Projects

Explore resources related to projects to help implement AI solutions for your business.

reddit

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vijaykurhade•This week

What marketing strategy will work for a startup with AI models to help real estate developers Design to Monetize projects better?

What marketing strategy will work for a startup with AI models to help real estate developers Design to Monetize projects better. PoC has been tested successfully on 4 large scale commercial to residential projects and promised results were delivered. Will Sending out small marketing write up to all the developers in concerned territory help?

#reddit#marketing#strategy

reddit

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Weekly-Offer-4172•This week

Technical Co-Founder Seeking Commercial/Marketing Partner for Micro SaaS Projects

Hi everyone, I’m looking for a commercial or marketing co-founder to join me in developing some Micro SaaS (MSaaS) apps. Here’s a bit about where I’m coming from and what I’m hoping to find: About Me: I’m a full-stack developer with over 15 years of experience, including some work in AI. I’m currently working part-time, which gives me the time to focus on developing MVPs quickly. I’m passionate about creating SaaS solutions and would love to find someone who can help bring these ideas to life. Based in french alps. What I’m Looking For: Role: Non-Technical Co-Founder (Commercial/Marketing) Location: Remote Equity: 50% co-founder stake What I’m Hoping You’ll Bring: Experience: Background in business development, marketing, or similar fields. Vision: An eye for potential in new SaaS ideas and a drive to help make them successful. Commitment: Enthusiasm for building and growing a business together. What’s In It For You: Revenue Potential: Share in the financial rewards of successful products with a 50% equity stake, giving you a direct share of the profits. Fast ROI: Benefit from rapid MVP development, which allows for quicker validation and faster revenue generation. Dynamic Approach: We move quickly—if an app doesn’t gain traction in a few weeks, we pivot to the next idea, keeping our efforts focused on what works. Financial Growth: As we iterate and scale, there are opportunities for significant financial upside based on the success of our products. Shared Success: Be an integral part of a partnership where both of us share equally in the risks and rewards, creating a strong incentive for mutual success. What’s In It For You: Partnership: Equal share in the business (50/50). Opportunity: Work on interesting MSaaS projects with room for creativity. Flexibility: A remote role that fits around your schedule. If you’re interested or would like to learn more, please reach out. I’d be thrilled to discuss how we might work together. Thank you for considering this!

#reddit#technical#co-founder

reddit

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SaraSavvy24•This week

My Manager Thinks ML Projects Takes 5 Minutes 🤦‍♀️

Hey, everyone! I’ve got to vent a bit because work has been something else lately. I’m a BI analyst at a bank, and I’m pretty much the only one dealing with machine learning and AI stuff. The rest of my team handles SQL and reporting—no Python, no R, no ML knowledge AT ALL. You could say I’m the only one handling data science stuff So, after I did a Python project for retail, my boss suddenly decided I’m the go-to for all things ML. Since then, I’ve been getting all the ML projects dumped on me (yay?), but here’s the kicker: my manager, who knows nothing about ML, acts like he’s some kind of expert. He keeps making suggestions that make zero sense and setting unrealistic deadlines. I swear, it’s like he read one article and thinks he’s cracked the code. And the best part? Whenever I finish a project, he’s all “we completed this” and “we came up with these insights.” Ummm, excuse me? We? I must’ve missed all those late-night coding sessions you didn’t show up for. The higher-ups know it’s my work and give me credit, but my manager just can’t help himself. Last week, he set a ridiculous deadline of 10 days for a super complex ML project. TEN DAYS! Like, does he even know that data preprocessing alone can take weeks? I’m talking about cleaning up messy datasets, handling missing values, feature engineering, and then model tuning. And that’s before even thinking about building the model! The actual model development is like the tip of the iceberg. But I just nodded and smiled because I was too exhausted to argue. 🤷‍♀️ And then, this one time, they didn’t even invite me to a meeting where they were presenting my work! The assistant manager came to me last minute, like, “Hey, can you explain these evaluation metrics to me so I can present them to the heads?” I was like, excuse me, what? Why not just invite me to the meeting to present my own work? But nooo, they wanted to play charades on me So, I gave the most complicated explanation ever, threw in all the jargon just to mess with him. He came back 10 minutes later, all flustered, and was like, “Yeah, you should probably do the presentation.” I just smiled and said, “I know… data science isn’t for everyone.” Anyway, they called me in at the last minute, and of course, I nailed it because I know my stuff. But seriously, the nerve of not including me in the first place and expecting me to swoop in like some kind of superhero. I mean, at least give me a cape if I’m going to keep saving the day! 🤦‍♀️ Honestly, I don’t know how much longer I can keep this up. I love the work, but dealing with someone who thinks they’re an ML guru when they can barely spell Python is just draining. I have built like some sort of defense mechanism to hit them with all the jargon and watch their eyes glaze over How do you deal with a manager who takes credit for your work and sets impossible deadlines? Should I keep pushing back or just let it go and keep my head down? Any advice! TL;DR: My manager thinks ML projects are plug-and-play, takes credit for my work, and expects me to clean and process data, build models, and deliver results in 10 days. How do I deal with this without snapping? #WorkDrama

#reddit#manager#thinks

reddit

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firebird8541154•This week

I made a bunch of side projects over the last 9 months, and even accrued 500+ accounts and some donations!

I just stumbled upon this subreddit and have a bunch of fun projects I'd like to present, any thoughts/feedback/criticism, etc. all welcome. So, first things first, a little about me, I work full time in an unrelated job, but have picked up full stack and mobile programming. I have two roommates who help a bit in their own way, one is a server expert and happened to have a server in our apartment basement, and the other is my brother and he picked up some frontend programming. We're all avid cyclists and decided to start building about 9 months ago. Our first idea was https://sherpa-map.com a SPA website allowing users to create cycling routes, send them to their Garmin devices, download them as GPX files, etc. This site uses the open-source software Graphhopper on the backend which I've augmented to send back surface type information. This site has a loooonnnggg list of features, from the simple, like a live weather radar, to the extreme like this functionality: ​ AI surface classification This video demonstrates the ability to classify road surface types in real time using high-resolution satellite imagery of road portions with unknown surface types! I trained a Pytorch resnet 50 model with tuned hyperparameters and 10 epochs on 200,000 satellite images of roads with known surface types! (We host a OSM Postgres server with coordinates of roads and their associated surface types, I made a script to pull images of said roads for training). I built the model into a secondary backend written in flask and piped the images being used back through live web sockets to my node.js backend to the person who is logged in! ​ Okay, on to the next side project, a cycling physics simulator! https://sherpa-map.com/cycling-route-calculator.html Cycling Physics Simulation This site lets users enter information about their bike setup, upload or use a preset route, and enter in their physical information to see how different changes in their setup might affect how fast they will be throughout a course! It can also pull complex weather information throughout the course and give a full suite of nutrition details! ​ Okay, Next project! The Activity Racer! https://sherpa-map.com/activity-racer.html Activity Racer This site lets users upload their own or competitors' GPX activity files and line them up against each other at any point in an event, to see who was faster where! It's great if you've done the same even year after year with differing setups, allowing you to get insights as to which might have done better at what point. ​ Okay, final project, this one's pretty half-baked as I'm still in the process of implementing so many other things, a podcast creation app! (I was bored and just started working on this a week or so ago, for no good reason). Currently, this one lives on https://sherpa-map.com/podcast.html This podcasting web app creates a peer to peer to peer... mesh network using webRTC so, small groups can communicate with the highest level of fidelity both in audio and video! Simply enter a room name and have other users enter the room name as well and they're connected! I've already used tensorflow.js AI to allow a blur background option, similar to MS Teams, whereby bodypix classifier AI picks out the person and I use a blur on a JS canvas behind them. I also went a little bit off the deep end and managed to implement the RNNoise background noise suppressor on the frontend, it's written in C, but I was able to use Windows Subsystem for Linux + emscrption to compile it in just the right way, with exposed malloc and free and a JS wrapper to use on the frontend in WASM. I actually use WASM (typically Rust) in many fun ways throughout all of these projects. I'm also in the middle of recreating the first site in React-Native + Maplibre for IOS and Android as individual APPs. In addition, I'm also working on the integration of my main site into a different project for a different group. So, I have a fun collection of side projects with slightly different GUIs, across different platforms with no coherent landing page as of yet but I've been having a blaaaast putting them together. As a final note, I even have a bit of an easter egg in the automated email system I use for account verifications and password resets do\not\reply@sherpa-map.com I hooked it up to ChatGPT API and told it it is a disgruntled worker whose sole task in life is to watch a do\not\reply email box and respond sarcastic/snarky to anyone who dares send a message to it, if AI comes for humanity, I bet I'll be on a list for this one lol.

#reddit#made#bunch

reddit

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tilopedia•This week

My Side Projects: From CEO to 4th Developer (Thanks, AI 🤖)

Hey Reddit 👋, I wanted to share a bit about some side projects I’ve been working on lately. Quick background for context: I’m the CEO of a mid-to-large-scale eCommerce company pulling in €10M+ annually in net turnover. We even built our own internal tracking software that’s now a SaaS (in early review stages on Shopify), competing with platforms like Lifetimely and TrueROAS. But! That’s not really the point of this post — there’s another journey I’ve been on that I’m super excited to share (and maybe get your feedback on!). AI Transformed My Role (and My Ideas List) I’m not a developer by trade — never properly learned how to code, and to be honest, I don’t intend to. But, I’ve always been the kind of guy who jots down ideas in a notes app and dreams about execution. My dev team calls me their “4th developer” (they’re a team of three) because I have solid theoretical knowledge and can kinda read code. And then AI happened. 🛠️ It basically turned my random ideas app into an MVP generation machine. I thought it’d be fun to share one of the apps I’m especially proud of. I am also planning to build this in public and therefore I am planning to post my progress on X and every project will have /stats page where live stats of the app will be available. Tackling My Task Management Problem 🚀 I’ve sucked at task management for YEARS, I still do! I’ve tried literally everything — Sheets, Todoist, Asana, ClickUp, Notion — you name it. I’d start… and then quit after a few weeks - always. What I struggle with the most is delegating tasks. As a CEO, I delegate a ton, and it’s super hard to track everything I’ve handed off to the team. Take this example: A few days ago, I emailed an employee about checking potential collaboration opportunities with a courier company. Just one of 10s of tasks like this I delegate daily. Suddenly, I thought: “Wouldn’t it be AMAZING if just typing out this email automatically created a task for me to track?” 💡 So… I jumped in. With the power of AI and a few intense days of work, I built a task manager that does just that. But of course, I couldn’t stop there. Research & Leveling It Up 📈 I looked at similar tools like TickTick and Todoist, scraped their G2 reviews (totally legally, promise! 😅), and ran them through AI for a deep SWOT analysis. I wanted to understand what their users liked/didn’t like and what gaps my app could fill. Some of the features people said they were missing didn’t align with the vision for my app (keeping it simple and personal), but I found some gold nuggets: Integration with calendars (Google) Reminders Customizable UX (themes) So, I started implementing what made sense and am keeping others on the roadmap for the future. And I’ve even built for that to, it still doesn’t have a name, however the point is you select on how many reviews of a specific app you want to make a SWOT analysis on and it will do it for you. Example for Todoist in comments. But more on that, some other time, maybe other post ... Key Features So Far: Here’s what’s live right now: ✅ Email to Task: Add an email as to, cc, or bcc — and it automatically creates a task with context, due dates, labels, etc. ✅ WhatsApp Reminders: Get nudged to handle your tasks via WhatsApp. ✅ WhatsApp to Task: Send a message like /task buy groceries — bam, it’s added with full context etc.. ✅ Chrome Extension (work-in-progress): Highlight text on any page, right-click, and send it straight to your task list. Next Steps: Build WITH the Community 👥 Right now, the app is 100% free while still in the early stages. But hey, API calls and server costs aren’t cheap, so pricing is something I’ll figure out with you as we grow. For now, my goal is to hit 100 users and iterate from there. My first pricing idea is, without monthly subscription, I don’t want to charge someone for something he didn’t use. So I am planning on charging "per task", what do you think? Here’s what I have planned: 📍 End of Year Goal: 100 users (starting from… 1 🥲). 💸 Revenue Roadmap: When we establish pricing, we’ll talk about that. 🛠️ Milestones: Post on Product Hunt when we hit 100 users. Clean up my self-written spaghetti code (hire a pro dev for review 🙃). Hire a part-time dev once we hit MRR that can cover its costs. You can check how are we doing on thisisatask.me/stats Other Side Projects I’m Working On: Because… what’s life without taking on too much, right? 😂 Full list of things I’m building: Internal HRM: Not public, tried and tested in-house. Android TV App: Syncs with HRM to post announcements to office TVs (streamlined and simple). Stats Tracker App: Connects to our internal software and gives me real-time company insights. Review Analyzer: Scrapes SaaS reviews (e.g., G2) and runs deep analysis via AI. This was originally for my Shopify SaaS but is quickly turning into something standalone. Coming soon! Mobile app game: secret for now. Let’s Build This Together! Would love it if you guys checked out https://thisisatask.me and gave it a spin! Still super early, super raw, but I’m pumped to hear your thoughts. Also, what’s a must-have task manager feature for you? Anything that frustrates you with current tools? I want to keep evolving this in public, so your feedback is gold. 🌟 Let me know, Reddit! Are you with me? 🙌

#reddit#side#projects:

reddit

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leanpreneur1•This week

Running and selling multiple side projects alongside a 9-5

My current side project started 56 days ago when I started writing 1,000 words per day. My core businesses are an agency and job board, and I just needed a creative outlet. The likes of Chris Guillebeau and Nathan Barry attribute their progression to writing so I thought I’d see if it might do the same for me. At first I was just vomiting words onto the screen, I made a blog and wrote mainly technical guides related to my skills. Over time I realised I was writing more and more about running a business as a solopreneur, or lean operator. There is tons of content out there giving you the Birds Eye of going from 0 to £10m. Inspiring stuff, but I think there is a void in real content, explaining the nuts and bolts of the how.  What is the day-to-day like for the solopreneurs who make a good living and have plenty of free time? That’s what I’m striving for anyway. I’m not talking about the 7-figure outliers. Or the ones teaching you to make content so you can have a business teaching others how to make content, and so on. I’m also sick of the ‘I made $X in 5 minutes and how you can too’  So, I started chatting to people in my network who run lean businesses and/or side hustles. I ask them a bit about their journey and ask them to teach something - how they operate, or a skill/process/system/tool that other people like you/me will find useful. One of my first chats was with Sam Dickie, who runs multiple side projects so thought I’d share here, see if others find it useful and get some feedback. I’ve removed all links as I’ve never posted on Reddit before so conscious of not being promotional, I’m posting this stuff to a tiny email list of friends with no upsells. Just finding my feet on whether others find it useful or not: — Sam is a serial entrepreneur who builds projects in his spare time whilst working a 9-5. He’s scaled and sold multiple ventures and currently runs one of the best newsletters out there for builders and entrepreneurs. Building audience through newsletters has always been a cornerstone strategy for him, so, along with sharing his advice on solopreneurism, he’s also generously shared his lean newsletter writing process. About Sam Sam is a Senior Product Manager who has spent the last 15 years working in the tech sector after starting his career as a town planner. In addition to his job he spends some of his spare time building side projects. These have included a 3D printing startup, a tech directory, a newsletter, a beta product directory, and consultancy. Sam is the epitome of making a success out of following your interest and curiosity. It’s clear he enjoys his business ventures and builds in a risk-free way.   It’s often touted by business gurus to avoid building around your interests, but Sam bucks the trend successfully. I think he’s someone who has already found his 1,000 true fans.  Descending rabbit holes, Sam’s journey of invention and curation 3D printing Sam’s first foray into launching a startup was with Fiilo, a 3D printing business. This was at the height of the 3D printing craze and he self-admits that he used the launch as an excuse to buy a 3D printer. He ended up with two and launching a product called GrowGo. GrowGo is a sustainable 3D-printed product that turns any bottle into somewhere that you can grow plants and herbs. He eventually sold this business and the printers, making around £10k. Along the way, he was exposed to various business tasks, including building a website in Weebly, the biggest nocode website builder of the time, and built an API that enabled print on demand for his product. NoCode.Tech The experiences of building as someone non-technical led to numerous friends asking how he built all of this tech. Back then, nocode wasn’t popular, and it had almost zero search volume, so Sam created a basic directory. A quick landing page on Weebly with a basic value prop, a short explanation and a list of the tools he had used before. It hit the top spot on Product Hunt, and he landed 2,000 subscribers in the first 48 hours. But, he hadn’t built it at this point, so he set about getting to work. He built the directory and list to 30,000 subs and monetised the site through advertising. At its peak with Sam, it was receiving about £2,000 per month in ad revenue. He was still working his 9-5 at this point, so thought it might be a good time to exit. The site was still growing, but it was becoming anxiety inducing whilst he was still working full-time. So, he ended up selling the site and making friend’s with the buyer. Fast forwarding a bit, Nocode.tech was eventually acquired by Stackr, a nocode app. Sam was working for their competitor at the time and ended up being offered a job by his friend who acquired the site. All of this from a side project in his area of passion. Creator Club After selling the directory, Sam lost his outlet for sharing his tools and learnings.  Being fascinated with curation and loving sifting through for nuggets, he invested more time into his personal website and launched Creator Club newsletter. Sam writes monthly and currently has over 8,000 subs. It’s one of the few newsletters that I let bypass my email filters and land in my main inbox. Life as a Part-Time Multipreneur Side Hustler If it’s not obvious already Sam is a curiosity led business creator. He’s found that the products without a revenue focus or intention have ironically outperformed those created for the sole purpose of creating money. He enjoys working on his side hustles. He could have run the Nocode.Tech for 10 more years and wouldn’t have tired of it as it’s a byproduct of his interest. For this reason, he has also created the Beta Directory, simply because he loves unearthing early-stage products. He admits he gets the fear when he thinks about quitting his 9-5, although he suspects if he devoted the same energy to one of his projects it could replace his income (no doubts from me here). This same fear means that he can run his ventures with less fear. This way, he can experiment with freedom and isn’t risking the ranch with a young family to consider. For example, recently he stopped paid sponsors on his newsletter as it was more stress than the value of the income to him. Sam divides his time on evenings and weekends (unequally) between the following: Creator Club Validation Co Beta directory Consultancy The pure side hustle status magnifies the need to run lean, let’s jump into his process…. Sam’s lean newsletter curation and creation process Starting out publishing his personal newsletter Going against his expertise, Sam originally over-engineered his process.  He curated with Feedly and tried to automate the full writing process with Zapier. The trouble is that there are too many points of failure which can lead the whole  chain to break down, and you spend more time fixing the system. For a 200 subscriber newsletter, he needed to pare things back. His set-up now Sam scaled back and now simple builds automations when he needs them. He keeps the process simple, right down to the design and any welcome automations. Keeping things real We touched on the trend that keeping things raw is better. Content has come full circle with the advent of AI. Everything looks too perfect and consequently, people’s tastes are changing. Sam mentioned watermarks that show content isn’t AI written, and we referenced content such as Greg Isenberg’s sketches, and Chris Donnelly’s image posts. \\Step by Step Process:\\ Using Stoop Inbox to manage sources Curation with Pocket Managing content with Airtable and Zapier Using Bearly to summarise Substack for writing Monitoring content sources Sam uses Stoop Inbox, an RSS curation tool, to manage his content sources. It gives him a dedicated email address for newsletters and he follows an Inbox Zero methodology. He checks in daily in Stoop, and on X, Reddit and IndieHackers. With X, he just uses the standard interface but has been careful to curate his feed, sometimes adding in extra notifications to hear from interesting people. Highlighting content When curating links, Sam uses Arc browser and the Pocket extension to save links. It’s super simple and lightweight. He creates tags which trigger an automation that curates the link to Airtable. If you watch the video, here’s a shoutout to Alice, the AI interface I use which has recently featured on Product Hunt. It’s a fantastic tool with bags of potential to enhance a solopreneur’s life. Ranking and sorting content He sends the links indexed using Pocket to a basic Airtable base via Zapier. From there, he grades the content and sets aside some time to read it in more depth. Pocket pulls through the title, metadata, and URL link. Review Sam does this manually but has used a tool as a shortcut for digesting long form content — Bearly.ai. Bearly.ai was created by Trung Phan and linking back to raw content, Trung is 1/3 of the hosts on the Not Investment Advice podcast. Its irreverent style and thumbnail are an example of a successful podcast that doesn’t over polish. Writing it all up Being a huge Notion fan (check out the free templates on his site), Sam originally used Notion for writing and linked it into Revue. When Elon sunsetted Revue, he switched to Substack. He loves the Substack interface so drafts in Substack based on a duplication of last month’s edition. Before publishing, Sam runs through a 10-point Notion checklist, which he shared with me. Parting Advice Keep your tool stack as lean as possible. Avoid tool switching to the shiny new object. Getting launched quickly is key. Don’t think that you have to be everywhere for distribution, Sam sticks with what he knows on X and LinkedIn. Overall, he advises just keeping things simple and therefore minimising risk. Resources He says they’re cliche, but I don’t agree; they’re timeless. Paul Graham of Y Combinator is someone Sam recommends following. He doesn’t write much, which is great as Sam gets anxiety when someone good often writes and he can’t keep up with the writing. His content is well thought out and distills complex concepts in entrepreneurship and startups. In addition, Sam loves Naval Ravikant’s approach. He mentions checking out the Almanac of Naval Ravikant for collected wisdom. Follow Sam’s Journey Again, not going to link here but you can find Sam’s stuff easily enough if you want to. His personal website is beautiful and contains loads of free downloads. He has also curated personal websites he admires if you need some inspiration. Sam is a super nice guy so reach out to him, I did before I started my personal blog recently, and he gave me some great advice. Also, worth keeping an eye on Validation Co, where he aims to help early-stage makers and creators validate their ideas. He’s building super slow — trying to enjoy the process without unachievable deadlines. Maintaining his stamina and passion. Amazing, I hope he writes more about that soon! -- That’s my second shot at an interview, hope you enjoyed it and found something useful in it. I’m talking to a marketplace founder who spends 2–3 hours per month his project, a multiple job board owner with a 9-5 and a leading book designer next. As this is my side project, should I keep going?

#reddit#running#selling

reddit

0

0.5

ANDYVO_•This week

Built a side project to help validate... side projects 🤔

Hey builders! Long-time lurker, first-time poster. Wanted to share something I've been working on after hours. The Problem Like many of you, I was building side projects at night while working full-time. My pattern was painfully consistent: \- Get excited about an idea \- Spend precious evening hours coding \- Launch on Reddit/Twitter \- \crickets\ \- Realize I should've validated first \- Repeat 🥲 The worst part? I was trying to do validation on Reddit, but with limited time after work, I couldn't: \- Properly research different communities \- Keep track of all the responses \- Find people actually interested in what I was building \- Or figure out what features to prioritize in my limited dev time The Solution So I built RediScope (yes, another side project 😅). It automates the Reddit validation process: Quick Loom demo How it works: Tell it what you're trying to validate AI helps write natural-sounding questions for each community Get instant analysis of responses Automatically spot potential early users The goal? Instead of spending your limited free time scrolling through Reddit, get clear validation and potential users in 48 hours. Tech Stack (since we all love this part): \- Frontend: React + Tailwind \- Backend: Node.js \- AI: OpenAI for the smart bits \- DB: PostgreSQL \- Hosting: Digital Ocean Current Status: \- ✅ Core validation engine working \- ✅ Community analysis \- ✅ Response analytics \- ✅ User dashboard \- 🔎 Lead Generator (in progress) \- 📋 API (planned) This is very much a nights-and-weekends project (like everything else we build 😄). Would love feedback from fellow side project builders: \- How do you validate your ideas currently? \- What would make this useful for your next project? \- Any features you'd want to see? Drop your thoughts below! If you want to try it when it launches: https://www.rediscope.app Also huge shoutout to this community - been learning so much from everyone's posts here 🙏

#reddit#built#side

reddit

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tilopedia•This week

My Side Projects: From CEO to 4th Developer (Thanks, AI 🤖)

Hey Reddit 👋, I wanted to share a bit about some side projects I’ve been working on lately. Quick background for context: I’m the CEO of a mid-to-large-scale eCommerce company pulling in €10M+ annually in net turnover. We even built our own internal tracking software that’s now a SaaS (in early review stages on Shopify), competing with platforms like Lifetimely and TrueROAS. But! That’s not really the point of this post — there’s another journey I’ve been on that I’m super excited to share (and maybe get your feedback on!). AI Transformed My Role (and My Ideas List) I’m not a developer by trade — never properly learned how to code, and to be honest, I don’t intend to. But, I’ve always been the kind of guy who jots down ideas in a notes app and dreams about execution. My dev team calls me their “4th developer” (they’re a team of three) because I have solid theoretical knowledge and can kinda read code. And then AI happened. 🛠️ It basically turned my random ideas app into an MVP generation machine. I thought it’d be fun to share one of the apps I’m especially proud of. I am also planning to build this in public and therefore I am planning to post my progress on X and every project will have /stats page where live stats of the app will be available. Tackling My Task Management Problem 🚀 I’ve sucked at task management for YEARS, I still do! I’ve tried literally everything — Sheets, Todoist, Asana, ClickUp, Notion — you name it. I’d start… and then quit after a few weeks - always. What I struggle with the most is delegating tasks. As a CEO, I delegate a ton, and it’s super hard to track everything I’ve handed off to the team. Take this example: A few days ago, I emailed an employee about checking potential collaboration opportunities with a courier company. Just one of 10s of tasks like this I delegate daily. Suddenly, I thought: “Wouldn’t it be AMAZING if just typing out this email automatically created a task for me to track?” 💡 So… I jumped in. With the power of AI and a few intense days of work, I built a task manager that does just that. But of course, I couldn’t stop there. Research & Leveling It Up 📈 I looked at similar tools like TickTick and Todoist, scraped their G2 reviews (totally legally, promise! 😅), and ran them through AI for a deep SWOT analysis. I wanted to understand what their users liked/didn’t like and what gaps my app could fill. Some of the features people said they were missing didn’t align with the vision for my app (keeping it simple and personal), but I found some gold nuggets: Integration with calendars (Google) Reminders Customizable UX (themes) So, I started implementing what made sense and am keeping others on the roadmap for the future. And I’ve even built for that to, it still doesn’t have a name, however the point is you select on how many reviews of a specific app you want to make a SWOT analysis on and it will do it for you. Example for Todoist in comments. But more on that, some other time, maybe other post ... Key Features So Far: Here’s what’s live right now: ✅ Email to Task: Add an email as to, cc, or bcc — and it automatically creates a task with context, due dates, labels, etc. ✅ WhatsApp Reminders: Get nudged to handle your tasks via WhatsApp. ✅ WhatsApp to Task: Send a message like /task buy groceries — bam, it’s added with full context etc.. ✅ Chrome Extension (work-in-progress): Highlight text on any page, right-click, and send it straight to your task list. Next Steps: Build WITH the Community 👥 Right now, the app is 100% free while still in the early stages. But hey, API calls and server costs aren’t cheap, so pricing is something I’ll figure out with you as we grow. For now, my goal is to hit 100 users and iterate from there. My first pricing idea is, without monthly subscription, I don’t want to charge someone for something he didn’t use. So I am planning on charging "per task", what do you think? Here’s what I have planned: 📍 End of Year Goal: 100 users (starting from… 1 🥲). 💸 Revenue Roadmap: When we establish pricing, we’ll talk about that. 🛠️ Milestones: Post on Product Hunt when we hit 100 users. Clean up my self-written spaghetti code (hire a pro dev for review 🙃). Hire a part-time dev once we hit MRR that can cover its costs. You can check how are we doing on thisisatask.me/stats Other Side Projects I’m Working On: Because… what’s life without taking on too much, right? 😂 Full list of things I’m building: Internal HRM: Not public, tried and tested in-house. Android TV App: Syncs with HRM to post announcements to office TVs (streamlined and simple). Stats Tracker App: Connects to our internal software and gives me real-time company insights. Review Analyzer: Scrapes SaaS reviews (e.g., G2) and runs deep analysis via AI. This was originally for my Shopify SaaS but is quickly turning into something standalone. Coming soon! Mobile app game: secret for now. Let’s Build This Together! Would love it if you guys checked out thisisatask.me and gave it a spin! Still super early, super raw, but I’m pumped to hear your thoughts. Also, what’s a must-have task manager feature for you? Anything that frustrates you with current tools? I want to keep evolving this in public, so your feedback is gold. 🌟 Let me know, Reddit! Are you with me? 🙌

#reddit#side#projects:

reddit

0

1

TheValueProvider•This week

10 Side Projects in 10 Years: Lessons from Failures and a $700 Exit

Hey folks, I'm sharing my journey so far in case it can help others. Entrepreneurship can sometimes be demotivating. In my case, I've always been involved in side projects and what I've realized is that every time you crash a project, the next one makes it a bit further. So this is a long-term game and consistency ends up paying off The $1 Android Game (2015, age 18) What Happened: 500 downloads, 1€ in ad revenue Ugly UI, performance issues Key Lessons: Don’t be afraid of launching. Delaying for “perfection” is often a sign that you fear being ignored. I was trying to perfect every aspect of the game. In reality, I was delaying the launch because I feared no one would download the app. Commit to the project or kill it. At some point, this project was no longer fun (it was just about fixing device responsiveness). Most importantly, I wasn't learning anything new so I moved to smth else. The Forex Bot Regret (2016, age 19) What Happened: Lost months identifying inexistent chart patterns Created a Trading bot that was never profitable Key Lessons: Day trading’s real winners are usually brokers. There are plenty of guys selling a bot or systems that are not making money trading, why would they sell a “money-printing machine” otherwise... Develop an unfair advantage. With these projects, I developed a strong coding foundation that gave me an edge when dealing with non-technical business people. Invest countless hours to create a skills gap between you and others, one that becomes increasingly difficult for them to close (coding, public speaking, networking, etc.) The $700 Instagram Exit (2018, age 21) What Happened: Grew a motivational account to 60k followers Sold it for $700 90% of followers were in low-income countries (hard to monetize) Key Lessons: Follower quality > quantity. I focused on growth and ended up with an audience I couldn’t truly define. If brands don’t see value, you won’t generate revenue. Also, if you do not know who you are creating content for, you'll end up demotivated and stop posting. Great 3rd party product + domain authority = Affiliate marketing works. In this case, I could easily promote an IG growing service because my 50k+ followers conveyed trust. Most importantly, the service I was promoting worked amazingly. The Illegal Amazon Review Marketplace (2020, age 23) What Happened: Sellers were reimbursing buyers for positive reviews Built a WordPress marketplace to facilitate “free products for reviews” Realized it violated Amazon’s terms Key Lessons: Check for “red flags” when doing idea assessment. There will always be red and orange flags. It’s about learning to differentiate between them (e.g. illegality, 100% dependence on a platform, etc.) If there’s competition, it’s good, if they are making money it’s even better. I was thrilled when I saw no competition for my “unique idea”. Later, I discovered the obvious reason. Copying a “Proven” Business Model (2020, age 23) What Happened: Tried recreating an Instagram “comment for comment” growth tool Instagram changed the algorithm and killed the growth strategy that the product used. Key Lessons: Do not build a business that depends 100% on another business, it is too risky. Mr. Musk can increase Twitter on API pricing to $42,000 monthly without notice and Tik Tok can be banned in the US. Due to the IG algorithm change, we had built a product that was not useful, and worse, now we had no idea how to grow an IG account. Consider future project synergies before selling. I regret having sold the 60k follower IG account since it could have saved me a lot of time when convincing users to try the service. NFT Marathon Medals (2021, age 24) What Happened: Created NFT race medals Sold 20 for 5€ each, but spent 95% of meetings explaining “what is an NFT?” Key Lessons: Market timing is crucial. As with every new technology, it is only useful as long as society is ready to adopt it. No matter how promising the tech is in the eyes of SV, society will end up dictating its success (blockchain, AI, etc). In this case, the runner community was not ready to adopt blockchain (it is not even prepared today). Race organizers did not know what they were selling, and runners did not know what they were buying. The 30-day rule in Fanatical Prospecting. Do not stop prospecting. I did prospecting and closed deals 3 months after the outbound efforts. Then I was busy executing the projects and had no clients once the projects were finished. AI Portal & Co-Founder Misalignment (2023, age 26) What Happened: Built a portal for SMEs to find AI use cases Co-founders disagreed on vision and execution Platform still gets \~1 new user/day Key Lessons: Define roles and equity clearly. Our biggest strength ended up killing us. Both founders had strong strategic skills and we were constantly arguing about decisions. NextJS + Vercel + Supabase: Great stack to create a SaaS MVP. (but do not use AI with frameworks unless you know how they work conceptually) SEO is king. One of our users creates a use case on “Changing Song Lyrics with AI.” Not being our target use case, it brings 90% of our traffic. Building an AI Tool & Getting Ghosted (2024, age 27) What Happened: SEO agency wanted to automate rewriting product descriptions Built it in 3 weeks, but the client vanished Key Lessons: Validate manually first. Don’t code a full-blown solution for a problem you haven’t tested in real-world workflows. I kept rewriting code only to throw it away. Jumping straight into building a solution ended up costing more time than it saved. Use templates, no-code, and open-source for prototyping. In my case, using a Next.js template saved me about four weeks of development only to hit the same dead end, but much faster. Fall in love with your ICP or walk away. I realized I didn’t enjoy working with SEO agencies. Looking back, I should have been honest with myself and admitted that I wasn’t motivated enough by this type of customer. Ignoring Code Perfection Doubled Traffic (2025, age 28) What Happened: Partnered with an ex-colleague to build an AI agents directory Focused on content & marketing, not endless bug fixes Traffic soared organically Key Lessons: Measure the impact of your actions and double down on what works. We set up an analytics system with PostHog and found wild imbalances (e.g. 1 post about frameworks outperformed 20 promotional posts). You have to start somewhere. For us, the AI agents directory is much more than just a standalone site, it's a strategic project that will allow us to discover new products, gain domain authority, and boost other projects. It builds the path for bigger opportunities. Less coding, more traction. Every day I have to fight against myself not to code “indispensable features”. Surprisingly, the directory keeps gaining consistent traffic despite being far from perfect Quitting My Job & Looking Ahead (2025, age 28) What Happened: Left full-time work to go all-in Plan to build vertical AI agents that handle entire business workflows (support, marketing, sales) Key Lessons: Bet on yourself. The opportunity cost of staying in my full-time job outweighed the benefits. It might be your case too I hope this post helps anyone struggling with their project and inspires those considering quitting their full-time job to take the leap with confidence.

#reddit#side#projects

github

0.467

0.03640675719934598

Apress•Mar 25, 2024

practical-python-ai-projects

Apress Source Code This repository accompanies Practical Python AI Projects by Serge Kruk (Apress, 2018). [comment]: #cover Download the files as a zip using the green button, or clone the repository to your machine using Git. Releases Release v1.0 corresponds to the code in the published book, without corrections or updates. Contributions See the file Contributing.md for more information on how you can contribute to this repository.

#github#practical-python-ai-projects

reddit

0

0

Neuro-AI•This week

[D] resources for avoiding common mistakes in machine learning projects?

There are often managers or entrepreneur types that think of "AI" as a magical solution and then millions of dollars get wasted because they don't understand how fragile the solutions can be, how do much more data is needed to create solutions that generalize, susceptibility to bias, training data needed, etc. What accessible information is there that would help those people understand what is involved for practicality of project and successful and ethical outcomes?

#reddit#resources#avoiding

youtube

0.371

0.5

AI BORDER•Mar 25, 2025

Vibe Coding For Non Coders - I built an online game in 30 seconds using AI

🚀 No coding skills? No problem! In this video, I show you how I built a working online game in just 30 seconds using AI-powered coding tools – perfect for beginners, creators, or anyone curious about AI development. 🔥 Try CodeLLM Teams FREE for 1 Month! 🎁👉 https://chatllm.abacus.ai/jTYLJgzFxy 👨‍💻 About CodeLLM Teams CodeLLM Teams is an advanced AI assistant that helps you write, optimize, and debug code across 10+ programming languages including Python, JavaScript, C++, PHP, and more. It works seamlessly with GitHub and all leading LLMs like Claude Sonnet 3.7, O3 Mini High, Quen, and others. 💻 Whether you're a solo developer or working in a team, CodeLLM makes your workflow faster and more efficient — even if you’ve never written a line of code before! #NoCode #AItools #GameDev #CodeLLM #AbacusAI #VibeCoding #LearnToCode #AIToolsForBeginners #CodingWithoutCode #BuildAGame #LLM #ChatGPT #Claude #GeminiAI #CodingTutorial #NonCoders #aifordevelopers ✨Contact AI Border: composition365@gmail.com✨ The videos use materials in a transformative and educational manner, following fair use guidelines and without any intention of copyright infringement. If you are the copyright owner or representative and have any concerns regarding the material used, please contact me at composition365@gmail.com, and we can address the issue. ✨Here are some more videos to watch 👍 ▶Top Free AI Video Generators: Image-to-Video and Text-to-Video Tools for 2025 https://youtu.be/VNDT2yA6zc0 ▶ Who Is the King of AI Video in 2025? Heygen vs Vozo AI vs Akool (Full Test) https://youtu.be/43up6iNj1wo ▶ GlobalGPT: The Ultimate All-in-One AI Tool for Writing, Proofreading, and Image Generation https://youtu.be/iPcFVC6Xz_8 ▶Uncensored AI Tool: Open Source Mimic PC Revolutionizes Content Creation https://youtu.be/4dvqDXQ09TY ▶AI Text-to-3D Animation: Effortlessly Create 3D Animated Videos from Text Prompts https://youtu.be/wzOCO8NYiLM ▶ Create Stunning Game & Film Concept Art with Shakker AI: AI Art Generation Tutorial https://youtu.be/OFv2CjWfq9U ▶ Create Viral Videos Using the Top AI Image and Video Generator https://youtu.be/1T3PxLdm2VY ▶ This video could help who are looking for: ai game builder,ai coding assistant,no code game development,code with ai,ai coding tutorial,build games with ai,image to game ai,html game with ai,free ai coding tools,how to build games with ai,ai game generator,learn coding with ai,ai tools for beginners,ai game development,ai for non coders,ai project tutorial,abacus ai,codeLLM tutorial,ai programming tools,ai powered coding,ai programming assistant,ai dev tools,build apps with ai,no code ai tools,code generator ai,ai video tutorial, #CodeWithAI #NoCodeTools #AIGameBuilder #AICodingAssistant #CodeLLM #AbacusAI #AIforBeginners #AIProjects #AIDevTools #LearnCodingWithAI #AITools2025 #AICodingTutorial #BuildWithAI #NoCodeDevelopment #AIProgramming #AIpowered #VibeCoding #CodingWithoutCode #CreateWithAI #HTMLGameWithAI #AIWorkflow #AIForEveryone #NonCodersWelcome #ShortVideoMaker #TextToCode #AIGeneratedCode #AIHack #AIForDevelopers #CreativeTools #ArtificialIntelligence #chatgpt #ClaudeAI

#NoCode#AItools#GameDev

youtube

0.409

0.71

AI Foundations•Aug 13, 2024

Automate any task using Claude! (my full Claude projects workflow)

This 25 minutes will save you hours. Automate any task with Claude! 👉🏼AI Foundations Community: https://swiy.co/aif-community-2 Claude custom instructions template for automation: https://docs.google.com/document/d/1jvYalwLY5f_6cUJre74FhYReMmomSLyEgujxhoYZ7P0/edit?usp=sharing 🛒AI Templates, Prompts and Courses for ChatGPT and Midjourney: https://aifoundations.io/shop 📰Subscribe to my newsletter: https://aifoundations.io 🔗Follow me on Linkedin: https://www.linkedin.com/in/drake-surach-823530203/ 🐥Follow me on twitter: https://twitter.com/drake0DTE ⌚Chapters: 0:00 - Claude for task automation 0:47 - Ai Community 2:05 - Creating a Claude Project 3:40 - How to use ‘Project Knowledge’ in Claude 8:17 - The task I will automate 9:40 - Adding company goals PDF 11:19 - Creating custom instructions in Claude 17:50 - Referencing project knowledge in custom instructions 20:22 - Testing my automation 21:40 - The results of the automation 23:35 - The BEST way to Learn AI #claude #anthropic

#claude#anthropic

coursera

0.486

0.45

UiPath•This week

UI Automation and Selectors

The UI Automation and Selectors course provides a deep understanding of the different methods used while interacting with the User Interface of different applications like Excel, Word, CRM, Websites while building automation projects.

#coursera#automation#selectors

reddit

0

1

DB010112•This week

Experienced Software Developer looking for startup to help. I will not promote

My passion for programming started at the age of 9 when I began playing video games. It was during this time that I first dived into programming, creating scripts for SA:MP (San Andreas Multiplayer) using the Pawn language. SA:MP is a modification for the popular game Grand Theft Auto: San Andreas, allowing players to experience multiplayer gameplay. My early experiences in programming were all about problem-solving—finding ways to enhance the game and improve the player experience. This was when I realized how satisfying it is to solve a problem through code, and that feeling has stayed with me throughout my career. I am a self-taught programmer, and everything I know today comes from my own initiative to learn and improve. After five years of working with local clients, I decided to expand my knowledge and started learning more widely applicable programming languages like Java and Python. I’ve always been the type of person who thrives on challenges. Whenever I encounter a problem, I don’t just look for a quick fix—I dive deep into researching and understanding the problem, and I find a solution that works in the long run. This is what drives me. The ability to solve problems, no matter how complex, and the satisfaction that comes with it is what fuels my passion for programming. My big break came when I had the opportunity to work at \\\\. There, I replaced two senior and two junior developers, which led to significant cost savings for the company. I completed all tasks ahead of schedule, focusing on Java-based applications that were multithreaded and communicated with embedded systems. This experience taught me how to work under pressure and how to manage and solve complex technical problems efficiently. Following my time at \\\\, I transitioned into freelance work as a FullStack Developer, working with technologies such as HTML, CSS, Bootstrap, JavaScript, Django, Spring, MySQL, and PostgreSQL. As a freelancer, I was responsible for finding solutions to a wide range of problems, often working independently and making decisions on the fly. I learned that self-reliance is key in this industry, and being resourceful is one of the most important qualities a developer can have. Later, I joined \\\\ elecom, where I worked on system integration with foreign teams, BPM process solutions, and the merging of complex systems in Oracle databases. I continued to solve challenges, often working with teams across borders and tackling technical obstacles that required creative and well-thought-out solutions. Eventually, I founded my own company, \\\\, where I focus on developing software solutions, Artificial Intelligence (AI), Cybersecurity, and Ethical Hacking. As an entrepreneur, I take pride in finding innovative solutions to problems, whether they come from clients or from technical obstacles I encounter along the way. I’ve also had the privilege of working with the Serbian Ministry of Defense and the police, handling sensitive projects that demand both technical expertise and trustworthiness. Being a self-taught programmer means that I have had to learn and adapt on my own, and I’ve learned to embrace challenges as opportunities for growth. I am constantly driven by the process of solving problems, and it is what keeps me engaged and fulfilled in my work. I am always open to new collaborations and am eager to take on new challenges that push my boundaries in technology, cybersecurity, and software development.

#reddit#experienced#software

reddit

0

1

stellarcitizen•This week

I fell into the builder's trap and need help getting out

Hi r/startups, First-time technical founder here. Two years ago, I decided to leave the 9-5 grind and build something meaningful. Now, I have (what I believe is) a brilliant technical solution but no clear business case. I’m seeking a cofounder with product and marketing expertise to help pivot my project into a viable business - or start a new one. Details below. About Me 36yo, born in Berlin and moved to San Francisco 8 years ago Master's in Software Engineering with 15 years of experience Worked with early-stage startups in Berlin and a venture studio in SF Spent the past years leading a team of 12 shipping enterprise software The tech I've built An AI engine that makes it easy for developers to automate their workflows. It works with code, issues, PRs and integrates with 3rd party systems like error trackers, wikis, ticketing systems, etc. It takes natural language instructions, fulfills them autonomously and responds with a result. The functionality is served as a platform, with an API and an SDK. On top of it, I've built a CLI and a web application with productivity tools for developers. Who and what I'm looking for My main goal is to leave my current job and build a company around a problem that matters to me, ideally with considerable equity. I’m looking for: A cofounder with product and marketing expertise who sees potential in my tech and can help turn it into a successful business—or someone with a strong business case who needs a technical founder. Mentorship from someone experienced in dev tool startups or as a successful solo founder. I’d love to learn from your journey and would be happy to offer my technical expertise or collaborate on projects in return. Happy to answer any questions or provide more details. Cheers!

#reddit#fell#into

reddit

0

0.667

GDbuildsGD•This week

I spent 6 months on building a tool, and got 0 zero users. Here is my story.

Edit Thank you all so much for your time reading my story. Your support, feedback, criticism, and skepticism; all helped me a lot, and I couldn't appreciate it enough \^\_\^ TL;DR I spent 6 months on a tool that currently has 0 users. Below is what I learned during my journey, sharing because I believe most mistakes are easily avoidable. Do not overestimate your product and assume it will be an exception to fundamental principles. Principles are there for a reason. Always look for validation before you start. Avoid building products with a low money-to-effort ratio/in very competitive fields. Unless you have the means, you probably won't make it. Pick a problem space, pick your target audience, and talk to them before thinking about a solution. Identify and match their pain points. Only then should you think of a solution. If people are not overly excited or willing to pay in advance for a discounted price, it might be a sign to rethink. Sell one and only one feature at a time. Avoid everything else. If people don't pay for that one core feature, no secondary feature will change their mind. Always spend twice as much time marketing as you do building. You will not get users if they don't know it exists. Define success metrics ("1000 users in 3 months" or "$6000 in the account at the end of 6 months") before you start. If you don't meet them, strongly consider quitting the project. If you can't get enough users to keep going, nothing else matters. VALIDATION, VALIDATION, VALIDATION. Success is not random, but most of our first products will not make a success story. Know when to admit failure, and move on. Even if a product of yours doesn't succeed, what you learned during its journey will turn out to be invaluable for your future. My story So, this is the story of a product, Summ, that I’ve been working on for the last 6 months. As it's the first product I’ve ever built, after watching you all from the sidelines, I have learned a lot, made many mistakes, and did only a few things right. Just sharing what I’ve learned and some insights from my journey so far. I hope that this post will help you avoid the mistakes I made — most of which I consider easily avoidable — while you enjoy reading it, and get to know me a little bit more 🤓. A slow start after many years Summ isn’t the first product I really wanted to build. Lacking enough dev skills to even get started was a huge blocker for so many years. In fact, the first product I would’ve LOVED to build was a smart personal shopping assistant. I had this idea 4 years ago; but with no GPT, no coding skills, no technical co-founder, I didn’t have the means to make it happen. I still do not know if such a tool exists and is good enough. All I wanted was a tool that could make data-based predictions about when to buy stuff (“buy a new toothpaste every three months”) and suggest physical products that I might need or be strongly interested in. AFAIK, Amazon famously still struggles with the second one. Fast-forward a few years, I learned the very basics of HTML, CSS, and Vanilla JS. Still was not there to build a product; but good enough to code my design portfolio from scratch. Yet, I couldn’t imagine myself building a product using Vanilla JS. I really hated it, I really sucked at it. So, back to tutorial hell, and to learn about this framework I just heard about: React.React introduced so many new concepts to me. “Thinking in React” is a phrase we heard a lot, and with quite good reasons. After some time, I was able to build very basic tutorial apps, both in React, and React Native; but I have to say that I really hated coding for mobile. At this point, I was already a fan of productivity apps, and had a concept for a time management assistant app in my design portfolio. So, why not build one? Surely, it must be easy, since every coding tutorial starts with a todo app. ❌ WRONG! Building a basic todo app is easy enough, but building one good enough for a place in the market was a challenge I took and failed. I wasted one month on that until I abandoned the project for good. Even if I continued working on it, as the productivity landscape is overly competitive, I wouldn’t be able to make enough money to cover costs, assuming I make any. Since I was (and still am) in between jobs, I decided to abandon the project. 👉 What I learned: Do not start projects with a low ratio of money to effort and time. Example: Even if I get 500 monthly users, 200 of which are paid users (unrealistically high number), assuming an average subscription fee of $5/m (such apps are quite cheap, mostly due to the high competition), it would make me around $1000 minus any occurring costs. Any founder with a product that has 500 active users should make more. Even if it was relatively successful, due to the high competition, I wouldn’t make any meaningful money. PS: I use Todoist today. Due to local pricing, I pay less than $2/m. There is no way I could beat this competitive pricing, let alone the app itself. But, somehow, with a project that wasn’t even functional — let alone being an MVP — I made my first Wi-Fi money: Someone decided that the domain I preemptively purchased is worth something. By this point, I had already abandoned the project, certainly wasn’t going to renew the domain, was looking for a FT job, and a new project that I could work on. And out of nowhere, someone hands me some free money — who am I not to take it? Of course, I took it. The domain is still unused, no idea why 🤔. Ngl, I still hate the fact that my first Wi-Fi money came from this. A new idea worth pursuing? Fast-forward some weeks now. Around March, I got this crazy idea of building an email productivity tool. We all use emails, yet we all hate them. So, this must be fixed. Everyone uses emails, in fact everyone HAS TO use emails. So, I just needed to build a tool and wait for people to come. This was all, really. After all, the problem space is huge, there is enough room for another product, everyone uses emails, no need for any further validation, right? ❌ WRONG ONCE AGAIN! We all hear from the greatest in the startup landscape that we must validate our ideas with real people, yet at least some of us (guilty here 🥸) think that our product will be hugely successful and prove them to be an exception. Few might, but most are not. I certainly wasn't. 👉 Lesson learned: Always validate your ideas with real people. Ask them how much they’d pay for such a tool (not if they would). Much better if they are willing to pay upfront for a discount, etc. But even this comes later, keep reading. I think the difference between “How much” and “If” is huge for two reasons: (1) By asking them for “How much”, you force them to think in a more realistic setting. (2) You will have a more realistic idea on your profit margins. Based on my competitive analysis, I already had a solution in my mind to improve our email usage standards and email productivity (huge mistake), but I did my best to learn about their problems regarding those without pushing the idea too hard. The idea is this: Generate concise email summaries with suggested actions, combine them into one email, and send it at their preferred times. Save as much as time the AI you end up with allows. After all, everyone loves to save time. So, what kind of validation did I seek for? Talked with only a few people around me about this crazy, internet-breaking idea. The responses I got were, now I see, mediocre; no one got excited about it, just said things along the lines of “Cool idea, OK”. So, any reasonable person in this situation would think “Okay, not might not be working”, right? Well, I did not. I assumed that they were the wrong audience for this product, and there was this magical land of user segments waiting eagerly for my product, yet unknowingly. To this day, I still have not reached this magical place. Perhaps, it didn’t exist in the first place. If I cannot find it, whether it exists or not doesn’t matter. I am certainly searching for it. 👉 What I should have done: Once I decide on a problem space (time management, email productivity, etc.), I should decide on my potential user segments, people who I plan to sell my product to. Then I should go talk to those people, ask them about their pains, then get to the problem-solving/ideation phase only later. ❗️ VALIDATION COMES FROM THE REALITY OUTSIDE. What validation looks like might change from product to product; but what invalidation looks like is more or less the same for every product. Nico Jeannen told me yesterday “validation = money in the account” on Twitter. This is the ultimate form of validation your product could get. If your product doesn’t make any money, then something is invalidated by reality: Your product, you, your idea, who knows? So, at this point, I knew a little bit of Python from spending some time in tutorial hell a few years ago, some HTML/CSS/JS, barely enough React to build a working app. React could work for this project, but I needed easy-to-implement server interactivity. Luckily, around this time, I got to know about this new gen of indie hackers, and learned (but didn’t truly understand) about their approach to indie hacking, and this library called Nextjs. How good Next.js still blows my mind. So, I was back to tutorial hell once again. But, this time, with a promise to myself: This is the last time I would visit tutorial hell. Time to start building this "ground-breaking idea" Learning the fundamentals of Next.js was easier than learning of React unsurprisingly. Yet, the first time I managed to run server actions on Next.js was one of the rarest moments that completely blew my mind. To this day, I reject the idea that it is something else than pure magic under its hood. Did I absolutely need Nextjs for this project though? I do not think so. Did it save me lots of time? Absolutely. Furthermore, learning Nextjs will certainly be quite helpful for other projects that I will be tackling in the future. Already got a few ideas that might be worth pursuing in the head in case I decide to abandon Summ in the future. Fast-forward few weeks again: So, at this stage, I had a barely working MVP-like product. Since the very beginning, I spent every free hour (and more) on this project as speed is essential. But, I am not so sure it was worth it to overwork in retrospect. Yet, I know I couldn’t help myself. Everything is going kinda smooth, so what’s the worst thing that could ever happen? Well, both Apple and Google announced their AIs (Apple Intelligence and Google Gemini, respectively) will have email summarization features for their products. Summarizing singular emails is no big deal, after all there were already so many similar products in the market. I still think that what truly matters is a frictionless user experience, and this is why I built this product in a certain way: You spend less than a few minutes setting up your account, and you get to enjoy your email summaries, without ever visiting its website again. This is still a very cool concept I really like a lot. So, at this point: I had no other idea that could be pursued, already spent too much time on this project. Do I quit or not? This was the question. Of course not. I just have to launch this product as quickly as possible. So, I did something right, a quite rare occurrence I might say: Re-planned my product, dropped everything secondary to the core feature immediately (save time on reading emails), tried launching it asap. 👉 Insight: Sell only one core feature at one time. Drop anything secondary to this core feature. Well, my primary occupation is product design. So one would expect that a product I build must have stellar design. I considered any considerable time spent on design at this stage would be simply wasted. I still think this is both true and wrong: True, because if your product’s core benefits suck, no one will care about your design. False, because if your design looks amateurish, no one will trust you and your product. So, I always targeted an average level design with it and the way this tool works made it quite easy as I had to design only 2 primary pages: Landing page and user portal (which has only settings and analytics pages). However, even though I knew spending time on design was not worth much of my time, I got a bit “greedy”: In fact, I redesigned those pages three times, and still ended up with a so-so design that I am not proud of. 👉 What I would do differently: Unless absolutely necessary, only one iteration per stage as long as it works. This, in my mind, applies to everything. If your product’s A feature works, then no need to rewrite it from scratch for any reason, or even refactor it. When your product becomes a success, and you absolutely need that part of your codebase to be written, do so, but only then. Ready to launch, now is th etime for some marketing, right? By July 26, I already had a “launchable” product that barely works (I marked this date on a Notion docs, this is how I know). Yet, I had spent almost no time on marketing, sales, whatever. After all, “You build and they will come”. Did I know that I needed marketing? Of course I did, but knowingly didn’t. Why, you might ask. Well, from my perspective, it had to be a dev-heavy product; meaning that you spend most of your time on developing it, mostly coding skills. But, this is simply wrong. As a rule of thumb, as noted by one of the greatests, Marc Louvion, you should spend at least twice of the building time on marketing. ❗️ Time spent on building \* 2 people don’t know your product > they don’t use your product > you don’t get users > you don’t make money Easy as that. Following the same reasoning, a slightly different approach to planning a project is possible. Determine an approximate time to complete the project with a high level project plan. Let’s say 6 months. By the reasoning above, 2 months should go into building, and 4 into marketing. If you need 4 months for building instead of 2, then you need 8 months of marketing, which makes the time to complete the project 12 months. If you don’t have that much time, then quit the project. When does a project count as completed? Well, in reality, never. But, I think we have to define success conditions even before we start for indie projects and startups; so we know when to quit when they are not met. A success condition could look like “Make $6000 in 12 months” or “Have 3000 users in 6 months”. It all depends on the project. But, once you set it, it should be set in stone: You don’t change it unless absolutely necessary. I suspect there are few principles that make a solopreneur successful; and knowing when to quit and when to continue is definitely one of them. Marc Louvion is famously known for his success, but he got there after failing so many projects. To my knowledge, the same applies to Nico Jeannen, Pieter Levels, or almost everyone as well. ❗️ Determining when to continue even before you start will definitely help in the long run. A half-aed launch Time-leap again. Around mid August, I “soft launched” my product. By soft launch, I mean lazy marketing. Just tweeting about it, posting it on free directories. Did I get any traffic? Surely I did. Did I get any users? Nope. Only after this time, it hit me: “Either something is wrong with me, or with this product” Marketing might be a much bigger factor for a project’s success after all. Even though I get some traffic, not convincing enough for people to sign up even for a free trial. The product was still perfect in my eyes at the time (well, still is ^(\_),) so the right people are not finding my product, I thought. Then, a question that I should have been asking at the very first place, one that could prevent all these, comes to my mind: “How do even people search for such tools?” If we are to consider this whole journey of me and my so-far-failed product to be an already destined failure, one metric suffices to show why. Search volume: 30. Even if people have such a pain point, they are not looking for email summaries. So, almost no organic traffic coming from Google. But, as a person who did zero marketing on this or any product, who has zero marketing knowledge, who doesn’t have an audience on social media, there is not much I could do. Finally, it was time to give up. Or not… In my eyes, the most important element that makes a founder (solo or not) successful (this, I am not by any means) is to solve problems. ❗️ So, the problem was this: “People are not finding my product by organic search” How do I make sure I get some organic traffic and gets more visibility? Learn digital marketing and SEO as much as I can within very limited time. Thankfully, without spending much time, I came across Neil Patel's YT channel, and as I said many times, it is an absolute gold mine. I learned a lot, especially about the fundamentals, and surely it will be fruitful; but there is no magic trick that could make people visit your website. SEO certainly helps, but only when people are looking for your keywords. However, it is truly a magical solution to get in touch with REAL people that are in your user segments: 👉 Understand your pains, understand their problems, help them to solve them via building products. I did not do this so far, have to admit. But, in case you would like to have a chat about your email usage, and email productivity, just get in touch; I’d be delighted to hear about them. Getting ready for a ProductHunt launch The date was Sept 1. And I unlocked an impossible achievement: Running out of Supabase’s free plan’s Egres limit while having zero users. I was already considering moving out of their Cloud server and managing a Supabase CLI service on my Hetzner VPS for some time; but never ever suspected that I would have to do this quickly. The cheapest plan Supabase offers is $25/month; yet, at that point, I am in between jobs for such a long time, basically broke, and could barely afford that price. One or two months could be okay, but why pay for it if I will eventually move out of their Cloud service? So, instead of paying $25, I spent two days migrating out of Supabase Cloud. Worth my time? Definitely not. But, when you are broke, you gotta do stupid things. This was the first time that I felt lucky to have zero users: I have no idea how I would manage this migration if I had any. I think this is one of the core tenets of an indie hacker: Controlling their own environment. I can’t remember whose quote this is, but I suspect it was Naval: Entrepreneurs have an almost pathological need to control their own fate. They will take any suffering if they can be in charge of their destiny, and not have it in somebody else’s hands. What’s truly scary is, at least in my case, we make people around us suffer at the expense of our attempting to control our own fates. I know this period has been quite hard on my wife as well, as I neglected her quite a bit, but sadly, I know that this will happen again. It is something that I can barely help with. Still, so sorry. After working the last two weeks on a ProductHunt Launch, I finally launched it this Tuesday. Zero ranking, zero new users, but 36 kind people upvoted my product, and many commented and provided invaluable feedback. I couldn't be more grateful for each one of them 🙏. Considering all these, what lies in the future of Summ though? I have no idea, to be honest. On one hand, I have zero users, have no job, no income. So, I need a way to make money asap. On the other hand, the whole idea of it revolves around one core premise (not an assumption) that I am not so willing to share; and I couldn’t have more trust in it. This might not be the best iteration of it, however I certainly believe that email usage is one of the best problem spaces one could work on. 👉 But, one thing is for certain: I need to get in touch with people, and talk with them about this product I built so far. In fact, this is the only item on my agenda. Nothing else will save my brainchild <3. Below are some other insights and notes that I got during my journey; as they do not 100% fit into this story, I think it is more suitable to list them here. I hope you enjoyed reading this. Give Summ a try, it comes with a generous free trial, no credit card required. Some additional notes and insights: Project planning is one of the most underestimated skills for solopreneurs. It saves you enormous time, and helps you to keep your focus up. Building B2C products beats building B2B products. Businesses are very willing to pay big bucks if your product helps them. On the other hand, spending a few hours per user who would pay $5/m probably is not worth your time. It doesn’t matter how brilliant your product is if no one uses it. If you cannot sell a product in a certain category/niche (or do not know how to sell it), it might be a good idea not to start a project in it. Going after new ideas and ventures is quite risky, especially if you don’t know how to market it. On the other hand, an already established category means that there is already demand. Whether this demand is sufficient or not is another issue. As long as there is enough demand for your product to fit in, any category/niche is good. Some might be better, some might be worse. Unless you are going hardcore B2B, you will need people to find your product by means of organic search. Always conduct thorough keyword research as soon as possible.

#reddit#spent#months

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asdasdasda134•This week

I'm a dev with zero fucking ideas. Help?

Long-time lurker, first-time poster here. I'm hoping you guys can help me out. I consider myself an above average engineer. With over 8 years of industry experience, I can whip out an MVP fast and iterate quickly. I love coding and learning new tech, but here's the issue—I've got absolutely no clue what to build. It's like I'm the least creative person I know, and can't find even one problem to solve. I've tried everything I can think of: Scrolling through ProductHunt until my eyes bled Asking non-tech friends about their "pain points" Stalking Twitter/X to see what people are building Experimenting with new AI tech to explore possibilities I've even attempted to build products. Almost 6 months ago, I started working on an AI conversation app to help non-native speakers like myself improve their English. But I soon realized there were already hundreds of apps doing this, and doing it much better than I could. I abandoned the project, figuring it wasn't unique enough. Same story with a couple of other projects that I started working on and abandoned later. So my question is how the heck you all come up with ideas? Any advice, commiseration, or hell—even random ideas you don’t want to build—would be greatly appreciated.

#reddit#zero#fucking

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wlynncork•This week

How to get funding for startup ? I will not promote

I will not promote. Software startup based out of Minnesota us. I've built and launched a product that is gaining traction, solving a problem that has frustrated software developers and product teams for years. The problem: Software development is slow, expensive, and full of inefficiencies. Developers spend hours on repetitive coding tasks, project managers struggle with bottlenecks, and businesses waste time translating product requirements into actual code. The solution: My product automates a large portion of software development. It acts as an AI-powered assistant for developers, taking high-level requirements and turning them into functional code while integrating with existing codebases. It can read, understand, and modify software projects in a structured way—cutting development time drastically. The potential: Businesses are always looking for ways to cut costs and speed up development. With the rise of AI, companies are increasingly adopting automation, and this tool fits perfectly into that wave. Imagine a world where software teams are 10x more efficient because AI handles the grunt work, and developers focus on the bigger picture. It’s not about replacing developers—it’s about supercharging them. The current status: The product is live and in use. The user base is growing, and I’ve proven demand. Now, I need to figure out the best funding model to scale—whether that’s bootstrapping, VC, grants, or some hybrid approach. If you have experience in startup funding or have scaled a tech product, I'd love to hear your insights. DM me if you're open to discussing strategies!

#reddit#funding#startup

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Rude-Onion6744•This week

Looking to Connect with Healthcare Tech Founders & Early-Stage Startups(Product, AI Strategy – Canada-based, Open to US). I will not promote.

Hey everyone, I’m exploring product and strategy roles in healthcare tech startups and looking to connect with early-stage founders working on AI-driven healthcare solutions. I’m currently based in Canada but looking for opportunities to work with U.S.-based startups in healthcare AI, digital health, and automation. My background is in tech, compliance, and cybersecurity, and I’m transitioning into product management for AI healthcare solutions. I’ve been working on a portfolio of AI-powered healthcare projects, including: A senior care digital assistant (health tracking, reminders) An AI companion for men's health (preventative care & chronic disease management) More projects coming in preventative health and healthcare automation I’m interested in learning from and working with fast-moving startups, particularly those at the intersection of AI and healthcare. If you’re working on something exciting, I’d love to chat. Drop a comment or DM me if you’re interested in connecting!

#reddit#looking#connect

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stepitup9600•This week

I just had my best month after 18 months as a solopreneur

Last month I reached important milestones both financially (60+ sales) and in terms of my personal brand (2.500+ new followers) But the most important part is that it has reinforced a belief in myself: it is possible, as long as I keep going, improving, learning and iterating. For the last year and a half, I've been grinding and launching project after project. But there was always something wrong: Product didn't solve a real problem Bad marketing (very often lol) Target market had low purchasing power Super-competitive niche (usually b2c) It's difficult to have failure after failure and keep going on. At times it would feel like everyone was making money, except for me. I was hacking on my projects every single day before and after my 9-5 and had mostly given up all my free time for this. But results were far from being what I wanted. So I would doubt myself all the time. One thing I had going for me is that I really enjoy building things - so that helped me a lot in staying consistent. I always knew this was a long-term thing and that I'd probably have to fail again and again before seeing some success. But even so, it was really hard to keep up the spirits at all time, especially after working so hard for so long. I wasn't going to give up but I also knew that continuing like this would lead nowhere. So I decided that for my next project I would do 2 things: 1) prioritize marketing and 2) build something strategic 1) Prioritize marketing I decided I was going to put in the same amount of effort into marketing as I put into building. Usually my time would be split 90% coding - 10% marketing. Now, for the first time ever it's probably 65% coding - 35% marketing. I organized myself and made an entire gameplan for it. This forced me to learn a lot about: Video editing Cold emails Copywriting Running ads Short-form content There are a lot of items I still need to execute on - but at least I have a good idea of how to approach most things. 2) Build something strategic I had to build something that I would be able to use even if nobody else did. For the last year and a half I had been building AI apps and my plan was to continue doing that. So I decided to leverage that and thought about how I could build something that would give me an unfair advantage + have a compounding effect over the long term: a) Unfair advantage Having AI demo apps that cover all type of AI functionalities would make my life easier & would allow me to ship new apps quickly, regardless of the required model/functionality So even if nobody bought this - I'd have built something really useful for myself & would have a slight edge over other people b) Compound over the long term Building "AnotherWrapper' (my new project) would have a good synergy with my future projects: It would allow me to build new projects faster While building new projects, I'd learn new things, which I would then be able to implement into AnotherWrapper and improve the product that way A win-win. Closing thoughts I did not expect things to go this well - it's been an amazing month and I'm truly grateful to everyone that has been supporting me. But at the end of the day, there is still a lot of work to be done. The initial 'hype' & effects from some viral tweets are starting to wear off. I still don't have a reliable distribution channel that guarantees me traffic. So I need to figure that out. I think the product has a lot of potential - it has been well received and has been a success so far, but my distribution is still lacking. The good thing is that I now have some extra cash to spend on things like ads, influencers, freelancers etc. So it opens some new doors that were previously closed! I also have some other projects down the pipeline which are coming soon. Will keep you guys updated!

#reddit#just#best

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jamanfarhad•This week

Need advice: Getting first paid customers for our AI subtitle generator[i will not promote]

we've built an AI-animated subtitle generator that helps content creators and video editors make viral-ready short-form videos in just a few taps. Here's where we are right now: 4,400+ signups in our first month after launch 70%+ activation rate (people actually processing and exporting videos) Industry-best word-by-word timestamp accuracy Support for 100+ languages (while competitors offer only 58+) Currently getting about 10 new organic signups daily (zero marketing) Users create \~25 projects per day on average The problem? Zero paying customers so far. Despite solid engagement metrics and what seems like a product people actually use, we haven't converted anyone to a paid plan. I'm guessing our pricing, packaging, or conversion strategy needs work. For those who've been through this stage: what was your breakthrough moment in getting those first paid users? Did you have to change your pricing model, add specific features, or implement particular conversion tactics? Any advice from those who've successfully crossed this milestone would be incredibly helpful. Thanks in advance!

#reddit#need#advice:

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colbyn-wadman•This week

Content aggregation that acts as a middleman for content discovery via third-party marketplace & revenue sharing (i will not promote but I'm looking for fellow researchers)

High level I’m considering a content aggregation business model, but one that acts as an open marketplace where third party devs and where world class data scientists compete to build the best recommenders for different use cases. (E.g. the incentives can be ad revenue sharing or subscription based for niche professional markets.) The idea is to facilitate more bottom up innovation from third party data scientists. The platform itself just acts as the middleman. (Also something that strips out original ads and makes it easy to skip paid sponsorship sections would be great.)  I’ve seen startups building web crawlers and content aggregation systems for other AI startups. My proposal is better in the sense that third party devs are instead responsible for implementing whatever questionable hacks are necessarily to scrape platforms that don’t necessarily want to be scraped.  Personally, I’m more concerned about getting the right information than ever before, to this end I can’t rely on platform specific recommenders. The solution is more bottom up innovation in content promotion. More generally, if you’re also concerned about consuming game changing information that’s too easily missed: we need a platform that incentivizes bottom up innovation of content promotion. What we need is a platform that functions like a marketplace where third party devs and where world class data scientists compete to build the best recommenders for different use cases. Here’s some elevator pitches I’m considering:  Did you know that the magic behind YouTube is its recommendation engine? Now, imagine an open platform where independent engines compete to deliver the most personalized content feed—from news to local events—directly to you. Interested in rethinking how we find content? “In today’s fragmented digital landscape, a single platform no longer holds sway over content discovery. The Network Effect is dead: audiences are more mobile than ever; and big tech killed it. In such a fragmented landscape we’re building a bottom-up, decentralized marketplace for recommendation engines—a solution that taps into diverse revenue streams through subscriptions, ad revenue, and affiliate partnerships. Invest in the future of personalized content aggregation.” “Are you a developer passionate about algorithms and content discovery? Our open marketplace lets you build and monetize your own recommendation engine, competing to deliver the most engaging, personalized feeds. Join a revolution where your innovation can directly shape how the world finds content.” “Are you tired of being told what to watch or read by one mysterious algorithm? Imagine taking control—choosing from a marketplace of smart recommendation engines that curate content just for you. It’s a revolution in content discovery where you hold the power.” (As a Utahn this one is interesting because even mormons are talking about the dangers of “doom scrolling” though it’s seldom discussed in society at large.) As far as simple hooks I’m considering:  One platform to rule them all and in the darkness bind them.  Choose how you discover—content recommenders that work for you.  The area where recommender engines battle to win your feed. Request I would love to start prototyping this idea and see what else I can uncover from such preliminary research. But I want to get a couple other likeminded individuals onboard.  I'm the best when it comes to iOS/macOS development, but there's tons of backend work that needs to be done which I wouldn’t have the time for if i'm focused on the native clients. Who am I 'ideally' looking for?  I’ve heard of weird stats to the effect that if you scale up a population to billions of people, the number of life overlaps starts skyrocketing. Not just physical lookalikes, but people with eerily similar life paths, personalities, habits, and even thoughts — without ever knowing each other. Where are my clones? Such is whom I’m looking for in an ideal world.  Take a hunch  People nowadays have no concept of going out on a limb, taking a ‘hunch’, and backing their instincts. Everything has to be calculated, proven, and guaranteed before they make a move. In contrast consider the success of the Chinese DeepSeek project: According to Asianometry’s YouTube video on DeepSeek, their “memory-saving multi-head latent architecture” (whatever that means, just quoting the name) came about from a researchers ‘hunch’, which the company bet big on and the result was drastically improved performance on low end hardware…  Here in the west the idea of betting on a hunch is inconceivable. We have no balls to chase long term insights. My own instincts when it comes to software is such because I’ve wasted too much of my life on small scale projects. All I’m trying to do is attempt a more scaled up experiment based on some hunches with me and a few other likeminded individuals.  Just as the early oil prospectors didn’t have precise maps—just intuition and test drills. They had to drill, analyze the pressure, and adjust. The best oil fields weren’t found by foresight alone, but by adaptive exploration. The startup space itself is liken to the first prospectors who got the gold nuggets lying in the riverbed. In such an environment moving first has its advantages but nowadays I wish I could have all those shitty ‘engineers’ sent to their maker.  Today the reality is such that you’ve got to dig deep—where vast stores of wealth can be found—or go home, and those who dig into the depths cannot use mere forethought, for what lies beneath cannot be seen by the mind’s eye.  I will not promote but I'm looking for fellow research oriented minds.

#reddit#content#aggregation

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0.667

GDbuildsGD•This week

I spent 6 months on building a tool, and got 0 zero users. Here is my story.

Edit Thank you all so much for your time reading my story. Your support, feedback, criticism, and skepticism; all helped me a lot, and I couldn't appreciate it enough \^\_\^ TL;DR I spent 6 months on a tool that currently has 0 users. Below is what I learned during my journey, sharing because I believe most mistakes are easily avoidable. Do not overestimate your product and assume it will be an exception to fundamental principles. Principles are there for a reason. Always look for validation before you start. Avoid building products with a low money-to-effort ratio/in very competitive fields. Unless you have the means, you probably won't make it. Pick a problem space, pick your target audience, and talk to them before thinking about a solution. Identify and match their pain points. Only then should you think of a solution. If people are not overly excited or willing to pay in advance for a discounted price, it might be a sign to rethink. Sell one and only one feature at a time. Avoid everything else. If people don't pay for that one core feature, no secondary feature will change their mind. Always spend twice as much time marketing as you do building. You will not get users if they don't know it exists. Define success metrics ("1000 users in 3 months" or "$6000 in the account at the end of 6 months") before you start. If you don't meet them, strongly consider quitting the project. If you can't get enough users to keep going, nothing else matters. VALIDATION, VALIDATION, VALIDATION. Success is not random, but most of our first products will not make a success story. Know when to admit failure, and move on. Even if a product of yours doesn't succeed, what you learned during its journey will turn out to be invaluable for your future. My story So, this is the story of a product, Summ, that I’ve been working on for the last 6 months. As it's the first product I’ve ever built, after watching you all from the sidelines, I have learned a lot, made many mistakes, and did only a few things right. Just sharing what I’ve learned and some insights from my journey so far. I hope that this post will help you avoid the mistakes I made — most of which I consider easily avoidable — while you enjoy reading it, and get to know me a little bit more 🤓. A slow start after many years Summ isn’t the first product I really wanted to build. Lacking enough dev skills to even get started was a huge blocker for so many years. In fact, the first product I would’ve LOVED to build was a smart personal shopping assistant. I had this idea 4 years ago; but with no GPT, no coding skills, no technical co-founder, I didn’t have the means to make it happen. I still do not know if such a tool exists and is good enough. All I wanted was a tool that could make data-based predictions about when to buy stuff (“buy a new toothpaste every three months”) and suggest physical products that I might need or be strongly interested in. AFAIK, Amazon famously still struggles with the second one. Fast-forward a few years, I learned the very basics of HTML, CSS, and Vanilla JS. Still was not there to build a product; but good enough to code my design portfolio from scratch. Yet, I couldn’t imagine myself building a product using Vanilla JS. I really hated it, I really sucked at it. So, back to tutorial hell, and to learn about this framework I just heard about: React.React introduced so many new concepts to me. “Thinking in React” is a phrase we heard a lot, and with quite good reasons. After some time, I was able to build very basic tutorial apps, both in React, and React Native; but I have to say that I really hated coding for mobile. At this point, I was already a fan of productivity apps, and had a concept for a time management assistant app in my design portfolio. So, why not build one? Surely, it must be easy, since every coding tutorial starts with a todo app. ❌ WRONG! Building a basic todo app is easy enough, but building one good enough for a place in the market was a challenge I took and failed. I wasted one month on that until I abandoned the project for good. Even if I continued working on it, as the productivity landscape is overly competitive, I wouldn’t be able to make enough money to cover costs, assuming I make any. Since I was (and still am) in between jobs, I decided to abandon the project. 👉 What I learned: Do not start projects with a low ratio of money to effort and time. Example: Even if I get 500 monthly users, 200 of which are paid users (unrealistically high number), assuming an average subscription fee of $5/m (such apps are quite cheap, mostly due to the high competition), it would make me around $1000 minus any occurring costs. Any founder with a product that has 500 active users should make more. Even if it was relatively successful, due to the high competition, I wouldn’t make any meaningful money. PS: I use Todoist today. Due to local pricing, I pay less than $2/m. There is no way I could beat this competitive pricing, let alone the app itself. But, somehow, with a project that wasn’t even functional — let alone being an MVP — I made my first Wi-Fi money: Someone decided that the domain I preemptively purchased is worth something. By this point, I had already abandoned the project, certainly wasn’t going to renew the domain, was looking for a FT job, and a new project that I could work on. And out of nowhere, someone hands me some free money — who am I not to take it? Of course, I took it. The domain is still unused, no idea why 🤔. Ngl, I still hate the fact that my first Wi-Fi money came from this. A new idea worth pursuing? Fast-forward some weeks now. Around March, I got this crazy idea of building an email productivity tool. We all use emails, yet we all hate them. So, this must be fixed. Everyone uses emails, in fact everyone HAS TO use emails. So, I just needed to build a tool and wait for people to come. This was all, really. After all, the problem space is huge, there is enough room for another product, everyone uses emails, no need for any further validation, right? ❌ WRONG ONCE AGAIN! We all hear from the greatest in the startup landscape that we must validate our ideas with real people, yet at least some of us (guilty here 🥸) think that our product will be hugely successful and prove them to be an exception. Few might, but most are not. I certainly wasn't. 👉 Lesson learned: Always validate your ideas with real people. Ask them how much they’d pay for such a tool (not if they would). Much better if they are willing to pay upfront for a discount, etc. But even this comes later, keep reading. I think the difference between “How much” and “If” is huge for two reasons: (1) By asking them for “How much”, you force them to think in a more realistic setting. (2) You will have a more realistic idea on your profit margins. Based on my competitive analysis, I already had a solution in my mind to improve our email usage standards and email productivity (huge mistake), but I did my best to learn about their problems regarding those without pushing the idea too hard. The idea is this: Generate concise email summaries with suggested actions, combine them into one email, and send it at their preferred times. Save as much as time the AI you end up with allows. After all, everyone loves to save time. So, what kind of validation did I seek for? Talked with only a few people around me about this crazy, internet-breaking idea. The responses I got were, now I see, mediocre; no one got excited about it, just said things along the lines of “Cool idea, OK”. So, any reasonable person in this situation would think “Okay, not might not be working”, right? Well, I did not. I assumed that they were the wrong audience for this product, and there was this magical land of user segments waiting eagerly for my product, yet unknowingly. To this day, I still have not reached this magical place. Perhaps, it didn’t exist in the first place. If I cannot find it, whether it exists or not doesn’t matter. I am certainly searching for it. 👉 What I should have done: Once I decide on a problem space (time management, email productivity, etc.), I should decide on my potential user segments, people who I plan to sell my product to. Then I should go talk to those people, ask them about their pains, then get to the problem-solving/ideation phase only later. ❗️ VALIDATION COMES FROM THE REALITY OUTSIDE. What validation looks like might change from product to product; but what invalidation looks like is more or less the same for every product. Nico Jeannen told me yesterday “validation = money in the account” on Twitter. This is the ultimate form of validation your product could get. If your product doesn’t make any money, then something is invalidated by reality: Your product, you, your idea, who knows? So, at this point, I knew a little bit of Python from spending some time in tutorial hell a few years ago, some HTML/CSS/JS, barely enough React to build a working app. React could work for this project, but I needed easy-to-implement server interactivity. Luckily, around this time, I got to know about this new gen of indie hackers, and learned (but didn’t truly understand) about their approach to indie hacking, and this library called Nextjs. How good Next.js still blows my mind. So, I was back to tutorial hell once again. But, this time, with a promise to myself: This is the last time I would visit tutorial hell. Time to start building this "ground-breaking idea" Learning the fundamentals of Next.js was easier than learning of React unsurprisingly. Yet, the first time I managed to run server actions on Next.js was one of the rarest moments that completely blew my mind. To this day, I reject the idea that it is something else than pure magic under its hood. Did I absolutely need Nextjs for this project though? I do not think so. Did it save me lots of time? Absolutely. Furthermore, learning Nextjs will certainly be quite helpful for other projects that I will be tackling in the future. Already got a few ideas that might be worth pursuing in the head in case I decide to abandon Summ in the future. Fast-forward few weeks again: So, at this stage, I had a barely working MVP-like product. Since the very beginning, I spent every free hour (and more) on this project as speed is essential. But, I am not so sure it was worth it to overwork in retrospect. Yet, I know I couldn’t help myself. Everything is going kinda smooth, so what’s the worst thing that could ever happen? Well, both Apple and Google announced their AIs (Apple Intelligence and Google Gemini, respectively) will have email summarization features for their products. Summarizing singular emails is no big deal, after all there were already so many similar products in the market. I still think that what truly matters is a frictionless user experience, and this is why I built this product in a certain way: You spend less than a few minutes setting up your account, and you get to enjoy your email summaries, without ever visiting its website again. This is still a very cool concept I really like a lot. So, at this point: I had no other idea that could be pursued, already spent too much time on this project. Do I quit or not? This was the question. Of course not. I just have to launch this product as quickly as possible. So, I did something right, a quite rare occurrence I might say: Re-planned my product, dropped everything secondary to the core feature immediately (save time on reading emails), tried launching it asap. 👉 Insight: Sell only one core feature at one time. Drop anything secondary to this core feature. Well, my primary occupation is product design. So one would expect that a product I build must have stellar design. I considered any considerable time spent on design at this stage would be simply wasted. I still think this is both true and wrong: True, because if your product’s core benefits suck, no one will care about your design. False, because if your design looks amateurish, no one will trust you and your product. So, I always targeted an average level design with it and the way this tool works made it quite easy as I had to design only 2 primary pages: Landing page and user portal (which has only settings and analytics pages). However, even though I knew spending time on design was not worth much of my time, I got a bit “greedy”: In fact, I redesigned those pages three times, and still ended up with a so-so design that I am not proud of. 👉 What I would do differently: Unless absolutely necessary, only one iteration per stage as long as it works. This, in my mind, applies to everything. If your product’s A feature works, then no need to rewrite it from scratch for any reason, or even refactor it. When your product becomes a success, and you absolutely need that part of your codebase to be written, do so, but only then. Ready to launch, now is th etime for some marketing, right? By July 26, I already had a “launchable” product that barely works (I marked this date on a Notion docs, this is how I know). Yet, I had spent almost no time on marketing, sales, whatever. After all, “You build and they will come”. Did I know that I needed marketing? Of course I did, but knowingly didn’t. Why, you might ask. Well, from my perspective, it had to be a dev-heavy product; meaning that you spend most of your time on developing it, mostly coding skills. But, this is simply wrong. As a rule of thumb, as noted by one of the greatests, Marc Louvion, you should spend at least twice of the building time on marketing. ❗️ Time spent on building \* 2 people don’t know your product > they don’t use your product > you don’t get users > you don’t make money Easy as that. Following the same reasoning, a slightly different approach to planning a project is possible. Determine an approximate time to complete the project with a high level project plan. Let’s say 6 months. By the reasoning above, 2 months should go into building, and 4 into marketing. If you need 4 months for building instead of 2, then you need 8 months of marketing, which makes the time to complete the project 12 months. If you don’t have that much time, then quit the project. When does a project count as completed? Well, in reality, never. But, I think we have to define success conditions even before we start for indie projects and startups; so we know when to quit when they are not met. A success condition could look like “Make $6000 in 12 months” or “Have 3000 users in 6 months”. It all depends on the project. But, once you set it, it should be set in stone: You don’t change it unless absolutely necessary. I suspect there are few principles that make a solopreneur successful; and knowing when to quit and when to continue is definitely one of them. Marc Louvion is famously known for his success, but he got there after failing so many projects. To my knowledge, the same applies to Nico Jeannen, Pieter Levels, or almost everyone as well. ❗️ Determining when to continue even before you start will definitely help in the long run. A half-aed launch Time-leap again. Around mid August, I “soft launched” my product. By soft launch, I mean lazy marketing. Just tweeting about it, posting it on free directories. Did I get any traffic? Surely I did. Did I get any users? Nope. Only after this time, it hit me: “Either something is wrong with me, or with this product” Marketing might be a much bigger factor for a project’s success after all. Even though I get some traffic, not convincing enough for people to sign up even for a free trial. The product was still perfect in my eyes at the time (well, still is ^(\_),) so the right people are not finding my product, I thought. Then, a question that I should have been asking at the very first place, one that could prevent all these, comes to my mind: “How do even people search for such tools?” If we are to consider this whole journey of me and my so-far-failed product to be an already destined failure, one metric suffices to show why. Search volume: 30. Even if people have such a pain point, they are not looking for email summaries. So, almost no organic traffic coming from Google. But, as a person who did zero marketing on this or any product, who has zero marketing knowledge, who doesn’t have an audience on social media, there is not much I could do. Finally, it was time to give up. Or not… In my eyes, the most important element that makes a founder (solo or not) successful (this, I am not by any means) is to solve problems. ❗️ So, the problem was this: “People are not finding my product by organic search” How do I make sure I get some organic traffic and gets more visibility? Learn digital marketing and SEO as much as I can within very limited time. Thankfully, without spending much time, I came across Neil Patel's YT channel, and as I said many times, it is an absolute gold mine. I learned a lot, especially about the fundamentals, and surely it will be fruitful; but there is no magic trick that could make people visit your website. SEO certainly helps, but only when people are looking for your keywords. However, it is truly a magical solution to get in touch with REAL people that are in your user segments: 👉 Understand your pains, understand their problems, help them to solve them via building products. I did not do this so far, have to admit. But, in case you would like to have a chat about your email usage, and email productivity, just get in touch; I’d be delighted to hear about them. Getting ready for a ProductHunt launch The date was Sept 1. And I unlocked an impossible achievement: Running out of Supabase’s free plan’s Egres limit while having zero users. I was already considering moving out of their Cloud server and managing a Supabase CLI service on my Hetzner VPS for some time; but never ever suspected that I would have to do this quickly. The cheapest plan Supabase offers is $25/month; yet, at that point, I am in between jobs for such a long time, basically broke, and could barely afford that price. One or two months could be okay, but why pay for it if I will eventually move out of their Cloud service? So, instead of paying $25, I spent two days migrating out of Supabase Cloud. Worth my time? Definitely not. But, when you are broke, you gotta do stupid things. This was the first time that I felt lucky to have zero users: I have no idea how I would manage this migration if I had any. I think this is one of the core tenets of an indie hacker: Controlling their own environment. I can’t remember whose quote this is, but I suspect it was Naval: Entrepreneurs have an almost pathological need to control their own fate. They will take any suffering if they can be in charge of their destiny, and not have it in somebody else’s hands. What’s truly scary is, at least in my case, we make people around us suffer at the expense of our attempting to control our own fates. I know this period has been quite hard on my wife as well, as I neglected her quite a bit, but sadly, I know that this will happen again. It is something that I can barely help with. Still, so sorry. After working the last two weeks on a ProductHunt Launch, I finally launched it this Tuesday. Zero ranking, zero new users, but 36 kind people upvoted my product, and many commented and provided invaluable feedback. I couldn't be more grateful for each one of them 🙏. Considering all these, what lies in the future of Summ though? I have no idea, to be honest. On one hand, I have zero users, have no job, no income. So, I need a way to make money asap. On the other hand, the whole idea of it revolves around one core premise (not an assumption) that I am not so willing to share; and I couldn’t have more trust in it. This might not be the best iteration of it, however I certainly believe that email usage is one of the best problem spaces one could work on. 👉 But, one thing is for certain: I need to get in touch with people, and talk with them about this product I built so far. In fact, this is the only item on my agenda. Nothing else will save my brainchild <3. Below are some other insights and notes that I got during my journey; as they do not 100% fit into this story, I think it is more suitable to list them here. I hope you enjoyed reading this. Give Summ a try, it comes with a generous free trial, no credit card required. Some additional notes and insights: Project planning is one of the most underestimated skills for solopreneurs. It saves you enormous time, and helps you to keep your focus up. Building B2C products beats building B2B products. Businesses are very willing to pay big bucks if your product helps them. On the other hand, spending a few hours per user who would pay $5/m probably is not worth your time. It doesn’t matter how brilliant your product is if no one uses it. If you cannot sell a product in a certain category/niche (or do not know how to sell it), it might be a good idea not to start a project in it. Going after new ideas and ventures is quite risky, especially if you don’t know how to market it. On the other hand, an already established category means that there is already demand. Whether this demand is sufficient or not is another issue. As long as there is enough demand for your product to fit in, any category/niche is good. Some might be better, some might be worse. Unless you are going hardcore B2B, you will need people to find your product by means of organic search. Always conduct thorough keyword research as soon as possible.

#reddit#spent#months

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Kato_Tedomoji•This week

Technical entrepreneur looking for next team

Hi everyone, I'm a 10+ year veteran software developer with a proven track record of building consumer-facing products (shipped 10+ projects, 5 for consumers). However, I'm not looking to build another consumer facing product (we all know how difficult that is) unless you have the network and funds. I'm gearing up for my next venture and searching for the perfect co-pilot (or two!) with equal splits. -- Here's the skinny: You (ideal): Experienced (ideally with at least one failed project under your belt - learning is key!) Ready to grind and U.S based. Titles are meaningless - passion and hustle matter most. Are tired of tarpit ideas (dating app, fitness app, etc.) Bonus Points: Another technical co-founder to build magic with. Non-technical rockstars with skills in customer acquisition, fundraising, or deep industry knowledge. -- Please send me a DM! Let's chat and see if we can build something together. -- Full disclosure: I did use AI to write some of these because I'm not super great at writing these out

#reddit#technical#entrepreneur

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jayisanxious•This week

Better ways to get leads for my MVP development service? I will not promote

Hi I'm Jay, I've been a dev for over 7 years. I've worked with organisations like the Qatar Airlines Currently I run a small dev shop focusing on building MVPs for non-tech founders specifically. Now I've been running meta ads and it's been okay. Working on 2 interesting projects currently. The workload is lower than our capacity but it's alright. The problem is- most of the leads don't seem to be qualified enough and fall through. Instead of actual founders who want to build something and know what it takes, I get wannabe entrepreneurs who have way too much expectations for absolute peanuts for budget Bare in mind, I already charge pretty low for the MVP as one of my USPs is cost-effective ($5k). I legit had a meeting with someone who expected me to develop a fully fledged AI powered MARKETPLACE for $1000😭 It's so hard not to take offense to things like that and absolutely lose my sh*t because WHAT💀 Any advice on where or how to get qualified and serious clients? Is there a way to target founders who've raised pre-seed or seed funding? I know it's a long shot since most startups don't get funded pre MVP but just something I'm trying to consider just in case Any and all advice would be appreciated, thank you🙏🏼 PS: Sorry about the rant halfway through😭🙏🏼

#reddit#better#ways

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1

DB010112•This week

Experienced Software Developer looking for startup to help. I will not promote

My passion for programming started at the age of 9 when I began playing video games. It was during this time that I first dived into programming, creating scripts for SA:MP (San Andreas Multiplayer) using the Pawn language. SA:MP is a modification for the popular game Grand Theft Auto: San Andreas, allowing players to experience multiplayer gameplay. My early experiences in programming were all about problem-solving—finding ways to enhance the game and improve the player experience. This was when I realized how satisfying it is to solve a problem through code, and that feeling has stayed with me throughout my career. I am a self-taught programmer, and everything I know today comes from my own initiative to learn and improve. After five years of working with local clients, I decided to expand my knowledge and started learning more widely applicable programming languages like Java and Python. I’ve always been the type of person who thrives on challenges. Whenever I encounter a problem, I don’t just look for a quick fix—I dive deep into researching and understanding the problem, and I find a solution that works in the long run. This is what drives me. The ability to solve problems, no matter how complex, and the satisfaction that comes with it is what fuels my passion for programming. My big break came when I had the opportunity to work at \\\\. There, I replaced two senior and two junior developers, which led to significant cost savings for the company. I completed all tasks ahead of schedule, focusing on Java-based applications that were multithreaded and communicated with embedded systems. This experience taught me how to work under pressure and how to manage and solve complex technical problems efficiently. Following my time at \\\\, I transitioned into freelance work as a FullStack Developer, working with technologies such as HTML, CSS, Bootstrap, JavaScript, Django, Spring, MySQL, and PostgreSQL. As a freelancer, I was responsible for finding solutions to a wide range of problems, often working independently and making decisions on the fly. I learned that self-reliance is key in this industry, and being resourceful is one of the most important qualities a developer can have. Later, I joined \\\\ elecom, where I worked on system integration with foreign teams, BPM process solutions, and the merging of complex systems in Oracle databases. I continued to solve challenges, often working with teams across borders and tackling technical obstacles that required creative and well-thought-out solutions. Eventually, I founded my own company, \\\\, where I focus on developing software solutions, Artificial Intelligence (AI), Cybersecurity, and Ethical Hacking. As an entrepreneur, I take pride in finding innovative solutions to problems, whether they come from clients or from technical obstacles I encounter along the way. I’ve also had the privilege of working with the Serbian Ministry of Defense and the police, handling sensitive projects that demand both technical expertise and trustworthiness. Being a self-taught programmer means that I have had to learn and adapt on my own, and I’ve learned to embrace challenges as opportunities for growth. I am constantly driven by the process of solving problems, and it is what keeps me engaged and fulfilled in my work. I am always open to new collaborations and am eager to take on new challenges that push my boundaries in technology, cybersecurity, and software development.

#reddit#experienced#software

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0.667

GDbuildsGD•This week

I spent 6 months on building a tool, and got 0 zero users. Here is my story.

Edit Thank you all so much for your time reading my story. Your support, feedback, criticism, and skepticism; all helped me a lot, and I couldn't appreciate it enough \^\_\^ TL;DR I spent 6 months on a tool that currently has 0 users. Below is what I learned during my journey, sharing because I believe most mistakes are easily avoidable. Do not overestimate your product and assume it will be an exception to fundamental principles. Principles are there for a reason. Always look for validation before you start. Avoid building products with a low money-to-effort ratio/in very competitive fields. Unless you have the means, you probably won't make it. Pick a problem space, pick your target audience, and talk to them before thinking about a solution. Identify and match their pain points. Only then should you think of a solution. If people are not overly excited or willing to pay in advance for a discounted price, it might be a sign to rethink. Sell one and only one feature at a time. Avoid everything else. If people don't pay for that one core feature, no secondary feature will change their mind. Always spend twice as much time marketing as you do building. You will not get users if they don't know it exists. Define success metrics ("1000 users in 3 months" or "$6000 in the account at the end of 6 months") before you start. If you don't meet them, strongly consider quitting the project. If you can't get enough users to keep going, nothing else matters. VALIDATION, VALIDATION, VALIDATION. Success is not random, but most of our first products will not make a success story. Know when to admit failure, and move on. Even if a product of yours doesn't succeed, what you learned during its journey will turn out to be invaluable for your future. My story So, this is the story of a product, Summ, that I’ve been working on for the last 6 months. As it's the first product I’ve ever built, after watching you all from the sidelines, I have learned a lot, made many mistakes, and did only a few things right. Just sharing what I’ve learned and some insights from my journey so far. I hope that this post will help you avoid the mistakes I made — most of which I consider easily avoidable — while you enjoy reading it, and get to know me a little bit more 🤓. A slow start after many years Summ isn’t the first product I really wanted to build. Lacking enough dev skills to even get started was a huge blocker for so many years. In fact, the first product I would’ve LOVED to build was a smart personal shopping assistant. I had this idea 4 years ago; but with no GPT, no coding skills, no technical co-founder, I didn’t have the means to make it happen. I still do not know if such a tool exists and is good enough. All I wanted was a tool that could make data-based predictions about when to buy stuff (“buy a new toothpaste every three months”) and suggest physical products that I might need or be strongly interested in. AFAIK, Amazon famously still struggles with the second one. Fast-forward a few years, I learned the very basics of HTML, CSS, and Vanilla JS. Still was not there to build a product; but good enough to code my design portfolio from scratch. Yet, I couldn’t imagine myself building a product using Vanilla JS. I really hated it, I really sucked at it. So, back to tutorial hell, and to learn about this framework I just heard about: React.React introduced so many new concepts to me. “Thinking in React” is a phrase we heard a lot, and with quite good reasons. After some time, I was able to build very basic tutorial apps, both in React, and React Native; but I have to say that I really hated coding for mobile. At this point, I was already a fan of productivity apps, and had a concept for a time management assistant app in my design portfolio. So, why not build one? Surely, it must be easy, since every coding tutorial starts with a todo app. ❌ WRONG! Building a basic todo app is easy enough, but building one good enough for a place in the market was a challenge I took and failed. I wasted one month on that until I abandoned the project for good. Even if I continued working on it, as the productivity landscape is overly competitive, I wouldn’t be able to make enough money to cover costs, assuming I make any. Since I was (and still am) in between jobs, I decided to abandon the project. 👉 What I learned: Do not start projects with a low ratio of money to effort and time. Example: Even if I get 500 monthly users, 200 of which are paid users (unrealistically high number), assuming an average subscription fee of $5/m (such apps are quite cheap, mostly due to the high competition), it would make me around $1000 minus any occurring costs. Any founder with a product that has 500 active users should make more. Even if it was relatively successful, due to the high competition, I wouldn’t make any meaningful money. PS: I use Todoist today. Due to local pricing, I pay less than $2/m. There is no way I could beat this competitive pricing, let alone the app itself. But, somehow, with a project that wasn’t even functional — let alone being an MVP — I made my first Wi-Fi money: Someone decided that the domain I preemptively purchased is worth something. By this point, I had already abandoned the project, certainly wasn’t going to renew the domain, was looking for a FT job, and a new project that I could work on. And out of nowhere, someone hands me some free money — who am I not to take it? Of course, I took it. The domain is still unused, no idea why 🤔. Ngl, I still hate the fact that my first Wi-Fi money came from this. A new idea worth pursuing? Fast-forward some weeks now. Around March, I got this crazy idea of building an email productivity tool. We all use emails, yet we all hate them. So, this must be fixed. Everyone uses emails, in fact everyone HAS TO use emails. So, I just needed to build a tool and wait for people to come. This was all, really. After all, the problem space is huge, there is enough room for another product, everyone uses emails, no need for any further validation, right? ❌ WRONG ONCE AGAIN! We all hear from the greatest in the startup landscape that we must validate our ideas with real people, yet at least some of us (guilty here 🥸) think that our product will be hugely successful and prove them to be an exception. Few might, but most are not. I certainly wasn't. 👉 Lesson learned: Always validate your ideas with real people. Ask them how much they’d pay for such a tool (not if they would). Much better if they are willing to pay upfront for a discount, etc. But even this comes later, keep reading. I think the difference between “How much” and “If” is huge for two reasons: (1) By asking them for “How much”, you force them to think in a more realistic setting. (2) You will have a more realistic idea on your profit margins. Based on my competitive analysis, I already had a solution in my mind to improve our email usage standards and email productivity (huge mistake), but I did my best to learn about their problems regarding those without pushing the idea too hard. The idea is this: Generate concise email summaries with suggested actions, combine them into one email, and send it at their preferred times. Save as much as time the AI you end up with allows. After all, everyone loves to save time. So, what kind of validation did I seek for? Talked with only a few people around me about this crazy, internet-breaking idea. The responses I got were, now I see, mediocre; no one got excited about it, just said things along the lines of “Cool idea, OK”. So, any reasonable person in this situation would think “Okay, not might not be working”, right? Well, I did not. I assumed that they were the wrong audience for this product, and there was this magical land of user segments waiting eagerly for my product, yet unknowingly. To this day, I still have not reached this magical place. Perhaps, it didn’t exist in the first place. If I cannot find it, whether it exists or not doesn’t matter. I am certainly searching for it. 👉 What I should have done: Once I decide on a problem space (time management, email productivity, etc.), I should decide on my potential user segments, people who I plan to sell my product to. Then I should go talk to those people, ask them about their pains, then get to the problem-solving/ideation phase only later. ❗️ VALIDATION COMES FROM THE REALITY OUTSIDE. What validation looks like might change from product to product; but what invalidation looks like is more or less the same for every product. Nico Jeannen told me yesterday “validation = money in the account” on Twitter. This is the ultimate form of validation your product could get. If your product doesn’t make any money, then something is invalidated by reality: Your product, you, your idea, who knows? So, at this point, I knew a little bit of Python from spending some time in tutorial hell a few years ago, some HTML/CSS/JS, barely enough React to build a working app. React could work for this project, but I needed easy-to-implement server interactivity. Luckily, around this time, I got to know about this new gen of indie hackers, and learned (but didn’t truly understand) about their approach to indie hacking, and this library called Nextjs. How good Next.js still blows my mind. So, I was back to tutorial hell once again. But, this time, with a promise to myself: This is the last time I would visit tutorial hell. Time to start building this "ground-breaking idea" Learning the fundamentals of Next.js was easier than learning of React unsurprisingly. Yet, the first time I managed to run server actions on Next.js was one of the rarest moments that completely blew my mind. To this day, I reject the idea that it is something else than pure magic under its hood. Did I absolutely need Nextjs for this project though? I do not think so. Did it save me lots of time? Absolutely. Furthermore, learning Nextjs will certainly be quite helpful for other projects that I will be tackling in the future. Already got a few ideas that might be worth pursuing in the head in case I decide to abandon Summ in the future. Fast-forward few weeks again: So, at this stage, I had a barely working MVP-like product. Since the very beginning, I spent every free hour (and more) on this project as speed is essential. But, I am not so sure it was worth it to overwork in retrospect. Yet, I know I couldn’t help myself. Everything is going kinda smooth, so what’s the worst thing that could ever happen? Well, both Apple and Google announced their AIs (Apple Intelligence and Google Gemini, respectively) will have email summarization features for their products. Summarizing singular emails is no big deal, after all there were already so many similar products in the market. I still think that what truly matters is a frictionless user experience, and this is why I built this product in a certain way: You spend less than a few minutes setting up your account, and you get to enjoy your email summaries, without ever visiting its website again. This is still a very cool concept I really like a lot. So, at this point: I had no other idea that could be pursued, already spent too much time on this project. Do I quit or not? This was the question. Of course not. I just have to launch this product as quickly as possible. So, I did something right, a quite rare occurrence I might say: Re-planned my product, dropped everything secondary to the core feature immediately (save time on reading emails), tried launching it asap. 👉 Insight: Sell only one core feature at one time. Drop anything secondary to this core feature. Well, my primary occupation is product design. So one would expect that a product I build must have stellar design. I considered any considerable time spent on design at this stage would be simply wasted. I still think this is both true and wrong: True, because if your product’s core benefits suck, no one will care about your design. False, because if your design looks amateurish, no one will trust you and your product. So, I always targeted an average level design with it and the way this tool works made it quite easy as I had to design only 2 primary pages: Landing page and user portal (which has only settings and analytics pages). However, even though I knew spending time on design was not worth much of my time, I got a bit “greedy”: In fact, I redesigned those pages three times, and still ended up with a so-so design that I am not proud of. 👉 What I would do differently: Unless absolutely necessary, only one iteration per stage as long as it works. This, in my mind, applies to everything. If your product’s A feature works, then no need to rewrite it from scratch for any reason, or even refactor it. When your product becomes a success, and you absolutely need that part of your codebase to be written, do so, but only then. Ready to launch, now is th etime for some marketing, right? By July 26, I already had a “launchable” product that barely works (I marked this date on a Notion docs, this is how I know). Yet, I had spent almost no time on marketing, sales, whatever. After all, “You build and they will come”. Did I know that I needed marketing? Of course I did, but knowingly didn’t. Why, you might ask. Well, from my perspective, it had to be a dev-heavy product; meaning that you spend most of your time on developing it, mostly coding skills. But, this is simply wrong. As a rule of thumb, as noted by one of the greatests, Marc Louvion, you should spend at least twice of the building time on marketing. ❗️ Time spent on building \* 2 people don’t know your product > they don’t use your product > you don’t get users > you don’t make money Easy as that. Following the same reasoning, a slightly different approach to planning a project is possible. Determine an approximate time to complete the project with a high level project plan. Let’s say 6 months. By the reasoning above, 2 months should go into building, and 4 into marketing. If you need 4 months for building instead of 2, then you need 8 months of marketing, which makes the time to complete the project 12 months. If you don’t have that much time, then quit the project. When does a project count as completed? Well, in reality, never. But, I think we have to define success conditions even before we start for indie projects and startups; so we know when to quit when they are not met. A success condition could look like “Make $6000 in 12 months” or “Have 3000 users in 6 months”. It all depends on the project. But, once you set it, it should be set in stone: You don’t change it unless absolutely necessary. I suspect there are few principles that make a solopreneur successful; and knowing when to quit and when to continue is definitely one of them. Marc Louvion is famously known for his success, but he got there after failing so many projects. To my knowledge, the same applies to Nico Jeannen, Pieter Levels, or almost everyone as well. ❗️ Determining when to continue even before you start will definitely help in the long run. A half-aed launch Time-leap again. Around mid August, I “soft launched” my product. By soft launch, I mean lazy marketing. Just tweeting about it, posting it on free directories. Did I get any traffic? Surely I did. Did I get any users? Nope. Only after this time, it hit me: “Either something is wrong with me, or with this product” Marketing might be a much bigger factor for a project’s success after all. Even though I get some traffic, not convincing enough for people to sign up even for a free trial. The product was still perfect in my eyes at the time (well, still is ^(\_),) so the right people are not finding my product, I thought. Then, a question that I should have been asking at the very first place, one that could prevent all these, comes to my mind: “How do even people search for such tools?” If we are to consider this whole journey of me and my so-far-failed product to be an already destined failure, one metric suffices to show why. Search volume: 30. Even if people have such a pain point, they are not looking for email summaries. So, almost no organic traffic coming from Google. But, as a person who did zero marketing on this or any product, who has zero marketing knowledge, who doesn’t have an audience on social media, there is not much I could do. Finally, it was time to give up. Or not… In my eyes, the most important element that makes a founder (solo or not) successful (this, I am not by any means) is to solve problems. ❗️ So, the problem was this: “People are not finding my product by organic search” How do I make sure I get some organic traffic and gets more visibility? Learn digital marketing and SEO as much as I can within very limited time. Thankfully, without spending much time, I came across Neil Patel's YT channel, and as I said many times, it is an absolute gold mine. I learned a lot, especially about the fundamentals, and surely it will be fruitful; but there is no magic trick that could make people visit your website. SEO certainly helps, but only when people are looking for your keywords. However, it is truly a magical solution to get in touch with REAL people that are in your user segments: 👉 Understand your pains, understand their problems, help them to solve them via building products. I did not do this so far, have to admit. But, in case you would like to have a chat about your email usage, and email productivity, just get in touch; I’d be delighted to hear about them. Getting ready for a ProductHunt launch The date was Sept 1. And I unlocked an impossible achievement: Running out of Supabase’s free plan’s Egres limit while having zero users. I was already considering moving out of their Cloud server and managing a Supabase CLI service on my Hetzner VPS for some time; but never ever suspected that I would have to do this quickly. The cheapest plan Supabase offers is $25/month; yet, at that point, I am in between jobs for such a long time, basically broke, and could barely afford that price. One or two months could be okay, but why pay for it if I will eventually move out of their Cloud service? So, instead of paying $25, I spent two days migrating out of Supabase Cloud. Worth my time? Definitely not. But, when you are broke, you gotta do stupid things. This was the first time that I felt lucky to have zero users: I have no idea how I would manage this migration if I had any. I think this is one of the core tenets of an indie hacker: Controlling their own environment. I can’t remember whose quote this is, but I suspect it was Naval: Entrepreneurs have an almost pathological need to control their own fate. They will take any suffering if they can be in charge of their destiny, and not have it in somebody else’s hands. What’s truly scary is, at least in my case, we make people around us suffer at the expense of our attempting to control our own fates. I know this period has been quite hard on my wife as well, as I neglected her quite a bit, but sadly, I know that this will happen again. It is something that I can barely help with. Still, so sorry. After working the last two weeks on a ProductHunt Launch, I finally launched it this Tuesday. Zero ranking, zero new users, but 36 kind people upvoted my product, and many commented and provided invaluable feedback. I couldn't be more grateful for each one of them 🙏. Considering all these, what lies in the future of Summ though? I have no idea, to be honest. On one hand, I have zero users, have no job, no income. So, I need a way to make money asap. On the other hand, the whole idea of it revolves around one core premise (not an assumption) that I am not so willing to share; and I couldn’t have more trust in it. This might not be the best iteration of it, however I certainly believe that email usage is one of the best problem spaces one could work on. 👉 But, one thing is for certain: I need to get in touch with people, and talk with them about this product I built so far. In fact, this is the only item on my agenda. Nothing else will save my brainchild <3. Below are some other insights and notes that I got during my journey; as they do not 100% fit into this story, I think it is more suitable to list them here. I hope you enjoyed reading this. Give Summ a try, it comes with a generous free trial, no credit card required. Some additional notes and insights: Project planning is one of the most underestimated skills for solopreneurs. It saves you enormous time, and helps you to keep your focus up. Building B2C products beats building B2B products. Businesses are very willing to pay big bucks if your product helps them. On the other hand, spending a few hours per user who would pay $5/m probably is not worth your time. It doesn’t matter how brilliant your product is if no one uses it. If you cannot sell a product in a certain category/niche (or do not know how to sell it), it might be a good idea not to start a project in it. Going after new ideas and ventures is quite risky, especially if you don’t know how to market it. On the other hand, an already established category means that there is already demand. Whether this demand is sufficient or not is another issue. As long as there is enough demand for your product to fit in, any category/niche is good. Some might be better, some might be worse. Unless you are going hardcore B2B, you will need people to find your product by means of organic search. Always conduct thorough keyword research as soon as possible.

#reddit#spent#months

reddit

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1

Rude-Onion6744•This week

Looking to Connect with Healthcare Tech Founders & Early-Stage Startups(Product, AI Strategy – Canada-based, Open to US). I will not promote.

Hey everyone, I’m exploring product and strategy roles in healthcare tech startups and looking to connect with early-stage founders working on AI-driven healthcare solutions. I’m currently based in Canada but looking for opportunities to work with U.S.-based startups in healthcare AI, digital health, and automation. My background is in tech, compliance, and cybersecurity, and I’m transitioning into product management for AI healthcare solutions. I’ve been working on a portfolio of AI-powered healthcare projects, including: A senior care digital assistant (health tracking, reminders) An AI companion for men's health (preventative care & chronic disease management) More projects coming in preventative health and healthcare automation I’m interested in learning from and working with fast-moving startups, particularly those at the intersection of AI and healthcare. If you’re working on something exciting, I’d love to chat. Drop a comment or DM me if you’re interested in connecting!

#reddit#looking#connect

reddit

0

1

wlynncork•This week

How to get funding for startup ? I will not promote

I will not promote. Software startup based out of Minnesota us. I've built and launched a product that is gaining traction, solving a problem that has frustrated software developers and product teams for years. The problem: Software development is slow, expensive, and full of inefficiencies. Developers spend hours on repetitive coding tasks, project managers struggle with bottlenecks, and businesses waste time translating product requirements into actual code. The solution: My product automates a large portion of software development. It acts as an AI-powered assistant for developers, taking high-level requirements and turning them into functional code while integrating with existing codebases. It can read, understand, and modify software projects in a structured way—cutting development time drastically. The potential: Businesses are always looking for ways to cut costs and speed up development. With the rise of AI, companies are increasingly adopting automation, and this tool fits perfectly into that wave. Imagine a world where software teams are 10x more efficient because AI handles the grunt work, and developers focus on the bigger picture. It’s not about replacing developers—it’s about supercharging them. The current status: The product is live and in use. The user base is growing, and I’ve proven demand. Now, I need to figure out the best funding model to scale—whether that’s bootstrapping, VC, grants, or some hybrid approach. If you have experience in startup funding or have scaled a tech product, I'd love to hear your insights. DM me if you're open to discussing strategies!

#reddit#funding#startup

reddit

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1

stellarcitizen•This week

I fell into the builder's trap and need help getting out

Hi r/startups, First-time technical founder here. Two years ago, I decided to leave the 9-5 grind and build something meaningful. Now, I have (what I believe is) a brilliant technical solution but no clear business case. I’m seeking a cofounder with product and marketing expertise to help pivot my project into a viable business - or start a new one. Details below. About Me 36yo, born in Berlin and moved to San Francisco 8 years ago Master's in Software Engineering with 15 years of experience Worked with early-stage startups in Berlin and a venture studio in SF Spent the past years leading a team of 12 shipping enterprise software The tech I've built An AI engine that makes it easy for developers to automate their workflows. It works with code, issues, PRs and integrates with 3rd party systems like error trackers, wikis, ticketing systems, etc. It takes natural language instructions, fulfills them autonomously and responds with a result. The functionality is served as a platform, with an API and an SDK. On top of it, I've built a CLI and a web application with productivity tools for developers. Who and what I'm looking for My main goal is to leave my current job and build a company around a problem that matters to me, ideally with considerable equity. I’m looking for: A cofounder with product and marketing expertise who sees potential in my tech and can help turn it into a successful business—or someone with a strong business case who needs a technical founder. Mentorship from someone experienced in dev tool startups or as a successful solo founder. I’d love to learn from your journey and would be happy to offer my technical expertise or collaborate on projects in return. Happy to answer any questions or provide more details. Cheers!

#reddit#fell#into

reddit

0

1

stellarcitizen•This week

I fell into the builder's trap and need help getting out

Hi r/startups, First-time technical founder here. Two years ago, I decided to leave the 9-5 grind and build something meaningful. Now, I have (what I believe is) a brilliant technical solution but no clear business case. I’m seeking a cofounder with product and marketing expertise to help pivot my project into a viable business - or start a new one. Details below. About Me 36yo, born in Berlin and moved to San Francisco 8 years ago Master's in Software Engineering with 15 years of experience Worked with early-stage startups in Berlin and a venture studio in SF Spent the past years leading a team of 12 shipping enterprise software The tech I've built An AI engine that makes it easy for developers to automate their workflows. It works with code, issues, PRs and integrates with 3rd party systems like error trackers, wikis, ticketing systems, etc. It takes natural language instructions, fulfills them autonomously and responds with a result. The functionality is served as a platform, with an API and an SDK. On top of it, I've built a CLI and a web application with productivity tools for developers. Who and what I'm looking for My main goal is to leave my current job and build a company around a problem that matters to me, ideally with considerable equity. I’m looking for: A cofounder with product and marketing expertise who sees potential in my tech and can help turn it into a successful business—or someone with a strong business case who needs a technical founder. Mentorship from someone experienced in dev tool startups or as a successful solo founder. I’d love to learn from your journey and would be happy to offer my technical expertise or collaborate on projects in return. Happy to answer any questions or provide more details. Cheers!

#reddit#fell#into

reddit

0

0.667

GDbuildsGD•This week

I spent 6 months on building a tool, and got 0 zero users. Here is my story.

Edit Thank you all so much for your time reading my story. Your support, feedback, criticism, and skepticism; all helped me a lot, and I couldn't appreciate it enough \^\_\^ TL;DR I spent 6 months on a tool that currently has 0 users. Below is what I learned during my journey, sharing because I believe most mistakes are easily avoidable. Do not overestimate your product and assume it will be an exception to fundamental principles. Principles are there for a reason. Always look for validation before you start. Avoid building products with a low money-to-effort ratio/in very competitive fields. Unless you have the means, you probably won't make it. Pick a problem space, pick your target audience, and talk to them before thinking about a solution. Identify and match their pain points. Only then should you think of a solution. If people are not overly excited or willing to pay in advance for a discounted price, it might be a sign to rethink. Sell one and only one feature at a time. Avoid everything else. If people don't pay for that one core feature, no secondary feature will change their mind. Always spend twice as much time marketing as you do building. You will not get users if they don't know it exists. Define success metrics ("1000 users in 3 months" or "$6000 in the account at the end of 6 months") before you start. If you don't meet them, strongly consider quitting the project. If you can't get enough users to keep going, nothing else matters. VALIDATION, VALIDATION, VALIDATION. Success is not random, but most of our first products will not make a success story. Know when to admit failure, and move on. Even if a product of yours doesn't succeed, what you learned during its journey will turn out to be invaluable for your future. My story So, this is the story of a product, Summ, that I’ve been working on for the last 6 months. As it's the first product I’ve ever built, after watching you all from the sidelines, I have learned a lot, made many mistakes, and did only a few things right. Just sharing what I’ve learned and some insights from my journey so far. I hope that this post will help you avoid the mistakes I made — most of which I consider easily avoidable — while you enjoy reading it, and get to know me a little bit more 🤓. A slow start after many years Summ isn’t the first product I really wanted to build. Lacking enough dev skills to even get started was a huge blocker for so many years. In fact, the first product I would’ve LOVED to build was a smart personal shopping assistant. I had this idea 4 years ago; but with no GPT, no coding skills, no technical co-founder, I didn’t have the means to make it happen. I still do not know if such a tool exists and is good enough. All I wanted was a tool that could make data-based predictions about when to buy stuff (“buy a new toothpaste every three months”) and suggest physical products that I might need or be strongly interested in. AFAIK, Amazon famously still struggles with the second one. Fast-forward a few years, I learned the very basics of HTML, CSS, and Vanilla JS. Still was not there to build a product; but good enough to code my design portfolio from scratch. Yet, I couldn’t imagine myself building a product using Vanilla JS. I really hated it, I really sucked at it. So, back to tutorial hell, and to learn about this framework I just heard about: React.React introduced so many new concepts to me. “Thinking in React” is a phrase we heard a lot, and with quite good reasons. After some time, I was able to build very basic tutorial apps, both in React, and React Native; but I have to say that I really hated coding for mobile. At this point, I was already a fan of productivity apps, and had a concept for a time management assistant app in my design portfolio. So, why not build one? Surely, it must be easy, since every coding tutorial starts with a todo app. ❌ WRONG! Building a basic todo app is easy enough, but building one good enough for a place in the market was a challenge I took and failed. I wasted one month on that until I abandoned the project for good. Even if I continued working on it, as the productivity landscape is overly competitive, I wouldn’t be able to make enough money to cover costs, assuming I make any. Since I was (and still am) in between jobs, I decided to abandon the project. 👉 What I learned: Do not start projects with a low ratio of money to effort and time. Example: Even if I get 500 monthly users, 200 of which are paid users (unrealistically high number), assuming an average subscription fee of $5/m (such apps are quite cheap, mostly due to the high competition), it would make me around $1000 minus any occurring costs. Any founder with a product that has 500 active users should make more. Even if it was relatively successful, due to the high competition, I wouldn’t make any meaningful money. PS: I use Todoist today. Due to local pricing, I pay less than $2/m. There is no way I could beat this competitive pricing, let alone the app itself. But, somehow, with a project that wasn’t even functional — let alone being an MVP — I made my first Wi-Fi money: Someone decided that the domain I preemptively purchased is worth something. By this point, I had already abandoned the project, certainly wasn’t going to renew the domain, was looking for a FT job, and a new project that I could work on. And out of nowhere, someone hands me some free money — who am I not to take it? Of course, I took it. The domain is still unused, no idea why 🤔. Ngl, I still hate the fact that my first Wi-Fi money came from this. A new idea worth pursuing? Fast-forward some weeks now. Around March, I got this crazy idea of building an email productivity tool. We all use emails, yet we all hate them. So, this must be fixed. Everyone uses emails, in fact everyone HAS TO use emails. So, I just needed to build a tool and wait for people to come. This was all, really. After all, the problem space is huge, there is enough room for another product, everyone uses emails, no need for any further validation, right? ❌ WRONG ONCE AGAIN! We all hear from the greatest in the startup landscape that we must validate our ideas with real people, yet at least some of us (guilty here 🥸) think that our product will be hugely successful and prove them to be an exception. Few might, but most are not. I certainly wasn't. 👉 Lesson learned: Always validate your ideas with real people. Ask them how much they’d pay for such a tool (not if they would). Much better if they are willing to pay upfront for a discount, etc. But even this comes later, keep reading. I think the difference between “How much” and “If” is huge for two reasons: (1) By asking them for “How much”, you force them to think in a more realistic setting. (2) You will have a more realistic idea on your profit margins. Based on my competitive analysis, I already had a solution in my mind to improve our email usage standards and email productivity (huge mistake), but I did my best to learn about their problems regarding those without pushing the idea too hard. The idea is this: Generate concise email summaries with suggested actions, combine them into one email, and send it at their preferred times. Save as much as time the AI you end up with allows. After all, everyone loves to save time. So, what kind of validation did I seek for? Talked with only a few people around me about this crazy, internet-breaking idea. The responses I got were, now I see, mediocre; no one got excited about it, just said things along the lines of “Cool idea, OK”. So, any reasonable person in this situation would think “Okay, not might not be working”, right? Well, I did not. I assumed that they were the wrong audience for this product, and there was this magical land of user segments waiting eagerly for my product, yet unknowingly. To this day, I still have not reached this magical place. Perhaps, it didn’t exist in the first place. If I cannot find it, whether it exists or not doesn’t matter. I am certainly searching for it. 👉 What I should have done: Once I decide on a problem space (time management, email productivity, etc.), I should decide on my potential user segments, people who I plan to sell my product to. Then I should go talk to those people, ask them about their pains, then get to the problem-solving/ideation phase only later. ❗️ VALIDATION COMES FROM THE REALITY OUTSIDE. What validation looks like might change from product to product; but what invalidation looks like is more or less the same for every product. Nico Jeannen told me yesterday “validation = money in the account” on Twitter. This is the ultimate form of validation your product could get. If your product doesn’t make any money, then something is invalidated by reality: Your product, you, your idea, who knows? So, at this point, I knew a little bit of Python from spending some time in tutorial hell a few years ago, some HTML/CSS/JS, barely enough React to build a working app. React could work for this project, but I needed easy-to-implement server interactivity. Luckily, around this time, I got to know about this new gen of indie hackers, and learned (but didn’t truly understand) about their approach to indie hacking, and this library called Nextjs. How good Next.js still blows my mind. So, I was back to tutorial hell once again. But, this time, with a promise to myself: This is the last time I would visit tutorial hell. Time to start building this "ground-breaking idea" Learning the fundamentals of Next.js was easier than learning of React unsurprisingly. Yet, the first time I managed to run server actions on Next.js was one of the rarest moments that completely blew my mind. To this day, I reject the idea that it is something else than pure magic under its hood. Did I absolutely need Nextjs for this project though? I do not think so. Did it save me lots of time? Absolutely. Furthermore, learning Nextjs will certainly be quite helpful for other projects that I will be tackling in the future. Already got a few ideas that might be worth pursuing in the head in case I decide to abandon Summ in the future. Fast-forward few weeks again: So, at this stage, I had a barely working MVP-like product. Since the very beginning, I spent every free hour (and more) on this project as speed is essential. But, I am not so sure it was worth it to overwork in retrospect. Yet, I know I couldn’t help myself. Everything is going kinda smooth, so what’s the worst thing that could ever happen? Well, both Apple and Google announced their AIs (Apple Intelligence and Google Gemini, respectively) will have email summarization features for their products. Summarizing singular emails is no big deal, after all there were already so many similar products in the market. I still think that what truly matters is a frictionless user experience, and this is why I built this product in a certain way: You spend less than a few minutes setting up your account, and you get to enjoy your email summaries, without ever visiting its website again. This is still a very cool concept I really like a lot. So, at this point: I had no other idea that could be pursued, already spent too much time on this project. Do I quit or not? This was the question. Of course not. I just have to launch this product as quickly as possible. So, I did something right, a quite rare occurrence I might say: Re-planned my product, dropped everything secondary to the core feature immediately (save time on reading emails), tried launching it asap. 👉 Insight: Sell only one core feature at one time. Drop anything secondary to this core feature. Well, my primary occupation is product design. So one would expect that a product I build must have stellar design. I considered any considerable time spent on design at this stage would be simply wasted. I still think this is both true and wrong: True, because if your product’s core benefits suck, no one will care about your design. False, because if your design looks amateurish, no one will trust you and your product. So, I always targeted an average level design with it and the way this tool works made it quite easy as I had to design only 2 primary pages: Landing page and user portal (which has only settings and analytics pages). However, even though I knew spending time on design was not worth much of my time, I got a bit “greedy”: In fact, I redesigned those pages three times, and still ended up with a so-so design that I am not proud of. 👉 What I would do differently: Unless absolutely necessary, only one iteration per stage as long as it works. This, in my mind, applies to everything. If your product’s A feature works, then no need to rewrite it from scratch for any reason, or even refactor it. When your product becomes a success, and you absolutely need that part of your codebase to be written, do so, but only then. Ready to launch, now is th etime for some marketing, right? By July 26, I already had a “launchable” product that barely works (I marked this date on a Notion docs, this is how I know). Yet, I had spent almost no time on marketing, sales, whatever. After all, “You build and they will come”. Did I know that I needed marketing? Of course I did, but knowingly didn’t. Why, you might ask. Well, from my perspective, it had to be a dev-heavy product; meaning that you spend most of your time on developing it, mostly coding skills. But, this is simply wrong. As a rule of thumb, as noted by one of the greatests, Marc Louvion, you should spend at least twice of the building time on marketing. ❗️ Time spent on building \* 2 people don’t know your product > they don’t use your product > you don’t get users > you don’t make money Easy as that. Following the same reasoning, a slightly different approach to planning a project is possible. Determine an approximate time to complete the project with a high level project plan. Let’s say 6 months. By the reasoning above, 2 months should go into building, and 4 into marketing. If you need 4 months for building instead of 2, then you need 8 months of marketing, which makes the time to complete the project 12 months. If you don’t have that much time, then quit the project. When does a project count as completed? Well, in reality, never. But, I think we have to define success conditions even before we start for indie projects and startups; so we know when to quit when they are not met. A success condition could look like “Make $6000 in 12 months” or “Have 3000 users in 6 months”. It all depends on the project. But, once you set it, it should be set in stone: You don’t change it unless absolutely necessary. I suspect there are few principles that make a solopreneur successful; and knowing when to quit and when to continue is definitely one of them. Marc Louvion is famously known for his success, but he got there after failing so many projects. To my knowledge, the same applies to Nico Jeannen, Pieter Levels, or almost everyone as well. ❗️ Determining when to continue even before you start will definitely help in the long run. A half-aed launch Time-leap again. Around mid August, I “soft launched” my product. By soft launch, I mean lazy marketing. Just tweeting about it, posting it on free directories. Did I get any traffic? Surely I did. Did I get any users? Nope. Only after this time, it hit me: “Either something is wrong with me, or with this product” Marketing might be a much bigger factor for a project’s success after all. Even though I get some traffic, not convincing enough for people to sign up even for a free trial. The product was still perfect in my eyes at the time (well, still is ^(\_),) so the right people are not finding my product, I thought. Then, a question that I should have been asking at the very first place, one that could prevent all these, comes to my mind: “How do even people search for such tools?” If we are to consider this whole journey of me and my so-far-failed product to be an already destined failure, one metric suffices to show why. Search volume: 30. Even if people have such a pain point, they are not looking for email summaries. So, almost no organic traffic coming from Google. But, as a person who did zero marketing on this or any product, who has zero marketing knowledge, who doesn’t have an audience on social media, there is not much I could do. Finally, it was time to give up. Or not… In my eyes, the most important element that makes a founder (solo or not) successful (this, I am not by any means) is to solve problems. ❗️ So, the problem was this: “People are not finding my product by organic search” How do I make sure I get some organic traffic and gets more visibility? Learn digital marketing and SEO as much as I can within very limited time. Thankfully, without spending much time, I came across Neil Patel's YT channel, and as I said many times, it is an absolute gold mine. I learned a lot, especially about the fundamentals, and surely it will be fruitful; but there is no magic trick that could make people visit your website. SEO certainly helps, but only when people are looking for your keywords. However, it is truly a magical solution to get in touch with REAL people that are in your user segments: 👉 Understand your pains, understand their problems, help them to solve them via building products. I did not do this so far, have to admit. But, in case you would like to have a chat about your email usage, and email productivity, just get in touch; I’d be delighted to hear about them. Getting ready for a ProductHunt launch The date was Sept 1. And I unlocked an impossible achievement: Running out of Supabase’s free plan’s Egres limit while having zero users. I was already considering moving out of their Cloud server and managing a Supabase CLI service on my Hetzner VPS for some time; but never ever suspected that I would have to do this quickly. The cheapest plan Supabase offers is $25/month; yet, at that point, I am in between jobs for such a long time, basically broke, and could barely afford that price. One or two months could be okay, but why pay for it if I will eventually move out of their Cloud service? So, instead of paying $25, I spent two days migrating out of Supabase Cloud. Worth my time? Definitely not. But, when you are broke, you gotta do stupid things. This was the first time that I felt lucky to have zero users: I have no idea how I would manage this migration if I had any. I think this is one of the core tenets of an indie hacker: Controlling their own environment. I can’t remember whose quote this is, but I suspect it was Naval: Entrepreneurs have an almost pathological need to control their own fate. They will take any suffering if they can be in charge of their destiny, and not have it in somebody else’s hands. What’s truly scary is, at least in my case, we make people around us suffer at the expense of our attempting to control our own fates. I know this period has been quite hard on my wife as well, as I neglected her quite a bit, but sadly, I know that this will happen again. It is something that I can barely help with. Still, so sorry. After working the last two weeks on a ProductHunt Launch, I finally launched it this Tuesday. Zero ranking, zero new users, but 36 kind people upvoted my product, and many commented and provided invaluable feedback. I couldn't be more grateful for each one of them 🙏. Considering all these, what lies in the future of Summ though? I have no idea, to be honest. On one hand, I have zero users, have no job, no income. So, I need a way to make money asap. On the other hand, the whole idea of it revolves around one core premise (not an assumption) that I am not so willing to share; and I couldn’t have more trust in it. This might not be the best iteration of it, however I certainly believe that email usage is one of the best problem spaces one could work on. 👉 But, one thing is for certain: I need to get in touch with people, and talk with them about this product I built so far. In fact, this is the only item on my agenda. Nothing else will save my brainchild <3. Below are some other insights and notes that I got during my journey; as they do not 100% fit into this story, I think it is more suitable to list them here. I hope you enjoyed reading this. Give Summ a try, it comes with a generous free trial, no credit card required. Some additional notes and insights: Project planning is one of the most underestimated skills for solopreneurs. It saves you enormous time, and helps you to keep your focus up. Building B2C products beats building B2B products. Businesses are very willing to pay big bucks if your product helps them. On the other hand, spending a few hours per user who would pay $5/m probably is not worth your time. It doesn’t matter how brilliant your product is if no one uses it. If you cannot sell a product in a certain category/niche (or do not know how to sell it), it might be a good idea not to start a project in it. Going after new ideas and ventures is quite risky, especially if you don’t know how to market it. On the other hand, an already established category means that there is already demand. Whether this demand is sufficient or not is another issue. As long as there is enough demand for your product to fit in, any category/niche is good. Some might be better, some might be worse. Unless you are going hardcore B2B, you will need people to find your product by means of organic search. Always conduct thorough keyword research as soon as possible.

#reddit#spent#months

reddit

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1

Disastrous-Airport88•This week

From Running a $350M Startup to Failing Big and Rediscovering What Really Matters in Life ❤️

This is my story. I’ve always been a hustler. I don’t remember a time I wasn’t working since I was 14. Barely slept 4 hours a night, always busy—solving problems, putting out fires. After college (LLB and MBA), I was lost. I tried regular jobs but couldn’t get excited, and when I’m not excited, I spiral. But I knew entrepreneurship; I just didn’t realize it was an option for adults. Then, in 2017 a friend asked me to help with their startup. “Cool,” I thought. Finally, a place where I could solve problems all day. It was a small e-commerce idea, tackling an interesting angle. I worked 17-hour days, delivering on a bike, talking to customers, vendors, and even random people on the street. Things moved fast. We applied to Y Combinator, got in, and raised $18M before Demo Day even started. We grew 100% month-over-month. Then came another $40M, and I moved to NYC. Before I knew it, we had 1,000 employees and raised $80M more. I was COO, managing 17 direct reports (VPs of Ops, Finance, HR, Data, and more) and 800 indirect employees. On the surface, I was on top of the world. But in reality, I was at rock bottom. I couldn’t sleep, drowning in anxiety, and eventually ended up on antidepressants. Then 2022 hit. We needed to raise $100M, but we couldn’t. In three brutal months, we laid off 900 people. It was the darkest period of my life. I felt like I’d failed everyone—myself, investors, my company, and my team. I took a year off. Packed up the car with my wife and drove across Europe, staying in remote places, just trying to calm my nervous system. I couldn’t speak to anyone, felt ashamed, and battled deep depression. It took over a year, therapy, plant medicine, intense morning routines, and a workout regimen to get back on my feet, physically and mentally. Now, I’m on the other side. In the past 6 months, I’ve been regaining my mojo, with a new respect for who I am and why I’m here. I made peace with what I went through over those 7 years—the lessons, the people, the experiences. I started reconnecting with my community, giving back. Every week, I have conversations with young founders, offering direction, or even jumping in to help with their operations. It’s been a huge gift. I also began exploring side projects. I never knew how to code, but I’ve always had ideas. Recent advances in AI gave me the push I needed. I built my first app, as my first attempt at my true passion—consumer products for kids. Today, I feel wholesome about my journey. I hope others can see that too. ❤️ EDIT: Wow, I didn’t expect this post to resonate with so many people. A lot of you have DM’d me, and I’ll try to respond. Just a heads-up, though—I’m juggling consulting and new projects, so I can’t jump on too many calls. Since I’m not promoting anything, I won’t be funneling folks to my page, so forgive me if I don’t get back to everyone. Anyway, it’s amazing to connect with so many of you. I’d love to write more, so let me know what topics you’d be interested in!

#reddit#running#$350m

reddit

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1

wlynncork•This week

How to get funding for startup ? I will not promote

I will not promote. Software startup based out of Minnesota us. I've built and launched a product that is gaining traction, solving a problem that has frustrated software developers and product teams for years. The problem: Software development is slow, expensive, and full of inefficiencies. Developers spend hours on repetitive coding tasks, project managers struggle with bottlenecks, and businesses waste time translating product requirements into actual code. The solution: My product automates a large portion of software development. It acts as an AI-powered assistant for developers, taking high-level requirements and turning them into functional code while integrating with existing codebases. It can read, understand, and modify software projects in a structured way—cutting development time drastically. The potential: Businesses are always looking for ways to cut costs and speed up development. With the rise of AI, companies are increasingly adopting automation, and this tool fits perfectly into that wave. Imagine a world where software teams are 10x more efficient because AI handles the grunt work, and developers focus on the bigger picture. It’s not about replacing developers—it’s about supercharging them. The current status: The product is live and in use. The user base is growing, and I’ve proven demand. Now, I need to figure out the best funding model to scale—whether that’s bootstrapping, VC, grants, or some hybrid approach. If you have experience in startup funding or have scaled a tech product, I'd love to hear your insights. DM me if you're open to discussing strategies!

#reddit#funding#startup

reddit

0

1

stellarcitizen•This week

I fell into the builder's trap and need help getting out

Hi r/startups, First-time technical founder here. Two years ago, I decided to leave the 9-5 grind and build something meaningful. Now, I have (what I believe is) a brilliant technical solution but no clear business case. I’m seeking a cofounder with product and marketing expertise to help pivot my project into a viable business - or start a new one. Details below. About Me 36yo, born in Berlin and moved to San Francisco 8 years ago Master's in Software Engineering with 15 years of experience Worked with early-stage startups in Berlin and a venture studio in SF Spent the past years leading a team of 12 shipping enterprise software The tech I've built An AI engine that makes it easy for developers to automate their workflows. It works with code, issues, PRs and integrates with 3rd party systems like error trackers, wikis, ticketing systems, etc. It takes natural language instructions, fulfills them autonomously and responds with a result. The functionality is served as a platform, with an API and an SDK. On top of it, I've built a CLI and a web application with productivity tools for developers. Who and what I'm looking for My main goal is to leave my current job and build a company around a problem that matters to me, ideally with considerable equity. I’m looking for: A cofounder with product and marketing expertise who sees potential in my tech and can help turn it into a successful business—or someone with a strong business case who needs a technical founder. Mentorship from someone experienced in dev tool startups or as a successful solo founder. I’d love to learn from your journey and would be happy to offer my technical expertise or collaborate on projects in return. Happy to answer any questions or provide more details. Cheers!

#reddit#fell#into

reddit

0

0.667

GDbuildsGD•This week

I spent 6 months on building a web product, and got zero users. Here is my story.

Edit Thank you all so much for your time reading my story. Your support, feedback, criticism, and skepticism; all helped me a lot, and I couldn't appreciate it enough \^\_\^ I have stuff to post on Reddit very rarely, but I share how my project is going on, random stuff, and memes on X. Just in case few might want to keep in touch 👀 TL;DR I spent 6 months on a tool that currently has 0 users. Below is what I learned during my journey, sharing because I believe most mistakes are easily avoidable. Do not overestimate your product and assume it will be an exception to fundamental principles. Principles are there for a reason. Always look for validation before you start. Avoid building products with a low money-to-effort ratio/in very competitive fields. Unless you have the means, you probably won't make it. Pick a problem space, pick your target audience, and talk to them before thinking about a solution. Identify and match their pain points. Only then should you think of a solution. If people are not overly excited or willing to pay in advance for a discounted price, it might be a sign to rethink. Sell one and only one feature at a time. Avoid everything else. If people don't pay for that one core feature, no secondary feature will change their mind. Always spend twice as much time marketing as you do building. You will not get users if they don't know it exists. Define success metrics ("1000 users in 3 months" or "$6000 in the account at the end of 6 months") before you start. If you don't meet them, strongly consider quitting the project. If you can't get enough users to keep going, nothing else matters. VALIDATION, VALIDATION, VALIDATION. Success is not random, but most of our first products will not make a success story. Know when to admit failure, and move on. Even if a product of yours doesn't succeed, what you learned during its journey will turn out to be invaluable for your future. My story So, this is the story of a product that I’ve been working on for the last 6 months. As it's the first product I’ve ever built, after watching you all from the sidelines, I have learned a lot, made many mistakes, and did only a few things right. Just sharing what I’ve learned and some insights from my journey so far. I hope that this post will help you avoid the mistakes I made — most of which I consider easily avoidable — while you enjoy reading it, and get to know me a little bit more 🤓. A slow start after many years Summ isn’t the first product I really wanted to build. Lacking enough dev skills to even get started was a huge blocker for so many years. In fact, the first product I would’ve LOVED to build was a smart personal shopping assistant. I had this idea 4 years ago; but with no GPT, no coding skills, no technical co-founder, I didn’t have the means to make it happen. I still do not know if such a tool exists and is good enough. All I wanted was a tool that could make data-based predictions about when to buy stuff (“buy a new toothpaste every three months”) and suggest physical products that I might need or be strongly interested in. AFAIK, Amazon famously still struggles with the second one. Fast-forward a few years, I learned the very basics of HTML, CSS, and Vanilla JS. Still was not there to build a product; but good enough to code my design portfolio from scratch. Yet, I couldn’t imagine myself building a product using Vanilla JS. I really hated it, I really sucked at it. So, back to tutorial hell, and to learn about this framework I just heard about: React.React introduced so many new concepts to me. “Thinking in React” is a phrase we heard a lot, and with quite good reasons. After some time, I was able to build very basic tutorial apps, both in React, and React Native; but I have to say that I really hated coding for mobile. At this point, I was already a fan of productivity apps, and had a concept for a time management assistant app in my design portfolio. So, why not build one? Surely, it must be easy, since every coding tutorial starts with a todo app. ❌ WRONG! Building a basic todo app is easy enough, but building one good enough for a place in the market was a challenge I took and failed. I wasted one month on that until I abandoned the project for good. Even if I continued working on it, as the productivity landscape is overly competitive, I wouldn’t be able to make enough money to cover costs, assuming I make any. Since I was (and still am) in between jobs, I decided to abandon the project. 👉 What I learned: Do not start projects with a low ratio of money to effort and time. Example: Even if I get 500 monthly users, 200 of which are paid users (unrealistically high number), assuming an average subscription fee of $5/m (such apps are quite cheap, mostly due to the high competition), it would make me around $1000 minus any occurring costs. Any founder with a product that has 500 active users should make more. Even if it was relatively successful, due to the high competition, I wouldn’t make any meaningful money. PS: I use Todoist today. Due to local pricing, I pay less than $2/m. There is no way I could beat this competitive pricing, let alone the app itself. But, somehow, with a project that wasn’t even functional — let alone being an MVP — I made my first Wi-Fi money: Someone decided that the domain I preemptively purchased is worth something. By this point, I had already abandoned the project, certainly wasn’t going to renew the domain, was looking for a FT job, and a new project that I could work on. And out of nowhere, someone hands me some free money — who am I not to take it? Of course, I took it. The domain is still unused, no idea why 🤔. Ngl, I still hate the fact that my first Wi-Fi money came from this. A new idea worth pursuing? Fast-forward some weeks now. Around March, I got this crazy idea of building an email productivity tool. We all use emails, yet we all hate them. So, this must be fixed. Everyone uses emails, in fact everyone HAS TO use emails. So, I just needed to build a tool and wait for people to come. This was all, really. After all, the problem space is huge, there is enough room for another product, everyone uses emails, no need for any further validation, right? ❌ WRONG ONCE AGAIN! We all hear from the greatest in the startup landscape that we must validate our ideas with real people, yet at least some of us (guilty here 🥸) think that our product will be hugely successful and prove them to be an exception. Few might, but most are not. I certainly wasn't. 👉 Lesson learned: Always validate your ideas with real people. Ask them how much they’d pay for such a tool (not if they would). Much better if they are willing to pay upfront for a discount, etc. But even this comes later, keep reading. I think the difference between “How much” and “If” is huge for two reasons: (1) By asking them for “How much”, you force them to think in a more realistic setting. (2) You will have a more realistic idea on your profit margins. Based on my competitive analysis, I already had a solution in my mind to improve our email usage standards and email productivity (huge mistake), but I did my best to learn about their problems regarding those without pushing the idea too hard. The idea is this: Generate concise email summaries with suggested actions, combine them into one email, and send it at their preferred times. Save as much as time the AI you end up with allows. After all, everyone loves to save time. So, what kind of validation did I seek for? Talked with only a few people around me about this crazy, internet-breaking idea. The responses I got were, now I see, mediocre; no one got excited about it, just said things along the lines of “Cool idea, OK”. So, any reasonable person in this situation would think “Okay, not might not be working”, right? Well, I did not. I assumed that they were the wrong audience for this product, and there was this magical land of user segments waiting eagerly for my product, yet unknowingly. To this day, I still have not reached this magical place. Perhaps, it didn’t exist in the first place. If I cannot find it, whether it exists or not doesn’t matter. I am certainly searching for it. 👉 What I should have done: Once I decide on a problem space (time management, email productivity, etc.), I should decide on my potential user segments, people who I plan to sell my product to. Then I should go talk to those people, ask them about their pains, then get to the problem-solving/ideation phase only later. ❗️ VALIDATION COMES FROM THE REALITY OUTSIDE. What validation looks like might change from product to product; but what invalidation looks like is more or less the same for every product. Nico Jeannen told me yesterday “validation = money in the account” on Twitter. This is the ultimate form of validation your product could get. If your product doesn’t make any money, then something is invalidated by reality: Your product, you, your idea, who knows? So, at this point, I knew a little bit of Python from spending some time in tutorial hell a few years ago, some HTML/CSS/JS, barely enough React to build a working app. React could work for this project, but I needed easy-to-implement server interactivity. Luckily, around this time, I got to know about this new gen of indie hackers, and learned (but didn’t truly understand) about their approach to indie hacking, and this library called Nextjs. How good Next.js still blows my mind. So, I was back to tutorial hell once again. But, this time, with a promise to myself: This is the last time I would visit tutorial hell. Time to start building this "ground-breaking idea" Learning the fundamentals of Next.js was easier than learning of React unsurprisingly. Yet, the first time I managed to run server actions on Next.js was one of the rarest moments that completely blew my mind. To this day, I reject the idea that it is something else than pure magic under its hood. Did I absolutely need Nextjs for this project though? I do not think so. Did it save me lots of time? Absolutely. Furthermore, learning Nextjs will certainly be quite helpful for other projects that I will be tackling in the future. Already got a few ideas that might be worth pursuing in the head in case I decide to abandon Summ in the future. Fast-forward few weeks again: So, at this stage, I had a barely working MVP-like product. Since the very beginning, I spent every free hour (and more) on this project as speed is essential. But, I am not so sure it was worth it to overwork in retrospect. Yet, I know I couldn’t help myself. Everything is going kinda smooth, so what’s the worst thing that could ever happen? Well, both Apple and Google announced their AIs (Apple Intelligence and Google Gemini, respectively) will have email summarization features for their products. Summarizing singular emails is no big deal, after all there were already so many similar products in the market. I still think that what truly matters is a frictionless user experience, and this is why I built this product in a certain way: You spend less than a few minutes setting up your account, and you get to enjoy your email summaries, without ever visiting its website again. This is still a very cool concept I really like a lot. So, at this point: I had no other idea that could be pursued, already spent too much time on this project. Do I quit or not? This was the question. Of course not. I just have to launch this product as quickly as possible. So, I did something right, a quite rare occurrence I might say: Re-planned my product, dropped everything secondary to the core feature immediately (save time on reading emails), tried launching it asap. 👉 Insight: Sell only one core feature at one time. Drop anything secondary to this core feature. Well, my primary occupation is product design. So one would expect that a product I build must have stellar design. I considered any considerable time spent on design at this stage would be simply wasted. I still think this is both true and wrong: True, because if your product’s core benefits suck, no one will care about your design. False, because if your design looks amateurish, no one will trust you and your product. So, I always targeted an average level design with it and the way this tool works made it quite easy as I had to design only 2 primary pages: Landing page and user portal (which has only settings and analytics pages). However, even though I knew spending time on design was not worth much of my time, I got a bit “greedy”: In fact, I redesigned those pages three times, and still ended up with a so-so design that I am not proud of. 👉 What I would do differently: Unless absolutely necessary, only one iteration per stage as long as it works. This, in my mind, applies to everything. If your product’s A feature works, then no need to rewrite it from scratch for any reason, or even refactor it. When your product becomes a success, and you absolutely need that part of your codebase to be written, do so, but only then. Ready to launch, now is th etime for some marketing, right? By July 26, I already had a “launchable” product that barely works (I marked this date on a Notion docs, this is how I know). Yet, I had spent almost no time on marketing, sales, whatever. After all, “You build and they will come”. Did I know that I needed marketing? Of course I did, but knowingly didn’t. Why, you might ask. Well, from my perspective, it had to be a dev-heavy product; meaning that you spend most of your time on developing it, mostly coding skills. But, this is simply wrong. As a rule of thumb, as noted by one of the greatests, Marc Louvion, you should spend at least twice of the building time on marketing. ❗️ Time spent on building \* 2 people don’t know your product > they don’t use your product > you don’t get users > you don’t make money Easy as that. Following the same reasoning, a slightly different approach to planning a project is possible. Determine an approximate time to complete the project with a high level project plan. Let’s say 6 months. By the reasoning above, 2 months should go into building, and 4 into marketing. If you need 4 months for building instead of 2, then you need 8 months of marketing, which makes the time to complete the project 12 months. If you don’t have that much time, then quit the project. When does a project count as completed? Well, in reality, never. But, I think we have to define success conditions even before we start for indie projects and startups; so we know when to quit when they are not met. A success condition could look like “Make $6000 in 12 months” or “Have 3000 users in 6 months”. It all depends on the project. But, once you set it, it should be set in stone: You don’t change it unless absolutely necessary. I suspect there are few principles that make a solopreneur successful; and knowing when to quit and when to continue is definitely one of them. Marc Louvion is famously known for his success, but he got there after failing so many projects. To my knowledge, the same applies to Nico Jeannen, Pieter Levels, or almost everyone as well. ❗️ Determining when to continue even before you start will definitely help in the long run. A half-aed launch Time-leap again. Around mid August, I “soft launched” my product. By soft launch, I mean lazy marketing. Just tweeting about it, posting it on free directories. Did I get any traffic? Surely I did. Did I get any users? Nope. Only after this time, it hit me: “Either something is wrong with me, or with this product” Marketing might be a much bigger factor for a project’s success after all. Even though I get some traffic, not convincing enough for people to sign up even for a free trial. The product was still perfect in my eyes at the time (well, still is ^(\_),) so the right people are not finding my product, I thought. Then, a question that I should have been asking at the very first place, one that could prevent all these, comes to my mind: “How do even people search for such tools?” If we are to consider this whole journey of me and my so-far-failed product to be an already destined failure, one metric suffices to show why. Search volume: 30. Even if people have such a pain point, they are not looking for email summaries. So, almost no organic traffic coming from Google. But, as a person who did zero marketing on this or any product, who has zero marketing knowledge, who doesn’t have an audience on social media, there is not much I could do. Finally, it was time to give up. Or not… In my eyes, the most important element that makes a founder (solo or not) successful (this, I am not by any means) is to solve problems. ❗️ So, the problem was this: “People are not finding my product by organic search” How do I make sure I get some organic traffic and gets more visibility? Learn digital marketing and SEO as much as I can within very limited time. Thankfully, without spending much time, I came across Neil Patel's YT channel, and as I said many times, it is an absolute gold mine. I learned a lot, especially about the fundamentals, and surely it will be fruitful; but there is no magic trick that could make people visit your website. SEO certainly helps, but only when people are looking for your keywords. However, it is truly a magical solution to get in touch with REAL people that are in your user segments: 👉 Understand your pains, understand their problems, help them to solve them via building products. I did not do this so far, have to admit. But, in case you would like to have a chat about your email usage, and email productivity, just get in touch; I’d be delighted to hear about them. Getting ready for a ProductHunt launch The date was Sept 1. And I unlocked an impossible achievement: Running out of Supabase’s free plan’s Egres limit while having zero users. I was already considering moving out of their Cloud server and managing a Supabase CLI service on my Hetzner VPS for some time; but never ever suspected that I would have to do this quickly. The cheapest plan Supabase offers is $25/month; yet, at that point, I am in between jobs for such a long time, basically broke, and could barely afford that price. One or two months could be okay, but why pay for it if I will eventually move out of their Cloud service? So, instead of paying $25, I spent two days migrating out of Supabase Cloud. Worth my time? Definitely not. But, when you are broke, you gotta do stupid things. This was the first time that I felt lucky to have zero users: I have no idea how I would manage this migration if I had any. I think this is one of the core tenets of an indie hacker: Controlling their own environment. I can’t remember whose quote this is, but I suspect it was Naval: Entrepreneurs have an almost pathological need to control their own fate. They will take any suffering if they can be in charge of their destiny, and not have it in somebody else’s hands. What’s truly scary is, at least in my case, we make people around us suffer at the expense of our attempting to control our own fates. I know this period has been quite hard on my wife as well, as I neglected her quite a bit, but sadly, I know that this will happen again. It is something that I can barely help with. Still, so sorry. After working the last two weeks on a ProductHunt Launch, I finally launched it this Tuesday. Zero ranking, zero new users, but 36 kind people upvoted my product, and many commented and provided invaluable feedback. I couldn't be more grateful for each one of them 🙏. Considering all these, what lies in the future of Summ though? I have no idea, to be honest. On one hand, I have zero users, have no job, no income. So, I need a way to make money asap. On the other hand, the whole idea of it revolves around one core premise (not an assumption) that I am not so willing to share; and I couldn’t have more trust in it. This might not be the best iteration of it, however I certainly believe that email usage is one of the best problem spaces one could work on. 👉 But, one thing is for certain: I need to get in touch with people, and talk with them about this product I built so far. In fact, this is the only item on my agenda. Nothing else will save my brainchild <3. Below are some other insights and notes that I got during my journey; as they do not 100% fit into this story, I think it is more suitable to list them here. I hope you enjoyed reading this. Give Summ a try, it comes with a generous free trial, no credit card required. Some additional notes and insights: Project planning is one of the most underestimated skills for solopreneurs. It saves you enormous time, and helps you to keep your focus up. Building B2C products beats building B2B products. Businesses are very willing to pay big bucks if your product helps them. On the other hand, spending a few hours per user who would pay $5/m probably is not worth your time. It doesn’t matter how brilliant your product is if no one uses it. If you cannot sell a product in a certain category/niche (or do not know how to sell it), it might be a good idea not to start a project in it. Going after new ideas and ventures is quite risky, especially if you don’t know how to market it. On the other hand, an already established category means that there is already demand. Whether this demand is sufficient or not is another issue. As long as there is enough demand for your product to fit in, any category/niche is good. Some might be better, some might be worse. Unless you are going hardcore B2B, you will need people to find your product by means of organic search. Always conduct thorough keyword research as soon as possible.

#reddit#spent#months

reddit

0

1

Awkward_Ad_9605•This week

Idea Validation Post: Seeking Feedback on My AI-Driven Quick Launch Application! 🚀

Hey Members! I’m excited to share an idea for a new application I’m planning to build: Quick Launch . This AI-driven platform is designed to assist solopreneurs or anyone with an idea in launching their Minimum Viable Products (MVPs) by taking on the roles of the entire team needed for the process. Goal: Assistance in quickly moving from Idea to MVP Before I dive into the details, I’d love to hear your thoughts and feedback. Key Features: Product Creation: From Idea to Product Detailing AI-Generated Q&A: Real-time questions generation one-at-a-time to define the product requirements based on their knowledge levels to convert an Idea into a Product. Market Research Reports: In-depth analysis that identifies product-market fit, competitive landscape, and potential marketing strategies. Sentiment Analysis: Evaluation of user feedback and reactions across multiple subreddits to gauge public opinion on ideas. Product Development: Product Detailing to Actual Product User Story Generation: Identification and creation of comprehensive user stories, tasks, and sub-tasks to facilitate development. AI Project Management: AI agents assume roles of project managers and UI/UX designers to streamline product detailing and development. Integration Capabilities: Seamless integration with popular project management tools like Jira, Asana, and Trello for better workflow management. Target Audience: Solopreneurs: Individuals looking to bring their business ideas to life without extensive resources. Indie Hackers: Entrepreneurs focused on building small projects or startups with minimal overhead. Idea Validators: Anyone with a concept seeking initial validation and market feedback before committing significant resources. If you’re interested in learning more, check out our teaser website: Quick Launch Discussion Question: What features would you find most valuable in an application like this? Are there specific pain points you face when launching an MVP? Your insights would be incredibly helpful as I refine this idea! Looking forward to your thoughts! 🙌

#reddit#idea#validation

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ferreiracarcara•This week

Share Your Expertise: AI, Automation, and Efficient Organizational Tools, Strategies and Routines!

Hello everyone, As we navigate through the advancements in AI and automation, it's clear that these technologies are reshaping the way we approach work and business management. To stay ahead, sharing our collective knowledge on these subjects is crucial. I'm inviting this community to share insights and experiences with AI tools, automation strategies, and especially, innovative organizational approaches you've found effective. From automating mundane tasks to optimizing digital marketing strategies, every piece of wisdom is valuable. Here’s what we’re specifically interested in: Automated Workflows: What are your strategies for creating automated workflows that enhance productivity and efficiency? Visual Organization: How do you utilize mind maps and other visual tools to organize thoughts and projects efficiently? Canvas Maps: Have you implemented CANVAS Maps in customer interaction, ideation, strategy development, or action planning? How has it improved your processes? AI in Marketing: How has AI helped you optimize your digital marketing strategies and data analysis? What tools or methodologies have you found most effective? This thread aims to be a resource for all of us to learn from each other's successes and innovations. Whether it’s a simple tip or a comprehensive strategy, your input can significantly impact someone’s approach to challenges. What groundbreaking AI solutions, automation hacks, or organizational methods have you discovered that made a noticeable difference in your work or business? Share your stories and let’s empower each other to achieve greater efficiency and success. Thank you for contributing to our shared journey toward innovation and improvement!

#reddit#share#your

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ElCapitandelmar•This week

Optimizing your business with AI

Everyone's talking about it...it can be a threatening or it can be an advantage, but either way, if you don't anticipate it, you're going to be left in the dust. I opened my sports management business nearly 2 years ago and things have been going really well. The clientele is growing, the revenue and projects are doubling every quarter, and headcount is tripling this year. That being said...as the owner and founder of the business, I'm looking for resources to learn more about streamlining operations and admin. I believe that understanding capabilities, the lynch pins, the applications, and the tools to unleash AI are going to be instrumental to how i grow the business to scale...does anyone have any knowledge or nudges about where I can dig into this? Traditional educational courses? Resources or AI integration businesses? Consultants? Thanks in advance for any and all your suggestions!

#reddit#optimizing#your

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GDbuildsGD•This week

I spent 6 months on building a web product, and got zero users. Here is my story.

Edit Thank you all so much for your time reading my story. Your support, feedback, criticism, and skepticism; all helped me a lot, and I couldn't appreciate it enough \^\_\^ I have stuff to post on Reddit very rarely, but I share how my project is going on, random stuff, and memes on X. Just in case few might want to keep in touch 👀 TL;DR I spent 6 months on a tool that currently has 0 users. Below is what I learned during my journey, sharing because I believe most mistakes are easily avoidable. Do not overestimate your product and assume it will be an exception to fundamental principles. Principles are there for a reason. Always look for validation before you start. Avoid building products with a low money-to-effort ratio/in very competitive fields. Unless you have the means, you probably won't make it. Pick a problem space, pick your target audience, and talk to them before thinking about a solution. Identify and match their pain points. Only then should you think of a solution. If people are not overly excited or willing to pay in advance for a discounted price, it might be a sign to rethink. Sell one and only one feature at a time. Avoid everything else. If people don't pay for that one core feature, no secondary feature will change their mind. Always spend twice as much time marketing as you do building. You will not get users if they don't know it exists. Define success metrics ("1000 users in 3 months" or "$6000 in the account at the end of 6 months") before you start. If you don't meet them, strongly consider quitting the project. If you can't get enough users to keep going, nothing else matters. VALIDATION, VALIDATION, VALIDATION. Success is not random, but most of our first products will not make a success story. Know when to admit failure, and move on. Even if a product of yours doesn't succeed, what you learned during its journey will turn out to be invaluable for your future. My story So, this is the story of a product that I’ve been working on for the last 6 months. As it's the first product I’ve ever built, after watching you all from the sidelines, I have learned a lot, made many mistakes, and did only a few things right. Just sharing what I’ve learned and some insights from my journey so far. I hope that this post will help you avoid the mistakes I made — most of which I consider easily avoidable — while you enjoy reading it, and get to know me a little bit more 🤓. A slow start after many years Summ isn’t the first product I really wanted to build. Lacking enough dev skills to even get started was a huge blocker for so many years. In fact, the first product I would’ve LOVED to build was a smart personal shopping assistant. I had this idea 4 years ago; but with no GPT, no coding skills, no technical co-founder, I didn’t have the means to make it happen. I still do not know if such a tool exists and is good enough. All I wanted was a tool that could make data-based predictions about when to buy stuff (“buy a new toothpaste every three months”) and suggest physical products that I might need or be strongly interested in. AFAIK, Amazon famously still struggles with the second one. Fast-forward a few years, I learned the very basics of HTML, CSS, and Vanilla JS. Still was not there to build a product; but good enough to code my design portfolio from scratch. Yet, I couldn’t imagine myself building a product using Vanilla JS. I really hated it, I really sucked at it. So, back to tutorial hell, and to learn about this framework I just heard about: React.React introduced so many new concepts to me. “Thinking in React” is a phrase we heard a lot, and with quite good reasons. After some time, I was able to build very basic tutorial apps, both in React, and React Native; but I have to say that I really hated coding for mobile. At this point, I was already a fan of productivity apps, and had a concept for a time management assistant app in my design portfolio. So, why not build one? Surely, it must be easy, since every coding tutorial starts with a todo app. ❌ WRONG! Building a basic todo app is easy enough, but building one good enough for a place in the market was a challenge I took and failed. I wasted one month on that until I abandoned the project for good. Even if I continued working on it, as the productivity landscape is overly competitive, I wouldn’t be able to make enough money to cover costs, assuming I make any. Since I was (and still am) in between jobs, I decided to abandon the project. 👉 What I learned: Do not start projects with a low ratio of money to effort and time. Example: Even if I get 500 monthly users, 200 of which are paid users (unrealistically high number), assuming an average subscription fee of $5/m (such apps are quite cheap, mostly due to the high competition), it would make me around $1000 minus any occurring costs. Any founder with a product that has 500 active users should make more. Even if it was relatively successful, due to the high competition, I wouldn’t make any meaningful money. PS: I use Todoist today. Due to local pricing, I pay less than $2/m. There is no way I could beat this competitive pricing, let alone the app itself. But, somehow, with a project that wasn’t even functional — let alone being an MVP — I made my first Wi-Fi money: Someone decided that the domain I preemptively purchased is worth something. By this point, I had already abandoned the project, certainly wasn’t going to renew the domain, was looking for a FT job, and a new project that I could work on. And out of nowhere, someone hands me some free money — who am I not to take it? Of course, I took it. The domain is still unused, no idea why 🤔. Ngl, I still hate the fact that my first Wi-Fi money came from this. A new idea worth pursuing? Fast-forward some weeks now. Around March, I got this crazy idea of building an email productivity tool. We all use emails, yet we all hate them. So, this must be fixed. Everyone uses emails, in fact everyone HAS TO use emails. So, I just needed to build a tool and wait for people to come. This was all, really. After all, the problem space is huge, there is enough room for another product, everyone uses emails, no need for any further validation, right? ❌ WRONG ONCE AGAIN! We all hear from the greatest in the startup landscape that we must validate our ideas with real people, yet at least some of us (guilty here 🥸) think that our product will be hugely successful and prove them to be an exception. Few might, but most are not. I certainly wasn't. 👉 Lesson learned: Always validate your ideas with real people. Ask them how much they’d pay for such a tool (not if they would). Much better if they are willing to pay upfront for a discount, etc. But even this comes later, keep reading. I think the difference between “How much” and “If” is huge for two reasons: (1) By asking them for “How much”, you force them to think in a more realistic setting. (2) You will have a more realistic idea on your profit margins. Based on my competitive analysis, I already had a solution in my mind to improve our email usage standards and email productivity (huge mistake), but I did my best to learn about their problems regarding those without pushing the idea too hard. The idea is this: Generate concise email summaries with suggested actions, combine them into one email, and send it at their preferred times. Save as much as time the AI you end up with allows. After all, everyone loves to save time. So, what kind of validation did I seek for? Talked with only a few people around me about this crazy, internet-breaking idea. The responses I got were, now I see, mediocre; no one got excited about it, just said things along the lines of “Cool idea, OK”. So, any reasonable person in this situation would think “Okay, not might not be working”, right? Well, I did not. I assumed that they were the wrong audience for this product, and there was this magical land of user segments waiting eagerly for my product, yet unknowingly. To this day, I still have not reached this magical place. Perhaps, it didn’t exist in the first place. If I cannot find it, whether it exists or not doesn’t matter. I am certainly searching for it. 👉 What I should have done: Once I decide on a problem space (time management, email productivity, etc.), I should decide on my potential user segments, people who I plan to sell my product to. Then I should go talk to those people, ask them about their pains, then get to the problem-solving/ideation phase only later. ❗️ VALIDATION COMES FROM THE REALITY OUTSIDE. What validation looks like might change from product to product; but what invalidation looks like is more or less the same for every product. Nico Jeannen told me yesterday “validation = money in the account” on Twitter. This is the ultimate form of validation your product could get. If your product doesn’t make any money, then something is invalidated by reality: Your product, you, your idea, who knows? So, at this point, I knew a little bit of Python from spending some time in tutorial hell a few years ago, some HTML/CSS/JS, barely enough React to build a working app. React could work for this project, but I needed easy-to-implement server interactivity. Luckily, around this time, I got to know about this new gen of indie hackers, and learned (but didn’t truly understand) about their approach to indie hacking, and this library called Nextjs. How good Next.js still blows my mind. So, I was back to tutorial hell once again. But, this time, with a promise to myself: This is the last time I would visit tutorial hell. Time to start building this "ground-breaking idea" Learning the fundamentals of Next.js was easier than learning of React unsurprisingly. Yet, the first time I managed to run server actions on Next.js was one of the rarest moments that completely blew my mind. To this day, I reject the idea that it is something else than pure magic under its hood. Did I absolutely need Nextjs for this project though? I do not think so. Did it save me lots of time? Absolutely. Furthermore, learning Nextjs will certainly be quite helpful for other projects that I will be tackling in the future. Already got a few ideas that might be worth pursuing in the head in case I decide to abandon Summ in the future. Fast-forward few weeks again: So, at this stage, I had a barely working MVP-like product. Since the very beginning, I spent every free hour (and more) on this project as speed is essential. But, I am not so sure it was worth it to overwork in retrospect. Yet, I know I couldn’t help myself. Everything is going kinda smooth, so what’s the worst thing that could ever happen? Well, both Apple and Google announced their AIs (Apple Intelligence and Google Gemini, respectively) will have email summarization features for their products. Summarizing singular emails is no big deal, after all there were already so many similar products in the market. I still think that what truly matters is a frictionless user experience, and this is why I built this product in a certain way: You spend less than a few minutes setting up your account, and you get to enjoy your email summaries, without ever visiting its website again. This is still a very cool concept I really like a lot. So, at this point: I had no other idea that could be pursued, already spent too much time on this project. Do I quit or not? This was the question. Of course not. I just have to launch this product as quickly as possible. So, I did something right, a quite rare occurrence I might say: Re-planned my product, dropped everything secondary to the core feature immediately (save time on reading emails), tried launching it asap. 👉 Insight: Sell only one core feature at one time. Drop anything secondary to this core feature. Well, my primary occupation is product design. So one would expect that a product I build must have stellar design. I considered any considerable time spent on design at this stage would be simply wasted. I still think this is both true and wrong: True, because if your product’s core benefits suck, no one will care about your design. False, because if your design looks amateurish, no one will trust you and your product. So, I always targeted an average level design with it and the way this tool works made it quite easy as I had to design only 2 primary pages: Landing page and user portal (which has only settings and analytics pages). However, even though I knew spending time on design was not worth much of my time, I got a bit “greedy”: In fact, I redesigned those pages three times, and still ended up with a so-so design that I am not proud of. 👉 What I would do differently: Unless absolutely necessary, only one iteration per stage as long as it works. This, in my mind, applies to everything. If your product’s A feature works, then no need to rewrite it from scratch for any reason, or even refactor it. When your product becomes a success, and you absolutely need that part of your codebase to be written, do so, but only then. Ready to launch, now is th etime for some marketing, right? By July 26, I already had a “launchable” product that barely works (I marked this date on a Notion docs, this is how I know). Yet, I had spent almost no time on marketing, sales, whatever. After all, “You build and they will come”. Did I know that I needed marketing? Of course I did, but knowingly didn’t. Why, you might ask. Well, from my perspective, it had to be a dev-heavy product; meaning that you spend most of your time on developing it, mostly coding skills. But, this is simply wrong. As a rule of thumb, as noted by one of the greatests, Marc Louvion, you should spend at least twice of the building time on marketing. ❗️ Time spent on building \* 2 people don’t know your product > they don’t use your product > you don’t get users > you don’t make money Easy as that. Following the same reasoning, a slightly different approach to planning a project is possible. Determine an approximate time to complete the project with a high level project plan. Let’s say 6 months. By the reasoning above, 2 months should go into building, and 4 into marketing. If you need 4 months for building instead of 2, then you need 8 months of marketing, which makes the time to complete the project 12 months. If you don’t have that much time, then quit the project. When does a project count as completed? Well, in reality, never. But, I think we have to define success conditions even before we start for indie projects and startups; so we know when to quit when they are not met. A success condition could look like “Make $6000 in 12 months” or “Have 3000 users in 6 months”. It all depends on the project. But, once you set it, it should be set in stone: You don’t change it unless absolutely necessary. I suspect there are few principles that make a solopreneur successful; and knowing when to quit and when to continue is definitely one of them. Marc Louvion is famously known for his success, but he got there after failing so many projects. To my knowledge, the same applies to Nico Jeannen, Pieter Levels, or almost everyone as well. ❗️ Determining when to continue even before you start will definitely help in the long run. A half-aed launch Time-leap again. Around mid August, I “soft launched” my product. By soft launch, I mean lazy marketing. Just tweeting about it, posting it on free directories. Did I get any traffic? Surely I did. Did I get any users? Nope. Only after this time, it hit me: “Either something is wrong with me, or with this product” Marketing might be a much bigger factor for a project’s success after all. Even though I get some traffic, not convincing enough for people to sign up even for a free trial. The product was still perfect in my eyes at the time (well, still is ^(\_),) so the right people are not finding my product, I thought. Then, a question that I should have been asking at the very first place, one that could prevent all these, comes to my mind: “How do even people search for such tools?” If we are to consider this whole journey of me and my so-far-failed product to be an already destined failure, one metric suffices to show why. Search volume: 30. Even if people have such a pain point, they are not looking for email summaries. So, almost no organic traffic coming from Google. But, as a person who did zero marketing on this or any product, who has zero marketing knowledge, who doesn’t have an audience on social media, there is not much I could do. Finally, it was time to give up. Or not… In my eyes, the most important element that makes a founder (solo or not) successful (this, I am not by any means) is to solve problems. ❗️ So, the problem was this: “People are not finding my product by organic search” How do I make sure I get some organic traffic and gets more visibility? Learn digital marketing and SEO as much as I can within very limited time. Thankfully, without spending much time, I came across Neil Patel's YT channel, and as I said many times, it is an absolute gold mine. I learned a lot, especially about the fundamentals, and surely it will be fruitful; but there is no magic trick that could make people visit your website. SEO certainly helps, but only when people are looking for your keywords. However, it is truly a magical solution to get in touch with REAL people that are in your user segments: 👉 Understand your pains, understand their problems, help them to solve them via building products. I did not do this so far, have to admit. But, in case you would like to have a chat about your email usage, and email productivity, just get in touch; I’d be delighted to hear about them. Getting ready for a ProductHunt launch The date was Sept 1. And I unlocked an impossible achievement: Running out of Supabase’s free plan’s Egres limit while having zero users. I was already considering moving out of their Cloud server and managing a Supabase CLI service on my Hetzner VPS for some time; but never ever suspected that I would have to do this quickly. The cheapest plan Supabase offers is $25/month; yet, at that point, I am in between jobs for such a long time, basically broke, and could barely afford that price. One or two months could be okay, but why pay for it if I will eventually move out of their Cloud service? So, instead of paying $25, I spent two days migrating out of Supabase Cloud. Worth my time? Definitely not. But, when you are broke, you gotta do stupid things. This was the first time that I felt lucky to have zero users: I have no idea how I would manage this migration if I had any. I think this is one of the core tenets of an indie hacker: Controlling their own environment. I can’t remember whose quote this is, but I suspect it was Naval: Entrepreneurs have an almost pathological need to control their own fate. They will take any suffering if they can be in charge of their destiny, and not have it in somebody else’s hands. What’s truly scary is, at least in my case, we make people around us suffer at the expense of our attempting to control our own fates. I know this period has been quite hard on my wife as well, as I neglected her quite a bit, but sadly, I know that this will happen again. It is something that I can barely help with. Still, so sorry. After working the last two weeks on a ProductHunt Launch, I finally launched it this Tuesday. Zero ranking, zero new users, but 36 kind people upvoted my product, and many commented and provided invaluable feedback. I couldn't be more grateful for each one of them 🙏. Considering all these, what lies in the future of Summ though? I have no idea, to be honest. On one hand, I have zero users, have no job, no income. So, I need a way to make money asap. On the other hand, the whole idea of it revolves around one core premise (not an assumption) that I am not so willing to share; and I couldn’t have more trust in it. This might not be the best iteration of it, however I certainly believe that email usage is one of the best problem spaces one could work on. 👉 But, one thing is for certain: I need to get in touch with people, and talk with them about this product I built so far. In fact, this is the only item on my agenda. Nothing else will save my brainchild <3. Below are some other insights and notes that I got during my journey; as they do not 100% fit into this story, I think it is more suitable to list them here. I hope you enjoyed reading this. Give Summ a try, it comes with a generous free trial, no credit card required. Some additional notes and insights: Project planning is one of the most underestimated skills for solopreneurs. It saves you enormous time, and helps you to keep your focus up. Building B2C products beats building B2B products. Businesses are very willing to pay big bucks if your product helps them. On the other hand, spending a few hours per user who would pay $5/m probably is not worth your time. It doesn’t matter how brilliant your product is if no one uses it. If you cannot sell a product in a certain category/niche (or do not know how to sell it), it might be a good idea not to start a project in it. Going after new ideas and ventures is quite risky, especially if you don’t know how to market it. On the other hand, an already established category means that there is already demand. Whether this demand is sufficient or not is another issue. As long as there is enough demand for your product to fit in, any category/niche is good. Some might be better, some might be worse. Unless you are going hardcore B2B, you will need people to find your product by means of organic search. Always conduct thorough keyword research as soon as possible.

#reddit#spent#months

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Clear-Leather•This week

AI Implementation

Hey guys. I am an entrepreneur. I started a service-based business in the Telecom construction industry. I have had my ups and downs. There are a lot of factors in that kind of business and I wanted to venture out to an industry that is more in demand now. The tech industry has been booming for the last two decades and the new AI revolution will help it keep the momentum. I am researching ideas about how and where AI could be useful and how to make it more accessible to those people who need them. In my service field, AI has helped me manage my projects and cut down on work time by half. A lot of organizational tasks and tracking can be done much faster with the help of AI. I'm looking for ideas on where else can someone use AI's help and what would make you want to use it. Also, what would be something that might attract your attention towards that? Looking for any and all opinions.

#reddit#implementation

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Awkward_Ad_9605•This week

Idea Validation Post: Seeking Feedback on My AI-Driven Quick Launch Application! 🚀

Hey Members! I’m excited to share an idea for a new application I’m planning to build: Quick Launch . This AI-driven platform is designed to assist solopreneurs or anyone with an idea in launching their Minimum Viable Products (MVPs) by taking on the roles of the entire team needed for the process. Goal: Assistance in quickly moving from Idea to MVP Before I dive into the details, I’d love to hear your thoughts and feedback. Key Features: Product Creation: From Idea to Product Detailing AI-Generated Q&A: Real-time questions generation one-at-a-time to define the product requirements based on their knowledge levels to convert an Idea into a Product. Market Research Reports: In-depth analysis that identifies product-market fit, competitive landscape, and potential marketing strategies. Sentiment Analysis: Evaluation of user feedback and reactions across multiple subreddits to gauge public opinion on ideas. Product Development: Product Detailing to Actual Product User Story Generation: Identification and creation of comprehensive user stories, tasks, and sub-tasks to facilitate development. AI Project Management: AI agents assume roles of project managers and UI/UX designers to streamline product detailing and development. Integration Capabilities: Seamless integration with popular project management tools like Jira, Asana, and Trello for better workflow management. Target Audience: Solopreneurs: Individuals looking to bring their business ideas to life without extensive resources. Indie Hackers: Entrepreneurs focused on building small projects or startups with minimal overhead. Idea Validators: Anyone with a concept seeking initial validation and market feedback before committing significant resources. If you’re interested in learning more, check out our teaser website: Quick Launch Discussion Question: What features would you find most valuable in an application like this? Are there specific pain points you face when launching an MVP? Your insights would be incredibly helpful as I refine this idea! Looking forward to your thoughts! 🙌

#reddit#idea#validation

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0.667

GDbuildsGD•This week

I spent 6 months on building a web product, and got zero users. Here is my story.

Edit Thank you all so much for your time reading my story. Your support, feedback, criticism, and skepticism; all helped me a lot, and I couldn't appreciate it enough \^\_\^ I have stuff to post on Reddit very rarely, but I share how my project is going on, random stuff, and memes on X. Just in case few might want to keep in touch 👀 TL;DR I spent 6 months on a tool that currently has 0 users. Below is what I learned during my journey, sharing because I believe most mistakes are easily avoidable. Do not overestimate your product and assume it will be an exception to fundamental principles. Principles are there for a reason. Always look for validation before you start. Avoid building products with a low money-to-effort ratio/in very competitive fields. Unless you have the means, you probably won't make it. Pick a problem space, pick your target audience, and talk to them before thinking about a solution. Identify and match their pain points. Only then should you think of a solution. If people are not overly excited or willing to pay in advance for a discounted price, it might be a sign to rethink. Sell one and only one feature at a time. Avoid everything else. If people don't pay for that one core feature, no secondary feature will change their mind. Always spend twice as much time marketing as you do building. You will not get users if they don't know it exists. Define success metrics ("1000 users in 3 months" or "$6000 in the account at the end of 6 months") before you start. If you don't meet them, strongly consider quitting the project. If you can't get enough users to keep going, nothing else matters. VALIDATION, VALIDATION, VALIDATION. Success is not random, but most of our first products will not make a success story. Know when to admit failure, and move on. Even if a product of yours doesn't succeed, what you learned during its journey will turn out to be invaluable for your future. My story So, this is the story of a product that I’ve been working on for the last 6 months. As it's the first product I’ve ever built, after watching you all from the sidelines, I have learned a lot, made many mistakes, and did only a few things right. Just sharing what I’ve learned and some insights from my journey so far. I hope that this post will help you avoid the mistakes I made — most of which I consider easily avoidable — while you enjoy reading it, and get to know me a little bit more 🤓. A slow start after many years Summ isn’t the first product I really wanted to build. Lacking enough dev skills to even get started was a huge blocker for so many years. In fact, the first product I would’ve LOVED to build was a smart personal shopping assistant. I had this idea 4 years ago; but with no GPT, no coding skills, no technical co-founder, I didn’t have the means to make it happen. I still do not know if such a tool exists and is good enough. All I wanted was a tool that could make data-based predictions about when to buy stuff (“buy a new toothpaste every three months”) and suggest physical products that I might need or be strongly interested in. AFAIK, Amazon famously still struggles with the second one. Fast-forward a few years, I learned the very basics of HTML, CSS, and Vanilla JS. Still was not there to build a product; but good enough to code my design portfolio from scratch. Yet, I couldn’t imagine myself building a product using Vanilla JS. I really hated it, I really sucked at it. So, back to tutorial hell, and to learn about this framework I just heard about: React.React introduced so many new concepts to me. “Thinking in React” is a phrase we heard a lot, and with quite good reasons. After some time, I was able to build very basic tutorial apps, both in React, and React Native; but I have to say that I really hated coding for mobile. At this point, I was already a fan of productivity apps, and had a concept for a time management assistant app in my design portfolio. So, why not build one? Surely, it must be easy, since every coding tutorial starts with a todo app. ❌ WRONG! Building a basic todo app is easy enough, but building one good enough for a place in the market was a challenge I took and failed. I wasted one month on that until I abandoned the project for good. Even if I continued working on it, as the productivity landscape is overly competitive, I wouldn’t be able to make enough money to cover costs, assuming I make any. Since I was (and still am) in between jobs, I decided to abandon the project. 👉 What I learned: Do not start projects with a low ratio of money to effort and time. Example: Even if I get 500 monthly users, 200 of which are paid users (unrealistically high number), assuming an average subscription fee of $5/m (such apps are quite cheap, mostly due to the high competition), it would make me around $1000 minus any occurring costs. Any founder with a product that has 500 active users should make more. Even if it was relatively successful, due to the high competition, I wouldn’t make any meaningful money. PS: I use Todoist today. Due to local pricing, I pay less than $2/m. There is no way I could beat this competitive pricing, let alone the app itself. But, somehow, with a project that wasn’t even functional — let alone being an MVP — I made my first Wi-Fi money: Someone decided that the domain I preemptively purchased is worth something. By this point, I had already abandoned the project, certainly wasn’t going to renew the domain, was looking for a FT job, and a new project that I could work on. And out of nowhere, someone hands me some free money — who am I not to take it? Of course, I took it. The domain is still unused, no idea why 🤔. Ngl, I still hate the fact that my first Wi-Fi money came from this. A new idea worth pursuing? Fast-forward some weeks now. Around March, I got this crazy idea of building an email productivity tool. We all use emails, yet we all hate them. So, this must be fixed. Everyone uses emails, in fact everyone HAS TO use emails. So, I just needed to build a tool and wait for people to come. This was all, really. After all, the problem space is huge, there is enough room for another product, everyone uses emails, no need for any further validation, right? ❌ WRONG ONCE AGAIN! We all hear from the greatest in the startup landscape that we must validate our ideas with real people, yet at least some of us (guilty here 🥸) think that our product will be hugely successful and prove them to be an exception. Few might, but most are not. I certainly wasn't. 👉 Lesson learned: Always validate your ideas with real people. Ask them how much they’d pay for such a tool (not if they would). Much better if they are willing to pay upfront for a discount, etc. But even this comes later, keep reading. I think the difference between “How much” and “If” is huge for two reasons: (1) By asking them for “How much”, you force them to think in a more realistic setting. (2) You will have a more realistic idea on your profit margins. Based on my competitive analysis, I already had a solution in my mind to improve our email usage standards and email productivity (huge mistake), but I did my best to learn about their problems regarding those without pushing the idea too hard. The idea is this: Generate concise email summaries with suggested actions, combine them into one email, and send it at their preferred times. Save as much as time the AI you end up with allows. After all, everyone loves to save time. So, what kind of validation did I seek for? Talked with only a few people around me about this crazy, internet-breaking idea. The responses I got were, now I see, mediocre; no one got excited about it, just said things along the lines of “Cool idea, OK”. So, any reasonable person in this situation would think “Okay, not might not be working”, right? Well, I did not. I assumed that they were the wrong audience for this product, and there was this magical land of user segments waiting eagerly for my product, yet unknowingly. To this day, I still have not reached this magical place. Perhaps, it didn’t exist in the first place. If I cannot find it, whether it exists or not doesn’t matter. I am certainly searching for it. 👉 What I should have done: Once I decide on a problem space (time management, email productivity, etc.), I should decide on my potential user segments, people who I plan to sell my product to. Then I should go talk to those people, ask them about their pains, then get to the problem-solving/ideation phase only later. ❗️ VALIDATION COMES FROM THE REALITY OUTSIDE. What validation looks like might change from product to product; but what invalidation looks like is more or less the same for every product. Nico Jeannen told me yesterday “validation = money in the account” on Twitter. This is the ultimate form of validation your product could get. If your product doesn’t make any money, then something is invalidated by reality: Your product, you, your idea, who knows? So, at this point, I knew a little bit of Python from spending some time in tutorial hell a few years ago, some HTML/CSS/JS, barely enough React to build a working app. React could work for this project, but I needed easy-to-implement server interactivity. Luckily, around this time, I got to know about this new gen of indie hackers, and learned (but didn’t truly understand) about their approach to indie hacking, and this library called Nextjs. How good Next.js still blows my mind. So, I was back to tutorial hell once again. But, this time, with a promise to myself: This is the last time I would visit tutorial hell. Time to start building this "ground-breaking idea" Learning the fundamentals of Next.js was easier than learning of React unsurprisingly. Yet, the first time I managed to run server actions on Next.js was one of the rarest moments that completely blew my mind. To this day, I reject the idea that it is something else than pure magic under its hood. Did I absolutely need Nextjs for this project though? I do not think so. Did it save me lots of time? Absolutely. Furthermore, learning Nextjs will certainly be quite helpful for other projects that I will be tackling in the future. Already got a few ideas that might be worth pursuing in the head in case I decide to abandon Summ in the future. Fast-forward few weeks again: So, at this stage, I had a barely working MVP-like product. Since the very beginning, I spent every free hour (and more) on this project as speed is essential. But, I am not so sure it was worth it to overwork in retrospect. Yet, I know I couldn’t help myself. Everything is going kinda smooth, so what’s the worst thing that could ever happen? Well, both Apple and Google announced their AIs (Apple Intelligence and Google Gemini, respectively) will have email summarization features for their products. Summarizing singular emails is no big deal, after all there were already so many similar products in the market. I still think that what truly matters is a frictionless user experience, and this is why I built this product in a certain way: You spend less than a few minutes setting up your account, and you get to enjoy your email summaries, without ever visiting its website again. This is still a very cool concept I really like a lot. So, at this point: I had no other idea that could be pursued, already spent too much time on this project. Do I quit or not? This was the question. Of course not. I just have to launch this product as quickly as possible. So, I did something right, a quite rare occurrence I might say: Re-planned my product, dropped everything secondary to the core feature immediately (save time on reading emails), tried launching it asap. 👉 Insight: Sell only one core feature at one time. Drop anything secondary to this core feature. Well, my primary occupation is product design. So one would expect that a product I build must have stellar design. I considered any considerable time spent on design at this stage would be simply wasted. I still think this is both true and wrong: True, because if your product’s core benefits suck, no one will care about your design. False, because if your design looks amateurish, no one will trust you and your product. So, I always targeted an average level design with it and the way this tool works made it quite easy as I had to design only 2 primary pages: Landing page and user portal (which has only settings and analytics pages). However, even though I knew spending time on design was not worth much of my time, I got a bit “greedy”: In fact, I redesigned those pages three times, and still ended up with a so-so design that I am not proud of. 👉 What I would do differently: Unless absolutely necessary, only one iteration per stage as long as it works. This, in my mind, applies to everything. If your product’s A feature works, then no need to rewrite it from scratch for any reason, or even refactor it. When your product becomes a success, and you absolutely need that part of your codebase to be written, do so, but only then. Ready to launch, now is th etime for some marketing, right? By July 26, I already had a “launchable” product that barely works (I marked this date on a Notion docs, this is how I know). Yet, I had spent almost no time on marketing, sales, whatever. After all, “You build and they will come”. Did I know that I needed marketing? Of course I did, but knowingly didn’t. Why, you might ask. Well, from my perspective, it had to be a dev-heavy product; meaning that you spend most of your time on developing it, mostly coding skills. But, this is simply wrong. As a rule of thumb, as noted by one of the greatests, Marc Louvion, you should spend at least twice of the building time on marketing. ❗️ Time spent on building \* 2 people don’t know your product > they don’t use your product > you don’t get users > you don’t make money Easy as that. Following the same reasoning, a slightly different approach to planning a project is possible. Determine an approximate time to complete the project with a high level project plan. Let’s say 6 months. By the reasoning above, 2 months should go into building, and 4 into marketing. If you need 4 months for building instead of 2, then you need 8 months of marketing, which makes the time to complete the project 12 months. If you don’t have that much time, then quit the project. When does a project count as completed? Well, in reality, never. But, I think we have to define success conditions even before we start for indie projects and startups; so we know when to quit when they are not met. A success condition could look like “Make $6000 in 12 months” or “Have 3000 users in 6 months”. It all depends on the project. But, once you set it, it should be set in stone: You don’t change it unless absolutely necessary. I suspect there are few principles that make a solopreneur successful; and knowing when to quit and when to continue is definitely one of them. Marc Louvion is famously known for his success, but he got there after failing so many projects. To my knowledge, the same applies to Nico Jeannen, Pieter Levels, or almost everyone as well. ❗️ Determining when to continue even before you start will definitely help in the long run. A half-aed launch Time-leap again. Around mid August, I “soft launched” my product. By soft launch, I mean lazy marketing. Just tweeting about it, posting it on free directories. Did I get any traffic? Surely I did. Did I get any users? Nope. Only after this time, it hit me: “Either something is wrong with me, or with this product” Marketing might be a much bigger factor for a project’s success after all. Even though I get some traffic, not convincing enough for people to sign up even for a free trial. The product was still perfect in my eyes at the time (well, still is ^(\_),) so the right people are not finding my product, I thought. Then, a question that I should have been asking at the very first place, one that could prevent all these, comes to my mind: “How do even people search for such tools?” If we are to consider this whole journey of me and my so-far-failed product to be an already destined failure, one metric suffices to show why. Search volume: 30. Even if people have such a pain point, they are not looking for email summaries. So, almost no organic traffic coming from Google. But, as a person who did zero marketing on this or any product, who has zero marketing knowledge, who doesn’t have an audience on social media, there is not much I could do. Finally, it was time to give up. Or not… In my eyes, the most important element that makes a founder (solo or not) successful (this, I am not by any means) is to solve problems. ❗️ So, the problem was this: “People are not finding my product by organic search” How do I make sure I get some organic traffic and gets more visibility? Learn digital marketing and SEO as much as I can within very limited time. Thankfully, without spending much time, I came across Neil Patel's YT channel, and as I said many times, it is an absolute gold mine. I learned a lot, especially about the fundamentals, and surely it will be fruitful; but there is no magic trick that could make people visit your website. SEO certainly helps, but only when people are looking for your keywords. However, it is truly a magical solution to get in touch with REAL people that are in your user segments: 👉 Understand your pains, understand their problems, help them to solve them via building products. I did not do this so far, have to admit. But, in case you would like to have a chat about your email usage, and email productivity, just get in touch; I’d be delighted to hear about them. Getting ready for a ProductHunt launch The date was Sept 1. And I unlocked an impossible achievement: Running out of Supabase’s free plan’s Egres limit while having zero users. I was already considering moving out of their Cloud server and managing a Supabase CLI service on my Hetzner VPS for some time; but never ever suspected that I would have to do this quickly. The cheapest plan Supabase offers is $25/month; yet, at that point, I am in between jobs for such a long time, basically broke, and could barely afford that price. One or two months could be okay, but why pay for it if I will eventually move out of their Cloud service? So, instead of paying $25, I spent two days migrating out of Supabase Cloud. Worth my time? Definitely not. But, when you are broke, you gotta do stupid things. This was the first time that I felt lucky to have zero users: I have no idea how I would manage this migration if I had any. I think this is one of the core tenets of an indie hacker: Controlling their own environment. I can’t remember whose quote this is, but I suspect it was Naval: Entrepreneurs have an almost pathological need to control their own fate. They will take any suffering if they can be in charge of their destiny, and not have it in somebody else’s hands. What’s truly scary is, at least in my case, we make people around us suffer at the expense of our attempting to control our own fates. I know this period has been quite hard on my wife as well, as I neglected her quite a bit, but sadly, I know that this will happen again. It is something that I can barely help with. Still, so sorry. After working the last two weeks on a ProductHunt Launch, I finally launched it this Tuesday. Zero ranking, zero new users, but 36 kind people upvoted my product, and many commented and provided invaluable feedback. I couldn't be more grateful for each one of them 🙏. Considering all these, what lies in the future of Summ though? I have no idea, to be honest. On one hand, I have zero users, have no job, no income. So, I need a way to make money asap. On the other hand, the whole idea of it revolves around one core premise (not an assumption) that I am not so willing to share; and I couldn’t have more trust in it. This might not be the best iteration of it, however I certainly believe that email usage is one of the best problem spaces one could work on. 👉 But, one thing is for certain: I need to get in touch with people, and talk with them about this product I built so far. In fact, this is the only item on my agenda. Nothing else will save my brainchild <3. Below are some other insights and notes that I got during my journey; as they do not 100% fit into this story, I think it is more suitable to list them here. I hope you enjoyed reading this. Give Summ a try, it comes with a generous free trial, no credit card required. Some additional notes and insights: Project planning is one of the most underestimated skills for solopreneurs. It saves you enormous time, and helps you to keep your focus up. Building B2C products beats building B2B products. Businesses are very willing to pay big bucks if your product helps them. On the other hand, spending a few hours per user who would pay $5/m probably is not worth your time. It doesn’t matter how brilliant your product is if no one uses it. If you cannot sell a product in a certain category/niche (or do not know how to sell it), it might be a good idea not to start a project in it. Going after new ideas and ventures is quite risky, especially if you don’t know how to market it. On the other hand, an already established category means that there is already demand. Whether this demand is sufficient or not is another issue. As long as there is enough demand for your product to fit in, any category/niche is good. Some might be better, some might be worse. Unless you are going hardcore B2B, you will need people to find your product by means of organic search. Always conduct thorough keyword research as soon as possible.

#reddit#spent#months

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ferreiracarcara•This week

Share Your Expertise: AI, Automation, and Efficient Organizational Tools, Strategies and Routines!

Hello everyone, As we navigate through the advancements in AI and automation, it's clear that these technologies are reshaping the way we approach work and business management. To stay ahead, sharing our collective knowledge on these subjects is crucial. I'm inviting this community to share insights and experiences with AI tools, automation strategies, and especially, innovative organizational approaches you've found effective. From automating mundane tasks to optimizing digital marketing strategies, every piece of wisdom is valuable. Here’s what we’re specifically interested in: Automated Workflows: What are your strategies for creating automated workflows that enhance productivity and efficiency? Visual Organization: How do you utilize mind maps and other visual tools to organize thoughts and projects efficiently? Canvas Maps: Have you implemented CANVAS Maps in customer interaction, ideation, strategy development, or action planning? How has it improved your processes? AI in Marketing: How has AI helped you optimize your digital marketing strategies and data analysis? What tools or methodologies have you found most effective? This thread aims to be a resource for all of us to learn from each other's successes and innovations. Whether it’s a simple tip or a comprehensive strategy, your input can significantly impact someone’s approach to challenges. What groundbreaking AI solutions, automation hacks, or organizational methods have you discovered that made a noticeable difference in your work or business? Share your stories and let’s empower each other to achieve greater efficiency and success. Thank you for contributing to our shared journey toward innovation and improvement!

#reddit#share#your

reddit

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0.333

lazymentors•This week

80+ Social Media Updates Related to Business Marketing That Occurred in last 5 months

Tiktok expanded its caption limits from 100 to 500 Characters. Reddit Updates Search tools, Now you can search User Comments. “Comment search is here”. Pinterest Announces New Partnership with WooCommerce to Expand Product Listings. Google’s launched ‘multisearch’ feature that lets you search using text and image at the same time. Etsy sellers went on strike after platform increases transaction fees. Reddit launched $1 million fund to support various projects going on platform. Instagram is updating its ranking algorithm to put more focus on Original Content LinkedIn Added New tools In creator mode: improved content analytics and Updates profile video Options. Tiktok launched its own gif library “Effect House”. Instagram Updates Reels editing tools adding reordering clips feature. Google Search got a new label to direct people to original news sources YouTube launches new Profile Rings for Stories and Live. Snapchat launched YouTube Link stickers to make video sharing easier! Messenger adds new shortcuts, including a slack like @everyone feature. Pinterest Expands it’s Creator funds program to help more Underrepresented creators. Reddit brings back r/place after 5 years. Google Adds New Seller Performance Badges, New Pricing Insights for eCommerce Brands. Meta and Google agrees to New Data Transfer agreement to keep Instagram and Facebook running in EU. Twitter tests New Interactive Ad types to boost its promotional Appeal. Instagram removed In-stream Ads from its Advertising Options. Tiktok launched new program “CAP” to help creative agencies reach its audience. Twitch shuts down its desktop app. Meta launched the ability to add “share to Reels” feature to third Party Apps. TikTok Adds New ‘Background Player’ Option for Live-Streams. Twitter rolls out ALT badge and improved image description. Fast, A Checkout Startup with $15 billion valuation shuts down after spending all the funds raised in 2021. Wordpress announced new pricing with more traffic and storage limits after receiving backlash from the community. Sales force upgrades marketing field services and sales tools with AI. Dropbox shop launches in open beta to allow creators to sell digital content. Tiktok is the most downloaded app in Quarter 1 of 2022. WhatsApp announced launch of ‘Communities’ - more structured group chats with admin controls. Tiktok expands testing a private dislike button for comments. Twitter acquired “Openback” A notification app to improve timeline and relevance of push notifications YouTube and Tiktok added New options for Automated Captions, Improving Accessibility. A new social media App “Be Real” is trending across the internet grabbing Gen-Zs attention to try the app. WhatsApp got permission to expand payment services to its Indian user base of 100 Million. YouTube Shorts now allows creators to splice in long-form videos. You can use long form video audios and clips for YT shorts. New Snapchat feature ‘Dynamic Stories’ uses a publisher’s RSS feed to automatically create Stories posts. Zoom launches AI-powered features aimed at sales teams. Tiktok started testing who viewed your profile feature. Ogilvy Announced they will no longer work with who edit their bodies and faces for ads. If you don’t know “Oglivy” is the most successful advertising agency of the decade. YouTube Launches New ‘Search Insights’ for all creators. Snapchat Added 13 million new users in Q1 2022 more than both Twitter and Facebook. Google is Introduced new options to reject tracking cookies in Europe after receiving fines from violating EU data laws. Sony & Microsoft are planning to integrate Ads into their gaming platforms Xbox and PlayStation. YouTube Adds new Shorts Shelf to Trending Tab to show Top Shorts in an alternative section. Instagram started testing a reels template feature which enables creators to copy formats from other reels. Google Tests “What People Are Saying” Search Results. Twitter Launches New Test of Promotions for Third Party Tools Within the App. Instagram is changing how hashtags work by experimenting removing Recents tab from hashtags section. Google Adds New Publisher Verification Badges to Extension Listings in the Google Web Store Amazon AWS launches $30M accelerator program aimed at minority founders. Meta launched more fundraising options for Instagram Reels in 30 countries. Brave Search and DuckDuckGo will no longer support Google AMP due to privacy issues. Instagram is working on a pinned post feature and will officially launch in next few months. Meta: You can now add Music to your Facebook comments Twitter tests new closed caption button to switch on captions in Video Clip Elon Musk Bought Twitter $44 Billion and Company is set to go private. Google now lets you request the removal of personal contact information from search results YouTube reveals that Ads between YT Shorts are being tested with selective brands. LinkedInis rolling out a new website link feature. Google Reduces Visibility Of Business Edits With Color Changes To Profile Updates. Instagram expands testing of 90 second Reels. Microsoft Advertising now offers incentive features like cash-back and adding stock images from your website. Facebook & Pinterest are growing again despite all the hype around slow growth of both platform in last quarter. Google Added 9 new Ad policies to prevent misleading ads taking place. Tiktok Introduces Third-party cookies to its Pixel. (like Facebook Pixel) Twitter reportedly overcounted number of daily active users for last 3 years. Google launched Media CDN to compete on content delivery. YouTube expands Thank You Monetisation tool to all eligible creators. Twitch is looking to expand their cut from streamers earnings from 30 to 50% and also thinks of boosting Ads. Snapchat launches a $230 flying drone camera and new e-commerce integrations in Snap Summit 2022. YouTube Expands its ‘Pre-Publish Checks’ Tool to the Mobile App Google Search Console’s URL parameter tool is officially removed for a time period. Twitter creators can now get paid through Cryptocurrency on Twitter with Stripe. Jellysmack- One of the Influencer marketing agency acquires YouTube analytics tool Google & Microsoft Ads brought more revenue in last quarter- 22% Gains! WhatsApp is working on a paid subscription for multi-phone and tablet chatting. Instagram users now spend 20% of their time in the reels section. Google tests new Color for clicked search results by you. Now Clicked results are in Purple. Twitter: Elon plans to remove employees and focus more on influencers for twitter’s growth + new monetisation ideas were shared. YouTube revenue falls as more users spend time on shorts tab than consuming long form content. Drop 👋 to receive June Updates!

#reddit#social#media

reddit

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ferreiracarcara•This week

Share Your Expertise: AI, Automation, and Efficient Organizational Tools, Strategies and Routines!

Hello everyone, As we navigate through the advancements in AI and automation, it's clear that these technologies are reshaping the way we approach work and business management. To stay ahead, sharing our collective knowledge on these subjects is crucial. I'm inviting this community to share insights and experiences with AI tools, automation strategies, and especially, innovative organizational approaches you've found effective. From automating mundane tasks to optimizing digital marketing strategies, every piece of wisdom is valuable. Here’s what we’re specifically interested in: Automated Workflows: What are your strategies for creating automated workflows that enhance productivity and efficiency? Visual Organization: How do you utilize mind maps and other visual tools to organize thoughts and projects efficiently? Canvas Maps: Have you implemented CANVAS Maps in customer interaction, ideation, strategy development, or action planning? How has it improved your processes? AI in Marketing: How has AI helped you optimize your digital marketing strategies and data analysis? What tools or methodologies have you found most effective? This thread aims to be a resource for all of us to learn from each other's successes and innovations. Whether it’s a simple tip or a comprehensive strategy, your input can significantly impact someone’s approach to challenges. What groundbreaking AI solutions, automation hacks, or organizational methods have you discovered that made a noticeable difference in your work or business? Share your stories and let’s empower each other to achieve greater efficiency and success. Thank you for contributing to our shared journey toward innovation and improvement!

#reddit#share#your

reddit

0

0.333

Otherwise_Soil39•This week

How do you explain, to a non-programmer why it's hard to replace programmers with AI?

to me it seems that AI is best at creative writing and absolutely dogshit at programming, it can't even get complex enough SQL no matter how much you try to correct it and feed it output. Let alone production code.. And since it's all just probability this isn't something that I see fixed in the near future. So from my perspective the last job that will be replaced is programming. But for some reason popular media has convinced everyone that programming is a dead profession that is currently being given away to robots. The best example I could come up with was saying: "It doesn't matter whether the AI says 'very tired' or 'exhausted' but in programming the equivalent would lead to either immediate issues or hidden issues in the future" other then that I made some bad attempts at explaining the scale, dependencies, legacy, and in-house services of large projects. But that did not win me the argument, because they saw a TikTok where the AI created a whole website! (generated boilerplate html) or heard that hundreds of thousands of programers are being laid off because "their 6 figure jobs are better done by AI already".

#reddit#explain,#non-programmer

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TechPrimo•This week

How I Started Learning Machine Learning

Hello, everyone. As promised, I'll write a longer post about how I entered the world of ML, hoping it will help someone shape their path. I'll include links to all the useful materials I used alongside the story, which you can use for learning. I like to call myself an AI Research Scientist who enjoys exploring new AI trends, delving deeper into understanding their background, and applying them to real products. This way, I try to connect science and entrepreneurship because I believe everything that starts as scientific research ends up "on the shelves" as a product that solves a specific user problem. I began my journey in ML in 2016 when it wasn't such a popular field. Everyone had heard of it, but few were applying it. I have several years of development experience and want to try my hand at ML. The first problem I encountered was where to start - whether to learn mathematics, statistics, or something else. That's when I came across a name and a course that completely changed my career. Let's start You guessed it. It was Professor Andrew Ng and his globally popular Machine Learning course available on Coursera (I still have the certificate, hehe). This was also my first official online course ever. Since that course no longer exists as it's been replaced by a new one, I recommend you check out: Machine Learning (Stanford CS229) Machine Learning Specialization These two courses start from the basics of ML and all the necessary calculus you need to know. Many always ask questions like whether to learn linear algebra, statistics, or probability, but you don't need to know everything in depth. This knowledge helps if you're a scientist developing a new architecture, but as an engineer, not really. You need to know some basics to understand, such as how the backpropagation algorithm works. I know that Machine Learning (Stanford CS229) is a very long and arduous course, but it's the right start if you want to be really good at ML. In my time, I filled two thick notebooks by hand while taking the course mentioned above. TensorFlow and Keras After the course, I didn't know how to apply my knowledge because I hadn't learned specifically how to code things. Then, I was looking for ways to learn how to code it. That's when I came across a popular framework called Keras, now part of TensorFlow. I started with a new course and acquiring practical knowledge: Deep Learning Specialization Deep Learning by Ian Goodfellow Machine Learning Yearning by Andrew Ng These resources above were my next step. I must admit that I learned the most from that course and from the book Deep Learning by Ian Goodfellow because I like reading books (although this one is quite difficult to read). Learn by coding To avoid just learning, I went through various GitHub repositories that I manually retyped and learned that way. It may be an old-fashioned technique, but it helped me a lot. Now, most of those repositories don't exist, so I'll share some that I found to be good: Really good Jupyter notebooks that can teach you the basics of TensorFlow Another good repo for learning TF and Keras Master the challenge After mastering the basics in terms of programming in TF/Keras, I wanted to try solving some real problems. There's no better place for that challenge than Kaggle and the popular Titanic dataset. Here, you can really find a bunch of materials and simple examples of ML applications. Here are some of my favorites: Titanic - Machine Learning from Disaster Home Credit Default Risk House Prices - Advanced Regression Techniques Two Sigma: Using News to Predict Stock Movements I then decided to further develop my career in the direction of applying ML to the stock market, first using predictions on time series and then using natural language processing. I've remained in this field until today and will defend my doctoral dissertation soon. How to deploy models To continue, before I move on to the topic of specialization, we need to address the topic of deployment. Now that we've learned how to make some basic models in Keras and how to use them, there are many ways and services, but I'll only mention what I use today. For all my ML models, whether simple regression models or complex GPT models, I use FastAPI. It's a straightforward framework, and you can quickly create API endpoints. I'll share a few older and useful tutorials for beginners: AI as an API tutorial series A step-by-step guide Productizing an ML Model with FastAPI and Cloud Run Personally, I've deployed on various cloud providers, of which I would highlight GCP and AWS because they have everything needed for model deployment, and if you know how to use them, they can be quite cheap. Chose your specialization The next step in developing my career, besides choosing finance as the primary area, was my specialization in the field of NLP. This happened in early 2020 when I started working with models based on the Transformer architecture. The first model I worked with was BERT, and the first tasks were related to classifications. My recommendations are to master the Transformer architecture well because 99% of today's LLM models are based on it. Here are some resources: The legendary paper "Attention Is All You Need" Hugging Face Course on Transformers Illustrated Guide to Transformers - Step by Step Explanation Good repository How large language models work, a visual intro to transformers After spending years using encoder-based Transformer models, I started learning GPT models. Good open-source models like Llama 2 then appear. Then, I started fine-tuning these models using the excellent Unsloth library: How to Finetune Llama-3 and Export to Ollama Fine-tune Llama 3.1 Ultra-Efficiently with Unsloth After that, I focused on studying various RAG techniques and developing Agent AI systems. This is now called AI engineering, and, as far as I can see, it has become quite popular. So I'll write more about that in another post, but here I'll leave what I consider to be the three most famous representatives, i.e., their tutorials: LangChain tutorial LangGraph tutorial CrewAI examples Here I am today Thanks to the knowledge I've generated over all these years in the field of ML, I've developed and worked on numerous projects. The most significant publicly available project is developing an agent AI system for well-being support, which I turned into a mobile application. Also, my entire doctoral dissertation is related to applying ML to the stock market in combination with the development of GPT models and reinforcement learning (more on that in a separate post). After long 6 years, I've completed my dissertation, and now I'm just waiting for its defense. I'll share everything I'm working on for the dissertation publicly on the project, and in tutorials I'm preparing to write. If you're interested in these topics, I announce that I'll soon start with activities of publishing content on Medium and a blog, but I'll share all of that here on Reddit as well. Now that I've gathered years of experience and knowledge in this field, I'd like to share it with others and help as much as possible. If you have any questions, feel free to ask them, and I'll try to answer all of them. Thank you for reading.

#reddit#started#learning

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TechPrimo•This week

How I Started Learning Machine Learning

Hello, everyone. As promised, I'll write a longer post about how I entered the world of ML, hoping it will help someone shape their path. I'll include links to all the useful materials I used alongside the story, which you can use for learning. I like to call myself an AI Research Scientist who enjoys exploring new AI trends, delving deeper into understanding their background, and applying them to real products. This way, I try to connect science and entrepreneurship because I believe everything that starts as scientific research ends up "on the shelves" as a product that solves a specific user problem. I began my journey in ML in 2016 when it wasn't such a popular field. Everyone had heard of it, but few were applying it. I have several years of development experience and want to try my hand at ML. The first problem I encountered was where to start - whether to learn mathematics, statistics, or something else. That's when I came across a name and a course that completely changed my career. Let's start You guessed it. It was Professor Andrew Ng and his globally popular Machine Learning course available on Coursera (I still have the certificate, hehe). This was also my first official online course ever. Since that course no longer exists as it's been replaced by a new one, I recommend you check out: Machine Learning (Stanford CS229) Machine Learning Specialization These two courses start from the basics of ML and all the necessary calculus you need to know. Many always ask questions like whether to learn linear algebra, statistics, or probability, but you don't need to know everything in depth. This knowledge helps if you're a scientist developing a new architecture, but as an engineer, not really. You need to know some basics to understand, such as how the backpropagation algorithm works. I know that Machine Learning (Stanford CS229) is a very long and arduous course, but it's the right start if you want to be really good at ML. In my time, I filled two thick notebooks by hand while taking the course mentioned above. TensorFlow and Keras After the course, I didn't know how to apply my knowledge because I hadn't learned specifically how to code things. Then, I was looking for ways to learn how to code it. That's when I came across a popular framework called Keras, now part of TensorFlow. I started with a new course and acquiring practical knowledge: Deep Learning Specialization Deep Learning by Ian Goodfellow Machine Learning Yearning by Andrew Ng These resources above were my next step. I must admit that I learned the most from that course and from the book Deep Learning by Ian Goodfellow because I like reading books (although this one is quite difficult to read). Learn by coding To avoid just learning, I went through various GitHub repositories that I manually retyped and learned that way. It may be an old-fashioned technique, but it helped me a lot. Now, most of those repositories don't exist, so I'll share some that I found to be good: Really good Jupyter notebooks that can teach you the basics of TensorFlow Another good repo for learning TF and Keras Master the challenge After mastering the basics in terms of programming in TF/Keras, I wanted to try solving some real problems. There's no better place for that challenge than Kaggle and the popular Titanic dataset. Here, you can really find a bunch of materials and simple examples of ML applications. Here are some of my favorites: Titanic - Machine Learning from Disaster Home Credit Default Risk House Prices - Advanced Regression Techniques Two Sigma: Using News to Predict Stock Movements I then decided to further develop my career in the direction of applying ML to the stock market, first using predictions on time series and then using natural language processing. I've remained in this field until today and will defend my doctoral dissertation soon. How to deploy models To continue, before I move on to the topic of specialization, we need to address the topic of deployment. Now that we've learned how to make some basic models in Keras and how to use them, there are many ways and services, but I'll only mention what I use today. For all my ML models, whether simple regression models or complex GPT models, I use FastAPI. It's a straightforward framework, and you can quickly create API endpoints. I'll share a few older and useful tutorials for beginners: AI as an API tutorial series A step-by-step guide Productizing an ML Model with FastAPI and Cloud Run Personally, I've deployed on various cloud providers, of which I would highlight GCP and AWS because they have everything needed for model deployment, and if you know how to use them, they can be quite cheap. Chose your specialization The next step in developing my career, besides choosing finance as the primary area, was my specialization in the field of NLP. This happened in early 2020 when I started working with models based on the Transformer architecture. The first model I worked with was BERT, and the first tasks were related to classifications. My recommendations are to master the Transformer architecture well because 99% of today's LLM models are based on it. Here are some resources: The legendary paper "Attention Is All You Need" Hugging Face Course on Transformers Illustrated Guide to Transformers - Step by Step Explanation Good repository How large language models work, a visual intro to transformers After spending years using encoder-based Transformer models, I started learning GPT models. Good open-source models like Llama 2 then appear. Then, I started fine-tuning these models using the excellent Unsloth library: How to Finetune Llama-3 and Export to Ollama Fine-tune Llama 3.1 Ultra-Efficiently with Unsloth After that, I focused on studying various RAG techniques and developing Agent AI systems. This is now called AI engineering, and, as far as I can see, it has become quite popular. So I'll write more about that in another post, but here I'll leave what I consider to be the three most famous representatives, i.e., their tutorials: LangChain tutorial LangGraph tutorial CrewAI examples Here I am today Thanks to the knowledge I've generated over all these years in the field of ML, I've developed and worked on numerous projects. The most significant publicly available project is developing an agent AI system for well-being support, which I turned into a mobile application. Also, my entire doctoral dissertation is related to applying ML to the stock market in combination with the development of GPT models and reinforcement learning (more on that in a separate post). After long 6 years, I've completed my dissertation, and now I'm just waiting for its defense. I'll share everything I'm working on for the dissertation publicly on the project, and in tutorials I'm preparing to write. If you're interested in these topics, I announce that I'll soon start with activities of publishing content on Medium and a blog, but I'll share all of that here on Reddit as well. Now that I've gathered years of experience and knowledge in this field, I'd like to share it with others and help as much as possible. If you have any questions, feel free to ask them, and I'll try to answer all of them. Thank you for reading.

#reddit#started#learning

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Little_Pangolin_164•This week

Getting Back Into AI/ML After a Career Break – Seeking Advice

Hi everyone, I’m a stay-at-home mom looking to restart my career in AI/ML. I have a Bachelor’s in AI and a Master’s in IT, along with experience teaching undergraduate courses like Fundamentals of AI and Artificial Neural Networks (ANN). However, I have a significant gap in my resume, which makes me feel a bit demotivated about re-entering the field. Despite the gap, my passion for AI hasn’t faded—I’m still fascinated by the field and would love to learn Machine Learning from scratch to build a strong foundation and transition into an ML career. I’d really appreciate any advice on: • The best way to relearn ML from scratch (courses, books, hands-on projects) • How to fill the resume gap and make myself a strong candidate again • Any communities, mentorship programs, or networking strategies that might help If anyone has been through a similar situation or has guidance on how to break back into AI/ML after a career break, I’d love to hear your thoughts! Thanks in advance.

#reddit#getting#back

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__proximity__•This week

How do I get a job in this job market? How do I stand out from the crowd?

About me - I am an international grad student graduating in Spring 2025. I have been applying for jobs and internships since September 2024 and so far I haven't even been able to land a single interview. I am not an absolute beginner in this field. Before coming to grad school I worked as an AI Software Engineer in a startup for more than a year. I have 2 publications one in the WACV workshop and another in ACM TALLIP. I have experience in computer vision and natural language processing, focusing on multimodal learning and real-world AI applications. My academic projects include building vision-language models, segmentation algorithms for medical imaging, and developing datasets with human attention annotations. I’ve also worked on challenging industry projects like automating AI pipelines and deploying real-time classifiers. How can I improve my chances in this competitive job market? Are there specific strategies for international students navigating U.S. tech job applications? How can I stand out, especially when competing with candidates from top schools and with more experience?

#reddit#market?#stand

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Primary_Cheesecake63•This week

[Help Needed] Developing an AI to Play Mini Metro – Struggling with Data Extraction & Strategy method

Hello everyone ! First of all, please excuse my English if i do mistakes, as it is not my native language and I am not necessarily comfortable with it :) Regarding this project, I will explain my initial intention. I know very little about coding, but I enjoy it and have had some Python lessons, along with a few small personal projects for fun, mostly using YouTube tutorials. Nothing too advanced... However, now I want to take it to the next level. Since I have some familiarity with coding, I’ve wanted to work on artificial intelligence for a while. I have never coded AI myself, but I enjoy downloading existing projects (for chess, checkers, cat-and-mouse games, etc.), testing their limits, and understanding how they work. One of my favorite strategy game genres is management games, especially Mini Metro. Given its relatively simple mechanics, I assumed there would already be AI projects for it. But to my surprise, I could only find mods that add maps ! I admit that I am neither the best nor the most patient researcher, so I haven’t spent hours searching, but the apparent lack of projects for this game struck me. Maybe the community is just small ? I haven't looked deeply into it. So, I got it into my head to create my own AI. After all, everything is on the internet, and perseverance is key ! However, perseverance alone is not enough when you are not particularly experienced, so I am turning to the community to find knowledgeable people who can help me. The First Obstacle: Getting Game Data I quickly realized that the biggest challenge is that Mini Metro does not have an accessible API (at least, not one I could find). This means I cannot easily extract game data. My initial idea was to have an AI analyze the game, think about the best move, and then write out the actions to be performed, instead of coding a bot that directly manipulates the game. But first, I needed a way to retrieve and store game data. Attempt #1: Image Recognition (Failed) Since there was no API, I tried using image recognition to gather game data. Unfortunately, it was a disaster. I used mss for screenshots ,Tesseract for OCR, andNumPy to manipulate images in the HSV color space but it produced unreliable results : It detected many false positives (labeling empty spaces as stations) It failed to consistently detect numbers (scores or resources like trains and lines) Dotted bridge indicators over rivers were misinterpreted as stations While I could detect stations, lines, and moving trains, the data was chaotic and unreliable Attempt #2: Manual Data Entry (Partially Successful but Impractical) Since image recognition was unreliable, I decided to manually update the game data in real-time. I created a script that : Displays an overlay when I press Shift+R. Allows me to manually input stations, lines, and other game elements. Saves the current state when I press Shift+R again, so I can resume playing. Implements a simple resource management system (trains, lines, etc.). This works better than image recognition because I control the input, but I’m running into serious limitations : Some game mechanics are hard to implement manually (adding a station in the middle of a line, extending the correct line when two lines overlap at a station) Keeping track of station demands (the shapes passengers want to travel to) becomes overwhelming as the game progresses Updating the score in real-time is practically impossible manually, and the score is essential for training an AI (for my reward systems) My Dilemma At this point, I am unsure of how to proceed. My questions for the community : Am I going in the right direction? Should I continue improving my manual tracking system or is it a dead end? Should I have persevered with image recognition instead? Is there a better way to extract game data that I haven’t thought of? I would appreciate any guidance or ideas. Thanks in advance ! if you need more info, i have posted my codes here : https://github.com/Dmsday/mini\metro\data\analyzer (for the image detection version I'm not sure that it's the latest version aka the most "functional" version that I could do because I think I deleted it out of boredom...)

#reddit#[help#needed]

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DizD•This week

ZeroToHeroML: Beginner-Friendly ML & AI Course (Free)

Hey r/learnmachinelearning! A friend of mine, who's been a software developer at Sony for 10 years, recently expressed interest in learning Machine Learning (ML) and Artificial Intelligence (AI). Leveraging my background in ML and neural computation (learned at UCSD) to create a beginner-friendly course guiding him through the basics and into more complex projects. Foundational Concepts: Predicting House Prices (Regression): Master regression techniques to forecast housing prices based on various factors. Iris Flower Species Prediction (Classification): Learn classification algorithms by predicting flower species using the famous Iris dataset. Overcoming Overfitting: Explore methods to prevent models from overfitting and enhance their generalizability. In Progress: Customer Segmentation (Unsupervised Learning): Delve into unsupervised learning to group customers based on purchase history or demographics (valuable for targeted marketing campaigns). Deep Learning for Image Recognition: Implement Convolutional Neural Networks (CNNs) to build models that recognize objects or scenes in images. Natural Language Processing Sentiment Analysis: Analyze the sentiment (positive, negative, or neutral) expressed in text data (e.g., reviews, social media posts) using NLP techniques. Introduction to Reinforcement Learning: Get acquainted with the fundamentals of reinforcement learning by creating an agent that learns to navigate a maze. Want to Learn or Contribute? I thought I'd share ZeroToHeroML here so others who want to learn ML/AI or know someone who does can benefit from this free resource! &#x200B; Fork the repo: https://github.com/DilrajS/ZeroToHeroML Share with others interested in ML/AI! Pull requests welcome (help the community grow!). All help is appriciated! Let's conquer ML/AI together!

#reddit#zerotoheroml:#beginner-friendly

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ParkMountain•This week

Scratch Machine Learning Algorithms Implementations

Hi there, other Redditors! Like many of you, when I first started working in the AI field, I wanted to build some basic Machine Learning models from scratch in order to better understand how each algorithm works, improve my programming and math skills, or simply produce an eye-catching, difficult project to put in the résumé. After spending some time searching for resources that could help me guide my studies, I discovered that the majority of scratch implementations that are currently available are either i) outdated (having been implemented years ago using Python 2 or an earlier version of Python 3); ii) too difficult to understand (using a lot of difficult, unfriendly optimization techniques or with poorly written code); or iii) too simple (only covering binary classification). With that in mind, I made the decision to develop user-friendly, uncomplicated, organized, and simple implementations from scratch. Aside from all of that, I've always wanted to create an open-source project so that others, particularly novices and those with less than a year's experience (like me), can collaborate with others, contribute to public projects, and experience Git firsthand (some of these implementations were made by other contributors!). Here are some implementations that are available: Algorithms (Random Forest Classifier and Regressor, Decision Tree Classifier and Regressor, KMeans, KNN Classifier and Regressor, Gaussian Naive Bayes, Linear Regression, Logistic Regression, PCA, Perceptron, MLP Classifier and Regressor, SVM Classifier and Regressor); Regression and classification metrics; Distance metrics (such as Euclidean); Data split functions (such as KFold); Activation and loss functions; Scalers (such as MinMaxScaler) and encoders (such as One Hot Encoder); and a few things more! Project's link: https://github.com/rafaelgreca/scratchml Disclaimer: The goal of this library is to provide code that is simpler, easier to understand, and more approachable for artificial intelligence enthusiasts and beginners who want to contribute to an open-source repository or who want to learn more about how algorithms work. It is not meant to replace existing libraries that are better, more optimized, and have a wider variety of implemented algorithms (such as scikit-learn, PyTorch, Keras, and Tensorflow). If you want to use optimized implementations with accurate results, please use one of the previously mentioned libraries. P.S.: I accidentally deleted the other post, so I am posting again. :-)

#reddit#scratch#machine

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OneTrueDuce•This week

Sophomore computer science student, looking at ISLP vs ESL vs mlcourse.ai

For background, I am currently a computer science sophomore, with intermediate skills in Python and C++. I have taken university courses on data structure and algorithms, calc 1-3, linear algebra, and an introductory stat course (which covered confidence interval, Z and T sample test, and hypothesis testing). I also have read up to Chapter 5 of the MML book and am currently self-studying probability theory (through STAT 110 video and textbook by Joe Blitzstein). I have done a few beginner ML projects with Tensorflow and scikit-learn, but most of the work is in EDA and feature engineering, while the ML model is just a black box that I plug and chug. So now, I want to learn how to implement ML models from scratch. I've been skimming over ISLP, which many people online recommended, but it seems that while it talks about mathematical equations used, I don't really get to implement it; as the labs are a lot of importing an already implemented model then plug and chug. So now, I am looking at ESL, which I believe is the more detailed and mathematically rigorous version of ISL. However, there aren't any labs or code along to ease beginners in (which I somewhat understand given the intended audience of the book). Another option I am looking at is mlcourse.ai, which seems to cover mathematics and has some lab/code along for it. But it doesn't seem to span as many subjects as ESL does. Given these options, I am unsure of which one to pick, should I first finish my self-study on probability theory and then Chapters 6-8 of MML? Then should I do ISLP first or just get into ESL? Or maybe I should do mlcourse.ai first then into ESL? Or should I just do the ML course/book along with the maths? In addition, there is also the data science + feature engineering stuff which I wonder if I should study more about. Sorry if this seems like a mess, there are just so many things to ML that I am kinda overwhelmed.

#reddit#sophomore#computer

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Suspicious_Dig_3849•This week

Advice Needed

Hey everyone, I’ve been diving into Artificial Intelligence, Machine Learning, and Deep Learning recently, but I find myself a little confused about how to approach the learning process effectively. My goal isn’t just to secure a job but to actually build cool AI products or startups—something innovative and impactful, like what companies such as OpenAI, Anthropic, or ElevenLabs are doing. I often see founders or engineers building incredible AI-driven startups, and I can’t help but wonder: • What kind of learning path did these people follow? • Surely they didn’t just stick to basic Udemy or YouTube courses that most people use for job prep. • What resources or approaches do serious AI practitioners use? I’ve heard that implementing research papers is a great way to gain a deep, intuitive understanding of AI concepts. But as someone who is still a beginner, I’m unsure how to start implementing papers without feeling overwhelmed. Here’s what I’m hoping to get clarity on: Where should I begin as a complete beginner? What resources, projects, or habits would you recommend to build solid fundamentals in AI/ML? How do I progress from beginner to a level where I can implement research papers? Are there intermediate steps I need to take before diving into papers? What would the ideal roadmap look like for someone who wants to build startups in AI? If you’re an AI practitioner, researcher, or startup founder, I’d love to hear about your experiences and learning pathways. What worked for you? What didn’t? Any advice or resources would be immensely appreciated. I’m ready to put in the hard work, I just want to make sure I’m moving in the right direction. Thanks in advance! Looking forward to learning from this community.

#reddit#advice#needed

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terobau007•This week

RAG with LLM project code walkthrough for beginners

Hello Guys, I have shared a code walkthrough which focuses on a RAG project using DeepSeek. It is a beginner friendly project that any fresher can implement with basic knowledge of python. Do let me know what you think about the project. Also I am trying to share beginner friendly projects for freshers in AI/ML field. I will soon be sharing a in depth tutorial for ML project that helped me get a job in ML field, once I am comfortable with making youtube videos as I am new to this. Do give feedbacks for improvements and stay connected for more projects. https://www.youtube.com/watch?v=aeWJjBrpyok&list=PLVGnN2aG2ioMr3VHOSur5n1LLm1FAdc0\&index=6

#reddit#project#code

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The-Cactus-Flower•This week

Looking for the Best Resources to Level Up in Python, AI, ML, and Data Science!

Hey everyone, I've been diving into AI, ML, and Data Science for a while now and have completed the IBM AI Engineering Course and the Machine Learning Specialization by Andrew Ng on Coursera. Both were super helpful to get me started on beginner-level implementation. However, I'm really looking to take my skills to the next level and go pro! 🎯 I’m looking for awesome from 0 to total hero tutorials/coding videos, especially on YouTube, and the best books you’ve come across. I'm particularly interested in: Python for AI/ML/Data Science Advanced AI/ML topics (hands-on projects would be a plus!) Awesome books (I already have a PDF of O'Reilly's Data Science* book with the black and yellow reptile on the cover and just started reading that since its a bit pricy in India) Any recommendations for resources (free or affordable if possible) would be amazing. Also, if anyone could suggest a solid book for Java Data Structures and Algorithms, that’d be awesome. I already know the concepts but want something to keep on hand for reference. Thanks a ton in advance for any suggestions! 🙌

#reddit#looking#best

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Any-Reserve-4403•This week

Learning Resources + Side Project Ideas

I made a post last night about my journey to landing an AI internship and have received a lot of responses asking about side projects and learning resources, so I am making another thread here consolidating this information for all those that are curious! Learning Process Step 1) Learn the basic fundamentals of the Math USE YOUTUBE!!! Literally just type in 'Machine Learning Math" and you will get tons of playlists covering nearly every topic. Personally I would focus on Linear Algebra and Calculus - specifically matrices/vector operations, dot products, eigenvectors/eigenvalues, derivatives and gradients. It might take a few tries until you find someone that meshes well with your learning style, but 3Blue1Brown is my top recommendation. I also read the book "Why Machines Learn" and found that extremely insightful. Work on implementing the math both with pen and paper then in Python. Step 2) Once you have a grip on the math fundamentals, I would pick up Hands-on Machine Learning with Sci-kit Learn, Keras and TensorFlow. This book was a game changer for me. It goes more in depth on the math and covers every topic from Linear Regression to the Transformers architecture. It also introduces you to Kaggle and some beginner level side projects. Step 3) After that book I would begin on side projects and also checking out other similar books, specifically Hands on Large Language Models and Hands on Generative AI. Step 4) If you have read all three of these books, and fully comprehend everything, then I would start looking up papers. I would just ask ChatGPT to feed you papers that are most relevant to your interests. Beginner Side Project Ideas 1) Build a Neural Network from scratch, using just Numpy. It can be super basic - have one input layer with 2 nodes, 1 hidden layer with 2 nodes, and output layer with one node. Learn about the forward feed process and play around with different activation functions and loss functions. Learn how these activation functions and loss functions impact backpropagation (hint: the derivatives of the activation functions and loss functions are all different). Get really good at this and understand the difference between regression models and classification models and which activation/loss functions go with which type of model. If you are really feeling crazy and are more focused on a SWE type of role, try doing it in a language other than python and try building a frontend for it so there is an interface where a user can input data and select their model architecture. 2) Build a CNN Image Classifier for the MNIST - Get familiar with the intricacies of CNN's, image manipulation, and basic computer vision concepts. 3) Build on top of open source LLM's. Go to Hugging Face's models page and start playing around with some. 4) KAGGLE COMPETITIONS - I will not explain further, do Kaggle Competitions. Other Resources I've mentioned YouTube, several books and Hugging Face. I also recommend: DataLemur.com \- Python practice, SQL practices, ML questions - his book Ace the Data Science Interview is also very good. X.com \- follow people that are prominent in the space. I joined an AI and Math Group that is constantly posting resources in there deep-ml.com If you have found any of this helpful - feel free to give me a follow on X and stay in touch @ x.com/hark0nnen\

#reddit#learning#resources

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oba2311•This week

Starting with Deep Learning in 2025 - Suggestion

I'm aware this has been asked many times here. so I'm not here to ask for a general advice - I've done some homework. My questions is - what do you think about this curriculum I put together (research + GPT)? Context: \- I'm a product manger with technical background and want to get back to a more technical depth. \- BSc in stats, familiar with all basic ML concepts, some maths (linear algebra etc), python. Basically, I got the basics covered a while ago so I'm looking to go back into the basics and I can learn and relearn anything I might need to with the internet. My focus is on getting hands on feel on where AI and deep learning is at in 2025, and understand the "under the hood" of key models used and LLMs specifically. Veterans - whats missing? what's redundant? Thanks so much! 🙏🏻 PS - hoping others will find this useful, you very well might too! |Week/Day|Goals|Resource|Activity| |:-|:-|:-|:-| |Week 1|Foundations of AI and Deep Learning||| |Day 1-2|Learn AI terminology and applications|DeepLearning.AI's "AI for Everyone"|Complete Module 1. Understand basic AI concepts and its applications.| |Day 3-5|Explore deep learning fundamentals|Fast.ai's Practical Deep Learning for Coders (2024)|Watch first 2 lessons. Code an image classifier as your first DL project.| |Day 6-7|Familiarize with ML/LLM terminology|Hugging Face Machine Learning Glossary|Study glossary terms and review foundational ML/LLM concepts.| |Week 2|Practical Deep Learning||| |Day 8-10|Build with PyTorch basics|PyTorch Beginner Tutorials|Complete the 60-minute blitz and create a simple neural network.| |Day 11-12|Explore more projects|Fast.ai Lesson 3|Implement a project such as text classification or tabular data analysis.| |Day 13-14|Fine-tune pre-trained models|Hugging Face Tutorials|Learn and apply fine-tuning techniques for a pre-trained model on a simple dataset.| |Week 3|Understanding LLMs||| |Day 15-17|Learn GPT architecture basics|OpenAI Documentation|Explore GPT architecture and experiment with OpenAI API Playground.| |Day 18-19|Understand tokenization and transformers|Hugging Face NLP Course|Complete the tokenization and transformers sections of the course.| |Day 20-21|Build LLM-based projects|TensorFlow NLP Tutorials|Create a text generator or summarizer using LLM techniques.| |Week 4|Advanced Concepts and Applications||| |Day 22-24|Review cutting-edge LLM research|Stanford's CRFM|Read recent LLM-related research and discuss its product management implications.| |Day 25-27|Apply knowledge to real-world projects|Kaggle|Select a dataset and build an NLP project using Hugging Face tools.| |Day 28-30|Explore advanced API use cases|OpenAI Cookbook and Forums|Experiment with advanced OpenAI API scenarios and engage in discussions to solidify knowledge.|

#reddit#starting#deep

reddit

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Afreen19•This week

Best AI/ML course for Beginners to advanced - recommendations?

Hey everyone, I’m looking for some solid AI/ML courses that cover everything from the basics to advanced topics. I want a structured learning path that helps me understand fundamental concepts like linear regression, neural networks, and deep learning, all the way to advanced topics like transformers, reinforcement learning, and real-world applications. Ideally, the course(s) should: • Be beginner-friendly but progress to advanced topics • Have practical, hands-on projects • Cover both theory and implementation (Python, TensorFlow, PyTorch, etc.) • Be well-structured and up to date I’m open to free and paid options (Coursera, Udemy, YouTube, etc.). What are some of the best courses you’d recommend? Thanks in advance!

#reddit#best#ai/ml

reddit

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Baby-Boss0506•This week

[Help Needed] How to tackle AI for DNS Security in a Hackathon for Beginner.

Hi everyone! I've been selected to participate in an AI and Cybersecurity Hackathon, and the group I'm in focuses on AI for DNS Security. Our goal is to implement AI algorithms to detect anomalies and enhance DNS security. Here’s the catch: I have no prior background in cybersecurity, and I’m also a beginner in applying AI to real-world security problems. I’d really appreciate some guidance from this amazing community on how to approach this challenge. A bit more about the project: Objective: Detect anomalies in DNS traffic (e.g., malicious requests, tunneling, etc.). AI tools: We’re free to choose algorithms, but I’m unsure where to start—supervised vs. unsupervised learning? My skillset: Decent grasp of Python (Pandas, Scikit-learn, etc.) and basic ML concepts. No practical experience in network security or analyzing DNS traffic. What I’m looking for: Datasets: Any recommendations for open-source DNS datasets or synthetic data creation methods? AI methods: Which models work best for anomaly detection in DNS logs? Are there any relevant GitHub projects? Learning resources: Beginner-friendly material on DNS security and the application of AI in this domain. Hackathon tips: How can I make the most of this opportunity and contribute effectively to my team? Bonus question: If you’ve participated in similar hackathons, what strategies helped you balance learning and execution within a short timeframe? Thank you so much in advance for any advice, resources, or personal experiences you can share! I’ll make sure to share our project results and lessons learned after the hackathon.

#reddit#[help#needed]

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NoConsideration4473•This week

Seeking Guidance on Implementing LLM and Generative AI in a New Role.

Hi everyone, I recently got a job that involves implementing LLM and Generative AI. During the interview, we discussed topics like Reinforcement Learning, Deep Learning, and Machine Learning. I’m finishing my MSc in Computer Science, where I’ve worked on projects involving simple Deep Learning implementations (data extraction, cleaning some MLP). Essentially, I will have access to servers for computing and a laptop, and I’ll be the sole programmer responsible for establishing and demonstrating value. I am looking for resources and maybe some examples of how to do that so far I have recommended: Hugging Face - Documentation Intro to Large Language Models by Andrej Karpathy I also plan to attend local meetups about LLM and PyTorch. I’m looking for practical advice on learning these technologies while developing them, particularly regarding development workflows, cloud backups, and coding standards, as I will be working independently. I know if I can achieve something easier and faster with a simple Machine Learning model I will do that. Any help would be greatly appreciated.

#reddit#seeking#guidance

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Repulsive_News1717•This week

AI Hackathon in Berlin

Hey there! We’re excited to host the Factory Network x {Tech: Berlin} AI Hackathon at Factory Berlin Mitte from September 28th at 10:00 AM to September 29th at 8:00 PM. This is a great chance for entrepreneurs, startup teams, and builders to dive into AI projects, whether you're improving an existing idea or starting something new.

#reddit#hackathon#berlin

reddit

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AssistanceOk2217•This week

How I Built a Simple 1-Person Startup With 100% AI

AI Made It Possible For All Of Us To Be An Entrepreneur Or Say Solopreneur Full Article https://i.redd.it/i4kzcaqvr18d1.gif AI Made It Possible For All Of Us To Be An Entrepreneur Or Say Solopreneur ● What's This Article About? • Creating a one-person startup called "Aniket Mart Shipping Solutions" using AI • Using AI to simplify product shipping • AI decides shipping methods based on product description purchased by customer • No humans needed in the shipping process ● Why Read This Article? • Learn how AI can help run a business with few people • See how AI can make business tasks easier • Get ideas for using AI in your own projects ● Let's Design: • The website uses Streamlit for easy shopping • A Flask server works with AI to sort products for shipping • The code is organized to make updates easy ● Project Structure: • UI Component: Main website code controls how the app runs Special code makes the website look nice Code to show products and let people buy them • Server Component: Sets up the main server Manages important information like API keys Stores product data Uses AI to decide shipping methods Handles requests from the website ● Closing Thoughts: • AI is changing how we can run businesses • In the future, AI will do even more in business • This project shows that one person with AI can do big things in business

#reddit#built#simple

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Repulsive_News1717•This week

ML Hackathon in Berlin

Join us for the Factory Network x {Tech: Berlin} Hackathon at Factory Berlin Mitte from September 28th, 10:00 AM to September 29th, 8:00 PM. It’s a perfect chance for ambitious students, entrepreneurs and teams to dive into AI projects, whether you're building on an existing idea or starting fresh.

#reddit#hackathon#berlin

reddit

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daniel-data•This week

Study Plan for Learning Data Science Over the Next 12 Months [D]

In this thread, I address a study plan for 2021. In case you're interested, I wrote a whole article about this topic: Study Plan for Learning Data Science Over the Next 12 Months Let me know your thoughts on this. &#x200B; https://preview.redd.it/emg20nzhet661.png?width=1170&format=png&auto=webp&s=cf09e4dc5e82ba2fd7b57c706ba2873be57fe8de We are ending 2020 and it is time to make plans for next year, and one of the most important plans and questions we must ask is what do we want to study?, what do we want to enhance?, what changes do we want to make?, and what is the direction we are going to take (or continue) in our professional careers?. Many of you will be starting on the road to becoming a data scientist, in fact you may be evaluating it, since you have heard a lot about it, but you have some doubts, for example about the amount of job offers that may exist in this area, doubts about the technology itself, and about the path you should follow, considering the wide range of options to learn. I’m a believer that we should learn from various sources, from various mentors, and from various formats. By sources I mean the various virtual platforms and face-to-face options that exist to study. By mentors I mean that it is always a good idea to learn from different points of view and learning from different teachers/mentors, and by formats I mean the choices between books, videos, classes, and other formats where the information is contained. When we extract information from all these sources we reinforce the knowledge learned, but we always need a guide, and this post aims to give you some practical insights and strategies in this regard. To decide on sources, mentors and formats it is up to you to choose. It depends on your preferences and ease of learning: for example, some people are better at learning from books, while others prefer to learn from videos. Some prefer to study on platforms that are practical (following online code), and others prefer traditional platforms: like those at universities (Master’s Degree, PHDs or MOOCs). Others prefer to pay for quality content, while others prefer to look only for free material. That’s why I won’t give a specific recommendation in this post, but I’ll give you the whole picture: a study plan. To start you should consider the time you’ll spend studying and the depth of learning you want to achieve, because if you find yourself without a job you could be available full time to study, which is a huge advantage. On the other hand, if you are working, you’ll have less time and you’ll have to discipline yourself to be able to have the time available in the evenings, mornings or weekends. Ultimately, the important thing is to meet the goal of learning and perhaps dedicating your career to this exciting area! We will divide the year into quarters as follows First Quarter: Learning the Basics Second Quarter: Upgrading the Level: Intermediate Knowledge Third Quarter: A Real World Project — A Full-stack Project Fourth Quarter: Seeking Opportunities While Maintaining Practice First Quarter: Learning the Basics &#x200B; https://preview.redd.it/u7t9bthket661.png?width=998&format=png&auto=webp&s=4ad29cb43618e7acf793259243aa5a60a8535f0a If you want to be more rigorous you can have start and end dates for this period of study of the bases. It could be something like: From January 1 to March 30, 2021 as deadline. During this period you will study the following: A programming language that you can apply to data science: Python or R. We recommend Python due to the simple fact that approximately 80% of data science job offers ask for knowledge in Python. That same percentage is maintained with respect to the real projects you will find implemented in production. And we add the fact that Python is multipurpose, so you won’t “waste” your time if at some point you decide to focus on web development, for example, or desktop development. This would be the first topic to study in the first months of the year. Familiarize yourself with statistics and mathematics. There is a big debate in the data science community about whether we need this foundation or not. I will write a post later on about this, but the reality is that you DO need it, but ONLY the basics (at least in the beginning). And I want to clarify this point before continuing. We could say that data science is divided in two big fields: Research on one side and putting Machine Learning algorithms into production on the other side. If you later decide to focus on Research then you are going to need mathematics and statistics in depth (very in depth). If you are going to go for the practical part, the libraries will help you deal with most of it, under the hood. It should be noted that most job offers are in the practical part. For both cases, and in this first stage you will only need the basics of: Statistics (with Python and NumPy) Descriptive statistics Inferential Statistics Hypothesis testing Probability Mathematics (with Python and NumPy) Linear Algebra (For example: SVD) Multivariate Calculus Calculus (For example: gradient descent) Note: We recommend that you study Python first before seeing statistics and mathematics, because the challenge is to implement these statistical and mathematical bases with Python. Don’t look for theoretical tutorials that show only slides or statistical and/or mathematical examples in Excel/Matlab/Octave/SAS and other different to Python or R, it gets very boring and impractical! You should choose a course, program or book that teaches these concepts in a practical way and using Python. Remember that Python is what we finally use, so you need to choose well. This advice is key so you don’t give up on this part, as it will be the most dense and difficult. If you have these basics in the first three months, you will be ready to make a leap in your learning for the next three months. Second Quarter: Upgrading the Level: Intermediate Knowledge &#x200B; https://preview.redd.it/y1y55vynet661.png?width=669&format=png&auto=webp&s=bd3e12bb112943025c39a8975faf4d64514df275 If you want to be more rigorous you can have start and end dates for this period of study at the intermediate level. It could be something like: From April 1 to June 30, 2021 as deadline. Now that you have a good foundation in programming, statistics and mathematics, it is time to move forward and learn about the great advantages that Python has for applying data analysis. For this stage you will be focused on: Data science Python stack Python has the following libraries that you should study, know and practice at this stage Pandas: for working with tabular data and make in-depth analysis Matplotlib and Seaborn: for data visualization Pandas is the in-facto library for data analysis, it is one of the most important (if not the most important) and powerful tools you should know and master during your career as a data scientist. Pandas will make it much easier for you to manipulate, cleanse and organize your data. Feature Engineering Many times people don’t go deep into Feature Engineering, but if you want to have Machine Learning models that make good predictions and improve your scores, spending some time on this subject is invaluable! Feature engineering is the process of using domain knowledge to extract features from raw data using data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself. To achieve the goal of good feature engineering you must know the different techniques that exist, so it is a good idea to at least study the main ones. Basic Models of Machine Learning At the end of this stage you will start with the study of Machine Learning. This is perhaps the most awaited moment! This is where you start to learn about the different algorithms you can use, which particular problems you can solve and how you can apply them in real life. The Python library we recommend you to start experimenting with ML is: scikit-learn. However it is a good idea that you can find tutorials where they explain the implementation of the algorithms (at least the simplest ones) from scratch with Python, since the library could be a “Black Box” and you might not understand what is happening under the hood. If you learn how to implement them with Python, you can have a more solid foundation. If you implement the algorithms with Python (without a library), you will put into practice everything seen in the statistics, mathematics and Pandas part. These are some recommendations of the algorithms that you should at least know in this initial stage Supervised learning Simple Linear Regression Multiple Linear Regression K-nearest neighbors (KNN) Logistic Regression Decision Trees Random Forest Unsupervised Learning K-Means PCA Bonus: if you have the time and you are within the time ranges, you can study these others Gradient Boosting Algorithms GBM XGBoost LightGBM CatBoost Note: do not spend more than the 3 months stipulated for this stage. Because you will be falling behind and not complying with the study plan. We all have shortcomings at this stage, it is normal, go ahead and then you can resume some concepts that did not understand in detail. The important thing is to have the basic knowledge and move forward! If at least you succeed to study the mentioned algorithms of supervised and unsupervised learning, you will have a very clear idea of what you will be able to do in the future. So don’t worry about covering everything, remember that it is a process, and ideally you should have some clearly established times so that you don’t get frustrated and feel you are advancing. So far, here comes your “theoretical” study of the basics of data science. Now we’ll continue with the practical part! Third Quarter: A Real World Project — A Full-stack Project &#x200B; https://preview.redd.it/vrn783vqet661.png?width=678&format=png&auto=webp&s=664061b3d33b34979b74b10b9f8a3d0f7b8b99ee If you want to be more rigorous you can have start and end dates for this period of study at the intermediate level. It could be something like: From July 1 to September 30, 2021 as deadline. Now that you have a good foundation in programming, statistics, mathematics, data analysis and machine learning algorithms, it is time to move forward and put into practice all this knowledge. Many of these suggestions may sound out of the box, but believe me they will make a big difference in your career as a data scientist. The first thing is to create your web presence: Create a Github (or GitLab) account, and learn Git*. Being able to manage different versions of your code is important, you should have version control over them, not to mention that having an active Github account is very valuable in demonstrating your true skills. On Github, you can also set up your Jupyter Notebooks and make them public, so you can show off your skills as well. This is mine for example: https://github.com/danielmoralesp Learn the basics of web programming*. The advantage is that you already have Python as a skill, so you can learn Flask to create a simple web page. Or you can use a template engine like Github Pages, Ghost or Wordpress itself and create your online portfolio. Buy a domain with your name*. Something like myname.com, myname.co, myname.dev, etc. This is invaluable so you can have your CV online and update it with your projects. There you can make a big difference, showing your projects, your Jupyter Notebooks and showing that you have the practical skills to execute projects in this area. There are many front-end templates for you to purchase for free or for payment, and give it a more personalized and pleasant look. Don’t use free sub-domains of Wordpress, Github or Wix, it looks very unprofessional, make your own. Here is mine for example: https://www.danielmorales.dev/ Choose a project you are passionate about and create a Machine Learning model around it. The final goal of this third quarter is to create ONE project, that you are passionate about, and that is UNIQUE among others. It turns out that there are many typical projects in the community, such as predicting the Titanic Survivors, or predicting the price of Houses in Boston. Those kinds of projects are good for learning, but not for showing off as your UNIQUE projects. If you are passionate about sports, try predicting the soccer results of your local league. If you are passionate about finance, try predicting your country’s stock market prices. If you are passionate about marketing, try to find someone who has an e-commerce and implement a product recommendation algorithm and upload it to production. If you are passionate about business: make a predictor of the best business ideas for 2021 :) As you can see, you are limited by your passions and your imagination. In fact, those are the two keys for you to do this project: Passion and Imagination. However don’t expect to make money from it, you are in a learning stage, you need that algorithm to be deployed in production, make an API in Flask with it, and explain in your website how you did it and how people can access it. This is the moment to shine, and at the same time it’s the moment of the greatest learning. You will most likely face obstacles, if your algorithm gives 60% of Accuracy after a huge optimization effort, it doesn’t matter, finish the whole process, deploy it to production, try to get a friend or family member to use it, and that will be the goal achieved for this stage: Make a Full-stack Machine Learning project. By full-stack I mean that you did all the following steps: You got the data from somewhere (scrapping, open data or API) You did a data analysis You cleaned and transformed the data You created Machine Learning Models You deployed the best model to production for other people to use. This does not mean that this whole process is what you will always do in your daily job, but it does mean that you will know every part of the pipeline that is needed for a data science project for a company. You will have a unique perspective! Fourth Quarter: Seeking Opportunities While Maintaining Practice &#x200B; https://preview.redd.it/qd0osystet661.png?width=1056&format=png&auto=webp&s=2da456b15985b2793041256f5e45bca99a23b51a If you want to be more rigorous you can have start and end dates for this period of study at the final level. It could be something like: From October 1 to December 31, 2021 as deadline. Now you have theoretical and practical knowledge. You have implemented a model in production. The next step depends on you and your personality. Let’s say you are an entrepreneur, and you have the vision to create something new from something you discovered or saw an opportunity to do business with this discipline, so it’s time to start planning how to do it. If that’s the case, obviously this post won’t cover that process, but you should know what the steps might be (or start figuring them out). But if you are one of those who want to get a job as a data scientist, here is my advice. Getting a job as a data scientist “You’re not going to get a job as fast as you think, if you keep thinking the same way”.Author It turns out that all people who start out as data scientists imagine themselves working for the big companies in their country or region. Or even remote. It turns out that if you aspire to work for a large company like data scientist you will be frustrated by the years of experience they ask for (3 or more years) and the skills they request. Large companies don’t hire Juniors (or very few do), precisely because they are already large companies. They have the financial muscle to demand experience and skills and can pay a commensurate salary (although this is not always the case). The point is that if you focus there you’re going to get frustrated! Here we must return to the following advise: “You need creativity to get a job in data science”. Like everything else in life we have to start at different steps, in this case, from the beginning. Here are the scenarios If you are working in a company and in a non-engineering role you must demonstrate your new skills to the company you are working for*. If you are working in the customer service area, you should apply it to your work, and do for example, detailed analysis of your calls, conversion rates, store data and make predictions about it! If you can have data from your colleagues, you could try to predict their sales! This may sound funny, but it’s about how creatively you can apply data science to your current work and how to show your bosses how valuable it is and EVANGELIZE them about the benefits of implementation. You’ll be noticed and they could certainly create a new data related department or job. And you already have the knowledge and experience. The key word here is Evangelize. Many companies and entrepreneurs are just beginning to see the power of this discipline, and it is your task to nurture that reality. If you are working in an area related to engineering, but that is not data science*. Here the same applies as the previous example, but you have some advantages, and that is that you could access the company’s data, and you could use it for the benefit of the company, making analyses and/or predictions about it, and again EVANGELIZING your bosses your new skills and the benefits of data science. If you are unemployed (or do not want, or do not feel comfortable following the two examples above)*, you can start looking outside, and what I recommend is that you look for technology companies and / or startups where they are just forming the first teams and are paying some salary, or even have options shares of the company. Obviously here the salaries will not be exorbitant, and the working hours could be longer, but remember that you are in the learning and practice stage (just in the first step), so you can not demand too much, you must land your expectations and fit that reality, and stop pretending to be paid $ 10,000 a month at this stage. But, depending of your country $1.000 USD could be something very interesting to start this new career. Remember, you are a Junior at this stage. The conclusion is: don’t waste your time looking at and/or applying to offers from big companies, because you will get frustrated. Be creative, and look for opportunities in smaller or newly created companies. Learning never stops While you are in that process of looking for a job or an opportunity, which could take half of your time (50% looking for opportunities, 50% staying in practice), you have to keep learning, you should advance to concepts such as Deep Learning, Data Engineer or other topics that you feel were left loose from the past stages or focus on the topics that you are passionate about within this group of disciplines in data science. At the same time you can choose a second project, and spend some time running it from end-to-end, and thus increase your portfolio and your experience. If this is the case, try to find a completely different project: if the first one was done with Machine Learning, let this second one be done with Deep learning. If the first one was deployed to a web page, that this second one is deployed to a mobile platform. Remember, creativity is the key! Conclusion We are at an ideal time to plan for 2021, and if this is the path you want to take, start looking for the platforms and media you want to study on. Get to work and don’t miss this opportunity to become a data scientist in 2021! Note: we are building a private community in Slack of data scientist, if you want to join us write to the email: support@datasource.ai I hope you enjoyed this reading! you can follow me on twitter or linkedin Thank you for reading!

#reddit#study#plan

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Brilliant_Fishing110•This week

Randomly asked ChatGPT and Claude for a 4 year roadmap for an ML Engineer

Title, Is it actually a good plan ?? If no, why not ?? \\🚀 4-Year Roadmap to Becoming a High-Earning ML Engineer & Entrepreneur\\ \\(With Smartwork & Realistic 60-70% Execution Feasibility)\\ \\🟢 Year 1: Strong Foundation & Initial Projects (0-12 Months)\\ 🎯 \\Goal: Master Python & ML Fundamentals\\ \\🔹 1-4 Months (Python & Math Strengthening)\\ ✅ Python Mastery \- Daily LeetCode Easy problems (minimum 2) \- Build automation projects \- NumPy & Pandas mastery \- DSA fundamentals ✅ Mathematics Foundation \- Linear Algebra basics \- Statistics fundamentals \- Basic calculus concepts ✅ First Mini-Hackathon Participation \- Join beginner-friendly hackathons \- Focus on Python-based challenges \- Team up with other beginners 💡 \\Smart Move:\\ \- Join Discord/Slack hackathon communities \- Practice collaborative coding \- Build network with fellow participants \\🔹 5-8 Months (ML Foundations)\\ ✅ Machine Learning Basics \- Supervised Learning \- Model evaluation \- Feature engineering \- scikit-learn projects ✅ Participate in 2-3 ML Hackathons \- Kaggle Getting Started competitions \- Local ML hackathons \- University hackathons ✅ Start LinkedIn & GitHub Portfolio 💡 \\Smart Move:\\ \- Document hackathon experiences \- Share learnings on LinkedIn \- Focus on completion over winning \\🔹 9-12 Months (Deep Learning Introduction)\\ ✅ Basic Deep Learning \- Neural network fundamentals \- PyTorch basics \- Computer vision tasks \- Basic NLP ✅ Advanced Hackathon Participation \- AI/ML specific hackathons \- Team lead in 1-2 hackathons \- Start mentoring beginners \\🔵 Year 1 Expected Outcome (60-70% Execution)\\ ✔ \\Strong Python & ML foundations\\ ✔ \\5-6 hackathon participations\\ ✔ \\Active GitHub (100+ commits)\\ ✔ \\Growing LinkedIn (300+ connections)\\ 💰 \\Earning Expectation → ₹8K-₹20K per month (Projects/Internship)\\ \\🟢 Year 2: Professional Growth & Specialization (12-24 Months)\\ 🎯 \\Goal: Build Professional Experience & Recognition\\ \\🔹 1-6 Months (Technical Depth)\\ ✅ Advanced ML Topics \- Deep Learning architectures \- Computer Vision OR NLP \- MLOps basics (Docker, FastAPI) \- Cloud fundamentals (AWS/GCP) ✅ Hackathon Achievements \- Win minor prizes in 2-3 hackathons \- Lead teams in major hackathons \- Network with sponsors ✅ Start Technical Blogging 💡 \\Smart Move:\\ \- Focus on hackathon projects that align with career goals \- Build relationships with companies at hackathons \- Create detailed project documentation \\🔹 7-12 Months (Professional Experience)\\ ✅ Secure ML Role/Internship ✅ Advanced Project Building ✅ Open Source Contributions ✅ Organize Small Hackathons 💡 \\Smart Move:\\ \- Use hackathon network for job referrals \- Convert hackathon projects into full products \- Build mentor reputation \\🔵 Year 2 Expected Outcome (60-70% Execution)\\ ✔ \\Professional ML experience\\ ✔ \\10+ hackathon participations\\ ✔ \\1-2 hackathon wins\\ ✔ \\Strong industry network\\ 💰 \\Earning Expectation → ₹40K-₹70K per month (Job/Freelancing)\\ \\🟢 Year 3: Scaling & Business Foundation (24-36 Months)\\ 🎯 \\Goal: Establish Multiple Income Streams\\ \\🔹 1-4 Months (Expertise Building)\\ ✅ Choose Specialization \- MLOps \- Computer Vision \- NLP/LLMs \- Generative AI ✅ Advanced Competitions \- International hackathons \- High-prize competitions \- Corporate ML challenges ✅ Start Consulting Services 💡 \\Smart Move:\\ \- Use hackathon wins for marketing \- Build service packages around expertise \- Network with corporate sponsors \\🔹 5-8 Months (Business Development)\\ ✅ Scale Services ✅ Build Client Network ✅ Create Training Programs ✅ Hackathon Mentorship Program 💡 \\Smart Move:\\ \- Convert hackathon projects to products \- Use event networks for client acquisition \- Build authority through speaking \\🔹 9-12 Months (Growth & Innovation)\\ ✅ Product Development ✅ Team Building ✅ Innovation Focus ✅ Hackathon Organization \\🔵 Year 3 Expected Outcome (60-70% Execution)\\ ✔ \\Established ML business/career\\ ✔ \\Known in hackathon community\\ ✔ \\Multiple income streams\\ ✔ \\Strong industry presence\\ 💰 \\Earning Expectation → ₹1L-₹2L per month (Multiple Streams)\\ \\🟢 Year 4: Scale & Leadership (36-48 Months)\\ 🎯 \\Goal: Build AI Company & Achieve Financial Freedom\\ \\🔹 1-4 Months (Business Scaling)\\ ✅ Company Formation \- AI consulting firm \- Product development \- Training programs ✅ Hackathon Innovation \- Launch own hackathon series \- Corporate partnerships \- Prize sponsorships ✅ Team Expansion 💡 \\Smart Move:\\ \- Use hackathon network for hiring \- Create unique event formats \- Build corporate relationships \\🔹 5-8 Months (Market Leadership)\\ ✅ Product Launch ✅ Service Expansion ✅ International Presence ✅ Innovation Hub Creation 💡 \\Smart Move:\\ \- Create hackathon-to-hiring pipeline \- Build educational programs \- Establish thought leadership \\🔹 9-12 Months (Empire Building)\\ ✅ Multiple Revenue Streams \- AI products \- Consulting services \- Educational programs \- Event organization \- Investment returns ✅ Industry Leadership \- Conference speaking \- Published content \- Community leadership \\🔵 Year 4 Expected Outcome (60-70% Execution)\\ ✔ \\Established AI company\\ ✔ \\Major hackathon organizer\\ ✔ \\Multiple product lines\\ ✔ \\Industry authority status\\ 💰 \\Earning Expectation → ₹3L-₹5L+ per month (Business Income)\\ \\📊 FINAL RATING\\ ✅ \\Comprehensive growth plan\\ ✅ \\Strong community focus\\ ✅ \\Multiple income pathways\\ 💡 \\If 100% Execution → 8.5/10 Feasibility\\ 💡 \\If 50% Execution → 6/10 Feasibility\\ 🔥 \\Conclusion: A balanced path to ML mastery and entrepreneurship, built through consistent growth and community engagement!\\ 🚀 \\Key Success Factors:\\ Regular hackathon participation Strong community involvement Consistent skill development Strategic network building Focus on both technical and business growth

#reddit#randomly#asked

reddit

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Repulsive_News1717•This week

AI Hackathon in Berlin

Join us for the {Tech: Berlin} AI Hackathon at CODE University Berlin from January 18th, 10:00 AM to January 19th, 8:00 PM. It’s a perfect chance for ambitious students, entrepreneurs and teams to dive into AI projects, whether you're building on an existing idea or starting fresh.

#reddit#hackathon#berlin

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Horror_Brief5524•This week

Seeking Guidance to Transition from SRE to Quant Developer/HFT

Hey Reddit! Let me introduce myself: I’m a 2024 B.Tech (IT) graduate from a state-level college in India. Currently, I’m working as an SRE (Site Reliability Engineer) at one of the world’s largest custodian banks (not naming the company). I’ve been with the organization for about a year now, combining my internship and full-time role. During this time, I’ve developed a strong foundation in AI/ML and have primarily been working on projects to automate BAU (Business-As-Usual) activities using these technologies. Outside of work, I have a deep interest in trading and stock markets—something that runs in the family, as my father has been trading for over 40 years. My ultimate goal is to transition into the field of quantitative development or high-frequency trading (HFT) and work for top-tier firms like HRT, Optiver, or Tower Research. To make this dream a reality, I’ve started preparing in the following ways: Learning the basics of stock markets and financial instruments. Studying statistics and experimenting with different algorithms to analyze stocks (mainly on a fundamental level for now). Getting back to practicing DSA (Data Structures and Algorithms). I’ll admit I’m rusty here since I haven’t been consistent with it for a while. I’m aware that transitioning from SRE to Quant Developer/HFT is ambitious and might seem like a pipe dream, but it’s a challenge I’m willing to take on. I’d love to hear from people in the quant field or those who’ve made similar career transitions. What should I focus on? Any recommended resources, books, or courses? Are there any specific skill sets or certifications that could make me stand out? Any guidance, advice, or encouragement would mean a lot. Thank you!

#reddit#seeking#guidance

reddit

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Brilliant_Fishing110•This week

Randomly asked ChatGPT and Claude for a 4 year roadmap for an ML Engineer

Title, Is it actually a good plan ?? If no, why not ?? \\🚀 4-Year Roadmap to Becoming a High-Earning ML Engineer & Entrepreneur\\ \\(With Smartwork & Realistic 60-70% Execution Feasibility)\\ \\🟢 Year 1: Strong Foundation & Initial Projects (0-12 Months)\\ 🎯 \\Goal: Master Python & ML Fundamentals\\ \\🔹 1-4 Months (Python & Math Strengthening)\\ ✅ Python Mastery \- Daily LeetCode Easy problems (minimum 2) \- Build automation projects \- NumPy & Pandas mastery \- DSA fundamentals ✅ Mathematics Foundation \- Linear Algebra basics \- Statistics fundamentals \- Basic calculus concepts ✅ First Mini-Hackathon Participation \- Join beginner-friendly hackathons \- Focus on Python-based challenges \- Team up with other beginners 💡 \\Smart Move:\\ \- Join Discord/Slack hackathon communities \- Practice collaborative coding \- Build network with fellow participants \\🔹 5-8 Months (ML Foundations)\\ ✅ Machine Learning Basics \- Supervised Learning \- Model evaluation \- Feature engineering \- scikit-learn projects ✅ Participate in 2-3 ML Hackathons \- Kaggle Getting Started competitions \- Local ML hackathons \- University hackathons ✅ Start LinkedIn & GitHub Portfolio 💡 \\Smart Move:\\ \- Document hackathon experiences \- Share learnings on LinkedIn \- Focus on completion over winning \\🔹 9-12 Months (Deep Learning Introduction)\\ ✅ Basic Deep Learning \- Neural network fundamentals \- PyTorch basics \- Computer vision tasks \- Basic NLP ✅ Advanced Hackathon Participation \- AI/ML specific hackathons \- Team lead in 1-2 hackathons \- Start mentoring beginners \\🔵 Year 1 Expected Outcome (60-70% Execution)\\ ✔ \\Strong Python & ML foundations\\ ✔ \\5-6 hackathon participations\\ ✔ \\Active GitHub (100+ commits)\\ ✔ \\Growing LinkedIn (300+ connections)\\ 💰 \\Earning Expectation → ₹8K-₹20K per month (Projects/Internship)\\ \\🟢 Year 2: Professional Growth & Specialization (12-24 Months)\\ 🎯 \\Goal: Build Professional Experience & Recognition\\ \\🔹 1-6 Months (Technical Depth)\\ ✅ Advanced ML Topics \- Deep Learning architectures \- Computer Vision OR NLP \- MLOps basics (Docker, FastAPI) \- Cloud fundamentals (AWS/GCP) ✅ Hackathon Achievements \- Win minor prizes in 2-3 hackathons \- Lead teams in major hackathons \- Network with sponsors ✅ Start Technical Blogging 💡 \\Smart Move:\\ \- Focus on hackathon projects that align with career goals \- Build relationships with companies at hackathons \- Create detailed project documentation \\🔹 7-12 Months (Professional Experience)\\ ✅ Secure ML Role/Internship ✅ Advanced Project Building ✅ Open Source Contributions ✅ Organize Small Hackathons 💡 \\Smart Move:\\ \- Use hackathon network for job referrals \- Convert hackathon projects into full products \- Build mentor reputation \\🔵 Year 2 Expected Outcome (60-70% Execution)\\ ✔ \\Professional ML experience\\ ✔ \\10+ hackathon participations\\ ✔ \\1-2 hackathon wins\\ ✔ \\Strong industry network\\ 💰 \\Earning Expectation → ₹40K-₹70K per month (Job/Freelancing)\\ \\🟢 Year 3: Scaling & Business Foundation (24-36 Months)\\ 🎯 \\Goal: Establish Multiple Income Streams\\ \\🔹 1-4 Months (Expertise Building)\\ ✅ Choose Specialization \- MLOps \- Computer Vision \- NLP/LLMs \- Generative AI ✅ Advanced Competitions \- International hackathons \- High-prize competitions \- Corporate ML challenges ✅ Start Consulting Services 💡 \\Smart Move:\\ \- Use hackathon wins for marketing \- Build service packages around expertise \- Network with corporate sponsors \\🔹 5-8 Months (Business Development)\\ ✅ Scale Services ✅ Build Client Network ✅ Create Training Programs ✅ Hackathon Mentorship Program 💡 \\Smart Move:\\ \- Convert hackathon projects to products \- Use event networks for client acquisition \- Build authority through speaking \\🔹 9-12 Months (Growth & Innovation)\\ ✅ Product Development ✅ Team Building ✅ Innovation Focus ✅ Hackathon Organization \\🔵 Year 3 Expected Outcome (60-70% Execution)\\ ✔ \\Established ML business/career\\ ✔ \\Known in hackathon community\\ ✔ \\Multiple income streams\\ ✔ \\Strong industry presence\\ 💰 \\Earning Expectation → ₹1L-₹2L per month (Multiple Streams)\\ \\🟢 Year 4: Scale & Leadership (36-48 Months)\\ 🎯 \\Goal: Build AI Company & Achieve Financial Freedom\\ \\🔹 1-4 Months (Business Scaling)\\ ✅ Company Formation \- AI consulting firm \- Product development \- Training programs ✅ Hackathon Innovation \- Launch own hackathon series \- Corporate partnerships \- Prize sponsorships ✅ Team Expansion 💡 \\Smart Move:\\ \- Use hackathon network for hiring \- Create unique event formats \- Build corporate relationships \\🔹 5-8 Months (Market Leadership)\\ ✅ Product Launch ✅ Service Expansion ✅ International Presence ✅ Innovation Hub Creation 💡 \\Smart Move:\\ \- Create hackathon-to-hiring pipeline \- Build educational programs \- Establish thought leadership \\🔹 9-12 Months (Empire Building)\\ ✅ Multiple Revenue Streams \- AI products \- Consulting services \- Educational programs \- Event organization \- Investment returns ✅ Industry Leadership \- Conference speaking \- Published content \- Community leadership \\🔵 Year 4 Expected Outcome (60-70% Execution)\\ ✔ \\Established AI company\\ ✔ \\Major hackathon organizer\\ ✔ \\Multiple product lines\\ ✔ \\Industry authority status\\ 💰 \\Earning Expectation → ₹3L-₹5L+ per month (Business Income)\\ \\📊 FINAL RATING\\ ✅ \\Comprehensive growth plan\\ ✅ \\Strong community focus\\ ✅ \\Multiple income pathways\\ 💡 \\If 100% Execution → 8.5/10 Feasibility\\ 💡 \\If 50% Execution → 6/10 Feasibility\\ 🔥 \\Conclusion: A balanced path to ML mastery and entrepreneurship, built through consistent growth and community engagement!\\ 🚀 \\Key Success Factors:\\ Regular hackathon participation Strong community involvement Consistent skill development Strategic network building Focus on both technical and business growth

#reddit#randomly#asked

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0.333

Starxel•This week

Is it too late for me to do a PhD in the US?

In 2019 I started an integrated Masters of Physics at Oxford. Graduated summer of 2023. During that time I first authored an AI research paper with the Oxford AI Society. We tried to get it into ICLR but it got rejected. Managed to get it into a NeurIPS workshop though, however I'm unsure if that holds much weight. The paper also got 21 citations on arxiv which is nice. After graduating, my gf and I broke up (mutually, long distance was too much) and life after university made me quite down. Bad market and struggled to get a job. A friend reached out to me about doing a startup in San Francisco. Did that startup until January 2024 when I quit because I had no money left. Through the connections I made out there I landed a gig at Chroma DB. Did a research contract with them. We didn't make a paper but instead made a technical report. The GitHub repo for the project has gained over 200 stars. However, since I was remote and US visas are a pain, my contract wasn't renewed. I tried starting my own business from July 2024 till December. I managed to secure a long term contract with a US construction company building them software that automates admin via GPT. Still doing this contract now and they've said they're happy to keep me for as long as I want. That's the context. During the winter of 2024 I thought heavily about applying for a PhD in the US. At: CMU, Stanford, Berkeley, MIT, CalTech, etc. However, I knew my profile wasn't strong enough. So I want to apply the winter of 2025. I'm in talks with a few institutions and research groups about doing projects. But is it possible that, starting in February 2025, I can co-author, submit and have accepted a paper into a top conference by December 2025? I feel like I'm too late to this decision and should have skipped that San Francisco startup to just do research projects from the start.

#reddit#late

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bendee983•This week

I’m AI/ML product manager. What I would have done differently on Day 1 if I knew what I know today

I’m a software engineer and product manager, and I’ve working with and studying machine learning models for several years. But nothing has taught me more than applying ML in real-world projects. Here are some of top product management lessons I learned from applying ML: Work backwards: In essence, creating ML products and features is no different than other products. Don’t jump into Jupyter notebooks and data analysis before you talk to the key stakeholders. Establish deployment goals (how ML will affect your operations), prediction goals (what exactly the model should predict), and evaluation metrics (metrics that matter and required level of accuracy) before gathering data and exploring models.  Bridge the tech/business gap in your organization: Business professionals don’t know enough about the intricacies of machine learning, and ML professionals don’t know about the practical needs of businesses. Educate your business team on the basics of ML and create joint teams of data scientists and business analysts to define and measure goals and progress of ML projects. ML projects are more likely to fail when business and data science teams work in silos. Adjust your priorities at different stages of the project: In the early stages of your ML project, aim for speed. Choose the solution that validates/rejects your hypotheses the fastest, whether it’s an API, a pre-trained model, or even a non-ML solution (always consider non-ML solutions). In the more advanced stages of the project, look for ways to optimize your solution (increase accuracy and speed, reduce costs, increase flexibility). There is a lot more to share, but these are some of the top experiences that would have made my life a lot easier if I had known them before diving into applied ML.  What is your experience?

#reddit#ai/ml#product

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Any-Reserve-4403•This week

How I landed an internship in AI

For motivational purposes only! I see a lot of posts on here from people without “traditional” machine learning, data science, etc.. backgrounds asking how they can break into the field, so I wanted to share my experience. EDIT Learning Resources and Side Project Ideas * My background: I graduated from a decent undergraduate school with a degree in Political Science several years ago. Following school I worked in both a client services role at a market research company and an account management role at a pretty notable fintech start-up. Both of these roles exposed me to ML, AI and more sophisticated software concepts in general, and I didn’t really care for the sales side of things, so I decided to make an attempt at switching careers into something more technical. While working full time I began taking night classes at a local community college, starting with pre calculus all the way up to Calc 2 and eventually more advanced classes like linear algebra and applied probability. I also took some programming courses including DSA. I took these classes for about two years while working, and on the side had been working through various ML books and videos on YouTube. What worked the best for me was Hands-on Machine Learning with Scikit Learn, Keara’s and Tensorflow. I eventually had enough credits where I was able to begin applying to MS in Data Science programs and was fortunate enough to get accepted into one and also get a position in their Robotics Lab doing Computer Vision work. When it came time to apply for internships, it was a BLOODBATH. I must have applied to over 100 roles with my only responses being video interviews and OA’s. Finally I got an interview for an AI Model Validation internship with a large insurance company and after completing the interviews was told I performed well but they were still interviewing several candidates. I ended up getting the offer and accepting the role where I’ll be working on a Computer Vision model and some LLM related tasks this summer and could not be more fortunate / excited. A couple things stood out to them during the interview process. 1, the fact that I was working and taking night classes with the intent to break into the field. It showed a genuine passion as opposed to someone who watched a YouTube video and claims they are now an expert. 2, side projects. I not only had several projects, but I had some that were relevant to the work I’d be doing this summer from the computer vision standpoint. 3, business sense. I emphasized during my interviews how working in a business role prior to beginning my masters would give me a leg up as intern because I would be able to apply the work of a data scientist to solving actual business challenges. For those of you trying to break into the field, keep pushing, keep building, and focus on what makes you unique and able to help a company! Please feel free to contact me if you would like any tips I can share, examples of projects, or anything that would be helpful to your journey.

#reddit#landed#internship

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Willing-Ear-8271•This week

MarkDrop

I’m excited to share my Python package, Markdrop, which has hit 5.01k+ downloads in just a month, so updated it just now! 🚀 It’s a powerful tool for converting PDF documents into structured formats like Markdown (.md) and HTML (.html) while automatically processing images and tables into descriptions for downstream use. Here's what Markdrop does: Key Features: PDF to Markdown/HTML Conversion: Converts PDFs into clean, structured Markdown files (.md) or HTML outputs, preserving the content layout. AI-Powered Descriptions: Replaces tables and images with descriptive summaries generated by LLM, making the content fully textual and easy to analyze. Earlier I added support of 6 different LLM Clients, but to improve the inference time, now this supports only GEMINI\API\KEY and OPENAI\API\KEY. Downloadable Tables: Can add accurate download buttons in HTML for tables, allowing users to download them as Excel files. Seamless Table and Image Handling: Extracts tables and images, generating detailed summaries for each, which are then embedded into the final Markdown document. At the end, one can have a .md file that contains only textual data, including the AI-generated summaries of tables, images, graphs, etc. This results in a highly portable format that can be used directly for several downstream tasks, such as: Can be directly integrated into a RAG pipeline for enhanced content understanding and querying on documents containg useful images and tabular data. Ideal for automated content summarization and report generation. Facilitates extracting key data points from tables and images for further analysis. The .md files can serve as input for machine learning tasks or data-driven projects. Ideal for data extraction, simplifying the task of gathering key data from tables and images. The downloadable table feature is perfect for analysts, reducing the manual task of copying tables into Excel. Markdrop streamlines workflows for document processing, saving time and enhancing productivity. You can easily install it via: pip install markdrop There’s also a Colab demo available to try it out directly: Open in Colab. Github Repo If you've used Markdrop or plan to, I’d love to hear your feedback! Share your experience, any improvements, or how it helped in your workflow. Check it out on PyPI and let me know your thoughts!

#reddit#markdrop

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Sensitive-Pie1997•This week

MS in Computer Science student (non-tech bachelor) applying for first Machine Learning intern/coop

Hi, I am an international student in the U.S applying for my first co-op for a Machine Learning/AI role, would love to get some advice on my current resume, thanks. I am currently in my second last semester of my 2 year's masters program in CS. I had no tech background before this program and was working in higher education administration. I have applied to about 50 positions so far on LinkedIn and HandShake and have had no responses but a few rejection emails. If you are a Machine learning professional, please help if I could improve my current project sections, I have two projects I am working on and hoping to swap them in after I finish them by the end of this semester: (1)Adaptive Learning curriculum for Spanish Language Learning (Duolingo-Inspired), which tailors lessons based on user's daily interactions (e.g., bar conversations, gym interactions). (2) DEI analytics and how they impact student performances and career outcome in Higher Ed. My interests lies in using ML in EdTech and Higher Education. But before finishing them, are my current projects passable? Also any suggestion to enhance my work experience section since they are not tech-related? Thanks! https://preview.redd.it/ww7smowjazne1.png?width=5100&format=png&auto=webp&s=289cde9aee45b2066dcfbb375fc318e7a3c5f5d7

#reddit#computer#science

reddit

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1

chipmux•This week

Backend dev wants to learn ML

Hello ML Experts, I am staff engineer, working in a product based organization, handling the backend services. I see myself becoming Solution Architect and then Enterprise Architect one day. With the AI and ML trending now a days, So i feel ML should be an additional skill that i should acquire which can help me leading and architecting providing solutions to the problems more efficiently, I think however it might not replace the traditional SWEs working on backend APIs completely, but ML will be just an additional diamention similar to the knowledge of Cloud services and DevOps. So i would like to acquire ML knowledge, I dont have any plans to be an expert at it right now, nor i want to become a full time data scientist or ML engineer as of today. But who knows i might diverge, but thats not the plan currently. I did some quick promting with ChatGPT and was able to comeup with below learning path for me. So i would appreciate if some of you ML experts can take a look at below learning path and provide your suggestions 📌 PHASE 1: Core AI/ML & Python for AI (3-4 Months) Goal: Build a solid foundation in AI/ML with Python, focusing on practical applications. 1️⃣ Python for AI/ML (2-3 Weeks) Course: [Python for Data Science and Machine Learning Bootcamp]() (Udemy) Topics: Python, Pandas, NumPy, Matplotlib, Scikit-learn basics 2️⃣ Machine Learning Fundamentals (4-6 Weeks) Course: Machine Learning Specialization by Andrew Ng (C0ursera) Topics: Linear & logistic regression, decision trees, SVMs, overfitting, feature engineering Project: Build an ML model using Scikit-learn (e.g., predicting house prices) 3️⃣ Deep Learning & AI Basics (4-6 Weeks) Course: Deep Learning Specialization by Andrew Ng (C0ursera) Topics: Neural networks, CNNs, RNNs, transformers, generative AI (GPT, Stable Diffusion) Project: Train an image classifier using TensorFlow/Keras 📌 PHASE 2: AI/ML for Enterprise & Cloud Applications (3-4 Months) Goal: Learn how AI is integrated into cloud applications & enterprise solutions. 4️⃣ AI/ML Deployment & MLOps (4 Weeks) Course: MLOps Specialization by Andrew Ng (C0ursera) Topics: Model deployment, monitoring, CI/CD for ML, MLflow, TensorFlow Serving Project: Deploy an ML model as an API using FastAPI & Docker 5️⃣ AI/ML in Cloud (Azure, AWS, OpenAI APIs) (4-6 Weeks) Azure AI Services: Course: Microsoft AI Fundamentals (C0ursera) Topics: Azure ML, Azure OpenAI API, Cognitive Services AWS AI Services: Course: [AWS Certified Machine Learning – Specialty]() (Udemy) Topics: AWS Sagemaker, AI workflows, AutoML 📌 PHASE 3: AI Applications in Software Development & Future Trends (Ongoing Learning) Goal: Explore AI-powered tools & future-ready AI applications. 6️⃣ Generative AI & LLMs (ChatGPT, GPT-4, LangChain, RAG, Vector DBs) (4 Weeks) Course: [ChatGPT Prompt Engineering for Developers]() (DeepLearning.AI) Topics: LangChain, fine-tuning, RAG (Retrieval-Augmented Generation) Project: Build an LLM-based chatbot with Pinecone + OpenAI API 7️⃣ AI-Powered Search & Recommendations (Semantic Search, Personalization) (4 Weeks) Course: [Building Recommendation Systems with Python]() (Udemy) Topics: Collaborative filtering, knowledge graphs, AI search 8️⃣ AI-Driven Software Development (Copilot, AI Code Generation, Security) (Ongoing) Course: AI-Powered Software Engineering (C0ursera) Topics: AI code completion, AI-powered security scanning 🚀 Final Step: Hands-on Projects & Portfolio Once comfortable, work on real-world AI projects: AI-powered document processing (OCR + LLM) AI-enhanced search (Vector Databases) Automated ML pipelines with MLOps Enterprise AI Chatbot using LLMs ⏳ Suggested Timeline 📅 6-9 Months Total (10-12 hours/week) 1️⃣ Core ML & Python (3-4 months) 2️⃣ Enterprise AI/ML & Cloud (3-4 months) 3️⃣ AI Future Trends & Applications (Ongoing) Would you like a customized plan with weekly breakdowns? 🚀

#reddit#backend#wants

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1

gigicr1•This week

What AI tools sounds best for you?

Hey, I'm a developer who's been lurking here for a while and I want to build something actually useful or at least fun(or both) that is AI-related. Here are 4 ideas (but I'm open to completely different ones): AGI Timeline Prediction Market (🎯 no real money, just bragging rights) Finally settle those "AGI by 2030?" debates with some skin in the game Watch your prediction accuracy score climb (or hilariously tank) Monthly milestone tracking to see who really knows their stuff Like fantasy football, but for the robot apocalypse AI Model Release Calendar Never miss another GPT launch again Compare models without diving into 50 different Discord servers Clear "what can it actually do?" benchmarks Get pinged when something wild drops Research Paper Difficulty Rater Browser extension that saves you from opening papers way above your pay grade Community ratings from "ELI5" to "PhD Required" Shows what you need to know before diving in "Time to comprehend" estimates (coffee breaks vs weekend projects) AI Progress Newsletter + Extension Track what you've actually read vs. saved for "later" (we all know how that goes) Weekly TL;DR of the important stuff Focus on real breakthroughs, not just PR Impact ratings from "neat trick" to "call Elon" Before I dive in and build one of these - has anyone seen something similar already? What would make these actually useful vs just another tool that collects dust? P.S. Open to wildly different ideas or improvements too. Maybe we need something completely different to track our march towards AGI?

#reddit#tools#sounds

reddit

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1

jagger_b•This week

Tried no-code AI builder Lovable

So I recently got some time to check out Lovable, a new no-code platform that’s been making waves. I built three projects: Crypto Coin Tracker Simple CRM Netflix-Style Streaming Site What I Liked: • Editable Display Items: Lovable makes tweaking components straightforward. You can select a specific element and prompt around just that (eg: a button) instead of the whole app/website. • Community-Driven Templates: A ton of pre-built templates are available for remixing, simplifying prototyping. Plus, any project created on the free tier becomes publicly accessible, thus creating a bunch of community templates to be used as inspiration. Pricing: Lovable’s pricing is so similar to Bolt.new. Both their free tiers suck and don’t give you much. The starter tiers are worth it at $20/m. For a detailed walkthrough of my experience with Lovable, check out this video. https://youtu.be/QZi9JvIKgJE?si=E8umG6LilCJQNmJy I’d love to hear your thoughts—are you using Lovable, Bolt.new, or another platform? Share your experiences below!

#reddit#tried#no-code

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1

Content_Limit_9723•This week

Building a No-Code AI Customer Service Tool While Working 9-5 | All real - No BS

I want to share my journey of building Chaterimo, my first revenue-generating side project that I've been working on for the past 1.5 years alongside my day job. What started as a solution to make AI chatbots more accessible has grown to over 300 signups, 30 paying customers, and 50,000+ customer queries handled. The Problem I Wanted to Solve: It started with my father's business struggling with customer service - hiring staff was expensive and they would eventually leave, creating a constant cycle of training new people. I decided to help by building a livechat chatbot powered by AI to handle customer queries. The first version was basic (running on ChatGPT-3 with 4k tokens), but it worked! Seeing its success at my father's business, I realized this could help many other businesses too. As I kept improving it and adding features, I expanded to focus on e-commerce stores facing similar challenges. What Makes Chaterimo Different: True no-code setup: Install and run in seconds Choice of AI Models: ChatGPT by default, with options for Claude and the latest Gemini Flexible API Integration: Bring your own API keys for cheaper, unlimited messaging Smart Context Understanding: Can search Google or scan the current webpage to provide relevant answers Lead Generation: Capture and manage potential customer information Rich Integrations: Works with Shopify, Facebook Messenger, and Make for automation Customizable Bot Personality: Edit your chatbot's role and behavior through system prompts The Journey: This is my first side project that's actually generating revenue ($500+ MRR), unlike my previous "just for fun" projects. The past 1.5 years have been a learning experience, balancing development with a full-time job. What started as a simple idea has evolved based on real user feedback and needs. Current Metrics: 300+ total signups 30 paying customers 50,000+ customer queries successfully handled by AI $500+ monthly recurring revenue All while maintaining a 9-5 job Some Things I've Learned: Focus on making things simpler, not adding more features Listen to users - they'll tell you what they really need Flexibility matters - letting users use their own API keys was a game-changer Building something you believe in makes all the difference I'm still actively improving Chaterimo based on feedback. If you're running a website or e-commerce store and want to try it out, I'd love to hear your thoughts. What's Next: I'm focused on making the onboarding even smoother and adding more customization options while keeping the core simplicity that makes Chaterimo work. Would love to hear your thoughts or answer any questions! Has anyone else built successful side projects while working full-time? What were your biggest learnings?

#reddit#building#no-code

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Disastrous_Expert_22•This week

500MRR.com

500MRR.com A few days ago, while browsing Hacker News, I came across a series of stories titled "Those making $500/month on side projects in 20xx - Show and tell," covering the years from 2017 to 2024. There were quite a few interesting projects, and on a whim, I pulled the data to create a website for visual exploration. The project URLs, income details, project overviews, and summaries from the comments section were extracted and summarized using a large language model. I spent about a day writing a Python script to process data, adjust LLM's prompt., using Nextra to generate a static website, and then deploying it on Vercel. 500MRR.com

#reddit#500mrr.com

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1

EffectiveTrifle7284•This week

Why I would encourage everyone to create a side project

Many people are afraid to start working on their projects, fearing they will be unsuccessful and waste a lot of time without gaining anything in return. However, this is the biggest trap of all: you actually gain much more than just money. Even if you don't become super successful and make millions of dollars, what is it really about? When you work on your project, you immerse yourself in development across all aspects. For example, when I used to work, I focused solely on my direct responsibilities. But when you create your project, you cannot limit yourself to a narrow range of tasks; you need to handle everything. If it's a website or an app, you must manage the frontend, backend, and deployment. If it's an app, you need to upload it to stores and understand legal nuances like terms of use and privacy policies. This is just one part of it. Here, you already realize the wealth of knowledge you can gain. Additionally, you are likely to enhance your competence in the technical aspects of your work. Now, let's move to part two—part one is about creating, and part two is about selling. Selling is essentially a separate art and often more complex than development. Thus, you will probably have to immerse yourself in a completely new area and gain experience in it. On top of that, there's another nice bonus: introductions. If you develop a product publicly, you will receive feedback, and perhaps someone will appreciate your project. That person may reach out to you, leading to new connections and acquaintances—often very valuable ones. So even if you don't earn a penny from your project, you will have gained tremendous experience. Of course, if your project consists merely of jumping into AI, writing something, and publishing it immediately without thoughtful consideration, it's unlikely you'll gain any benefit. Therefore, every time I complete a project, I never focus on making millions of dollars. Instead, I first thank the universe for the opportunities I had to create this project, gain experience, and meet wonderful people. Good luck!

#reddit#would#encourage

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smartynetwork•This week

What would be your strategy to go from $0 to $10K/month in 2025?

Hello guys, I hope we can brainstorm some strategies for my case and perhaps people reading this can learn a thing or two also. So I currently have a full time job (9-6) and I'm working on two side projects, one is a weight loss tracker and analysis app, the other is an AI website builder. I know, not groundbreaking technologies (is there anything left to be invented anymore?). So my goal is not to build this huge corporation and take over the world, no. My goal for 2025 is to launch these two products and to grow them to $10K/month, which would allow me to quit my job and focus full time on my projects. I was wondering what would be your strategy and launch timeline to achieve that? What can I expect in terms of effort and resources for marketing and adoption? What key metrics are important to monitor after launch?

#reddit#would#your

reddit

0

0.818

Michaelbetterecycle•This week

How me and my team made 15+ apps and not made a single sale in 2023

Hey, my name is Michael, I am in Auckland NZ. This year was the official beginning of my adult life. I graduated from university and started a full-time job. I’ve also really dug into indiehacking/bootstrapping and started 15 projects (and it will be at least 17 before the year ends). I think I’ve learned a lot but I consciously repeated mistakes. Upto (Nov) Discord Statuses + Your Location + Facebook Poke https://preview.redd.it/4nqt7tp2tf5c1.png?width=572&format=png&auto=webp&s=b0223484bc54b45b5c65e0b1afd0dc52f9c02ad1 This was the end of uni, I often messaged (and got messaged) requests of status and location to (and from my) friends. I thought, what if we make a social app that’s super basic and all it does is show you where your friends are? To differentiate from snap maps and others we wanted something with more privacy where you select the location. However, never finished the codebase or launched it. This is because I slowly started to realize that B2C (especially social networks) are way too hard to make into an actual business and the story with Fistbump would repeat itself. However, this decision not to launch it almost launched a curse on our team. From that point, we permitted ourselves to abandon projects even before launching. Lessons: Don’t do social networks if your goal is 10k MRR ASAP. If you build something to 90% competition ship it or you will think it’s okay to abandon projects Insight Bites (Nov) Youtube Summarizer Extension &#x200B; https://preview.redd.it/h6drqej4tf5c1.jpg?width=800&format=pjpg&auto=webp&s=0f211456c390ac06f4fcb54aa51f9d50b0826658 Right after Upto, we started ideating and conveniently the biggest revolution in the recent history of tech was released → GPT. We instantly began ideating. The first problem we chose to use AI for is to summarize YouTube videos. Comical. Nevertheless, I am convinced we have had the best UX because you could right-click on a video to get a slideshow of insights instead of how everyone else did it. We dropped it because there was too much competition and unit economics didn’t work out (and it was a B2C). PodPigeon (Dec) Podcast → Tweet Threads https://preview.redd.it/0ukge245tf5c1.png?width=2498&format=png&auto=webp&s=23303e1cab330578a3d25cd688fa67aa3b97fb60 Then we thought, to make unit economics work we need to make this worthwhile for podcasters. This is when I got into Twitter and started seeing people summarize podcasts. Then I thought, what if we make something that converts a podcast into tweets? This was probably one of the most important projects because it connected me with Jason and Jonaed, both of whom I regularly stay in contact with and are my go-to experts on ideas related to content creation. Jonaed was even willing to buy Podpigeon and was using it on his own time. However, the unit economics still didn’t work out (and we got excited about other things). Furthermore, we got scared of the competition because I found 1 - 2 other people who did similar things poorly. This was probably the biggest mistake we’ve made. Very similar projects made 10k MRR and more, launching later than we did. We didn’t have a coherent product vision, we didn’t understand the customer well enough, and we had a bad outlook on competition and a myriad of other things. Lessons: I already made another post about the importance of outlook on competition. Do not quit just because there are competitors or just because you can’t be 10x better. Indiehackers and Bootstrappers (or even startups) need to differentiate in the market, which can be via product (UX/UI), distribution, or both. Asking Ace Intro.co + Crowdsharing &#x200B; https://preview.redd.it/0hu2tt16tf5c1.jpg?width=1456&format=pjpg&auto=webp&s=3d397568ef2331e78198d64fafc1a701a3e75999 As I got into Twitter, I wanted to chat with some people I saw there. However, they were really expensive. I thought, what if we made some kind of crowdfunding service for other entrepreneurs to get a private lecture from their idols? It seemed to make a lot of sense on paper. It was solving a problem (validated via the fact that Intro.co is a thing and making things cheaper and accessible is a solid ground to stand on), we understood the market (or so we thought), and it could monetize relatively quickly. However, after 1-2 posts on Reddit and Indiehackers, we quickly learned three things. Firstly, no one cares. Secondly, even if they do, they think they can get the same information for free online. Thirdly, the reasons before are bad because for the first point → we barely talked to people, and for the second people → we barely talked to the wrong people. However, at least we didn’t code anything this time and tried to validate via a landing page. Lessons Don’t give up after 1 Redditor says “I don’t need this” Don’t be scared to choose successful people as your audience. Clarito Journaling with AI analyzer https://preview.redd.it/8ria2wq6tf5c1.jpg?width=1108&format=pjpg&auto=webp&s=586ec28ae75003d9f71b4af2520b748d53dd2854 Clarito is a classic problem all amateur entrepreneurs have. It’s where you lie to yourself that you have a real problem and therefore is validated but when your team asks you how much you would pay you say I guess you will pay, maybe, like 5 bucks a month…? Turns out, you’d have to pay me to use our own product lol. We sent it off to a few friends and posted on some forums, but never really got anything tangible and decided to move away. Honestly, a lot of it is us in our own heads. We say the market is too saturated, it’ll be hard to monetize, it’s B2C, etc. Lessons: You use the Mom Test on other people. You have to do it yourself as well. However, recognizing that the Mom Test requires a lot of creativity in its investigation because knowing what questions to ask can determine the outcome of the validation. I asked myself “Do I journal” but I didn’t ask myself “How often do I want GPT to chyme in on my reflections”. Which was practically never. That being said I think with the right audience and distribution, this product can work. I just don’t know (let alone care) about the audience that much (and I thought I was one of them)/ Horns & Claw Scrapes financial news texts you whether you should buy/sell the stock (news sentiment analysis) &#x200B; https://preview.redd.it/gvfxdgc7tf5c1.jpg?width=1287&format=pjpg&auto=webp&s=63977bbc33fe74147b1f72913cefee4a9ebec9c2 This one we didn’t even bother launching. Probably something internal in the team and also seemed too good to be true (because if this works, doesn’t that just make us ultra-rich fast?). I saw a similar tool making 10k MRR so I guess I was wrong. Lessons: This one was pretty much just us getting into our heads. I declared that without an audience it would be impossible to ship this product and we needed to start a YouTube channel. Lol, and we did. And we couldn’t even film for 1 minute. I made bold statements like “We will commit to this for at least 1 year no matter what”. Learnery Make courses about any subject https://preview.redd.it/1nw6z448tf5c1.jpg?width=1112&format=pjpg&auto=webp&s=f2c73e8af23b0a6c3747a81e785960d4004feb48 This is probably the most “successful” project we’ve made. It grew from a couple of dozen to a couple of hundred users. It has 11 buy events for $9.99 LTD (we couldn’t be bothered connecting Stripe because we thought no one would buy it anyway). However what got us discouraged from seriously pursuing it more is, that this has very low defensibility, “Why wouldn’t someone just use chatGPT?” and it’s B2C so it’s hard to monetize. I used it myself for a month or so but then stopped. I don’t think it’s the app, I think the act of learning a concept from scratch isn’t something you do constantly in the way Learnery delivers it (ie course). I saw a bunch of similar apps that look like Ass make like 10k MRR. Lessons: Don’t do B2C, or if you do, do it properly Don’t just Mixpanel the buy button, connect your Stripe otherwise, it doesn’t feel real and you won’t get momentum. I doubt anyone (even me) will make this mistake again. I live in my GPT bubble where I make assumptions that everyone uses GPT the same way and as much as I do. In reality, the argument that this has low defensibility against GPT is invalid. Platforms that deliver a differentiated UX from ChatGPT to audiences who are not tightly integrated into the habit of using ChatGPT (which is like - everyone except for SOME tech evangelists). CuriosityFM Make podcasts about any subject https://preview.redd.it/zmosrcp8tf5c1.jpg?width=638&format=pjpg&auto=webp&s=d04ddffabef9050050b0d87939273cc96a8637dc This was our attempt at making Learnery more unique and more differentiated from chatGPT. We never really launched it. The unit economics didn’t work out and it was actually pretty boring to listen to, I don’t think I even fully listened to one 15-minute episode. I think this wasn’t that bad, it taught us more about ElevenLabs and voice AI. It took us maybe only 2-3 days to build so I think building to learn a new groundbreaking technology is fine. SleepyTale Make children’s bedtime stories https://preview.redd.it/14ue9nm9tf5c1.jpg?width=807&format=pjpg&auto=webp&s=267e18ec6f9270e6d1d11564b38136fa524966a1 My 8-year-old sister gave me that idea. She was too scared of making tea and I was curious about how she’d react if she heard a bedtime story about that exact scenario with the moral that I wanted her to absorb (which is that you shouldn’t be scared to try new things ie stop asking me to make your tea and do it yourself, it’s not that hard. You could say I went full Goebbels on her). Zane messaged a bunch of parents on Facebook but no one really cared. We showed this to one Lady at the place we worked from at Uni and she was impressed and wanted to show it to her kids but we already turned off our ElevenLabs subscription. Lessons: However, the truth behind this is beyond just “you need to be able to distribute”. It’s that you have to care about the audience. I don’t particularly want to build products for kids and parents. I am far away from that audience because I am neither a kid anymore nor going to be a parent anytime soon, and my sister still asked me to make her tea so the story didn’t work. I think it’s important to ask yourself whether you care about the audience. The way you answer that even when you are in full bias mode is, do you engage with them? Are you interested in what’s happening in their communities? Are you friends with them? Etc. User Survey Analyzer Big User Survey → GPT → Insights Report Me and my coworker were chatting about AI when he asked me to help him analyze a massive survey for him. I thought that was some pretty decent validation. Someone in an actual company asking for help. Lessons Market research is important but moving fast is also important. Ie building momentum. Also don’t revolve around 1 user. This has been a problem in multiple projects. Finding as many users as possible in the beginning to talk to is key. Otherwise, you are just waiting for 1 person to get back to you. AutoI18N Automated Internationalization of the codebase for webapps This one I might still do. It’s hard to find a solid distribution strategy. However, the idea came from me having to do it at my day job. It seems a solid problem. I’d say it’s validated and has some good players already. The key will be differentiation via the simplicity of UX and distribution (which means a slightly different audience). In the backlog for now because I don’t care about the problem or the audience that much. Documate - Part 1 Converts complex PDFs into Excel https://preview.redd.it/8b45k9katf5c1.jpg?width=1344&format=pjpg&auto=webp&s=57324b8720eb22782e28794d2db674b073193995 My mom needed to convert a catalog of furniture into an inventory which took her 3 full days of data entry. I automated it for her and thought this could have a big impact but there was no distribution because there was no ICP. We tried to find the ideal customers by talking to a bunch of different demographics but I flew to Kazakhstan for a holiday and so this kind of fizzled out. I am not writing this blog post linearity, this is my 2nd hour and I am tired and don’t want to finish this later so I don’t even know what lessons I learned. Figmatic Marketplace of high-quality Figma mockups of real apps https://preview.redd.it/h13yv45btf5c1.jpg?width=873&format=pjpg&auto=webp&s=aaa2896aeac2f22e9b7d9eed98c28bb8a2d2cdf1 This was a collab between me and my friend Alex. It was the classic Clarito where we both thought we had this problem and would pay to fix it. In reality, this is a vitamin. Neither I, nor I doubt Alex have thought of this as soon as we bought the domain. We posted it on Gumroad, sent it to a bunch of forums, and called it a day. Same issue as almost all the other ones. No distribution strategy. However, apps like Mobin show us that this concept is indeed profitable but it takes time. It needs SEO. It needs a community. None of those things, me and Alex had or was interested in. However shortly after HTML → Figma came out and it’s the best plugin. Maybe that should’ve been the idea. Podcast → Course Turns Podcaster’s episodes into a course This one I got baited by Jason :P I described to him the idea of repurposing his content for a course. He told me this was epic and he would pay. Then after I sent him the demo, he never checked it out. Anyhow during the development, we realized that doesn’t actually work because A podcast doesn’t have the correct format for the course, the most you can extract are concepts and ideas, seldom explanations. Most creators want video-based courses to be hosted on Kajabi or Udemy Another lesson is that when you pitch something to a user, what you articulate is a platform or a process, they imagine an outcome. However, the end result of your platform can be a very different outcome to what they had in mind and there is even a chance that what they want is not possible. You need to understand really well what the outcome looks like before you design the process. This is a classic problem where we thought of the solution before the problem. Yes, the problem exists. Podcasters want to make courses. However, if you really understand what they want, you can see how repurposing a podcast isn’t the best way to get there. However I only really spoke to 1-2 podcasters about this so making conclusions is dangerous for this can just be another asking ace mistake with the Redditor. Documate Part 2 Same concept as before but now I want to run some ads. We’ll see what happens. https://preview.redd.it/xb3npj0ctf5c1.jpg?width=1456&format=pjpg&auto=webp&s=3cd4884a29fd11d870d010a2677b585551c49193 In conclusion https://preview.redd.it/2zrldc9dtf5c1.jpg?width=1840&format=pjpg&auto=webp&s=2b3105073e752ad41c23f205dbd1ea046c1da7ff It doesn’t actually matter that much whether you choose to do a B2C, or a social network or focus on growing your audience. All of these can make you successful. What’s important is that you choose. If I had to summarize my 2023 in one word it’s indecision. Most of these projects succeeded for other people, nothing was as fundamentally wrong about them as I proclaimed. In reality that itself was an excuse. New ideas seduce, and it is a form of discipline to commit to a single project for a respectful amount of time. https://preview.redd.it/zy9a2vzdtf5c1.jpg?width=1456&format=pjpg&auto=webp&s=901c621227bba0feb4efdb39142f66ab2ebb86fe Distribution is not just posting on Indiehackers and Reddit. It’s an actual strategy and you should think of it as soon as you think of the idea, even before the Figma designs. I like how Denis Shatalin taught me. You have to build a pipeline. That means a reliable way to get leads, launch campaigns at them, close deals, learn from them, and optimize. Whenever I get an idea now I always try to ask myself “Where can I find 1000s leads in one day?” If there is no good answer, this is not a good project to do now. &#x200B; https://preview.redd.it/2boh3fpetf5c1.jpg?width=1456&format=pjpg&auto=webp&s=1c0d5d7b000716fcbbb00cbad495e8b61e25be66 Talk to users before doing anything. Jumping on designing and coding to make your idea a reality is a satisfying activity in the short term. Especially for me, I like to create for the sake of creation. However, it is so important to understand the market, understand the audience, understand the distribution. There are a lot of things to understand before coding. https://preview.redd.it/lv8tt96ftf5c1.jpg?width=1456&format=pjpg&auto=webp&s=6c8735aa6ad795f216ff9ddfa2341712e8277724 Get out of your own head. The real reason we dropped so many projects is that we got into our own heads. We let the negative thoughts creep in and kill all the optimism. I am really good at coming up with excuses to start a project. However, I am equally as good at coming up with reasons to kill a project. And so you have this yin and yang of starting and stopping. Building momentum and not burning out. I can say with certainty my team ran out of juice this year. We lost momentum so many times we got burnt out towards the end. Realizing that the project itself has momentum is important. User feedback and sales bring momentum. Building also creates momentum but unless it is matched with an equal force of impact, it can stomp the project down. That is why so many of our projects died quickly after we launched. The smarter approach is to do things that have a low investment of momentum (like talking to users) but result in high impact (sales or feedback). Yes, that means the project can get invalidated which makes it more short-lived than if we built it first, but it preserves team life energy. At the end of 2023 here is a single sentence I am making about how I think one becomes a successful indiehacker. One becomes a successful Indiehacker when one starts to solve pain-killer problems in the market they understand, for an audience they care about and consistently engage with for a long enough timeframe. Therefore an unsuccessful Indiehacker in a single sentence is An unsuccessful Indiehacker constantly enters new markets they don’t understand to build solutions for people whose problems they don’t care about, in a timeframe that is shorter than than the time they spent thinking about distribution. However, an important note to be made. Life is not just about indiehacking. It’s about learning and having fun. In the human world, the best journey isn’t the one that gets you the fastest to your goals but the one you enjoy the most. I enjoyed making those silly little projects and although I do not regret them, I will not repeat the same mistakes in 2024. But while it’s still 2023, I have 2 more projects I want to do :) EDIT: For Devs, frontend is always react with vite (ts) and backend is either node with express (ts) or python. For DB either Postgres or mongo (usually Prisma for ORM). For deployment all of it is on AWS (S3, EC2). In terms of libraries/APIs Whisper.cpp is best open source for transcription Obviously the gpt apis Eleven labs for voice related stuff And other random stuff here and there

#reddit#team#made

reddit

0

1

Ok_Damage_1764•This week

How I built my SaaS and earned $273 MRR in the first month

Hi everyone! I’m Alex Varga, an indie developer. Last year, I focused on accelerating my development speed and launched 10 projects in 12 months. One of them called Bulk Image Generation started growing through SEO, so I decided to focus on it. After one month of SEO efforts, it’s generating $273 MRR. I hope my experience will be useful to others. Concept bulkimagegeneration.com website helps to generate up to 100 images in 15 seconds using AI I was using Google, started with keywords like "Bulk Image ..." a lot of them are Bulk Image Resizer, Downloader etc. But there was no Bulk Image Generator. I thought: yeah, this domain is available, let's buy. So I bought bulkimagegeneration.com and bulkimagegenerator.com So, the app concept is to help people generate images with AI at scale: let\`s say 100 images in 15 seconds. Marketing Gap https://preview.redd.it/4luzib02bbie1.png?width=1905&format=png&auto=webp&s=cbe845107aca46ae5729dfe121fefd5e9cdab9ac Most builders create a product first and figure out how to sell it later. I took a completely different approach with Bulk Image Generator. I identified a market gap and secured a domain name that matched exactly what people were searching for and launched app. https://preview.redd.it/h6vwur34bbie1.png?width=1905&format=png&auto=webp&s=9a163ff6f503be4c175c6e5e82e2003b32df1fe0 Growth Strategy SEO has become the main acquisition channel, so I’ve decided to focus even more on it with this experiment. Almost every day, I publish either a new article or a free micro-app (as a lead magnet) for Bulk Image Generator. I also tried Google Ads, spent $20, and got a $0.35 CPC. https://preview.redd.it/3rhnzvs6bbie1.png?width=1905&format=png&auto=webp&s=f9819d1e82d3e2429d6ccb7b00dcac86a7a351c2 In comparison, the Free Image to Text Prompt Converter (one of the lead magnets) has a $0.011 CPC, which is more than 30 times cheaper than Google Ads. So I decided not to focus now on paid ads. https://preview.redd.it/p333fyl9bbie1.png?width=1905&format=png&auto=webp&s=2e96532d7709b44b7459e7ccf37ef9a0fa784728 After using our free tools, some users explore our main product - a bulk image generation service. Users pay a monthly subscription to get credits, which they can spend on image generation, face swaps, and bulk background removal. Currently, this app generates around $250 in Monthly Recurring Revenue: https://preview.redd.it/9wcm0tjfbbie1.png?width=1905&format=png&auto=webp&s=41bcdd4f7594b09087c51cc5044e4b9c94c129c8 SEO Keyword Research I use Semrush or similar tools to find keywords with a search volume greater than 300 and then write articles targeting those keywords. If the topic has enough potential, I might create a free tool (e.g., a Free Image to Text Prompt Converter) to attract more users. Occasions matter. For instance, I wrote an article about creating images for Super Bowl ads, which led to one paying user who replicated the exact creatives showcased in the article https://preview.redd.it/shpax6mlbbie1.png?width=1905&format=png&auto=webp&s=d491385761df126424c2f9ba14c5da15f8cbb603 AI Tools Aggregators This can be an excellent acquisition channel. When BulkImageGeneration.com was featured in an article on Toolify.ai, I immediately gained three paying users (\~$60). I took 2 more AI Aggregators, and on average I had CPC = $0.2, which is a fair price and usually it has ROAs > 100%. However, some major aggregators are expensive ($300–400 per placement). I want to try it once I reach $500+ MRR. Next Steps bulkimagegeneration.com currently ranks #1 in search results for relevant keywords (e.g., “bulk image generation,” “bulk image generator”). I plan to keep producing content targeting niche keywords and timely occasions. buy more places in AI Aggregators I also want to reach out to YouTubers and ask them to include Bulk in their reviews for free

#reddit#built#saas

reddit

0

1

inputorigin•This week

I retired at 32 from my side project. Here's the path I took.

EDIT 2: Thanks for the award kind stranger! I've stopped responding to reddit comments for this post. I'm adding an FAQ to the original post based on the most common high quality questions. If you have a question that you're dying to know the answer to and that only I can help you with (vs. Google, ChatGPT, etc.), DM me. EDIT: I love how controversial this post has become (50% upvote rate), and only in this subreddit (vs. other subreddits that I posted the same content in). I trust that the open-minded half of you will find something useful in this post and my other posts and comments. I retired at 32 years old, in large part thanks to a B2C SaaS app that I developed on my own. Now, I don't have to work in order to cover my living expenses, and wouldn't have to work for quite a while. In other words, I can finally sip mai tais at the beach. I've condensed how I got there into this post. First, a super simplified timeline of events, followed by some critical details. Timeline 2013 Graduated college in the US 2013 Started first corporate job 2013 Started side project (B2C app) that would eventually lead to my retirement 2020 Started charging for use of my B2C app (was free, became freemium) 2021 Quit my last corporate job 2022 Retired: time freedom attained Details First, some summary statistics of my path to retirement: 9 years: time between graduating college and my retirement. 8 years: total length of my career where I worked at some corporate day job. 7 years: time it took my B2C app to make its first revenue dollar 2 years: time between my first dollar of SaaS revenue and my retirement. "Something something overnight success a decade in the making". I got extremely lucky on my path to retirement, both in terms of the business environment I was in and who I am as a person. I'd also like to think that some of the conscious decisions I made along the way contributed to my early retirement. Lucky Breaks Was born in the US middle class. Had a natural affinity for computer programming and entrepreneurial mindset (initiative, resourcefulness, pragmatism, courage, growth mindset). Had opportunities to develop these mindsets throughout life. Got into a good college which gave me the credentials to get high paying corporate jobs. Was early to a platform that saw large adoption (see "barnacle on whale" strategy). Business niche is shareworthy: my SaaS received free media. Business niche is relatively stable, and small enough to not be competitive. "Skillful" Decisions I decided to spend the nights and weekends of my early career working on side projects in the hopes that one would hit. I also worked a day job to support myself and build my savings. My launch funnel over roughly 7 years of working on side projects: Countless side projects prototyped. 5 side projects publically launched. 2 side projects made > $0. 1 side project ended up becoming the SaaS that would help me retire. At my corporate day jobs, I optimized for learning and work-life balance. My learning usually stalled after a year or two at one company, so I’d quit and find another job. I invested (and continute to do so) in physical and mental wellbeing via regular workouts, meditation, journaling, traveling, and good food. My fulfilling non-work-life re-energized me for my work-life, and my work-life supported my non-work-life: a virtuous cycle. I automated the most time-consuming aspects of my business (outside of product development). Nowadays, I take long vacations and work at most 20 hours a week / a three-day work week . I decided to keep my business entirely owned and operated by me. It's the best fit for my work-style (high autonomy, deep focus, fast decision-making) and need to have full creative freedom and control. I dated and married a very supportive and inspiring partner. I try not to succumb to outrageous lifestyle creep, which keeps my living expenses low and drastically extends my burn-rate. Prescription To share some aphorisms I’ve leaned with the wantrepreneurs or those who want to follow a similar path: Maximize your at bats, because you only need one hit. Bias towards action. Launch quickly. Get your ideas out into the real world for feedback. Perfect is the enemy of good. If you keep swinging and improving, you'll hit the ball eventually. Keep the big picture in mind. You don't necessarily need a home-run to be happy: a base hit will often do the job. Think about what matters most to you in life: is it a lot of money or status? Or is it something more satisfying, and often just as if not more attainable, like freedom, loving relationships, or fulfillment? Is what you’re doing now a good way to get what you want? Or is there a better way? At more of a micro-level of "keep the big picture in mind", I often see talented wantrepreneurs get stuck in the weeds of lower-level optimizations, usually around technical design choices. They forget (or maybe subconsciously avoid) the higher-level and more important questions of customer development, user experience, and distribution. For example: “Are you solving a real problem?” or “Did you launch an MVP and what did your users think?” Adopt a growth mindset. Believe that you are capable of learning whatever you need to learn in order to do what you want to do. The pain of regret is worse than the pain of failure. I’ve noticed that fear of failure is the greatest thing holding people back from taking action towards their dreams. Unless failure means death in your case, a debilitating fear of failure is a surmountable mental block. You miss 100% of the shots you don't take. When all is said and done, we often regret the things we didn't do in life than the things we did. There’s more to life than just work. Blasphemous (at least among my social circle)! But the reality is that many of the dying regret having worked too much in their lives. As Miss Frizzle from The Magic Schoolbus says: "Take chances, make mistakes, get messy!" Original post

#reddit#retired#side

reddit

0

0.5

Ok_Bread_6005•This week

Day 1 of my BIP for my AdonisJS Boilerplate (turbosaas) [Built in public]

Hello everyone, here is day 1 (not really, I started a bit earlier) of my project: A boilerplate using AdonisJS, Inertia What technologies are used/present? AdonisJS Inertia Stripe OpenAI TailwindCSS Vite (React) Why? Firstly, I want to save time when launching my projects, and I think you do too, so I've included as many relevant features as possible. I'm tired of seeing attitudes like 'develop your SaaS in 1 hour and produce terrible code!' The purpose of this codebase is to provide the highest quality code possible and to maintain that standard throughout the development process. You might spend an extra 20 minutes doing things right, but you'll save 2 hours on refactoring. And no, you won't have to pay for updates. (WTF by the way?) Why these technologies? I've seen a lot of NextJS for boilerplates, and I've also used NextJS before, but I quickly abandoned it. It quickly becomes a mess You lose track of what is what, and start doing anything Every update breaks your application Whereas with AdonisJS, life is beautiful. There are plenty of community packages already available, and everything you need is here. What am I offering? Authentication: Social authentication, OTP, Magic Links, and credentials, along with complete account management features like password recovery. Payment & Mailing Integration: Seamless integration from start to finish, with multiple options to choose from. Detailed Documentation: Thorough explanations of every aspect, covering even the smallest, potentially confusing details in the code. Maintainable & Scalable Code: Organized by features, allowing you to easily drag and drop features to extend functionality. Developer Tools: Handy commands for generating new features and automatically adding necessary imports; a complete config to enable/disable a feature in less than 10 seconds... Pre-made Pages: Ready-to-use pages such as an admin dashboard for tasks like automatically updating products on Stripe. Extensive Component Library: A variety of components to streamline development. I've designed this boilerplate to be as developer-friendly and robust as possible, aiming to support maintainability and scalability from the get-go. Summary of today and previous days Day 2 Stripe is a nightmare to set up if you've never done it before, it quickly becomes tedious. But I've finally finished setting everything up: one-time payments, subscriptions, and subscription updates. It was complicated. Today I finally implemented the 'forgot password' option, and I've completed all the authentication by adding magic links (working with OTP). I also set up automatic deployment with GitHub Actions, and everything works well. The build runs with the action to ensure everything goes smoothly, then using SSH, I pull the project, build it, and launch it. Tomorrow: What I want to do tomorrow Tomorrow, I want to create the blog, because yes, I want to include a blog as well, and especially complete it as soon as possible so it can be available on turbosaas(dot)dev, and write my build in public. It will probably use markdown. Thank you for reading this short build in public, you can also check out how it's going on turbosaas(dot)dev.

#reddit#adonisjs#boilerplate

reddit

0

0

an732001•This week

Learn how to code in 24 hours with a Private AI Tutor

Learning how to code is REALLY HARD. I remember I struggled a lot learning how to code. I just wanted to build a simple app or website but the task was super daunting. There were so many steps, so many things to learn. Terminal, git, command prompt, IDE, library, package, React, Express, GAH! It was hard. I did a lot of courses but I always got 10-20% through and gave up. They were just SO BORING. Some of the courses were too advanced, some were too beginner, and all required me to keep pausing & playing. So, I built Athens: https://www.tryathens.com/ Athens gives you a personal AI tutor that understands your skill level and modifies each challenge/lesson to your skill level. No nothing about programming? Learn from the basics of HTML. Already know the basics of React? Implement it, and then learn the backend. All of this while you build a fully-fledged clone of Instagram, Netflix, or one of the many other projects! What do you guys think about this? Would you use it? Do you have any feedback? PS: Athens will normally go for $299/year but currently it's on pre-sale for $159 (sometimes $99 with lifetime discount)

#reddit#learn#code

reddit

0

0.667

GDbuildsGD•This week

I spent 6 months on a web app as a side project, and got 0 users. Here is my story.

Edit Thank you all so much for your time reading my story. Your support, feedback, criticism, and skepticism; all helped me a lot, and I couldn't appreciate it enough \^\_\^ I very rarely have stuff to post on Reddit, but I share how my project is going on, just random stuff, and memes on X. In case few might want to keep up 👀 TL;DR I spent 6 months on a tool that currently has 0 users. Below is what I learned during my journey, sharing because I believe most mistakes are easily avoidable. Do not overestimate your product and assume it will be an exception to fundamental principles. Principles are there for a reason. Always look for validation before you start. Avoid building products with a low money-to-effort ratio/in very competitive fields. Unless you have the means, you probably won't make it. Pick a problem space, pick your target audience, and talk to them before thinking about a solution. Identify and match their pain points. Only then should you think of a solution. If people are not overly excited or willing to pay in advance for a discounted price, it might be a sign to rethink. Sell one and only one feature at a time. Avoid everything else. If people don't pay for that one core feature, no secondary feature will change their mind. Always spend twice as much time marketing as you do building. You will not get users if they don't know it exists. Define success metrics ("1000 users in 3 months" or "$6000 in the account at the end of 6 months") before you start. If you don't meet them, strongly consider quitting the project. If you can't get enough users to keep going, nothing else matters. VALIDATION, VALIDATION, VALIDATION. Success is not random, but most of our first products will not make a success story. Know when to admit failure, and move on. Even if a product of yours doesn't succeed, what you learned during its journey will turn out to be invaluable for your future. My story So, this is the story of a product that I’ve been working on for the last 6 months. As it's the first product I’ve ever built, after watching you all from the sidelines, I have learned a lot, made many mistakes, and did only a few things right. Just sharing what I’ve learned and some insights from my journey so far. I hope that this post will help you avoid the mistakes I made — most of which I consider easily avoidable — while you enjoy reading it, and get to know me a little bit more 🤓. A slow start after many years Summ isn’t the first product I really wanted to build. Lacking enough dev skills to even get started was a huge blocker for so many years. In fact, the first product I would’ve LOVED to build was a smart personal shopping assistant. I had this idea 4 years ago; but with no GPT, no coding skills, no technical co-founder, I didn’t have the means to make it happen. I still do not know if such a tool exists and is good enough. All I wanted was a tool that could make data-based predictions about when to buy stuff (“buy a new toothpaste every three months”) and suggest physical products that I might need or be strongly interested in. AFAIK, Amazon famously still struggles with the second one. Fast-forward a few years, I learned the very basics of HTML, CSS, and Vanilla JS. Still was not there to build a product; but good enough to code my design portfolio from scratch. Yet, I couldn’t imagine myself building a product using Vanilla JS. I really hated it, I really sucked at it. So, back to tutorial hell, and to learn about this framework I just heard about: React.React introduced so many new concepts to me. “Thinking in React” is a phrase we heard a lot, and with quite good reasons. After some time, I was able to build very basic tutorial apps, both in React, and React Native; but I have to say that I really hated coding for mobile. At this point, I was already a fan of productivity apps, and had a concept for a time management assistant app in my design portfolio. So, why not build one? Surely, it must be easy, since every coding tutorial starts with a todo app. ❌ WRONG! Building a basic todo app is easy enough, but building one good enough for a place in the market was a challenge I took and failed. I wasted one month on that until I abandoned the project for good. Even if I continued working on it, as the productivity landscape is overly competitive, I wouldn’t be able to make enough money to cover costs, assuming I make any. Since I was (and still am) in between jobs, I decided to abandon the project. 👉 What I learned: Do not start projects with a low ratio of money to effort and time. Example: Even if I get 500 monthly users, 200 of which are paid users (unrealistically high number), assuming an average subscription fee of $5/m (such apps are quite cheap, mostly due to the high competition), it would make me around $1000 minus any occurring costs. Any founder with a product that has 500 active users should make more. Even if it was relatively successful, due to the high competition, I wouldn’t make any meaningful money. PS: I use Todoist today. Due to local pricing, I pay less than $2/m. There is no way I could beat this competitive pricing, let alone the app itself. But, somehow, with a project that wasn’t even functional — let alone being an MVP — I made my first Wi-Fi money: Someone decided that the domain I preemptively purchased is worth something. By this point, I had already abandoned the project, certainly wasn’t going to renew the domain, was looking for a FT job, and a new project that I could work on. And out of nowhere, someone hands me some free money — who am I not to take it? Of course, I took it. The domain is still unused, no idea why 🤔. Ngl, I still hate the fact that my first Wi-Fi money came from this. A new idea worth pursuing? Fast-forward some weeks now. Around March, I got this crazy idea of building an email productivity tool. We all use emails, yet we all hate them. So, this must be fixed. Everyone uses emails, in fact everyone HAS TO use emails. So, I just needed to build a tool and wait for people to come. This was all, really. After all, the problem space is huge, there is enough room for another product, everyone uses emails, no need for any further validation, right? ❌ WRONG ONCE AGAIN! We all hear from the greatest in the startup landscape that we must validate our ideas with real people, yet at least some of us (guilty here 🥸) think that our product will be hugely successful and prove them to be an exception. Few might, but most are not. I certainly wasn't. 👉 Lesson learned: Always validate your ideas with real people. Ask them how much they’d pay for such a tool (not if they would). Much better if they are willing to pay upfront for a discount, etc. But even this comes later, keep reading. I think the difference between “How much” and “If” is huge for two reasons: (1) By asking them for “How much”, you force them to think in a more realistic setting. (2) You will have a more realistic idea on your profit margins. Based on my competitive analysis, I already had a solution in my mind to improve our email usage standards and email productivity (huge mistake), but I did my best to learn about their problems regarding those without pushing the idea too hard. The idea is this: Generate concise email summaries with suggested actions, combine them into one email, and send it at their preferred times. Save as much as time the AI you end up with allows. After all, everyone loves to save time. So, what kind of validation did I seek for? Talked with only a few people around me about this crazy, internet-breaking idea. The responses I got were, now I see, mediocre; no one got excited about it, just said things along the lines of “Cool idea, OK”. So, any reasonable person in this situation would think “Okay, not might not be working”, right? Well, I did not. I assumed that they were the wrong audience for this product, and there was this magical land of user segments waiting eagerly for my product, yet unknowingly. To this day, I still have not reached this magical place. Perhaps, it didn’t exist in the first place. If I cannot find it, whether it exists or not doesn’t matter. I am certainly searching for it. 👉 What I should have done: Once I decide on a problem space (time management, email productivity, etc.), I should decide on my potential user segments, people who I plan to sell my product to. Then I should go talk to those people, ask them about their pains, then get to the problem-solving/ideation phase only later. ❗️ VALIDATION COMES FROM THE REALITY OUTSIDE. What validation looks like might change from product to product; but what invalidation looks like is more or less the same for every product. Nico Jeannen told me yesterday “validation = money in the account” on Twitter. This is the ultimate form of validation your product could get. If your product doesn’t make any money, then something is invalidated by reality: Your product, you, your idea, who knows? So, at this point, I knew a little bit of Python from spending some time in tutorial hell a few years ago, some HTML/CSS/JS, barely enough React to build a working app. React could work for this project, but I needed easy-to-implement server interactivity. Luckily, around this time, I got to know about this new gen of indie hackers, and learned (but didn’t truly understand) about their approach to indie hacking, and this library called Nextjs. How good Next.js still blows my mind. So, I was back to tutorial hell once again. But, this time, with a promise to myself: This is the last time I would visit tutorial hell. Time to start building this "ground-breaking idea" Learning the fundamentals of Next.js was easier than learning of React unsurprisingly. Yet, the first time I managed to run server actions on Next.js was one of the rarest moments that completely blew my mind. To this day, I reject the idea that it is something else than pure magic under its hood. Did I absolutely need Nextjs for this project though? I do not think so. Did it save me lots of time? Absolutely. Furthermore, learning Nextjs will certainly be quite helpful for other projects that I will be tackling in the future. Already got a few ideas that might be worth pursuing in the head in case I decide to abandon Summ in the future. Fast-forward few weeks again: So, at this stage, I had a barely working MVP-like product. Since the very beginning, I spent every free hour (and more) on this project as speed is essential. But, I am not so sure it was worth it to overwork in retrospect. Yet, I know I couldn’t help myself. Everything is going kinda smooth, so what’s the worst thing that could ever happen? Well, both Apple and Google announced their AIs (Apple Intelligence and Google Gemini, respectively) will have email summarization features for their products. Summarizing singular emails is no big deal, after all there were already so many similar products in the market. I still think that what truly matters is a frictionless user experience, and this is why I built this product in a certain way: You spend less than a few minutes setting up your account, and you get to enjoy your email summaries, without ever visiting its website again. This is still a very cool concept I really like a lot. So, at this point: I had no other idea that could be pursued, already spent too much time on this project. Do I quit or not? This was the question. Of course not. I just have to launch this product as quickly as possible. So, I did something right, a quite rare occurrence I might say: Re-planned my product, dropped everything secondary to the core feature immediately (save time on reading emails), tried launching it asap. 👉 Insight: Sell only one core feature at one time. Drop anything secondary to this core feature. Well, my primary occupation is product design. So one would expect that a product I build must have stellar design. I considered any considerable time spent on design at this stage would be simply wasted. I still think this is both true and wrong: True, because if your product’s core benefits suck, no one will care about your design. False, because if your design looks amateurish, no one will trust you and your product. So, I always targeted an average level design with it and the way this tool works made it quite easy as I had to design only 2 primary pages: Landing page and user portal (which has only settings and analytics pages). However, even though I knew spending time on design was not worth much of my time, I got a bit “greedy”: In fact, I redesigned those pages three times, and still ended up with a so-so design that I am not proud of. 👉 What I would do differently: Unless absolutely necessary, only one iteration per stage as long as it works. This, in my mind, applies to everything. If your product’s A feature works, then no need to rewrite it from scratch for any reason, or even refactor it. When your product becomes a success, and you absolutely need that part of your codebase to be written, do so, but only then. Ready to launch, now is th etime for some marketing, right? By July 26, I already had a “launchable” product that barely works (I marked this date on a Notion docs, this is how I know). Yet, I had spent almost no time on marketing, sales, whatever. After all, “You build and they will come”. Did I know that I needed marketing? Of course I did, but knowingly didn’t. Why, you might ask. Well, from my perspective, it had to be a dev-heavy product; meaning that you spend most of your time on developing it, mostly coding skills. But, this is simply wrong. As a rule of thumb, as noted by one of the greatests, Marc Louvion, you should spend at least twice of the building time on marketing. ❗️ Time spent on building \* 2 people don’t know your product > they don’t use your product > you don’t get users > you don’t make money Easy as that. Following the same reasoning, a slightly different approach to planning a project is possible. Determine an approximate time to complete the project with a high level project plan. Let’s say 6 months. By the reasoning above, 2 months should go into building, and 4 into marketing. If you need 4 months for building instead of 2, then you need 8 months of marketing, which makes the time to complete the project 12 months. If you don’t have that much time, then quit the project. When does a project count as completed? Well, in reality, never. But, I think we have to define success conditions even before we start for indie projects and startups; so we know when to quit when they are not met. A success condition could look like “Make $6000 in 12 months” or “Have 3000 users in 6 months”. It all depends on the project. But, once you set it, it should be set in stone: You don’t change it unless absolutely necessary. I suspect there are few principles that make a solopreneur successful; and knowing when to quit and when to continue is definitely one of them. Marc Louvion is famously known for his success, but he got there after failing so many projects. To my knowledge, the same applies to Nico Jeannen, Pieter Levels, or almost everyone as well. ❗️ Determining when to continue even before you start will definitely help in the long run. A half-aed launch Time-leap again. Around mid August, I “soft launched” my product. By soft launch, I mean lazy marketing. Just tweeting about it, posting it on free directories. Did I get any traffic? Surely I did. Did I get any users? Nope. Only after this time, it hit me: “Either something is wrong with me, or with this product” Marketing might be a much bigger factor for a project’s success after all. Even though I get some traffic, not convincing enough for people to sign up even for a free trial. The product was still perfect in my eyes at the time (well, still is ^(\_),) so the right people are not finding my product, I thought. Then, a question that I should have been asking at the very first place, one that could prevent all these, comes to my mind: “How do even people search for such tools?” If we are to consider this whole journey of me and my so-far-failed product to be an already destined failure, one metric suffices to show why. Search volume: 30. Even if people have such a pain point, they are not looking for email summaries. So, almost no organic traffic coming from Google. But, as a person who did zero marketing on this or any product, who has zero marketing knowledge, who doesn’t have an audience on social media, there is not much I could do. Finally, it was time to give up. Or not… In my eyes, the most important element that makes a founder (solo or not) successful (this, I am not by any means) is to solve problems. ❗️ So, the problem was this: “People are not finding my product by organic search” How do I make sure I get some organic traffic and gets more visibility? Learn digital marketing and SEO as much as I can within very limited time. Thankfully, without spending much time, I came across Neil Patel's YT channel, and as I said many times, it is an absolute gold mine. I learned a lot, especially about the fundamentals, and surely it will be fruitful; but there is no magic trick that could make people visit your website. SEO certainly helps, but only when people are looking for your keywords. However, it is truly a magical solution to get in touch with REAL people that are in your user segments: 👉 Understand your pains, understand their problems, help them to solve them via building products. I did not do this so far, have to admit. But, in case you would like to have a chat about your email usage, and email productivity, just get in touch; I’d be delighted to hear about them. Getting ready for a ProductHunt launch The date was Sept 1. And I unlocked an impossible achievement: Running out of Supabase’s free plan’s Egres limit while having zero users. I was already considering moving out of their Cloud server and managing a Supabase CLI service on my Hetzner VPS for some time; but never ever suspected that I would have to do this quickly. The cheapest plan Supabase offers is $25/month; yet, at that point, I am in between jobs for such a long time, basically broke, and could barely afford that price. One or two months could be okay, but why pay for it if I will eventually move out of their Cloud service? So, instead of paying $25, I spent two days migrating out of Supabase Cloud. Worth my time? Definitely not. But, when you are broke, you gotta do stupid things. This was the first time that I felt lucky to have zero users: I have no idea how I would manage this migration if I had any. I think this is one of the core tenets of an indie hacker: Controlling their own environment. I can’t remember whose quote this is, but I suspect it was Naval: Entrepreneurs have an almost pathological need to control their own fate. They will take any suffering if they can be in charge of their destiny, and not have it in somebody else’s hands. What’s truly scary is, at least in my case, we make people around us suffer at the expense of our attempting to control our own fates. I know this period has been quite hard on my wife as well, as I neglected her quite a bit, but sadly, I know that this will happen again. It is something that I can barely help with. Still, so sorry. After working the last two weeks on a ProductHunt Launch, I finally launched it this Tuesday. Zero ranking, zero new users, but 36 kind people upvoted my product, and many commented and provided invaluable feedback. I couldn't be more grateful for each one of them 🙏. Considering all these, what lies in the future of Summ though? I have no idea, to be honest. On one hand, I have zero users, have no job, no income. So, I need a way to make money asap. On the other hand, the whole idea of it revolves around one core premise (not an assumption) that I am not so willing to share; and I couldn’t have more trust in it. This might not be the best iteration of it, however I certainly believe that email usage is one of the best problem spaces one could work on. 👉 But, one thing is for certain: I need to get in touch with people, and talk with them about this product I built so far. In fact, this is the only item on my agenda. Nothing else will save my brainchild <3. Below are some other insights and notes that I got during my journey; as they do not 100% fit into this story, I think it is more suitable to list them here. I hope you enjoyed reading this. Give Summ a try, it comes with a generous free trial, no credit card required. Some additional notes and insights: Project planning is one of the most underestimated skills for solopreneurs. It saves you enormous time, and helps you to keep your focus up. Building B2B products beats building B2C products. Businesses are very willing to pay big bucks if your product helps them. On the other hand, spending a few hours per user who would pay $5/m probably is not worth your time. It doesn’t matter how brilliant your product is if no one uses it. If you cannot sell a product in a certain category/niche (or do not know how to sell it), it might be a good idea not to start a project in it. Going after new ideas and ventures is quite risky, especially if you don’t know how to market it. On the other hand, an already established category means that there is already demand. Whether this demand is sufficient or not is another issue. As long as there is enough demand for your product to fit in, any category/niche is good. Some might be better, some might be worse. Unless you are going hardcore B2B, you will need people to find your product by means of organic search. Always conduct thorough keyword research as soon as possible.

#reddit#spent#months

reddit

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nebulasync•This week

I Made $20K in 2 Months by Building in Public on X

Hey everyone, I wanted to share my journey of making $20K in just 2 months by leveraging Twitter (X) and building in public. It’s been an exciting ride, and I hope my story inspires others to take action on their ideas. Here’s exactly what I did: Building in Public I started sharing everything about my work openly. My wins, struggles, and process. I showed: How I build MVPs for clients. The tools I use (Next.js, Supabase, Cursor AI, etc.). The challenges I face and how I solve them. Transparency builds trust, and trust brings clients. Consistency is Key For the past 2 months, I’ve posted consistently on X, even when I felt like no one was watching. Here’s what I focused on: Sharing value (pro tips, workflows, tools). Asking for advice and engaging with my community. Highlighting my projects and client work. Building an audience takes time, but showing up daily pays off. Personal Brand = Inbound Clients I never did any “engagement farming” or gimmicky posts. I just shared my knowledge, and it led to over 35M views on my tweets and 7K followers. Many of these followers turned into inbound client leads. I’ve always believed: Share value for free, and charge for implementation. The Power of Community Engaging with my community on X has been game-changing. People have: Helped refine my processes. Shared valuable tools and advice. Connected me to opportunities I wouldn’t have found otherwise. Key Takeaway: You don’t need a perfect process or a huge following to start. Be consistent. Build in public. Share your journey. In 2 months, I’ve gone from wondering if this would work to making $20K by simply showing up and adding value. If you’re thinking about building in public or starting a personal brand, DO IT. It works. Feel free to ask me anything. I’m happy to share more details about my process, tools, or lessons learned! Let’s build together.

#reddit#made#$20k

reddit

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Adept_Data_6153•This week

What's the s way to implement AI - Free Tire API - Initial Phase

hi there, I'm working on a project where I need to implement AI for text generation.For that I use gemini api but it has limitations for 15 users per minute just like others. so in your projects how did you manage this in initial phase to get this effectively with no budgets. I'm just starting with this and I don't have that much idea about model as I'm learning it and exploring.

#reddit#what's#implement

reddit

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Inner_Lengthiness697•This week

What are Boilerplates?

What are Boilerplates? Boilerplate originally referred to the rolled steel used to make boilers for steam engines in the 19th century. Over time, the term evolved to describe any standardized piece of text or code that can be reused without significant changes. Interest in SaaS has been on the rise, and many more people now want to build products. However, building products from scratch takes a lot of time, and it can be extremely frustrating. Enter SaaS Boilerplates With the standardization of stacks and basic systems that govern SaaS tools, it has become evident that there was a need, and the time was ripe for SaaS Boilerplates. SaaS Boilerplates come with landing pages, website components, authentication modules, payment modules, and various other standard features that can save developers a significant amount of time and cost. The market is flooded with Boilerplates for various tech stacks, such as NextJS, Laravel, Swift, NuxtJS, and so forth. Pros and Cons of Boilerplates Pros Save a significant amount of time and money Reduce frustration for developers as the redundant tasks are taken care of Boilerplates often follow best practices For anywhere between $49 and $299, they provide terrific value for those looking to build something very quickly Most importantly, Boilerplates also enable aspiring founders and builders with limited technical resources or abilities to ship their products faster and more cheaply. They are beacons of hope for non-technical founders looking to build a product quickly. Cons Limited flexibility May become outdated fairly quickly Setting them up still requires time Similar landing pages and design themes can make the product look like a clone Marc Lou’s Shipfast For most of us, Marc Lou popularized the idea of SaaS Boilerplate. Marc Lou launched Shipfast in August 2023. He had built 27 projects prior to this and Shipfast was nothing but all his basic code organised properly. At that time, there were no solid NextJS boilerplates, and Shipfast just took off. He got traction via Product Hunt, Twitter and Hacker News and soon Shipfast went viral. Shipfast now generates $130K/mo, just 9 months after its launch. Marc has been building Shipfast in public, which has led to a lot of interest in SaaS Boilerplates. The market is now flooded with boilerplates for every major tech stack. Marc reaped the benefits of the first mover’s advantage as well as the social proof via his Shipfast community. I don’t think any other boilerplates are as successful as Shipfast, but there are quite a few good ones out there. Shipixen* has grossed over $20K in the 5 months Makerkit* does \~$3500/mo Moreover, there are many open-source boilerplates available for popular stacks such as NextJS. The Evolution of Boilerplates Boilerplates are quickly turning into no-code/low-code code generation tools. For instance, Shipixen allows you to generate custom code for landing pages, waitlist pages and blogs using a simple User Interface. Boilerplates are perfectly posied to sit between code and no-code. Allow the flexibility of code with the interface of a no-code tool — that will be the core value proposition of SaaS boilerplates. Should you build a Boilerplate? Well, the market is flooded, but I believe there’s still an opportunity to leverage boilerplates. You can build boilerplates for certain types of apps or tools, such as Chrome extensions Boilerplates can act as a great lead funnel for building out a great productized services business No-code/low-code code generation boilerplates can become a big thing if you can help build complex tools Niche tech stack boilerplates may still be lucrative Known strategies for successfully building a boilerplate 👇🏻 Shipfast thrives because of social proof and community SaaSRock generates most of its traffic from its Gumroad listings and blogs Usenextbase and Shipixen are being built in public Many boilerplates start with waitlists They have a very clear value proposition around saving time and cost Design & No-Code Boilerplates Here is the corrected version with improved grammar and clarity: While SaaS (code) boilerplates have become fairly popular, other types of boilerplates are emerging in the market, such as design boilerplates and no-code boilerplates. To be honest, design boilerplates have been around for a while. You will find numerous landing page packs, component libraries, and so forth. Makers are now building kits that leverage standard libraries and technologies such as Tailwind CSS, Daisy UI, and more. Nick Buzz from the famous baked.design has this *50 Landing Page Design Kit* in Tailwind CSS & Figma which is wildly popular. Lastly, there is a trend of no-code boilerplates as well. Mohit is building a Bubble Boilerplate for the popular no-code platform — Bubble. All in all, I think that people want to build products and build them fast. Boilerplates help them save a significant amount of time and cost. More importantly, boilerplates are impulse purchases for people who have not shipped but who want to ship. Introducing BuilderKit.ai We have been building AI SaaS tools for quite a while now. 10+ products across text, image, speech, RAG — we have built em all. We figured that it seems easy but actually building these so called AI Wrappers can be time consuming and frustrating — there is a lot of nuance to it. So we built BuidlerKit.ai — a NextJS SaaS Boilerpalte It takes care of everything from landing pages, authentication, dashboarding, emails, SEO to payments — everything that you need to build your tool. It also comes with 8+ production-ready apps. Moreover, the BuilderKit community is an exclusive community of AI SaaS builders (Pro Only Access) The Pre Orders are now live at https://www.builderkit.ai (First 100 Customers get $100 Off — I think we have already done \~20 odd orders since the announcement yesterday, Grab your seat asap!) Starter Plan $49, Pro Plan @ $99

#reddit#boilerplates?

reddit

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bts_23•This week

Solopreneur making $40k MRR with a No Code SaaS sideproject

Hey, I'm Elias and I do case studies analyzing successful startups and solopreneurs. I wanted to share the summarized version of this one with you because this entrepreneurial journey blew my mind. This post will be about FormulaBot (ExcelFormulaBot), an AI No Code SaaS founded by David Bressler back in August 2022. FormulaBot is currently making $40k MRR (monthly recurring revenue). How did the founder come up with the idea. David is a data guy who worked in analytics for several years. In July 2022, David got really interested in AI, especially ChatGPT. One night, he tried it out at home, just like we all did back in the time. But in his case, trying ChatGPT gave him a big idea. That idea ended up making him a lot of money and changing the life of 750 million people who use Excel. That night David started by asking GPT easy questions, then complex ones. Since he used Excel a lot and helped his colleagues with it, he thought about an AI that could make Excel easier, like generating formulas from text. He looked online but found nothing. Seeing a big chance, he decided to do something about it. What challenges did the founder face. But David didn’t have any idea about how to develop an app. However, with no-code tools this is not a problem anymore. He discovered Bubble, a no-code web app tool that could connect with the OpenAI API.After, learning Bubble from YouTube tutorials and through trial and error and spending his nights studying the OpenAI API documentation, he launched the first version of the app in around three weeks. Strategies that made the project successful. David validated his idea by posting about ExcelFormulaBot on a Reddit Excel subreddit, receiving surprising attention with 10,000 upvotes. This encouraged him to offer the tool for free to gather feedback. Facing a hefty $4,999 API bill after the Reddit post, David quickly monetized his product with a subscription-based SaaS website. On launch day, 82 customers signed up, surpassing his expectations. A successful Product Hunt launch followed, generating $2.4k in sales within 24 hours, and a TikTok influencer with 4.5 million followers brought in thousands of new users overnight with a viral video. Marketing approach: -Paid ads: FormulaBot boosted website traffic with Paid Ads, notably on Google Ads, prioritizing Quality Score. This ensured ads aligned better with user searches, maximizing visibility and cost-efficiency, targeting those seeking Excel formula assistance. -SEO: a) Content/Keyword optimization: FormulaBot improved its SEO by making helpful pages about Excel formulas, like guides on topics such as "How to use SUMIFS." b) Site Speed Enhancement: David boosted FormulaBot's marketing site speed by moving it from Bubble to Framer, aiming to improve user experience and SEO performance. c) On-page optimization: David optimized FormulaBot's on-page elements by adjusting title tags, meta descriptions, and content to enhance SEO performance and align with search intent. These strategic refinements aimed to address ranking declines and emphasize FormulaBot's uniqueness, ultimately improving its visibility and competitiveness in search results. -Virality: FormulaBot went viral as users found it highly useful and cool. Influencers on platforms like TikTok and Twitter shared it with their followers because they found it valuable. Offering numerous free features further enhanced its appeal. Lessons: successes and mistakes. ✅ Leverage industry expertise: David identified a problem in analytics and used his experience to start an online business addressing it, turning an industry challenge into a profitable venture. ✅ Embrace learning new skills: Despite lacking initial technical know-how, David learned what he needed to develop the software himself, demonstrating a commitment to continuous learning and adaptability crucial for success. ❌ Minimize dependency on third parties: Relying solely on the ChatGPT API poses risks for FormulaBot. Any issues with the API could disrupt functionality and limit scalability. ⁉️ Caution with free tools: Offering a free tool can attract users and drive viral growth, but converting them to paying customers is challenging. Avoid relying solely on a 100% free model unless your revenue comes from non-user sources like ads. For businesses dependent on user subscriptions or purchases, balancing user attraction with conversion challenges is crucial. How could you replicate this idea step-by-step. To replicate the success of FormulaBot and similar AI wrapper startups, it's crucial to tread carefully in a competitive market. Avoid mere replication of existing solutions unless you can offer something distinct or superior. Consider these steps to effectively develop an AI Wrapper/ChatGPT wrapper product using Bubble as a no-code tool: Design the user interface: Utilize Bubble's drag-and-drop editor to create a user-friendly interface with input fields, buttons, and result displays. Set up workflows: Define workflows to connect the interface with the ChatGPT API, enabling seamless interaction between users and the AI. Integrate the ChatGPT API: Obtain the API key from OpenAI and integrate it into your app using Bubble's API connector feature. Test and gather feedback: Thoroughly test your app, soliciting feedback to refine functionality and usability. Refine and optimize: Continuously improve your app based on user input and testing results to enhance performance and user experience. The in-depth version of the case study was originally posted here. Feel free to comment if you have any questions, and let me know which similar ideas you'd like me to analyze.

#reddit#solopreneur#making

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1

laike9m•This week

Things I did to promote my product, and how they turned out

(I will share more updates in the future, you can find me on Twitter and/or Mastodon) Ask any ten indie developers about the toughest part of their job, and nine will likely say "marketing." I recently got a taste of this firsthand when I launched Xylect. Here's a rundown of my promotional attempts - hopefully, my experiences can help fellow developers out there. Podcast Community (✅ Success) I kicked things off by promoting Xylect in my podcast listener group. It wasn't a blockbuster, but I managed to sell a few copies and got some invaluable feedback from friends. Shoutout to those early supporters! Reddit r/macapps (✅ Success) Having had some luck promoting open-source projects on Reddit before, I decided to make r/macapps my first stop in the English-speaking world. I made an app to help you automate boring tasks with one click This post turned out to be a hit! I sold about ten copies and got a ton of useful feedback. Users pointed out compatibility issues with PopClip and suggested improvements for the website. One Italian user even requested localization, which I happily added. https://preview.redd.it/y4fuwh6hleqd1.png?width=959&format=png&auto=webp&s=7bb1b68cbf8a4f94998999e0832b9b7bd85bac67 https://preview.redd.it/8uu4cmyhleqd1.png?width=683&format=png&auto=webp&s=8f1744636aee8074b0e7491a334ef06076b143b0 I also got an intriguing email from a French user - more on that later. More Reddit Posts (❌ Failure) Riding high on my r/macapps success, I branched out to r/SideProject, r/Entrepreneur, and r/indiehackers. These subreddits frown upon direct self-promotion, so I took a softer approach with an article: The unexpected emotional cost of being an indiehacker While the article was heartfelt, it fell flat. Across all three posts, I got a grand total of three comments - two of which were complaints about the font size on mobile. Needless to say, I didn't sell a single copy. Hacker News (❌ Failure) As one of the tech world's major forums, I had to give Hacker News a shot. I wasn't too optimistic, given my past experiences there. Posting on HN feels like a mix of luck and dark magic. As expected, my post vanished without a trace - no comments, no sales. I might give it another go someday. If you're curious, you can check out my previous HN submissions. Tools Directory Websites (❌ Failure) These sites have a simple premise: you list your app, they display it. Seemed like an easy way to get some backlinks, right? Well, I learned the hard way that it's not that simple. I stumbled upon a Reddit post where someone claimed to have made a killing with their directory site in just a few days. The catch? Each listing cost $19. The site had a handful of apps listed, so I thought, "Why not? Early bird gets the worm." I paid up and listed Xylect. Spoiler alert: all I got was $19 poorer 🥲 Lesson learned: These directory sites won't magically sell your product. At best, they're just glorified backlinks. There might be some value in paid promotions on these platforms, but I can't speak to that from experience. V2EX (❌ Failure) After striking out in the English-speaking world, I turned my attention to the Chinese market, starting with V2EX (think of it as China's hybrid of HN and Reddit). This turned out to be my most unexpected flop. Here's the post: [\[Launch Discount\] Mac's most powerful AI search (Perplexity + Wikipedia + Google), boost your efficiency tenfold with one click. No API key required, no prompt needed, no token limit 🔥 - V2EX](https://www.v2ex.com/t/1064930?p=1#reply36) I'd seen decent engagement on other promo posts, so I had high hopes. I posted late at night (US time) and went to bed dreaming of waking up to a flood of comments. Reality check: The next morning, I had exactly one reply - from Kilerd, a loyal podcast listener showing some love. I was baffled. After re-reading my post, I realized I'd missed a crucial element: promo codes. A quick scan of popular posts confirmed my suspicion. Nearly every successful promo post was offering codes, and most comments were just base64-encoded email addresses. Talk about a facepalm moment. I scrambled to add a note about an upcoming free trial and invited users to drop their emails. This got the ball rolling with some code requests, but by then, the damage was done. The post fizzled out, and I didn't sell a single copy 🫠 A French Friend's Newsletter (✅ Success) At this point, my promotional efforts were looking pretty grim. My sales chart had a depressing stretch of flatline. But then, a glimmer of hope appeared in my inbox. Remember that French user I mentioned earlier? He ran a newsletter called vvmac and offered to feature Xylect if I added French support and sent him a free license. It was an offer I couldn't refuse. What followed was a crash course in French localization (thank you, Claude!) and the start of an incredible partnership. This guy was the most thorough beta tester I've ever encountered. We exchanged over sixty emails, covering everything from translations to UI tweaks to bug fixes. His response time was lightning-fast - I'd fix a bug, and five minutes later, he'd confirm it was sorted. The result? A much-improved Xylect and a glowing feature in his newsletter. https://preview.redd.it/ylcq2wxoleqd1.png?width=991&format=png&auto=webp&s=ee395110f50417d5c7f61318f27bf3dc30247809 I'm still in awe of his dedication. He single-handedly transformed Xylect from a buggy mess into a polished product. I'll be forever grateful for his help. The newsletter feature led to a few more sales, but honestly, that felt like a bonus at that point. Influencers (❌ Failure) I knew from the start that to really make waves, I'd need influencer backing. So, I added a note offering free licenses to content creators willing to collaborate. https://preview.redd.it/tyb2m1rqleqd1.png?width=799&format=png&auto=webp&s=56eabf126e772515322595613c546e6ba69fb431 I did get one taker: Hey, I'll be honest, I am not a huge content creator but I think I put a lot of effort in evaluating and figuring out which apps work... So I was wondering if I could get a license in case you are willing to share it. Thank you for considering. Have a great weekend. But I knew I needed to aim higher. With the new French localization, I thought I'd try my luck with some French-speaking Mac YouTubers. I crafted emails highlighting how Xylect could help their French audience with English content. https://preview.redd.it/07oqzemrleqd1.png?width=542&format=png&auto=webp&s=3d160c1d149f28e9029816a277c6ab2496fcd57e After days of silence, I got one reply. It was... not what I was hoping for: Hi, Thank you for your proposal. I can help you to promote your service on Tiktok, Instagram et YouTube, with unique short video. Price for this project is 3500€. Unless I've completely lost my marbles, there's no way I'm dropping 3500€ on promotion. Sure, given their follower count (YouTube: 348K, TikTok: 2.7M, Instagram: 400K), it's not an outrageous ask. For some products, it might even be worth it. But for Xylect? No way. I also reached out to a Chinese influencer on Xiaohongshu, but they weren't interested. Back to the drawing board. Conclusion If you've made it this far, you've probably realized this isn't exactly a success story. My search for effective promotional channels came up largely empty-handed. I'd naively thought that my success with open-source projects would translate seamlessly to the indie dev world. Boy, was I wrong. As I mentioned in my previous article, open-source projects create a dynamic where users feel indebted to developers for their free labor. But in the commercial world of indie development, that dynamic completely flips. While this experience was often frustrating, it was also enlightening - which was kind of the point. As my first foray into indie development, my main goal was to learn the ropes and understand the process. Making money would've been nice, sure, but it wasn't my primary focus. Thanks for sticking with me through this post. I will share more updates in the future, you can follow me on  Twitter and/or Mastodon.

#reddit#things#promote

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0

Traditional-Cat7094•This week

I have developed targethub.ai for 2 months as a side project, need your thoughts

Hello everyone, I have built https://targethub.ai to give my children the tools I wished I had at the start of my career and entrepreneurship journey. Now, I’m sharing that same framework with you, so you can transform your ambitions into measurable achievements. Whether you’re a student tackling challenging coursework, a professional climbing the career ladder, an entrepreneur driving innovative business ideas, a startuper disrupting traditional models, an influencer expanding your personal brand, or a blogger turning creative passion into consistent results, Targethub.ai is tailored for you. My AI-powered platform combines proven strategies with real-world insights to empower you to: • Break down complex goals into clear, actionable steps • Strategically plan and execute academic, professional, or personal projects • Maintain focus and track progress using advanced, user-friendly tools • Receive personalized guidance that evolves with your needs Check and advise please your thoughts targethub.ai

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0.667

GDbuildsGD•This week

I spent 6 months on a web app as a side project, and got 0 users. Here is my story.

Edit Thank you all so much for your time reading my story. Your support, feedback, criticism, and skepticism; all helped me a lot, and I couldn't appreciate it enough \^\_\^ I very rarely have stuff to post on Reddit, but I share how my project is going on, just random stuff, and memes on X. In case few might want to keep up 👀 TL;DR I spent 6 months on a tool that currently has 0 users. Below is what I learned during my journey, sharing because I believe most mistakes are easily avoidable. Do not overestimate your product and assume it will be an exception to fundamental principles. Principles are there for a reason. Always look for validation before you start. Avoid building products with a low money-to-effort ratio/in very competitive fields. Unless you have the means, you probably won't make it. Pick a problem space, pick your target audience, and talk to them before thinking about a solution. Identify and match their pain points. Only then should you think of a solution. If people are not overly excited or willing to pay in advance for a discounted price, it might be a sign to rethink. Sell one and only one feature at a time. Avoid everything else. If people don't pay for that one core feature, no secondary feature will change their mind. Always spend twice as much time marketing as you do building. You will not get users if they don't know it exists. Define success metrics ("1000 users in 3 months" or "$6000 in the account at the end of 6 months") before you start. If you don't meet them, strongly consider quitting the project. If you can't get enough users to keep going, nothing else matters. VALIDATION, VALIDATION, VALIDATION. Success is not random, but most of our first products will not make a success story. Know when to admit failure, and move on. Even if a product of yours doesn't succeed, what you learned during its journey will turn out to be invaluable for your future. My story So, this is the story of a product that I’ve been working on for the last 6 months. As it's the first product I’ve ever built, after watching you all from the sidelines, I have learned a lot, made many mistakes, and did only a few things right. Just sharing what I’ve learned and some insights from my journey so far. I hope that this post will help you avoid the mistakes I made — most of which I consider easily avoidable — while you enjoy reading it, and get to know me a little bit more 🤓. A slow start after many years Summ isn’t the first product I really wanted to build. Lacking enough dev skills to even get started was a huge blocker for so many years. In fact, the first product I would’ve LOVED to build was a smart personal shopping assistant. I had this idea 4 years ago; but with no GPT, no coding skills, no technical co-founder, I didn’t have the means to make it happen. I still do not know if such a tool exists and is good enough. All I wanted was a tool that could make data-based predictions about when to buy stuff (“buy a new toothpaste every three months”) and suggest physical products that I might need or be strongly interested in. AFAIK, Amazon famously still struggles with the second one. Fast-forward a few years, I learned the very basics of HTML, CSS, and Vanilla JS. Still was not there to build a product; but good enough to code my design portfolio from scratch. Yet, I couldn’t imagine myself building a product using Vanilla JS. I really hated it, I really sucked at it. So, back to tutorial hell, and to learn about this framework I just heard about: React.React introduced so many new concepts to me. “Thinking in React” is a phrase we heard a lot, and with quite good reasons. After some time, I was able to build very basic tutorial apps, both in React, and React Native; but I have to say that I really hated coding for mobile. At this point, I was already a fan of productivity apps, and had a concept for a time management assistant app in my design portfolio. So, why not build one? Surely, it must be easy, since every coding tutorial starts with a todo app. ❌ WRONG! Building a basic todo app is easy enough, but building one good enough for a place in the market was a challenge I took and failed. I wasted one month on that until I abandoned the project for good. Even if I continued working on it, as the productivity landscape is overly competitive, I wouldn’t be able to make enough money to cover costs, assuming I make any. Since I was (and still am) in between jobs, I decided to abandon the project. 👉 What I learned: Do not start projects with a low ratio of money to effort and time. Example: Even if I get 500 monthly users, 200 of which are paid users (unrealistically high number), assuming an average subscription fee of $5/m (such apps are quite cheap, mostly due to the high competition), it would make me around $1000 minus any occurring costs. Any founder with a product that has 500 active users should make more. Even if it was relatively successful, due to the high competition, I wouldn’t make any meaningful money. PS: I use Todoist today. Due to local pricing, I pay less than $2/m. There is no way I could beat this competitive pricing, let alone the app itself. But, somehow, with a project that wasn’t even functional — let alone being an MVP — I made my first Wi-Fi money: Someone decided that the domain I preemptively purchased is worth something. By this point, I had already abandoned the project, certainly wasn’t going to renew the domain, was looking for a FT job, and a new project that I could work on. And out of nowhere, someone hands me some free money — who am I not to take it? Of course, I took it. The domain is still unused, no idea why 🤔. Ngl, I still hate the fact that my first Wi-Fi money came from this. A new idea worth pursuing? Fast-forward some weeks now. Around March, I got this crazy idea of building an email productivity tool. We all use emails, yet we all hate them. So, this must be fixed. Everyone uses emails, in fact everyone HAS TO use emails. So, I just needed to build a tool and wait for people to come. This was all, really. After all, the problem space is huge, there is enough room for another product, everyone uses emails, no need for any further validation, right? ❌ WRONG ONCE AGAIN! We all hear from the greatest in the startup landscape that we must validate our ideas with real people, yet at least some of us (guilty here 🥸) think that our product will be hugely successful and prove them to be an exception. Few might, but most are not. I certainly wasn't. 👉 Lesson learned: Always validate your ideas with real people. Ask them how much they’d pay for such a tool (not if they would). Much better if they are willing to pay upfront for a discount, etc. But even this comes later, keep reading. I think the difference between “How much” and “If” is huge for two reasons: (1) By asking them for “How much”, you force them to think in a more realistic setting. (2) You will have a more realistic idea on your profit margins. Based on my competitive analysis, I already had a solution in my mind to improve our email usage standards and email productivity (huge mistake), but I did my best to learn about their problems regarding those without pushing the idea too hard. The idea is this: Generate concise email summaries with suggested actions, combine them into one email, and send it at their preferred times. Save as much as time the AI you end up with allows. After all, everyone loves to save time. So, what kind of validation did I seek for? Talked with only a few people around me about this crazy, internet-breaking idea. The responses I got were, now I see, mediocre; no one got excited about it, just said things along the lines of “Cool idea, OK”. So, any reasonable person in this situation would think “Okay, not might not be working”, right? Well, I did not. I assumed that they were the wrong audience for this product, and there was this magical land of user segments waiting eagerly for my product, yet unknowingly. To this day, I still have not reached this magical place. Perhaps, it didn’t exist in the first place. If I cannot find it, whether it exists or not doesn’t matter. I am certainly searching for it. 👉 What I should have done: Once I decide on a problem space (time management, email productivity, etc.), I should decide on my potential user segments, people who I plan to sell my product to. Then I should go talk to those people, ask them about their pains, then get to the problem-solving/ideation phase only later. ❗️ VALIDATION COMES FROM THE REALITY OUTSIDE. What validation looks like might change from product to product; but what invalidation looks like is more or less the same for every product. Nico Jeannen told me yesterday “validation = money in the account” on Twitter. This is the ultimate form of validation your product could get. If your product doesn’t make any money, then something is invalidated by reality: Your product, you, your idea, who knows? So, at this point, I knew a little bit of Python from spending some time in tutorial hell a few years ago, some HTML/CSS/JS, barely enough React to build a working app. React could work for this project, but I needed easy-to-implement server interactivity. Luckily, around this time, I got to know about this new gen of indie hackers, and learned (but didn’t truly understand) about their approach to indie hacking, and this library called Nextjs. How good Next.js still blows my mind. So, I was back to tutorial hell once again. But, this time, with a promise to myself: This is the last time I would visit tutorial hell. Time to start building this "ground-breaking idea" Learning the fundamentals of Next.js was easier than learning of React unsurprisingly. Yet, the first time I managed to run server actions on Next.js was one of the rarest moments that completely blew my mind. To this day, I reject the idea that it is something else than pure magic under its hood. Did I absolutely need Nextjs for this project though? I do not think so. Did it save me lots of time? Absolutely. Furthermore, learning Nextjs will certainly be quite helpful for other projects that I will be tackling in the future. Already got a few ideas that might be worth pursuing in the head in case I decide to abandon Summ in the future. Fast-forward few weeks again: So, at this stage, I had a barely working MVP-like product. Since the very beginning, I spent every free hour (and more) on this project as speed is essential. But, I am not so sure it was worth it to overwork in retrospect. Yet, I know I couldn’t help myself. Everything is going kinda smooth, so what’s the worst thing that could ever happen? Well, both Apple and Google announced their AIs (Apple Intelligence and Google Gemini, respectively) will have email summarization features for their products. Summarizing singular emails is no big deal, after all there were already so many similar products in the market. I still think that what truly matters is a frictionless user experience, and this is why I built this product in a certain way: You spend less than a few minutes setting up your account, and you get to enjoy your email summaries, without ever visiting its website again. This is still a very cool concept I really like a lot. So, at this point: I had no other idea that could be pursued, already spent too much time on this project. Do I quit or not? This was the question. Of course not. I just have to launch this product as quickly as possible. So, I did something right, a quite rare occurrence I might say: Re-planned my product, dropped everything secondary to the core feature immediately (save time on reading emails), tried launching it asap. 👉 Insight: Sell only one core feature at one time. Drop anything secondary to this core feature. Well, my primary occupation is product design. So one would expect that a product I build must have stellar design. I considered any considerable time spent on design at this stage would be simply wasted. I still think this is both true and wrong: True, because if your product’s core benefits suck, no one will care about your design. False, because if your design looks amateurish, no one will trust you and your product. So, I always targeted an average level design with it and the way this tool works made it quite easy as I had to design only 2 primary pages: Landing page and user portal (which has only settings and analytics pages). However, even though I knew spending time on design was not worth much of my time, I got a bit “greedy”: In fact, I redesigned those pages three times, and still ended up with a so-so design that I am not proud of. 👉 What I would do differently: Unless absolutely necessary, only one iteration per stage as long as it works. This, in my mind, applies to everything. If your product’s A feature works, then no need to rewrite it from scratch for any reason, or even refactor it. When your product becomes a success, and you absolutely need that part of your codebase to be written, do so, but only then. Ready to launch, now is th etime for some marketing, right? By July 26, I already had a “launchable” product that barely works (I marked this date on a Notion docs, this is how I know). Yet, I had spent almost no time on marketing, sales, whatever. After all, “You build and they will come”. Did I know that I needed marketing? Of course I did, but knowingly didn’t. Why, you might ask. Well, from my perspective, it had to be a dev-heavy product; meaning that you spend most of your time on developing it, mostly coding skills. But, this is simply wrong. As a rule of thumb, as noted by one of the greatests, Marc Louvion, you should spend at least twice of the building time on marketing. ❗️ Time spent on building \* 2 people don’t know your product > they don’t use your product > you don’t get users > you don’t make money Easy as that. Following the same reasoning, a slightly different approach to planning a project is possible. Determine an approximate time to complete the project with a high level project plan. Let’s say 6 months. By the reasoning above, 2 months should go into building, and 4 into marketing. If you need 4 months for building instead of 2, then you need 8 months of marketing, which makes the time to complete the project 12 months. If you don’t have that much time, then quit the project. When does a project count as completed? Well, in reality, never. But, I think we have to define success conditions even before we start for indie projects and startups; so we know when to quit when they are not met. A success condition could look like “Make $6000 in 12 months” or “Have 3000 users in 6 months”. It all depends on the project. But, once you set it, it should be set in stone: You don’t change it unless absolutely necessary. I suspect there are few principles that make a solopreneur successful; and knowing when to quit and when to continue is definitely one of them. Marc Louvion is famously known for his success, but he got there after failing so many projects. To my knowledge, the same applies to Nico Jeannen, Pieter Levels, or almost everyone as well. ❗️ Determining when to continue even before you start will definitely help in the long run. A half-aed launch Time-leap again. Around mid August, I “soft launched” my product. By soft launch, I mean lazy marketing. Just tweeting about it, posting it on free directories. Did I get any traffic? Surely I did. Did I get any users? Nope. Only after this time, it hit me: “Either something is wrong with me, or with this product” Marketing might be a much bigger factor for a project’s success after all. Even though I get some traffic, not convincing enough for people to sign up even for a free trial. The product was still perfect in my eyes at the time (well, still is ^(\_),) so the right people are not finding my product, I thought. Then, a question that I should have been asking at the very first place, one that could prevent all these, comes to my mind: “How do even people search for such tools?” If we are to consider this whole journey of me and my so-far-failed product to be an already destined failure, one metric suffices to show why. Search volume: 30. Even if people have such a pain point, they are not looking for email summaries. So, almost no organic traffic coming from Google. But, as a person who did zero marketing on this or any product, who has zero marketing knowledge, who doesn’t have an audience on social media, there is not much I could do. Finally, it was time to give up. Or not… In my eyes, the most important element that makes a founder (solo or not) successful (this, I am not by any means) is to solve problems. ❗️ So, the problem was this: “People are not finding my product by organic search” How do I make sure I get some organic traffic and gets more visibility? Learn digital marketing and SEO as much as I can within very limited time. Thankfully, without spending much time, I came across Neil Patel's YT channel, and as I said many times, it is an absolute gold mine. I learned a lot, especially about the fundamentals, and surely it will be fruitful; but there is no magic trick that could make people visit your website. SEO certainly helps, but only when people are looking for your keywords. However, it is truly a magical solution to get in touch with REAL people that are in your user segments: 👉 Understand your pains, understand their problems, help them to solve them via building products. I did not do this so far, have to admit. But, in case you would like to have a chat about your email usage, and email productivity, just get in touch; I’d be delighted to hear about them. Getting ready for a ProductHunt launch The date was Sept 1. And I unlocked an impossible achievement: Running out of Supabase’s free plan’s Egres limit while having zero users. I was already considering moving out of their Cloud server and managing a Supabase CLI service on my Hetzner VPS for some time; but never ever suspected that I would have to do this quickly. The cheapest plan Supabase offers is $25/month; yet, at that point, I am in between jobs for such a long time, basically broke, and could barely afford that price. One or two months could be okay, but why pay for it if I will eventually move out of their Cloud service? So, instead of paying $25, I spent two days migrating out of Supabase Cloud. Worth my time? Definitely not. But, when you are broke, you gotta do stupid things. This was the first time that I felt lucky to have zero users: I have no idea how I would manage this migration if I had any. I think this is one of the core tenets of an indie hacker: Controlling their own environment. I can’t remember whose quote this is, but I suspect it was Naval: Entrepreneurs have an almost pathological need to control their own fate. They will take any suffering if they can be in charge of their destiny, and not have it in somebody else’s hands. What’s truly scary is, at least in my case, we make people around us suffer at the expense of our attempting to control our own fates. I know this period has been quite hard on my wife as well, as I neglected her quite a bit, but sadly, I know that this will happen again. It is something that I can barely help with. Still, so sorry. After working the last two weeks on a ProductHunt Launch, I finally launched it this Tuesday. Zero ranking, zero new users, but 36 kind people upvoted my product, and many commented and provided invaluable feedback. I couldn't be more grateful for each one of them 🙏. Considering all these, what lies in the future of Summ though? I have no idea, to be honest. On one hand, I have zero users, have no job, no income. So, I need a way to make money asap. On the other hand, the whole idea of it revolves around one core premise (not an assumption) that I am not so willing to share; and I couldn’t have more trust in it. This might not be the best iteration of it, however I certainly believe that email usage is one of the best problem spaces one could work on. 👉 But, one thing is for certain: I need to get in touch with people, and talk with them about this product I built so far. In fact, this is the only item on my agenda. Nothing else will save my brainchild <3. Below are some other insights and notes that I got during my journey; as they do not 100% fit into this story, I think it is more suitable to list them here. I hope you enjoyed reading this. Give Summ a try, it comes with a generous free trial, no credit card required. Some additional notes and insights: Project planning is one of the most underestimated skills for solopreneurs. It saves you enormous time, and helps you to keep your focus up. Building B2B products beats building B2C products. Businesses are very willing to pay big bucks if your product helps them. On the other hand, spending a few hours per user who would pay $5/m probably is not worth your time. It doesn’t matter how brilliant your product is if no one uses it. If you cannot sell a product in a certain category/niche (or do not know how to sell it), it might be a good idea not to start a project in it. Going after new ideas and ventures is quite risky, especially if you don’t know how to market it. On the other hand, an already established category means that there is already demand. Whether this demand is sufficient or not is another issue. As long as there is enough demand for your product to fit in, any category/niche is good. Some might be better, some might be worse. Unless you are going hardcore B2B, you will need people to find your product by means of organic search. Always conduct thorough keyword research as soon as possible.

#reddit#spent#months

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0

0.818

Michaelbetterecycle•This week

How me and my team made 15+ apps and not made a single sale in 2023

Hey, my name is Michael, I am in Auckland NZ. This year was the official beginning of my adult life. I graduated from university and started a full-time job. I’ve also really dug into indiehacking/bootstrapping and started 15 projects (and it will be at least 17 before the year ends). I think I’ve learned a lot but I consciously repeated mistakes. Upto (Nov) Discord Statuses + Your Location + Facebook Poke https://preview.redd.it/4nqt7tp2tf5c1.png?width=572&format=png&auto=webp&s=b0223484bc54b45b5c65e0b1afd0dc52f9c02ad1 This was the end of uni, I often messaged (and got messaged) requests of status and location to (and from my) friends. I thought, what if we make a social app that’s super basic and all it does is show you where your friends are? To differentiate from snap maps and others we wanted something with more privacy where you select the location. However, never finished the codebase or launched it. This is because I slowly started to realize that B2C (especially social networks) are way too hard to make into an actual business and the story with Fistbump would repeat itself. However, this decision not to launch it almost launched a curse on our team. From that point, we permitted ourselves to abandon projects even before launching. Lessons: Don’t do social networks if your goal is 10k MRR ASAP. If you build something to 90% competition ship it or you will think it’s okay to abandon projects Insight Bites (Nov) Youtube Summarizer Extension &#x200B; https://preview.redd.it/h6drqej4tf5c1.jpg?width=800&format=pjpg&auto=webp&s=0f211456c390ac06f4fcb54aa51f9d50b0826658 Right after Upto, we started ideating and conveniently the biggest revolution in the recent history of tech was released → GPT. We instantly began ideating. The first problem we chose to use AI for is to summarize YouTube videos. Comical. Nevertheless, I am convinced we have had the best UX because you could right-click on a video to get a slideshow of insights instead of how everyone else did it. We dropped it because there was too much competition and unit economics didn’t work out (and it was a B2C). PodPigeon (Dec) Podcast → Tweet Threads https://preview.redd.it/0ukge245tf5c1.png?width=2498&format=png&auto=webp&s=23303e1cab330578a3d25cd688fa67aa3b97fb60 Then we thought, to make unit economics work we need to make this worthwhile for podcasters. This is when I got into Twitter and started seeing people summarize podcasts. Then I thought, what if we make something that converts a podcast into tweets? This was probably one of the most important projects because it connected me with Jason and Jonaed, both of whom I regularly stay in contact with and are my go-to experts on ideas related to content creation. Jonaed was even willing to buy Podpigeon and was using it on his own time. However, the unit economics still didn’t work out (and we got excited about other things). Furthermore, we got scared of the competition because I found 1 - 2 other people who did similar things poorly. This was probably the biggest mistake we’ve made. Very similar projects made 10k MRR and more, launching later than we did. We didn’t have a coherent product vision, we didn’t understand the customer well enough, and we had a bad outlook on competition and a myriad of other things. Lessons: I already made another post about the importance of outlook on competition. Do not quit just because there are competitors or just because you can’t be 10x better. Indiehackers and Bootstrappers (or even startups) need to differentiate in the market, which can be via product (UX/UI), distribution, or both. Asking Ace Intro.co + Crowdsharing &#x200B; https://preview.redd.it/0hu2tt16tf5c1.jpg?width=1456&format=pjpg&auto=webp&s=3d397568ef2331e78198d64fafc1a701a3e75999 As I got into Twitter, I wanted to chat with some people I saw there. However, they were really expensive. I thought, what if we made some kind of crowdfunding service for other entrepreneurs to get a private lecture from their idols? It seemed to make a lot of sense on paper. It was solving a problem (validated via the fact that Intro.co is a thing and making things cheaper and accessible is a solid ground to stand on), we understood the market (or so we thought), and it could monetize relatively quickly. However, after 1-2 posts on Reddit and Indiehackers, we quickly learned three things. Firstly, no one cares. Secondly, even if they do, they think they can get the same information for free online. Thirdly, the reasons before are bad because for the first point → we barely talked to people, and for the second people → we barely talked to the wrong people. However, at least we didn’t code anything this time and tried to validate via a landing page. Lessons Don’t give up after 1 Redditor says “I don’t need this” Don’t be scared to choose successful people as your audience. Clarito Journaling with AI analyzer https://preview.redd.it/8ria2wq6tf5c1.jpg?width=1108&format=pjpg&auto=webp&s=586ec28ae75003d9f71b4af2520b748d53dd2854 Clarito is a classic problem all amateur entrepreneurs have. It’s where you lie to yourself that you have a real problem and therefore is validated but when your team asks you how much you would pay you say I guess you will pay, maybe, like 5 bucks a month…? Turns out, you’d have to pay me to use our own product lol. We sent it off to a few friends and posted on some forums, but never really got anything tangible and decided to move away. Honestly, a lot of it is us in our own heads. We say the market is too saturated, it’ll be hard to monetize, it’s B2C, etc. Lessons: You use the Mom Test on other people. You have to do it yourself as well. However, recognizing that the Mom Test requires a lot of creativity in its investigation because knowing what questions to ask can determine the outcome of the validation. I asked myself “Do I journal” but I didn’t ask myself “How often do I want GPT to chyme in on my reflections”. Which was practically never. That being said I think with the right audience and distribution, this product can work. I just don’t know (let alone care) about the audience that much (and I thought I was one of them)/ Horns & Claw Scrapes financial news texts you whether you should buy/sell the stock (news sentiment analysis) &#x200B; https://preview.redd.it/gvfxdgc7tf5c1.jpg?width=1287&format=pjpg&auto=webp&s=63977bbc33fe74147b1f72913cefee4a9ebec9c2 This one we didn’t even bother launching. Probably something internal in the team and also seemed too good to be true (because if this works, doesn’t that just make us ultra-rich fast?). I saw a similar tool making 10k MRR so I guess I was wrong. Lessons: This one was pretty much just us getting into our heads. I declared that without an audience it would be impossible to ship this product and we needed to start a YouTube channel. Lol, and we did. And we couldn’t even film for 1 minute. I made bold statements like “We will commit to this for at least 1 year no matter what”. Learnery Make courses about any subject https://preview.redd.it/1nw6z448tf5c1.jpg?width=1112&format=pjpg&auto=webp&s=f2c73e8af23b0a6c3747a81e785960d4004feb48 This is probably the most “successful” project we’ve made. It grew from a couple of dozen to a couple of hundred users. It has 11 buy events for $9.99 LTD (we couldn’t be bothered connecting Stripe because we thought no one would buy it anyway). However what got us discouraged from seriously pursuing it more is, that this has very low defensibility, “Why wouldn’t someone just use chatGPT?” and it’s B2C so it’s hard to monetize. I used it myself for a month or so but then stopped. I don’t think it’s the app, I think the act of learning a concept from scratch isn’t something you do constantly in the way Learnery delivers it (ie course). I saw a bunch of similar apps that look like Ass make like 10k MRR. Lessons: Don’t do B2C, or if you do, do it properly Don’t just Mixpanel the buy button, connect your Stripe otherwise, it doesn’t feel real and you won’t get momentum. I doubt anyone (even me) will make this mistake again. I live in my GPT bubble where I make assumptions that everyone uses GPT the same way and as much as I do. In reality, the argument that this has low defensibility against GPT is invalid. Platforms that deliver a differentiated UX from ChatGPT to audiences who are not tightly integrated into the habit of using ChatGPT (which is like - everyone except for SOME tech evangelists). CuriosityFM Make podcasts about any subject https://preview.redd.it/zmosrcp8tf5c1.jpg?width=638&format=pjpg&auto=webp&s=d04ddffabef9050050b0d87939273cc96a8637dc This was our attempt at making Learnery more unique and more differentiated from chatGPT. We never really launched it. The unit economics didn’t work out and it was actually pretty boring to listen to, I don’t think I even fully listened to one 15-minute episode. I think this wasn’t that bad, it taught us more about ElevenLabs and voice AI. It took us maybe only 2-3 days to build so I think building to learn a new groundbreaking technology is fine. SleepyTale Make children’s bedtime stories https://preview.redd.it/14ue9nm9tf5c1.jpg?width=807&format=pjpg&auto=webp&s=267e18ec6f9270e6d1d11564b38136fa524966a1 My 8-year-old sister gave me that idea. She was too scared of making tea and I was curious about how she’d react if she heard a bedtime story about that exact scenario with the moral that I wanted her to absorb (which is that you shouldn’t be scared to try new things ie stop asking me to make your tea and do it yourself, it’s not that hard. You could say I went full Goebbels on her). Zane messaged a bunch of parents on Facebook but no one really cared. We showed this to one Lady at the place we worked from at Uni and she was impressed and wanted to show it to her kids but we already turned off our ElevenLabs subscription. Lessons: However, the truth behind this is beyond just “you need to be able to distribute”. It’s that you have to care about the audience. I don’t particularly want to build products for kids and parents. I am far away from that audience because I am neither a kid anymore nor going to be a parent anytime soon, and my sister still asked me to make her tea so the story didn’t work. I think it’s important to ask yourself whether you care about the audience. The way you answer that even when you are in full bias mode is, do you engage with them? Are you interested in what’s happening in their communities? Are you friends with them? Etc. User Survey Analyzer Big User Survey → GPT → Insights Report Me and my coworker were chatting about AI when he asked me to help him analyze a massive survey for him. I thought that was some pretty decent validation. Someone in an actual company asking for help. Lessons Market research is important but moving fast is also important. Ie building momentum. Also don’t revolve around 1 user. This has been a problem in multiple projects. Finding as many users as possible in the beginning to talk to is key. Otherwise, you are just waiting for 1 person to get back to you. AutoI18N Automated Internationalization of the codebase for webapps This one I might still do. It’s hard to find a solid distribution strategy. However, the idea came from me having to do it at my day job. It seems a solid problem. I’d say it’s validated and has some good players already. The key will be differentiation via the simplicity of UX and distribution (which means a slightly different audience). In the backlog for now because I don’t care about the problem or the audience that much. Documate - Part 1 Converts complex PDFs into Excel https://preview.redd.it/8b45k9katf5c1.jpg?width=1344&format=pjpg&auto=webp&s=57324b8720eb22782e28794d2db674b073193995 My mom needed to convert a catalog of furniture into an inventory which took her 3 full days of data entry. I automated it for her and thought this could have a big impact but there was no distribution because there was no ICP. We tried to find the ideal customers by talking to a bunch of different demographics but I flew to Kazakhstan for a holiday and so this kind of fizzled out. I am not writing this blog post linearity, this is my 2nd hour and I am tired and don’t want to finish this later so I don’t even know what lessons I learned. Figmatic Marketplace of high-quality Figma mockups of real apps https://preview.redd.it/h13yv45btf5c1.jpg?width=873&format=pjpg&auto=webp&s=aaa2896aeac2f22e9b7d9eed98c28bb8a2d2cdf1 This was a collab between me and my friend Alex. It was the classic Clarito where we both thought we had this problem and would pay to fix it. In reality, this is a vitamin. Neither I, nor I doubt Alex have thought of this as soon as we bought the domain. We posted it on Gumroad, sent it to a bunch of forums, and called it a day. Same issue as almost all the other ones. No distribution strategy. However, apps like Mobin show us that this concept is indeed profitable but it takes time. It needs SEO. It needs a community. None of those things, me and Alex had or was interested in. However shortly after HTML → Figma came out and it’s the best plugin. Maybe that should’ve been the idea. Podcast → Course Turns Podcaster’s episodes into a course This one I got baited by Jason :P I described to him the idea of repurposing his content for a course. He told me this was epic and he would pay. Then after I sent him the demo, he never checked it out. Anyhow during the development, we realized that doesn’t actually work because A podcast doesn’t have the correct format for the course, the most you can extract are concepts and ideas, seldom explanations. Most creators want video-based courses to be hosted on Kajabi or Udemy Another lesson is that when you pitch something to a user, what you articulate is a platform or a process, they imagine an outcome. However, the end result of your platform can be a very different outcome to what they had in mind and there is even a chance that what they want is not possible. You need to understand really well what the outcome looks like before you design the process. This is a classic problem where we thought of the solution before the problem. Yes, the problem exists. Podcasters want to make courses. However, if you really understand what they want, you can see how repurposing a podcast isn’t the best way to get there. However I only really spoke to 1-2 podcasters about this so making conclusions is dangerous for this can just be another asking ace mistake with the Redditor. Documate Part 2 Same concept as before but now I want to run some ads. We’ll see what happens. https://preview.redd.it/xb3npj0ctf5c1.jpg?width=1456&format=pjpg&auto=webp&s=3cd4884a29fd11d870d010a2677b585551c49193 In conclusion https://preview.redd.it/2zrldc9dtf5c1.jpg?width=1840&format=pjpg&auto=webp&s=2b3105073e752ad41c23f205dbd1ea046c1da7ff It doesn’t actually matter that much whether you choose to do a B2C, or a social network or focus on growing your audience. All of these can make you successful. What’s important is that you choose. If I had to summarize my 2023 in one word it’s indecision. Most of these projects succeeded for other people, nothing was as fundamentally wrong about them as I proclaimed. In reality that itself was an excuse. New ideas seduce, and it is a form of discipline to commit to a single project for a respectful amount of time. https://preview.redd.it/zy9a2vzdtf5c1.jpg?width=1456&format=pjpg&auto=webp&s=901c621227bba0feb4efdb39142f66ab2ebb86fe Distribution is not just posting on Indiehackers and Reddit. It’s an actual strategy and you should think of it as soon as you think of the idea, even before the Figma designs. I like how Denis Shatalin taught me. You have to build a pipeline. That means a reliable way to get leads, launch campaigns at them, close deals, learn from them, and optimize. Whenever I get an idea now I always try to ask myself “Where can I find 1000s leads in one day?” If there is no good answer, this is not a good project to do now. &#x200B; https://preview.redd.it/2boh3fpetf5c1.jpg?width=1456&format=pjpg&auto=webp&s=1c0d5d7b000716fcbbb00cbad495e8b61e25be66 Talk to users before doing anything. Jumping on designing and coding to make your idea a reality is a satisfying activity in the short term. Especially for me, I like to create for the sake of creation. However, it is so important to understand the market, understand the audience, understand the distribution. There are a lot of things to understand before coding. https://preview.redd.it/lv8tt96ftf5c1.jpg?width=1456&format=pjpg&auto=webp&s=6c8735aa6ad795f216ff9ddfa2341712e8277724 Get out of your own head. The real reason we dropped so many projects is that we got into our own heads. We let the negative thoughts creep in and kill all the optimism. I am really good at coming up with excuses to start a project. However, I am equally as good at coming up with reasons to kill a project. And so you have this yin and yang of starting and stopping. Building momentum and not burning out. I can say with certainty my team ran out of juice this year. We lost momentum so many times we got burnt out towards the end. Realizing that the project itself has momentum is important. User feedback and sales bring momentum. Building also creates momentum but unless it is matched with an equal force of impact, it can stomp the project down. That is why so many of our projects died quickly after we launched. The smarter approach is to do things that have a low investment of momentum (like talking to users) but result in high impact (sales or feedback). Yes, that means the project can get invalidated which makes it more short-lived than if we built it first, but it preserves team life energy. At the end of 2023 here is a single sentence I am making about how I think one becomes a successful indiehacker. One becomes a successful Indiehacker when one starts to solve pain-killer problems in the market they understand, for an audience they care about and consistently engage with for a long enough timeframe. Therefore an unsuccessful Indiehacker in a single sentence is An unsuccessful Indiehacker constantly enters new markets they don’t understand to build solutions for people whose problems they don’t care about, in a timeframe that is shorter than than the time they spent thinking about distribution. However, an important note to be made. Life is not just about indiehacking. It’s about learning and having fun. In the human world, the best journey isn’t the one that gets you the fastest to your goals but the one you enjoy the most. I enjoyed making those silly little projects and although I do not regret them, I will not repeat the same mistakes in 2024. But while it’s still 2023, I have 2 more projects I want to do :) EDIT: For Devs, frontend is always react with vite (ts) and backend is either node with express (ts) or python. For DB either Postgres or mongo (usually Prisma for ORM). For deployment all of it is on AWS (S3, EC2). In terms of libraries/APIs Whisper.cpp is best open source for transcription Obviously the gpt apis Eleven labs for voice related stuff And other random stuff here and there

#reddit#team#made

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lmcaraig•This week

I built an app to find who’s interested in your app by monitoring social media

Hi everyone! I hope you’re all doing great folks! I’d love to know your thoughts about what I’ve been working on recently! 🙏 If you’re busy or wanna see the app scroll to the bottom to see the video demo, otherwise, continue reading. Very brief presentation of myself first: I’m Marvin, and I live in Florence, Italy, 👋 This year I decided to go all-in on solopreneurship, I’ve been in tech as Software Engineer first, and then in Engineering Leadership for 10+ years, I’ve always worked in startups, except for last year, when I was the Director of Engineering at the Linux Foundation. Follow me on X or subscribe to my newsletter if you’re curious about this journey. The vision Most founders start building digital startups because they love crafting and being impactful by helping other people or companies. First-time founders then face reality when they realize that nailing distribution is key. All other founders already learned this, most likely the hard way. The outcome is the same: a great product will unlikely succeed without great distribution. Letting people know about your product should be easier and not an unfair advantage. The following meme is so true, but also quite sad. I wanna help this to change by easing the marketing and distribution part. https://preview.redd.it/g52pz46upqtd1.png?width=679&format=png&auto=webp&s=cf8398a3592f25c05c396bb2ff5d028331a36315 The story behind Distribution is a huge space: lead generation, demand generation, content marketing, social media marketing, cold outreach, etc. I cannot solve everything altogether. A few months ago I was checking the traffic to a job board I own (NextCommit). That's when I noticed that the “baseline” traffic increased by almost 10x. 🤯 I started investigating why. I realized that the monthly traffic from Reddit increased from 10-ish to 350+. Yeah, the job board doesn’t get much traffic in total, but this was an interesting finding. After digging more, it seems that all that increase came from a single Reddit comment: https://www.reddit.com/r/remotework/comments/1crwcei/comment/l5fb1yy/ This is the moment when I realized two things: It’s cool that someone quoted it! Engaging with people on Reddit, even just through comments, can be VERY powerful. And this was just one single comment! https://preview.redd.it/nhxcv4h2qqtd1.png?width=1192&format=png&auto=webp&s=d31905f56ae59426108ddbb61f2d6b668eedf27a Some weeks later I started noticing a few apps like ReplyGuy. These were automatically engaging with Reddit posts identified through keywords. I decided to sign up for the free plan of ReplyGuy to know more, but many things didn’t convince me: One of the keywords I used for my job board was “remote” and that caused a lot of false positives, The generated replies were good as a kickstart, but most of the time they needed to be tuned to sound more like me. The latter is expected. In the end, the platform doesn’t know me, doesn’t know my opinions, doesn’t know my story, etc.. The only valuable feature left for me was identifying the posts, but that also didn’t work well for me due to false positives. I ended up using it after only 15 minutes. I’m not saying they did a poor job, but it was not working well for me. In the end, the product got quite some traction, so it helped confirm there’s interest in that kind of tool. What bothered me was the combination of auto-replies that felt non-authentic. It’s not that I’m against bots, automation is becoming more common, and people are getting used to it. But in this context, I believe bots should act as an extension of ourselves, enhancing our interactions rather than just generating generic responses (like tools such as HeyGen, Synthesia, PhotoAI). I’m not there yet with my app, but a lot can be done. I'd love to reach the point where a user feels confident to automate the replies because they sound as written by themselves. I then decided to start from the same space, helping engage with Reddit posts, for these reasons: I experienced myself that it can be impactful, It aligns with my vision to ease distribution, Some competitors validated that there’s interest in this specific feature and I could use it as a starting point, I’m confident I can provide a better experience even with what I already have. The current state The product currently enables you to: Create multiple projects and assign keywords, Find the posts that are relevant for engagement using a fuzzy match of keywords and post-filtered using AI to avoid false positives, Provide an analysis of each post to assess the best way to engage, Generate a helpful reply that you’d need to review and post. So currently the product is more on the demand gen side, but this is just the beginning. I’m speaking with people from Marketing, Sales, RevOps, and Growth agencies to better understand their lives, struggles, and pain points. This will help me ensure that I build a product that enables them to help users find the products they need. I’m currently looking for up to 10 people to join the closed beta for free. If you’re interested in joining or to get notified once generally available you can do it here! https://tally.so/r/3XYbj4 After the closed beta, I will start onboarding people in batches. This will let me gather feedback, iterate, and provide a great experience to everyone aligned with my vision. I’m not going to add auto-reply unless the conditions I explained above are met or someone convinces me there’s a good reason for doing so. Each batch will probably get bigger with an increasing price until I’m confident about making it generally available. The next steps The next steps will depend on the feedback I get from the customers and the learnings from the discovery calls I’m having. I will talk about future developments in another update, but I have some ideas already. Check out the demo video below, and I'd love to hear your thoughts! ❤️ Oh and BTW, the app is called HaveYouHeard! https://reddit.com/link/1fzsnrd/video/34lat9snpqtd1/player This is the link to Loom in case the upload doesn't work: https://www.loom.com/share/460c4033b1f94e3bb5e1d081a05eedfd

#reddit#built#find

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Odd-Foundation4714•This week

[Self Promo] We made an AI accountability Buddy for ADHD Entrepreneurs

Hi Reddit, My girlfriend and I are both entrepreneurs with ADHD. We were sick of constantly starting projects and not finishing them. We both loved the idea of an accountability partner and wanted to see whether we could use AI as our own personal accountability buddy. So we built Goalhound: https://goalhound-app.vercel.app/ Try it out completely free and let us know your thoughts.

#reddit#[self#promo]

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Potential-Promise-50•This week

I am planning to build ai seo friendly blog generator (i know the idea sounds cliche but hear me out)

So basically i come from a digital marketing background, have promoted a lot of businesses, have done a couple of seo projects as well. I found a major gap, there are many saas that do create blogs, but still i do have to add the images, format it, add anchor texts, add alt texts and so on. basically make it “seo friendly”, there is no blog generator that i found does that or as far as i know. I know you will say seo is dead, google only will rank human written content and so on. But i have seen so many websites ranked at the top just because of automated ai content. I plan to add many features, to make this entire process automated. It does require some basic seo expertise to operate but most tasks i plan on automating. It will automatically publish blogs periodically on wordpress or through api connection. Let me know what your feedback is good or bad, and if this type of software comes to market will you pay for it (i plan on selling basic plan for 30 usd/month) And what all pain points are there, that i need to take care of, what features are a must.

#reddit#planning#build

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williezx•This week

I’ve Compiled the Best AI Side Project Playbooks and Tested Them Myself—Anyone Interested?

I’ve spent the past year diving deep into AI-powered side projects, testing out all the best playbooks I could find. Some worked amazingly well, some flopped, and I learned a ton along the way. Now, I’ve compiled everything—only the most effective, step-by-step playbooks that actually work. These cover everything from idea validation to scaling, and I’ve tested them all myself to make sure they’re legit. Some of the things I’ve successfully built with AI: ✅ Automated content businesses ✅ Niche websites that generate passive income ✅ AI-powered digital product businesses ✅ Simple AI tools that people actually pay for ✅ AI-assisted freelancing strategies Would anyone be interested in checking these out? Just trying to see if there’s demand before I share more! Let me know what kind of AI side hustle you’d want to try.

#reddit#i’ve#compiled

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0.6

Drobushevskiy•This week

Finding domains for a business: The troubles faced and how they were solved.

Hey everyone! I’m sure some of you have experience searching for a domain name for your project or startup. And you know how hard it can be to find the right one. You want it to be short, memorable, SEO-friendly, free of a bad history, and relevant to your project’s meaning. As a solo entrepreneur, I’ve faced the same challenges. I tried using domain auctions and drop-catching platforms to find short and valuable domain names for my projects and for resale. But these platforms can be frustrating – there’s too much competition, bidding wars drive up prices, and waiting for a domain to become available takes forever. GoDaddy auctions can last up to 10 days, and placing a backorder doesn’t always guarantee success. This process can be stressful and time-consuming. I just wanted a way to quickly grab the right domain and start using it immediately – without all the waiting and worrying. One day, I found a great domain on Product Hunt. The product was abandoned, and the domain was available. I thought, "What if I could find more domains like this in the same niche from this site?" and "How can I automate this?” That’s how I ended up creating GoneDomains GoneDomains helps to find available domain names from popular websites like Product Hunt, Medium, Hacker News, Forbes, and others. It saves hours of searching and eliminates the stress of competing with other buyers. Recently, I added a Domain Rating (DR) metric for each domain, making it easier to find valuable domains for SEO. If you’re familiar with DR, you know that domains with high DR can boost SEO rankings. Dashboard of GoneDomains with the filter Now, I’m working on new features: A feature that shows the average price of domains across multiple sources. A tool to check how many domain extensions are already registered for a specific name. AI-powered analysis to determine a domain’s niche and keywords, plus a filter for one-, two-, or three-word domains. Today, GoneDomains has over 30,000 available domain names sourced from platforms like Product Hunt, Medium, Hacker News, Forbes, TechCrunch, and more. New domains are added daily. GoneDomains saves you from spending hours manually searching, dealing with bidding wars, waiting for auctions to end, and unnecessary stress.

#reddit#finding#domains

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Educational-Sand8635•This week

Enhancing Time Management & Journaling with AI: A Hybrid Physical-Digital Approach

Hey everyone! I wanted to share my experience combining AI, physical journaling, and time tracking - and get your thoughts on taking this further. Background: My AI-Enhanced Productivity Journey I recently did an intensive experiment tracking my time down to the minute (as a software engineer juggling multiple projects, Kendo practice, and side hustles). I used Claude/ChatGPT to analyze my patterns and got some fascinating insights about my productivity and habits. The AIs helped me spot patterns I was blind to and asked surprisingly thoughtful questions that made me reflect deeper. What really struck me was how AI turned from just an analysis tool into something like a wise friend who remembers everything and asks the right questions at the right time. This got me thinking about creating a more structured approach. The Hybrid Model Concept I'm exploring an idea that combines: Physical journaling/tracking (for tactile experience and mindfulness) AI-powered digital companion (for insights and reflection) Flexible input methods (write in a notebook, take photos, type, or voice record) The key insight is: while AI can track digital activities, our lives happen both online and offline. Sometimes we're in meetings, reading books, or having coffee with friends. By combining human input with AI analysis, we get both accuracy and insight. How It Would Work: \- Write in your physical journal/planner as usual \- Optionally snap photos or type key points into the app \- AI companion provides: \- Smart comparisons (today vs last week/month/year) \- Pattern recognition ("I notice you're most creative after morning exercise...") \- Thoughtful reflection prompts ("How has your approach to \[recurring challenge\] evolved?") \- Connection-making between entries ("This reminds me of what you wrote about...") What Makes This Different Human Agency: You control what to track and share, maintaining mindfulness AI as Coach: Beyond just tracking, it asks meaningful questions based on your patterns Temporal Intelligence: Helps you see how your behaviors and thoughts evolve over time Flexibility: Works whether you prefer paper, digital, or both Early Insights from My Testing: \- Initial tracking caused some anxiety (couldn't sleep first two nights!) but became natural \- AI feedback varies by tool (Claude more encouraging, ChatGPT more direct) \- The combination of manual tracking + AI analysis led to better self-awareness \- Having AI ask unexpected questions led to deeper insights than solo journaling Questions for the Community: Have you tried combining AI with traditional productivity/journaling methods? What worked/didn't? What kinds of AI-generated insights/questions would be most valuable to you? How would you balance the convenience of automation with the benefits of manual tracking? What features would make this truly useful for your productivity practice? I believe there's something powerful in combining the mindfulness of manual tracking, the wisdom of AI, and the flexibility of modern tools. But I'd love to hear your thoughts and experiences! Looking forward to the discussion! 🤔✍️

#reddit#enhancing#time

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0.714

kasperoo•This week

I’ve built a gaming recommendation and exploration platform called Which Game Next

Hello there! Me and a few of my best friends are software engineers, and we’ve been working part-time on developing a side project for the past 12 months. It’s called www.whichgamenext.com, and we’ve recently launched into open beta for everyone to check out. Your feedback would be invaluable to us! Our aim has been to build a gaming recommendation engine, alongside providing market oversight for where you can legally and officially purchase or obtain modern games from multiple stores and/or subscriptions. It’s often difficult to figure out what you have access to if you only have a single specific subscription, like Game Pass PC, or if you’re only interested in games on GOG/Nintendo (what a mix!). We started by identifying the available digital stores and subscriptions and slowly compiling our database using multiple automated services to gather data on these games. Think JustWatch, but for games! One major service we’ve partnered with is IGDB, which has been supplying us with JSON data dumps that served as the initial seed for our game data. A massive thank you to them for their continued support! With the data in place, we’ve been focusing on exploring new features. So far, this has included private and public user-generated lists, personal backlog tracking, and the ability to like or dislike games. We’re now improving our recommendation engine, tackling the complexities that come with it, and having a lot of fun along the way. We’re utilising modern AI strategies and solving fascinating problems related to large-scale data aggregation. We truly can’t wait to share this fantastic work! In addition to this, you can soon expect curated collections, articles about games, and supporting links to help you make informed, unbiased purchasing decisions. Your shared data will drive the recommendations. But it doesn’t stop there—we have plenty of other features on our radar, such as importing games from your favourite stores, syncing your gameplay time, surfacing data like “How Long to Beat,” and creating new and exciting ways to interact with this growing community! This is a passion project created by a group of gamers who want to spend their time and money wisely, without purchasing biases. Since it’s a side project, we mostly work on it at night, but we’re excited to grow the community, share our vision, and, who knows, maybe one day make it our full-time job! Let’s dive into the technical details: • Monorepo architecture: This speeds up development by sharing libraries, living style guides, configs, etc. Nx.js has been brilliant, enabling us to create a dependency graph of changes and only build/deploy what’s modified in a PR. • AWS: We’re using the free tier (with a few exceptions where we pay for smaller services). Achieving self-sufficiency is critical for us. Additionally, we applied to the AWS Startup Foundation programme and received $1,000 in AWS credits, which has been incredibly helpful! • Infrastructure: Fully deployed as code with Terraform. • Backends: Built using Express and Nest.js, split into around 40 projects and counting! Each project plays a unique role in gathering and syncing game data. • Scalability: Designed from the ground up, utilising AWS Lambdas with auto-scaling and load balancing. • Databases: We use Postgres with RDS and DynamoDB for storing various data. • Frontend stack: Built with React, Next.js, Tailwind, Zustand, TanStack Query, Jest, and Storybook. • CI/CD: Managed with GitHub Actions and Amplify hooks for deploying the frontends. • Admin portal: We’ve built a bespoke CMS to control the main website. It synchronises with external services, tracks game data changes, and allows us to selectively apply ‘patches’ from sites like IGDB. The system also includes data override and rollback capabilities, ensuring we maintain control over game data. • Automation: Partially automated, so manual intervention is rarely needed. • Scraping tools: Fully integrated into the admin portal with log trail capabilities. • Cloudflare: Used for on-the-fly image transformations; we’re considering moving to it full-time as our CDN for free WebP conversions. • Authentication: Handled by Cognito, with a custom frontend built from scratch. Key learnings so far: • AWS cold starts: Not ideal! While the platform is still new, we ping endpoints to keep them responsive. This won’t be an issue once traffic increases. • Lambda memory matters: We learned the hard way that low-memory configurations can delay responses by 2-3 seconds. • DynamoDB partition keys: If not designed correctly from the start, you might have to start over (yes, we’ve been there!). • GitHub Actions: Setting up node\_modules cache reuse takes time, but it’s worth it—don’t give up! We don’t know where this project will take us yet, but it’s been a fantastic journey so far. We’ve learned a lot, explored technologies we don’t typically use in our day jobs, and built something we’re genuinely passionate about. Your feedback would mean the world to us. What do you think of what we’ve done so far? What would you like to see added? Is this a service you’d use? Do you see the value in it as we do? Thanks for reading, and we hope to see you in the comments! (or our newly created /r/whichgamenext

#reddit#i’ve#built

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OnlineJobsPHmod•This week

PlumbingJobs.com - I launched a niche job board with hand-curated jobs for plumbers. Here's the summary of how it's going after the 3rd month

On October 12th 2024, I launched PlumbingJobs.com, and this is my first update (January 2025) in what I hope will be a long journey. To stay accountable and track progress, I’ll be sharing monthly updates about the site's stats, achievements, challenges, and my plans moving forward. While these posts are mostly to document the journey, I hope they’ll also be helpful to others, especially members of r/SideProject who might be working on their own first online projects. If this post isn’t a good fit for this subreddit, I’m happy to remove it or move updates elsewhere. The goal for PlumbingJobs.com is clear: to become the #1 job board for plumber jobs, featuring hand-picked opportunities the plumbing industry. Let’s dive right in: Statistics update ~ 4th Quarter of 2024 |\-|October|November|December| |:-|:-|:-|:-| |Jobs Posted:|2|16|43| |Paid Post:|0|2|2| |Free Post:|0|1|2| |Visitors:|72|138|1,164| |Avg. Time Per Visit:|1 min. 24 sec|2 min. 15 sec|3 min. 41 sec| |Pageviews:|196|308|2,590| |Avg. Actions:|1.1|2.3|2.3| |Bounce Rate:|87%|73%|40%| I'm not a very technical guy and I don't know how to code. So the best way for me was learning to build it using Wordpress through YouTube. Also, I believe in the power of a great .COM domain name, and the stats from the first three months have only reinforced that belief: 49.2% of traffic comes directly from users typing the URL into their browsers. 48% of traffic is from search engines like Google and Bing. The remaining 1.8% comes from social media and other backlinks. Pricing Tiers and Early Wins I offer three pricing tiers for job listings: Free Listing: Basic exposure for job openings. Silver Listing ($45): Greater visibility and placement on the site. Gold Listing ($95): Premium visibility and enhanced promotion. To my surprise, my very first sale in October was a Gold Listing! That initial $95 sale was the motivation I needed to keep building. Later that month, I sold a Silver Listing, bringing my total revenue for October to $140. The same revenue was generated in December 2024, showing consistent early interest. Steps Taken in December To boost SEO and add value to the site, I created a Plumbing Directory, featuring: Plumbing companies across the U.S. Their stories, contact information, logos, addresses, business hours, and more. This directory serves as free marketing for these businesses and increases the likelihood they’ll discover my site and support it by posting job openings. Plans Moving Forward Social Media Marketing: I plan to automate posts using AI to expand reach and drive more traffic to the site. Consistency in Job Postings: I’m committed to posting 2–3 plumbing jobs daily to keep the site fresh and useful for plumbers seeking work. Looking forward to grow this niche job board slowly but surely this 2025. If you have any questions, concerns, come across glitches - feel free to reach out, happy to chat. Thank you all again, and see you in a month. Romel@plumbingjobs.com

#reddit#plumbingjobs.com#launched

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thewolfofslovenia•This week

0-20+ faceless AI automated YouTube channels in 1 year - my process and tools

First of all before diving deep into this process (scroll a bit below) I have to say something that everyone keeps asking me, is it profitable? Yes. It's by far my most profitable venture outside of my regular 9-5... But it took a lot of work, delegation and building processes to get here. So the one thing I would love to get out of this post - if you have any insights, feedback or tools I might be missing out post them below and let's help each other out. Now, how you can get started with (AI) YouTube automation: Pick a topic that is BOTH: a) in demand b) interesting to you & you have knowledge about Do everything yourself at first - delegate later No one cares about the videos as much as you do, so make sure to nail the ideation, scripts, editing, format and packaging yourself first. Now that we got that out of the way: Use this workflow: VidIQ - outliers sections is pure gold, I use it all the time to find trending video packaging, topics, etc. ChatGPT or Claude - high level video ideas at scale and your assistant (I use projects inside ChatGPT and its really good at managing and prioritizing). If you are using it for scripts please for the love of god, make final edits yourself by hand. Add character, personal insights, ideas, etc. Katalist AI - all in one video generator tool I use to quickly go from video idea to script, storyboard, AI voiceover and then final visuals. It's surprisingly good and to make a decent video it only takes about 1-2 hours in TOTAL. Once you understand how it works and have a process, delegate to tech savvy VAs / content creators for $5-$15/hour and you have final, good quality videos for less than $30. Pikzels / Krea AI - your AI thumbnail generator, I dont remember the last time we used Photoshop outside of quick text or image edits. Its basically AI image manipulation at scale and it costs 10-30x less than a human thumbnail designer and the thumbnails are really good. VidIQ+TubeBuddy - titles & optimization, but you have to know that most of the views come usually from recommended, so dont over obsess and add 392x keywords in your title and description. Its all about the packaging. Now whats left is track performance & iterate - it's practically impossible to nail it the first few times, but each video you make look at the data (not just in YT studio) and UNDERSTAND why it did not perform as well as you thought it would. Regarding monetization, adsense sucks - sell digital products. If I was relying on adsense alone I would never ever be profitable, but selling mini digital products and mentioning CTAs in the actual video not just in the description makes this super profitable and scaleable, especially since video production is so cheap. Final thoughts: (AI) YouTube automation absolutely works, but it’s not an overnight success or a total hands-off cashcow machine. It’s a real business and you need systems, consistent effort, iteration, failing and learning along the way. If you’ve got any tips, hidden gems or tools I might be missing, drop them below & let’s help each other out.

#reddit#0-20+#faceless

reddit

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0.818

Michaelbetterecycle•This week

How me and my team made 15+ apps and not made a single sale in 2023

Hey, my name is Michael, I am in Auckland NZ. This year was the official beginning of my adult life. I graduated from university and started a full-time job. I’ve also really dug into indiehacking/bootstrapping and started 15 projects (and it will be at least 17 before the year ends). I think I’ve learned a lot but I consciously repeated mistakes. Upto (Nov) Discord Statuses + Your Location + Facebook Poke https://preview.redd.it/4nqt7tp2tf5c1.png?width=572&format=png&auto=webp&s=b0223484bc54b45b5c65e0b1afd0dc52f9c02ad1 This was the end of uni, I often messaged (and got messaged) requests of status and location to (and from my) friends. I thought, what if we make a social app that’s super basic and all it does is show you where your friends are? To differentiate from snap maps and others we wanted something with more privacy where you select the location. However, never finished the codebase or launched it. This is because I slowly started to realize that B2C (especially social networks) are way too hard to make into an actual business and the story with Fistbump would repeat itself. However, this decision not to launch it almost launched a curse on our team. From that point, we permitted ourselves to abandon projects even before launching. Lessons: Don’t do social networks if your goal is 10k MRR ASAP. If you build something to 90% competition ship it or you will think it’s okay to abandon projects Insight Bites (Nov) Youtube Summarizer Extension &#x200B; https://preview.redd.it/h6drqej4tf5c1.jpg?width=800&format=pjpg&auto=webp&s=0f211456c390ac06f4fcb54aa51f9d50b0826658 Right after Upto, we started ideating and conveniently the biggest revolution in the recent history of tech was released → GPT. We instantly began ideating. The first problem we chose to use AI for is to summarize YouTube videos. Comical. Nevertheless, I am convinced we have had the best UX because you could right-click on a video to get a slideshow of insights instead of how everyone else did it. We dropped it because there was too much competition and unit economics didn’t work out (and it was a B2C). PodPigeon (Dec) Podcast → Tweet Threads https://preview.redd.it/0ukge245tf5c1.png?width=2498&format=png&auto=webp&s=23303e1cab330578a3d25cd688fa67aa3b97fb60 Then we thought, to make unit economics work we need to make this worthwhile for podcasters. This is when I got into Twitter and started seeing people summarize podcasts. Then I thought, what if we make something that converts a podcast into tweets? This was probably one of the most important projects because it connected me with Jason and Jonaed, both of whom I regularly stay in contact with and are my go-to experts on ideas related to content creation. Jonaed was even willing to buy Podpigeon and was using it on his own time. However, the unit economics still didn’t work out (and we got excited about other things). Furthermore, we got scared of the competition because I found 1 - 2 other people who did similar things poorly. This was probably the biggest mistake we’ve made. Very similar projects made 10k MRR and more, launching later than we did. We didn’t have a coherent product vision, we didn’t understand the customer well enough, and we had a bad outlook on competition and a myriad of other things. Lessons: I already made another post about the importance of outlook on competition. Do not quit just because there are competitors or just because you can’t be 10x better. Indiehackers and Bootstrappers (or even startups) need to differentiate in the market, which can be via product (UX/UI), distribution, or both. Asking Ace Intro.co + Crowdsharing &#x200B; https://preview.redd.it/0hu2tt16tf5c1.jpg?width=1456&format=pjpg&auto=webp&s=3d397568ef2331e78198d64fafc1a701a3e75999 As I got into Twitter, I wanted to chat with some people I saw there. However, they were really expensive. I thought, what if we made some kind of crowdfunding service for other entrepreneurs to get a private lecture from their idols? It seemed to make a lot of sense on paper. It was solving a problem (validated via the fact that Intro.co is a thing and making things cheaper and accessible is a solid ground to stand on), we understood the market (or so we thought), and it could monetize relatively quickly. However, after 1-2 posts on Reddit and Indiehackers, we quickly learned three things. Firstly, no one cares. Secondly, even if they do, they think they can get the same information for free online. Thirdly, the reasons before are bad because for the first point → we barely talked to people, and for the second people → we barely talked to the wrong people. However, at least we didn’t code anything this time and tried to validate via a landing page. Lessons Don’t give up after 1 Redditor says “I don’t need this” Don’t be scared to choose successful people as your audience. Clarito Journaling with AI analyzer https://preview.redd.it/8ria2wq6tf5c1.jpg?width=1108&format=pjpg&auto=webp&s=586ec28ae75003d9f71b4af2520b748d53dd2854 Clarito is a classic problem all amateur entrepreneurs have. It’s where you lie to yourself that you have a real problem and therefore is validated but when your team asks you how much you would pay you say I guess you will pay, maybe, like 5 bucks a month…? Turns out, you’d have to pay me to use our own product lol. We sent it off to a few friends and posted on some forums, but never really got anything tangible and decided to move away. Honestly, a lot of it is us in our own heads. We say the market is too saturated, it’ll be hard to monetize, it’s B2C, etc. Lessons: You use the Mom Test on other people. You have to do it yourself as well. However, recognizing that the Mom Test requires a lot of creativity in its investigation because knowing what questions to ask can determine the outcome of the validation. I asked myself “Do I journal” but I didn’t ask myself “How often do I want GPT to chyme in on my reflections”. Which was practically never. That being said I think with the right audience and distribution, this product can work. I just don’t know (let alone care) about the audience that much (and I thought I was one of them)/ Horns & Claw Scrapes financial news texts you whether you should buy/sell the stock (news sentiment analysis) &#x200B; https://preview.redd.it/gvfxdgc7tf5c1.jpg?width=1287&format=pjpg&auto=webp&s=63977bbc33fe74147b1f72913cefee4a9ebec9c2 This one we didn’t even bother launching. Probably something internal in the team and also seemed too good to be true (because if this works, doesn’t that just make us ultra-rich fast?). I saw a similar tool making 10k MRR so I guess I was wrong. Lessons: This one was pretty much just us getting into our heads. I declared that without an audience it would be impossible to ship this product and we needed to start a YouTube channel. Lol, and we did. And we couldn’t even film for 1 minute. I made bold statements like “We will commit to this for at least 1 year no matter what”. Learnery Make courses about any subject https://preview.redd.it/1nw6z448tf5c1.jpg?width=1112&format=pjpg&auto=webp&s=f2c73e8af23b0a6c3747a81e785960d4004feb48 This is probably the most “successful” project we’ve made. It grew from a couple of dozen to a couple of hundred users. It has 11 buy events for $9.99 LTD (we couldn’t be bothered connecting Stripe because we thought no one would buy it anyway). However what got us discouraged from seriously pursuing it more is, that this has very low defensibility, “Why wouldn’t someone just use chatGPT?” and it’s B2C so it’s hard to monetize. I used it myself for a month or so but then stopped. I don’t think it’s the app, I think the act of learning a concept from scratch isn’t something you do constantly in the way Learnery delivers it (ie course). I saw a bunch of similar apps that look like Ass make like 10k MRR. Lessons: Don’t do B2C, or if you do, do it properly Don’t just Mixpanel the buy button, connect your Stripe otherwise, it doesn’t feel real and you won’t get momentum. I doubt anyone (even me) will make this mistake again. I live in my GPT bubble where I make assumptions that everyone uses GPT the same way and as much as I do. In reality, the argument that this has low defensibility against GPT is invalid. Platforms that deliver a differentiated UX from ChatGPT to audiences who are not tightly integrated into the habit of using ChatGPT (which is like - everyone except for SOME tech evangelists). CuriosityFM Make podcasts about any subject https://preview.redd.it/zmosrcp8tf5c1.jpg?width=638&format=pjpg&auto=webp&s=d04ddffabef9050050b0d87939273cc96a8637dc This was our attempt at making Learnery more unique and more differentiated from chatGPT. We never really launched it. The unit economics didn’t work out and it was actually pretty boring to listen to, I don’t think I even fully listened to one 15-minute episode. I think this wasn’t that bad, it taught us more about ElevenLabs and voice AI. It took us maybe only 2-3 days to build so I think building to learn a new groundbreaking technology is fine. SleepyTale Make children’s bedtime stories https://preview.redd.it/14ue9nm9tf5c1.jpg?width=807&format=pjpg&auto=webp&s=267e18ec6f9270e6d1d11564b38136fa524966a1 My 8-year-old sister gave me that idea. She was too scared of making tea and I was curious about how she’d react if she heard a bedtime story about that exact scenario with the moral that I wanted her to absorb (which is that you shouldn’t be scared to try new things ie stop asking me to make your tea and do it yourself, it’s not that hard. You could say I went full Goebbels on her). Zane messaged a bunch of parents on Facebook but no one really cared. We showed this to one Lady at the place we worked from at Uni and she was impressed and wanted to show it to her kids but we already turned off our ElevenLabs subscription. Lessons: However, the truth behind this is beyond just “you need to be able to distribute”. It’s that you have to care about the audience. I don’t particularly want to build products for kids and parents. I am far away from that audience because I am neither a kid anymore nor going to be a parent anytime soon, and my sister still asked me to make her tea so the story didn’t work. I think it’s important to ask yourself whether you care about the audience. The way you answer that even when you are in full bias mode is, do you engage with them? Are you interested in what’s happening in their communities? Are you friends with them? Etc. User Survey Analyzer Big User Survey → GPT → Insights Report Me and my coworker were chatting about AI when he asked me to help him analyze a massive survey for him. I thought that was some pretty decent validation. Someone in an actual company asking for help. Lessons Market research is important but moving fast is also important. Ie building momentum. Also don’t revolve around 1 user. This has been a problem in multiple projects. Finding as many users as possible in the beginning to talk to is key. Otherwise, you are just waiting for 1 person to get back to you. AutoI18N Automated Internationalization of the codebase for webapps This one I might still do. It’s hard to find a solid distribution strategy. However, the idea came from me having to do it at my day job. It seems a solid problem. I’d say it’s validated and has some good players already. The key will be differentiation via the simplicity of UX and distribution (which means a slightly different audience). In the backlog for now because I don’t care about the problem or the audience that much. Documate - Part 1 Converts complex PDFs into Excel https://preview.redd.it/8b45k9katf5c1.jpg?width=1344&format=pjpg&auto=webp&s=57324b8720eb22782e28794d2db674b073193995 My mom needed to convert a catalog of furniture into an inventory which took her 3 full days of data entry. I automated it for her and thought this could have a big impact but there was no distribution because there was no ICP. We tried to find the ideal customers by talking to a bunch of different demographics but I flew to Kazakhstan for a holiday and so this kind of fizzled out. I am not writing this blog post linearity, this is my 2nd hour and I am tired and don’t want to finish this later so I don’t even know what lessons I learned. Figmatic Marketplace of high-quality Figma mockups of real apps https://preview.redd.it/h13yv45btf5c1.jpg?width=873&format=pjpg&auto=webp&s=aaa2896aeac2f22e9b7d9eed98c28bb8a2d2cdf1 This was a collab between me and my friend Alex. It was the classic Clarito where we both thought we had this problem and would pay to fix it. In reality, this is a vitamin. Neither I, nor I doubt Alex have thought of this as soon as we bought the domain. We posted it on Gumroad, sent it to a bunch of forums, and called it a day. Same issue as almost all the other ones. No distribution strategy. However, apps like Mobin show us that this concept is indeed profitable but it takes time. It needs SEO. It needs a community. None of those things, me and Alex had or was interested in. However shortly after HTML → Figma came out and it’s the best plugin. Maybe that should’ve been the idea. Podcast → Course Turns Podcaster’s episodes into a course This one I got baited by Jason :P I described to him the idea of repurposing his content for a course. He told me this was epic and he would pay. Then after I sent him the demo, he never checked it out. Anyhow during the development, we realized that doesn’t actually work because A podcast doesn’t have the correct format for the course, the most you can extract are concepts and ideas, seldom explanations. Most creators want video-based courses to be hosted on Kajabi or Udemy Another lesson is that when you pitch something to a user, what you articulate is a platform or a process, they imagine an outcome. However, the end result of your platform can be a very different outcome to what they had in mind and there is even a chance that what they want is not possible. You need to understand really well what the outcome looks like before you design the process. This is a classic problem where we thought of the solution before the problem. Yes, the problem exists. Podcasters want to make courses. However, if you really understand what they want, you can see how repurposing a podcast isn’t the best way to get there. However I only really spoke to 1-2 podcasters about this so making conclusions is dangerous for this can just be another asking ace mistake with the Redditor. Documate Part 2 Same concept as before but now I want to run some ads. We’ll see what happens. https://preview.redd.it/xb3npj0ctf5c1.jpg?width=1456&format=pjpg&auto=webp&s=3cd4884a29fd11d870d010a2677b585551c49193 In conclusion https://preview.redd.it/2zrldc9dtf5c1.jpg?width=1840&format=pjpg&auto=webp&s=2b3105073e752ad41c23f205dbd1ea046c1da7ff It doesn’t actually matter that much whether you choose to do a B2C, or a social network or focus on growing your audience. All of these can make you successful. What’s important is that you choose. If I had to summarize my 2023 in one word it’s indecision. Most of these projects succeeded for other people, nothing was as fundamentally wrong about them as I proclaimed. In reality that itself was an excuse. New ideas seduce, and it is a form of discipline to commit to a single project for a respectful amount of time. https://preview.redd.it/zy9a2vzdtf5c1.jpg?width=1456&format=pjpg&auto=webp&s=901c621227bba0feb4efdb39142f66ab2ebb86fe Distribution is not just posting on Indiehackers and Reddit. It’s an actual strategy and you should think of it as soon as you think of the idea, even before the Figma designs. I like how Denis Shatalin taught me. You have to build a pipeline. That means a reliable way to get leads, launch campaigns at them, close deals, learn from them, and optimize. Whenever I get an idea now I always try to ask myself “Where can I find 1000s leads in one day?” If there is no good answer, this is not a good project to do now. &#x200B; https://preview.redd.it/2boh3fpetf5c1.jpg?width=1456&format=pjpg&auto=webp&s=1c0d5d7b000716fcbbb00cbad495e8b61e25be66 Talk to users before doing anything. Jumping on designing and coding to make your idea a reality is a satisfying activity in the short term. Especially for me, I like to create for the sake of creation. However, it is so important to understand the market, understand the audience, understand the distribution. There are a lot of things to understand before coding. https://preview.redd.it/lv8tt96ftf5c1.jpg?width=1456&format=pjpg&auto=webp&s=6c8735aa6ad795f216ff9ddfa2341712e8277724 Get out of your own head. The real reason we dropped so many projects is that we got into our own heads. We let the negative thoughts creep in and kill all the optimism. I am really good at coming up with excuses to start a project. However, I am equally as good at coming up with reasons to kill a project. And so you have this yin and yang of starting and stopping. Building momentum and not burning out. I can say with certainty my team ran out of juice this year. We lost momentum so many times we got burnt out towards the end. Realizing that the project itself has momentum is important. User feedback and sales bring momentum. Building also creates momentum but unless it is matched with an equal force of impact, it can stomp the project down. That is why so many of our projects died quickly after we launched. The smarter approach is to do things that have a low investment of momentum (like talking to users) but result in high impact (sales or feedback). Yes, that means the project can get invalidated which makes it more short-lived than if we built it first, but it preserves team life energy. At the end of 2023 here is a single sentence I am making about how I think one becomes a successful indiehacker. One becomes a successful Indiehacker when one starts to solve pain-killer problems in the market they understand, for an audience they care about and consistently engage with for a long enough timeframe. Therefore an unsuccessful Indiehacker in a single sentence is An unsuccessful Indiehacker constantly enters new markets they don’t understand to build solutions for people whose problems they don’t care about, in a timeframe that is shorter than than the time they spent thinking about distribution. However, an important note to be made. Life is not just about indiehacking. It’s about learning and having fun. In the human world, the best journey isn’t the one that gets you the fastest to your goals but the one you enjoy the most. I enjoyed making those silly little projects and although I do not regret them, I will not repeat the same mistakes in 2024. But while it’s still 2023, I have 2 more projects I want to do :) EDIT: For Devs, frontend is always react with vite (ts) and backend is either node with express (ts) or python. For DB either Postgres or mongo (usually Prisma for ORM). For deployment all of it is on AWS (S3, EC2). In terms of libraries/APIs Whisper.cpp is best open source for transcription Obviously the gpt apis Eleven labs for voice related stuff And other random stuff here and there

#reddit#team#made

reddit

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1

inputorigin•This week

I retired at 32 from my side project. Here's the path I took.

EDIT 2: Thanks for the award kind stranger! I've stopped responding to reddit comments for this post. I'm adding an FAQ to the original post based on the most common high quality questions. If you have a question that you're dying to know the answer to and that only I can help you with (vs. Google, ChatGPT, etc.), DM me. EDIT: I love how controversial this post has become (50% upvote rate), and only in this subreddit (vs. other subreddits that I posted the same content in). I trust that the open-minded half of you will find something useful in this post and my other posts and comments. I retired at 32 years old, in large part thanks to a B2C SaaS app that I developed on my own. Now, I don't have to work in order to cover my living expenses, and wouldn't have to work for quite a while. In other words, I can finally sip mai tais at the beach. I've condensed how I got there into this post. First, a super simplified timeline of events, followed by some critical details. Timeline 2013 Graduated college in the US 2013 Started first corporate job 2013 Started side project (B2C app) that would eventually lead to my retirement 2020 Started charging for use of my B2C app (was free, became freemium) 2021 Quit my last corporate job 2022 Retired: time freedom attained Details First, some summary statistics of my path to retirement: 9 years: time between graduating college and my retirement. 8 years: total length of my career where I worked at some corporate day job. 7 years: time it took my B2C app to make its first revenue dollar 2 years: time between my first dollar of SaaS revenue and my retirement. "Something something overnight success a decade in the making". I got extremely lucky on my path to retirement, both in terms of the business environment I was in and who I am as a person. I'd also like to think that some of the conscious decisions I made along the way contributed to my early retirement. Lucky Breaks Was born in the US middle class. Had a natural affinity for computer programming and entrepreneurial mindset (initiative, resourcefulness, pragmatism, courage, growth mindset). Had opportunities to develop these mindsets throughout life. Got into a good college which gave me the credentials to get high paying corporate jobs. Was early to a platform that saw large adoption (see "barnacle on whale" strategy). Business niche is shareworthy: my SaaS received free media. Business niche is relatively stable, and small enough to not be competitive. "Skillful" Decisions I decided to spend the nights and weekends of my early career working on side projects in the hopes that one would hit. I also worked a day job to support myself and build my savings. My launch funnel over roughly 7 years of working on side projects: Countless side projects prototyped. 5 side projects publically launched. 2 side projects made > $0. 1 side project ended up becoming the SaaS that would help me retire. At my corporate day jobs, I optimized for learning and work-life balance. My learning usually stalled after a year or two at one company, so I’d quit and find another job. I invested (and continute to do so) in physical and mental wellbeing via regular workouts, meditation, journaling, traveling, and good food. My fulfilling non-work-life re-energized me for my work-life, and my work-life supported my non-work-life: a virtuous cycle. I automated the most time-consuming aspects of my business (outside of product development). Nowadays, I take long vacations and work at most 20 hours a week / a three-day work week . I decided to keep my business entirely owned and operated by me. It's the best fit for my work-style (high autonomy, deep focus, fast decision-making) and need to have full creative freedom and control. I dated and married a very supportive and inspiring partner. I try not to succumb to outrageous lifestyle creep, which keeps my living expenses low and drastically extends my burn-rate. Prescription To share some aphorisms I’ve leaned with the wantrepreneurs or those who want to follow a similar path: Maximize your at bats, because you only need one hit. Bias towards action. Launch quickly. Get your ideas out into the real world for feedback. Perfect is the enemy of good. If you keep swinging and improving, you'll hit the ball eventually. Keep the big picture in mind. You don't necessarily need a home-run to be happy: a base hit will often do the job. Think about what matters most to you in life: is it a lot of money or status? Or is it something more satisfying, and often just as if not more attainable, like freedom, loving relationships, or fulfillment? Is what you’re doing now a good way to get what you want? Or is there a better way? At more of a micro-level of "keep the big picture in mind", I often see talented wantrepreneurs get stuck in the weeds of lower-level optimizations, usually around technical design choices. They forget (or maybe subconsciously avoid) the higher-level and more important questions of customer development, user experience, and distribution. For example: “Are you solving a real problem?” or “Did you launch an MVP and what did your users think?” Adopt a growth mindset. Believe that you are capable of learning whatever you need to learn in order to do what you want to do. The pain of regret is worse than the pain of failure. I’ve noticed that fear of failure is the greatest thing holding people back from taking action towards their dreams. Unless failure means death in your case, a debilitating fear of failure is a surmountable mental block. You miss 100% of the shots you don't take. When all is said and done, we often regret the things we didn't do in life than the things we did. There’s more to life than just work. Blasphemous (at least among my social circle)! But the reality is that many of the dying regret having worked too much in their lives. As Miss Frizzle from The Magic Schoolbus says: "Take chances, make mistakes, get messy!" Original post

#reddit#retired#side

reddit

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1

Content-Shopping8791•This week

Looking for Innovators to Join my Stealth-Mode AI and Automation Startup

Hi everyone, I’m currently working on building a stealth-mode startup that focuses on AI, automation, management consulting, and streamlining business processes. Right now, it’s just me working on this, and I’m looking for passionate, creative people to join me and help shape the future of the company. A bit about me: I’m from the UK and have a Business Management degree and an IT diploma, so I’ve got a good mix of business and tech knowledge to push this forward. I’m currently using tools like UiPath, Python, Make.com, Automation Anywhere, and others to create innovative solutions, but I’m not tied to these. I’m open to using any tools or technologies that fit the business and help us succeed. This is unpaid for now, but once we hit revenue targets, the plan is to transition into paid positions. If you’re excited about startups, innovation, and building something meaningful, this might be for you. I’m building AI-powered tools that solve real business problems, workflows to automate processes, and management consulting services to help businesses streamline and work smarter. It’s about combining tech innovation with business strategy to deliver something that really works. I’d love to work with people who have skills in things like Python, TensorFlow, UiPath, Automation Anywhere, web development (frontend, backend, or full-stack), or just a talent for improving workflows. If you’re great at problem-solving, strategy, or even just brainstorming new ideas, there’s a place for you. What’s in it for you? First off, you’ll get real-world experience in AI, automation, and consulting. You’ll also get the chance to help shape the company as part of the founding team and grow with it. Once the startup hits revenue goals, paid roles will follow. It’s flexible too, work remotely and set your own schedule. If this sounds interesting to you, just comment or send me a DM with a bit about your experience, any projects you’ve worked on, and how you think you could contribute to the startup. I’ll be running interviews soon to chat with people and see how we can work together. If you’re excited about joining a startup from the ground up, let’s connect. I’d love to hear from you.

#reddit#looking#innovators

reddit

0

1

Feeling_Run_2556•This week

Free AI Tool Directory 🔥

Hey! I built an AI Website Tools Directory – a collection of free AI-powered tools designed to help website owners, marketers, designers, coders, and creators automate repetitive tasks. 💡 Why I Built This: I wanted a place where I can find a comprehensive list AI tools for free. Its a single hub of free AI utilities for website optimization, content creation, productivity and much more. 🚀 What’s Inside? (Use cases) 🔹 For Content Creators & Writers AI Blog Post Generator – Instantly create article ideas & drafts. AI Meta Tag Generator – SEO-optimized titles & descriptions. AI Content Improver – Rewrite & enhance your text. 🔹 For Coders & Developers AI Code Snippet Generator – Get working code snippets for web projects. AI Regex Generator – Easily create regex patterns. AI JSON Formatter – Auto-format & clean up JSON data. 🔹 For Designers & Video Creators AI UX Improvement Tool – Detects website usability & design issues. AI Image Optimizer – Compress & enhance images for better performance. AI Video Title Generator – Get engaging titles for YouTube & Reels. 🔹 For Audio & Music Creators AI Podcast Name Generator – Unique & catchy names for your show. AI Music Genre Classifier – Analyze & tag your music automatically. 🔹 For Productivity & SEO AI Keyword Research Tool – Find trending search terms. AI Headline Analyzer – Optimize titles for engagement. AI Email Subject Line Generator – Boost email open rates. 🔍 Looking for Feedback: What other AI-powered tools would be useful for your work? Any specific features you’d like to see added? 👉 Try it here: https://www.aiwebsitetools.com/ Would love your thoughts! 🚀

#reddit#free#tool

reddit

0

0.5

LemaLogic_com•This week

I made a Voice AI Automated Testing platform (because I hate making phone calls)

As my first New Year’s resolution, I’m excited to officially launch my side project: Testzilla.ai. While designing my Voice AI systems using VAPI, RetellAI, Bland, etc., I quickly got tired of the "Update system, test call flows, repeat" cycle that went with it. The whole point of Voice AI (for me) was that I could get off the phone, not spend even more time on it. So I made some Voice AI agents to test my Voice AI system so I didn't have to keep doing it manually. I showed it to developers friends who got excited and wanted to use it themselves with their systems (and sent me "Take My Money" meme, always a good sign). After hearing this a bunch of times, I decided to make it a platform I could share and easily use on multiple projects, have a simple UI, and let me run tests from my desktop or mobile with a click—and not spend 5-30 minutes of awkward time talking to phonebots in a crowded office. Win. It also has the benefit of being a way for an AI Agency to PROVE to clients that their AI system is working properly, answering questions the right way, NOT answering questions the wrong way, and that any advanced functionality (lookups, appointments, etc.) works properly. Key Features: Multi-Project Management: Simplifies the QA process across a diverse project portfolio, ideal for agencies handling multiple clients. Custom Test Management: Easily create, organize, and track test cases tailored to your project. Run Test Batches: Group and execute test cases efficiently to keep your workflow smooth and organized. Actionable Insights: Get analysis and suggestions that help you fix issues early and improve your releases. Client-Friendly Reporting: Provides clear, detailed reports that make it easy to share progress and results with stakeholders. Developer Tools: Easily manage (receive, email, view, listen, notify) your Transcripts from other systems (VAPI, Retell, etc) without having to create Zapier or Make automations with the provided Webhook URL. More dev tools coming soon, let us know what would make your life easier! I’m launching today and would love to get feedback from this awesome community! If you’re into QA, software development, or just love testing tools, give it a look and let me know what you think. I'll add $20 in credits to your new account so you can try it out risk free, no credit cards required. Here’s the link: Testzilla.ai Looking forward to hearing your thoughts! Cheers, Brian Gallagher

#reddit#made#voice

reddit

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1

DarkAutumn•This week

[P] Building an Reinforcement Learning Agent to play The Legend of Zelda

A year go I started trying to use PPO to play the original Legend of Zelda, and I was able to train a model to beat the first boss after a few months of work. I wanted to share the project just for show and tell. I'd love to hear feedback and suggestions as this is just a hobby project. I don't do this for a living. The code for that lives in the original-design branch of my Triforce repo. I'm currently tinkering with new designs so the main branch is much less stable. Here's a video of the agent beating the first dungeon, which was trained with 5,000,000+ steps. At 38 seconds, you can see it learned that it's invulnerable at the screen edge, and it exploits that to avoid damage from a projectile. At 53 seconds it steps up to avoid damage from an unblockable projectile, even though it takes a -0.06 penalty for moving the wrong way (taking damage would be a larger penalty.) At 55 seconds it walks towards the rock projectile to block it. And so on, lots of little things the model does is easy to miss if you don't know the game inside and out. As a TLDR, here's an early version of my new (single) model. This doesn't make it quite as far, but if you watch closely it's combat is already far better, and is only trained on 320,000 steps (~6% of the steps the first model was trained on). This is pretty far along from my very first model. Original Design I got the original project working using stable-baselines's PPO and default neural network (Shared NatureCNN, I believe). SB was great to get started but ultimately stifling. In the new version of the project I've implemented PPO from scratch with torch with my own simple neural network similar to stable-baseline's default. I'm playing with all kinds of changes and designs now that I have more flexibility and control. Here is my rough original design: Overall Strategy My first pass through this project was basically "imagine playing Zelda with your older sibling telling you where to go and what to do". I give the model an objective vector which points to where I want it to go on the screen (as a bird flies, the agent still had to learn path finding to avoid damage and navigate around the map). This includes either point at the nearest enemy I want it to kill or a NSEW vector if it's supposed to move to the next room. Due a few limitations with stable-baselines (especially around action masking), I ended up training unique models for traversing the overworld vs the dungeon (since they have entirely different tilesets). I also trained a different model for when we have sword beams vs not. In the video above you can see what model is being used onscreen. In my current project I've removed this objective vector as it felt too much like cheating. Instead I give it a one-hot encoded objective (move north to the next room, pickup items, kill enemies, etc). So far it's working quite well without that crutch. The new project also does a much better job of combat even without multiple models to handle beams vs not. Observation/Action Space Image - The standard neural network had a really tough time being fed the entire screen. No amount of training seemed to help. I solved this by creating a viewport around Link that keeps him centered. This REALLY helped the model learn. I also had absolutely zero success with stacking frames to give Link a way to see enemy/projectile movement. The model simply never trained with stable-baselines when I implemented frame stacking and I never figured out why. I just added it to my current neural network and it seems to be working... Though my early experiments show that giving it 3 frames (skipping two in between, so frames curr, curr-3, curr-6) doesn't really give us that much better performance. It might if I took away some of the vectors. We'll see. Vectors - Since the model cannot see beyond its little viewport, I gave the model a vector to the closest item, enemy, and projectile onscreen. This made it so the model can shoot enemies across the room outside of its viewport. My new model gives it multiple enemies/items/projectiles and I plan to try to use an attention mechanism as part of the network to see if I can just feed it all of that data. Information - It also gets a couple of one-off datapoints like whether it currently has sword beams. The new model also gives it a "source" room (to help better understand dungeons where we have to backtrack), and a one-hot encoded objective. Action Space My original project just has a few actions, 4 for moving in the cardinal directions and 4 for attacking in each direction (I also added bombs but never spent any time training it). I had an idea to use masking to help speed up training. I.E. if link bumps into a wall, don't let him move in that direction again until he moves elsewhere, as the model would often spend an entire memory buffer running headlong straight into a wall before an update...better to do it once and get a huge negative penalty which is essentially the same result but faster. Unfortunately SB made it really annoying architecturally to pass that info down to the policy layer. I could have hacked it together, but eventually I just reimplemented PPO and my own neural network so I could properly mask actions in the new version. For example, when we start training a fresh model, it cannot attack when there aren't enemies on screen and I can disallow it from leaving certain areas. The new model actually understands splitting swinging the sword short range vs firing sword beams as two different actions, though I haven't yet had a chance to fully train with the split yet. Frameskip/Cooldowns - In the game I don't use a fixed frame skip for actions. Instead I use the internal ram state of game to know when Link is animation locked or not and only allow the agent to take actions when it's actually possible to give meaningful input to the game. This greatly sped up training. We also force movement to be between tiles on the game map. This means that when the agent decides to move it loses control for longer than a player would...a player can make more split second decisions. This made it easier to implement movement rewards though and might be something to clean up in the future. Other interesting details Pathfinding - To facilitate rewards, the original version of this project used A* to pathfind from link to what he should be doing. Here's a video of it in action. This information wasn't giving to the model directly but instead the agent would only be given the rewards if it exactly followed that path or the transposed version of it. It would also pathfind around enemies and not walk through them. This was a nightmare though. The corner cases were significant, and pushing Link towards enemies but not into them was really tricky. The new verison just uses a wavefront algorithm. I calculate a wave from the tiles we want to get to outwards, then make sure we are following the gradient. Also calculating the A* around enemies every frame (even with caching) was super slow. Wavefront was faster, especially because I give the new model no special rewards for walking around enemies...faster to compute and it has to learn from taking damage or not. Either way, the both the old and new models successfully learned how to pathfind around danger and obstacles, with or without the cheaty objective vector. Rewards - I programmed very dense rewards in both the old and new model. At basically every step, the model is getting rewarded or punished for something. I actually have some ideas I can't wait to try out to make the rewards more sparse. Or maybe we start with dense rewards for the first training, then fine-tune the model with sparser rewards. We'll see. Predicting the Future - Speaking of rewards. One interesting wrinkle is that the agent can do a lot of things that will eventually deal damage but not on that frame. For example, when Link sets a bomb it takes several seconds before it explodes, killing things. This can be a massive reward or penalty since he spent an extremely valuable resource, but may have done massive damage. PPO and other RL propagates rewards backwards, of course, but that spike in reward could land on a weird frame where we took damage or moved in the wrong direction. I probably could have just not solved that problem and let it shake out over time, but instead I used the fact that we are in an emulator to just see what the outcome of every decision is. When planting a bomb, shooting sword beams, etc, we let the game run forward until impact, then rewind time and reward the agent appropriately, continuing on from when we first paused. This greatly speeds up training, even if it's expensive to do this savestate, play forward, restore state. Neural Networks - When I first started this project (knowing very little about ML and RL), I thought most of my time would be tuning the shape of the neural network that we are using. In reality, the default provided by stable-baselines and my eventual reimplemnentation has been enough to make massive progress. Now that I have a solid codebase though, I really want to revisit this. I'd like to see if trying CoordConvs and similar networks might make the viewport unncessary. Less interesting details/thoughts Hyperparameters - Setting the entropy coefficinet way lower helped a TON in training stable models. My new PPO implementation is way less stable than stable-baselines (ha, imagine that), but still converges most of the time. Infinite Rewards - As with all reinforcement learning, if you give some way for the model to get infinite rewards, it will do just that and nothing else. I spent days, or maybe weeks tweaking reward functions to just get it to train and not find a spot on the wall it could hump for infinite rewards. Even just neutral rewards, like +0.5 moving forward and -0.5 for moving backwards, would often result in a model that just stepped left, then right infinitely. There has to be a real reward or punishment (non-neutral) for forward progress. Debugging Rewards - In fact, building a rewards debugger was the only way I made progress in this project. If you are tackling something this big, do that very early. Stable-Retro is pretty great - Couldn't be happier with the clean design for implementing emulation for AI. Torch is Awesome - My early versions heavily used numpy and relied on stable-baselines, with its multiproc parallelization support. It worked great. Moving the project over to torch was night and day though. It gave me so much more flexibility, instant multithreading for matrix operations. I have a pretty beefy computer and I'm almost at the same steps per second as 20 proc stable-retro/numpy. Future Ideas This has already gone on too long. I have some ideas for future projects, but maybe I'll just make them another post when I actually do them. Special Thanks A special thanks to Brad Flaugher for help with the early version of this, Fiskbit from the Zelda1 speedrunning community for help pulling apart the raw assembly to build this thing, and MatPoliquin for maintaining Stable-Retro. Happy to answer any questions, really I just love nerding out about this stuff.

#reddit#building#reinforcement

reddit

0

1

thewolfofslovenia•This week

0-20+ faceless AI automated YouTube channels in 1 year - my process and tools

First of all before diving deep into this process (scroll a bit below) I have to say something that everyone keeps asking me, is it profitable? Yes. It's by far my most profitable venture outside of my regular 9-5... But it took a lot of work, delegation and building processes to get here. So the one thing I would love to get out of this post - if you have any insights, feedback or tools I might be missing out post them below and let's help each other out. Now, how you can get started with (AI) YouTube automation: Pick a topic that is BOTH: a) in demand b) interesting to you & you have knowledge about Do everything yourself at first - delegate later No one cares about the videos as much as you do, so make sure to nail the ideation, scripts, editing, format and packaging yourself first. Now that we got that out of the way: Use this workflow: VidIQ - outliers sections is pure gold, I use it all the time to find trending video packaging, topics, etc. ChatGPT or Claude - high level video ideas at scale and your assistant (I use projects inside ChatGPT and its really good at managing and prioritizing). If you are using it for scripts please for the love of god, make final edits yourself by hand. Add character, personal insights, ideas, etc. Katalist AI - all in one video generator tool I use to quickly go from video idea to script, storyboard, AI voiceover and then final visuals. It's surprisingly good and to make a decent video it only takes about 1-2 hours in TOTAL. Once you understand how it works and have a process, delegate to tech savvy VAs / content creators for $5-$15/hour and you have final, good quality videos for less than $30. Pikzels / Krea AI - your AI thumbnail generator, I dont remember the last time we used Photoshop outside of quick text or image edits. Its basically AI image manipulation at scale and it costs 10-30x less than a human thumbnail designer and the thumbnails are really good. VidIQ+TubeBuddy - titles & optimization, but you have to know that most of the views come usually from recommended, so dont over obsess and add 392x keywords in your title and description. Its all about the packaging. Now whats left is track performance & iterate - it's practically impossible to nail it the first few times, but each video you make look at the data (not just in YT studio) and UNDERSTAND why it did not perform as well as you thought it would. Regarding monetization, adsense sucks - sell digital products. If I was relying on adsense alone I would never ever be profitable, but selling mini digital products and mentioning CTAs in the actual video not just in the description makes this super profitable and scaleable, especially since video production is so cheap. Final thoughts: (AI) YouTube automation absolutely works, but it’s not an overnight success or a total hands-off cashcow machine. It’s a real business and you need systems, consistent effort, iteration, failing and learning along the way. If you’ve got any tips, hidden gems or tools I might be missing, drop them below & let’s help each other out.

#reddit#0-20+#faceless

reddit

0

0.333

milaworld•This week

[D] Misuse of Deep Learning in Nature Journal’s Earthquake Aftershock Paper

Recently, I saw a post by Rajiv Shah, Chicago-based data-scientist, regarding an article published in Nature last year called Deep learning of aftershock patterns following large earthquakes, written by scientists at Harvard in collaboration with Google. Below is the article: Stand Up for Best Practices: Misuse of Deep Learning in Nature’s Earthquake Aftershock Paper The Dangers of Machine Learning Hype Practitioners of AI, machine learning, predictive modeling, and data science have grown enormously over the last few years. What was once a niche field defined by its blend of knowledge is becoming a rapidly growing profession. As the excitement around AI continues to grow, the new wave of ML augmentation, automation, and GUI tools will lead to even more growth in the number of people trying to build predictive models. But here’s the rub: While it becomes easier to use the tools of predictive modeling, predictive modeling knowledge is not yet a widespread commodity. Errors can be counterintuitive and subtle, and they can easily lead you to the wrong conclusions if you’re not careful. I’m a data scientist who works with dozens of expert data science teams for a living. In my day job, I see these teams striving to build high-quality models. The best teams work together to review their models to detect problems. There are many hard-to-detect-ways that lead to problematic models (say, by allowing target leakage into their training data). Identifying issues is not fun. This requires admitting that exciting results are “too good to be true” or that their methods were not the right approach. In other words, it’s less about the sexy data science hype that gets headlines and more about a rigorous scientific discipline. Bad Methods Create Bad Results Almost a year ago, I read an article in Nature that claimed unprecedented accuracy in predicting earthquake aftershocks by using deep learning. Reading the article, my internal radar became deeply suspicious of their results. Their methods simply didn’t carry many of the hallmarks of careful predicting modeling. I started to dig deeper. In the meantime, this article blew up and became widely recognized! It was even included in the release notes for Tensorflow as an example of what deep learning could do. However, in my digging, I found major flaws in the paper. Namely, data leakage which leads to unrealistic accuracy scores and a lack of attention to model selection (you don’t build a 6 layer neural network when a simpler model provides the same level of accuracy). To my earlier point: these are subtle, but incredibly basic predictive modeling errors that can invalidate the entire results of an experiment. Data scientists are trained to recognize and avoid these issues in their work. I assumed that this was simply overlooked by the author, so I contacted her and let her know so that she could improve her analysis. Although we had previously communicated, she did not respond to my email over concerns with the paper. Falling On Deaf Ears So, what was I to do? My coworkers told me to just tweet it and let it go, but I wanted to stand up for good modeling practices. I thought reason and best practices would prevail, so I started a 6-month process of writing up my results and shared them with Nature. Upon sharing my results, I received a note from Nature in January 2019 that despite serious concerns about data leakage and model selection that invalidate their experiment, they saw no need to correct the errors, because “Devries et al. are concerned primarily with using machine learning as [a] tool to extract insight into the natural world, and not with details of the algorithm design.” The authors provided a much harsher response. You can read the entire exchange on my github. It’s not enough to say that I was disappointed. This was a major paper (it’s Nature!) that bought into AI hype and published a paper despite it using flawed methods. Then, just this week, I ran across articles by Arnaud Mignan and Marco Broccardo on shortcomings that they found in the aftershocks article. Here are two more data scientists with expertise in earthquake analysis who also noticed flaws in the paper. I also have placed my analysis and reproducible code on github. Standing Up For Predictive Modeling Methods I want to make it clear: my goal is not to villainize the authors of the aftershocks paper. I don’t believe that they were malicious, and I think that they would argue their goal was to just show how machine learning could be applied to aftershocks. Devries is an accomplished earthquake scientist who wanted to use the latest methods for her field of study and found exciting results from it. But here’s the problem: their insights and results were based on fundamentally flawed methods. It’s not enough to say, “This isn’t a machine learning paper, it’s an earthquake paper.” If you use predictive modeling, then the quality of your results are determined by the quality of your modeling. Your work becomes data science work, and you are on the hook for your scientific rigor. There is a huge appetite for papers that use the latest technologies and approaches. It becomes very difficult to push back on these papers. But if we allow papers or projects with fundamental issues to advance, it hurts all of us. It undermines the field of predictive modeling. Please push back on bad data science. Report bad findings to papers. And if they don’t take action, go to twitter, post about it, share your results and make noise. This type of collective action worked to raise awareness of p-values and combat the epidemic of p-hacking. We need good machine learning practices if we want our field to continue to grow and maintain credibility. Link to Rajiv's Article Original Nature Publication (note: paywalled) GitHub repo contains an attempt to reproduce Nature's paper Confrontational correspondence with authors

#reddit#misuse#deep

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inarrears•This week

[N] Netflix and European Space Agency no longer working with Siraj Raval

According to article in The Register: A Netflix spokesperson confirmed to The Register it wasn’t working with Raval, and the ESA has cancelled the whole workshop altogether. “The situation is as it is. The workshop is cancelled, and that’s all,” Guillaume Belanger, an astrophysicist and the INTEGRAL Science Operations Coordinator at the ESA, told The Register on Monday. Raval isn’t about to quit his work any time soon, however. He promised students who graduated from his course that they would be referred to recruiters at Nvidia, Intel, Google and Amazon for engineering positions, or matched with a startup co-founder or a consulting client. In an unlisted YouTube video recorded live for his students discussing week eight of his course, and seen by El Reg, he read out a question posed to him: “Will your referrals hold any value now?” “Um, yeah they’re going to hold value. I don’t see why they wouldn’t. I mean, yes, some people on Twitter were angry but that has nothing to do with… I mean… I’ve also had tons of support, you know. I’ve had tons of support from people, who, uh, you know, support me, who work at these companies. He continues to justify his actions: “Public figures called me in private to remind me that this happens. You know, people make mistakes. You just have to keep going. They’re basically just telling me to not to stop. Of course, you make mistakes but you just keep going,” he claimed. When The Register asked Raval for comment, he responded: I've hardly taken any time off to relax since I first started my YouTube channel almost four years ago. And despite the enormous amount of work it takes to release two high quality videos a week for my audience, I progressively started to take on multiple other projects simultaneously by myself – a book, a docu-series, podcasts, YouTube videos, the course, the school of AI. Basically, these past few weeks, I've been experiencing a burnout unlike anything I've felt before. As a result, all of my output has been subpar. I made the [neural qubits] video and paper in one week. I remember wishing I had three to six months to really dive into quantum machine-learning and make something awesome, but telling myself I couldn't take that long as it would hinder my other projects. I plagiarized large chunks of the paper to meet my self-imposed one-week deadline. The associated video with animations took a lot more work to make. I didn't expect the paper to be cited as serious research, I considered it an additional reading resource for people who enjoyed the associated video to learn more about quantum machine learning. If I had a second chance, I'd definitely take way more time to write the paper, and in my own words. I've given refunds to every student who's asked so far, and the majority of students are still enrolled in the course. There are many happy students, they're just not as vocal on social media. We're on week 8 of 10 of my course, fully committed to student success. “And, no, I haven't plagiarized research for any other paper,” he added. https://www.theregister.co.uk/2019/10/14/ravelaiyoutube/

#reddit#netflix#european

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Wiskkey•This week

[P] The Big Sleep: Text-to-image generation using BigGAN and OpenAI's CLIP via a Google Colab notebook from Twitter user Adverb

From https://twitter.com/advadnoun/status/1351038053033406468: The Big Sleep Here's the notebook for generating images by using CLIP to guide BigGAN. It's very much unstable and a prototype, but it's also a fair place to start. I'll likely update it as time goes on. colab.research.google.com/drive/1NCceX2mbiKOSlAd\o7IU7nA9UskKN5WR?usp=sharing I am not the developer of The Big Sleep. This is the developer's Twitter account; this is the developer's Reddit account. Steps to follow to generate the first image in a given Google Colab session: Optionally, if this is your first time using Google Colab, view this Colab introduction and/or this Colab FAQ. Click this link. Sign into your Google account if you're not already signed in. Click the "S" button in the upper right to do this. Note: Being signed into a Google account has privacy ramifications, such as your Google search history being recorded in your Google account. In the Table of Contents, click "Parameters". Find the line that reads "tx = clip.tokenize('''a cityscape in the style of Van Gogh''')" and change the text inside of the single quote marks to your desired text; example: "tx = clip.tokenize('''a photo of New York City''')". The developer recommends that you keep the three single quote marks on both ends of your desired text so that mult-line text can be used An alternative is to remove two of the single quotes on each end of your desired text; example: "tx = clip.tokenize('a photo of New York City')". In the Table of Contents, click "Restart the kernel...". Position the pointer over the first cell in the notebook, which starts with text "import subprocess". Click the play button (the triangle) to run the cell. Wait until the cell completes execution. Click menu item "Runtime->Restart and run all". In the Table of Contents, click "Diagnostics". The output appears near the end of the Train cell that immediately precedes the Diagnostics cell, so scroll up a bit. Every few minutes (or perhaps 10 minutes if Google assigned you relatively slow hardware for this session), a new image will appear in the Train cell that is a refinement of the previous image. This process can go on for as long as you want until Google ends your Google Colab session, which is a total of up to 12 hours for the free version of Google Colab. Steps to follow if you want to start a different run using the same Google Colab session: Click menu item "Runtime->Interrupt execution". Save any images that you want to keep by right-clicking on them and using the appropriate context menu command. Optionally, change the desired text. Different runs using the same desired text almost always results in different outputs. Click menu item "Runtime->Restart and run all". Steps to follow when you're done with your Google Colab session: Click menu item "Runtime->Manage sessions". Click "Terminate" to end the session. Optionally, log out of your Google account due to the privacy ramifications of being logged into a Google account. The first output image in the Train cell (using the notebook's default of seeing every 100th image generated) usually is a very poor match to the desired text, but the second output image often is a decent match to the desired text. To change the default of seeing every 100th image generated, change the number 100 in line "if itt % 100 == 0:" in the Train cell to the desired number. For free-tier Google Colab users, I recommend changing 100 to a small integer such as 5. Tips for the text descriptions that you supply: In Section 3.1.4 of OpenAI's CLIP paper (pdf), the authors recommend using a text description of the form "A photo of a {label}." or "A photo of a {label}, a type of {type}." for images that are photographs. A Reddit user gives these tips. The Big Sleep should generate these 1,000 types of things better on average than other types of things. Here is an article containing a high-level description of how The Big Sleep works. The Big Sleep uses a modified version of BigGAN as its image generator component. The Big Sleep uses the ViT-B/32 CLIP model to rate how well a given image matches your desired text. The best CLIP model according to the CLIP paper authors is the (as of this writing) unreleased ViT-L/14-336px model; see Table 10 on page 40 of the CLIP paper (pdf) for a comparison. There are many other sites/programs/projects that use CLIP to steer image/video creation to match a text description. Some relevant subreddits: r/bigsleep (subreddit for images/videos generated from text-to-image machine learning algorithms). r/deepdream (subreddit for images/videos generated from machine learning algorithms). r/mediasynthesis (subreddit for media generation/manipulation techniques that use artificial intelligence; this subreddit shouldn't be used to post images/videos unless new techniques are demonstrated, or the images/videos are of high quality relative to other posts). Example using text 'a black cat sleeping on top of a red clock': https://preview.redd.it/7xq58v7022c61.png?width=512&format=png&auto=webp&s=a229ae9add555cd1caba31c42b60d907ffe67773 Example using text 'the word ''hot'' covered in ice': https://preview.redd.it/6kxdp8u3k2c61.png?width=512&format=png&auto=webp&s=5bd078b0111575f5d88a1dc53b0aeb933f3b0da6 Example using text 'a monkey holding a green lightsaber': https://preview.redd.it/rdsybsoaz2c61.png?width=512&format=png&auto=webp&s=2769d4c6c883c1c35ae0b1c629bebe9bc1d41393 Example using text 'The White House in Washington D.C. at night with green and red spotlights shining on it': https://preview.redd.it/w4mg90xsf5c61.png?width=512&format=png&auto=webp&s=5f18318de2f77bcd8a86e71e87048fadd30383d1 Example using text '''A photo of the Golden Gate Bridge at night, illuminated by spotlights in a tribute to Prince''': https://preview.redd.it/cn4ecuafhic61.png?width=512&format=png&auto=webp&s=397c838fdc49f13c5f17110b92c78b95bf0dcac0 Example using text '''a Rembrandt-style painting titled "Robert Plant decides whether to take the stairway to heaven or the ladder to heaven"''': https://preview.redd.it/h7rb3y6j5jc61.png?width=512&format=png&auto=webp&s=537bfe8210af185647b00e7585c948aa2c4e0ffb Example using text '''A photo of the Empire State Building being shot at with the laser cannons of a TIE fighter.''': https://preview.redd.it/cwi7i639c5d61.png?width=512&format=png&auto=webp&s=0510c8b93adb40eee4d3f41607f1c215d41e55ff Example using text '''A cartoon of a new mascot for the Reddit subreddit DeepDream that has a mouse-like face and wears a cape''': https://preview.redd.it/wtxbduevcbd61.png?width=512&format=png&auto=webp&s=c5d266258922bc62f25c80a08cd9cabc07d9cb1c Example using text '''Bugs Bunny meets the Eye of Sauron, drawn in the Looney Tunes cartoon style''': https://preview.redd.it/gmljaeekuid61.png?width=512&format=png&auto=webp&s=9ea578de165e12afc3a62bf6886bc1ae9dc19bec Example using text '''Photo of a blue and red neon-colored frog at night.''': https://preview.redd.it/nzlypte6wzd61.png?width=512&format=png&auto=webp&s=7e10b06f22cfc57c64b6d05738c7486b895083df Example using text '''Hell begins to freeze over''': https://preview.redd.it/vn99we9ngmf61.png?width=512&format=png&auto=webp&s=2408efd607f0ab40a08db6ee67448791aa813993 Example using text '''A scene with vibrant colors''': https://preview.redd.it/4z133mvrgmf61.png?width=512&format=png&auto=webp&s=b78e7a8e3f736769655056093a9904ff09a355a1 Example using text '''The Great Pyramids were turned into prisms by a wizard''': https://preview.redd.it/zxt6op7vgmf61.png?width=512&format=png&auto=webp&s=53e578cfde14b28afe27957e95e610b89afadd44

#reddit#sleep:#text-to-image

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MrAcurite•This week

[D] I don't really trust papers out of "Top Labs" anymore

I mean, I trust that the numbers they got are accurate and that they really did the work and got the results. I believe those. It's just that, take the recent "An Evolutionary Approach to Dynamic Introduction of Tasks in Large-scale Multitask Learning Systems" paper. It's 18 pages of talking through this pretty convoluted evolutionary and multitask learning algorithm, it's pretty interesting, solves a bunch of problems. But two notes. One, the big number they cite as the success metric is 99.43 on CIFAR-10, against a SotA of 99.40, so woop-de-fucking-doo in the grand scheme of things. Two, there's a chart towards the end of the paper that details how many TPU core-hours were used for just the training regimens that results in the final results. The sum total is 17,810 core-hours. Let's assume that for someone who doesn't work at Google, you'd have to use on-demand pricing of $3.22/hr. This means that these trained models cost $57,348. Strictly speaking, throwing enough compute at a general enough genetic algorithm will eventually produce arbitrarily good performance, so while you can absolutely read this paper and collect interesting ideas about how to use genetic algorithms to accomplish multitask learning by having each new task leverage learned weights from previous tasks by defining modifications to a subset of components of a pre-existing model, there's a meta-textual level on which this paper is just "Jeff Dean spent enough money to feed a family of four for half a decade to get a 0.03% improvement on CIFAR-10." OpenAI is far and away the worst offender here, but it seems like everyone's doing it. You throw a fuckton of compute and a light ganache of new ideas at an existing problem with existing data and existing benchmarks, and then if your numbers are infinitesimally higher than their numbers, you get to put a lil' sticker on your CV. Why should I trust that your ideas are even any good? I can't check them, I can't apply them to my own projects. Is this really what we're comfortable with as a community? A handful of corporations and the occasional university waving their dicks at everyone because they've got the compute to burn and we don't? There's a level at which I think there should be a new journal, exclusively for papers in which you can replicate their experimental results in under eight hours on a single consumer GPU.

#reddit#don't#really

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More_Knowledge2000•This week

[N] Blog: Strategies for effective AI/LLM cost management

For those of you knee-deep in cloud infrastructure for AI/LLM projects, you know the cost complexities all too well. This guide from Yotascale delves into proven strategies that can help you navigate these challenges like a pro. Read the blog post here: https://yotascale.com/blog/the-enigma-of-ai-cloud-costs-strategies-for-effective-management/

#reddit#blog:#strategies

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milaworld•This week

[N] Stable-Baselines v2.0.0 Released

Has anyone tried to use Stable-Baselines? How does it compare to the official Baselines from OpenAI in your experience? https://github.com/hill-a/stable-baselines From the repo: Stable Baselines Stable Baselines is a set of improved implementations of reinforcement learning algorithms based on OpenAI Baselines. You can read a detailed presentation of Stable Baselines in the Medium article. These algorithms will make it easier for the research community and industry to replicate, refine, and identify new ideas, and will create good baselines to build projects on top of. We expect these tools will be used as a base around which new ideas can be added, and as a tool for comparing a new approach against existing ones. We also hope that the simplicity of these tools will allow beginners to experiment with a more advanced toolset, without being buried in implementation details. Main differences with OpenAI Baselines This toolset is a fork of OpenAI Baselines, with a major structural refactoring, and code cleanups: Unified structure for all algorithms PEP8 compliant (unified code style) Documented functions and classes More tests & more code coverage

#reddit#stable-baselines#v2.0.0

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serend1p1ty-lee•This week

[P] An elegant and strong PyTorch Trainer

For lightweight use, pytorch-lightning is too heavy, and its source code will be very difficult for beginners to read, at least for me. As we know, for a deep learning engineer, a powerful trainer is a sharp weapon. When reproducing the SOTA papers, you don't have to write a lot of template code every time and can pay more attention to the model implementation itself. I opened source some works (AAAI 21 SeqNet, ICCV 21 MAED, etc) and earned more than 500 stars. After referring to some popular projects (detectron2, pytorch-image-models, and mmcv), based on my personal development experience, I developed a SIMPLE enough, GENERIC enough, and STRONG enough PyTorch Trainer: core-pytorch-utils, also named CPU. CPU covers most details in the process of training a deep neural network, including: Auto logging to console and tensorboard. Auto checkpointing. Argument parser which can load a YAML configuration file. Make ALL PyTorch LR scheduler supporting warmup. Support distributed training. Support Automatically Mixed Precision (AMP) training. I try to keep the project code as simple and readable as possible. So the code comments are very detailed and everyone can understand them. What's more, a good document is also available: CPU document For deep learning green hands, you can learn how to: write a standard and clean training loop. use AMP to speed up your training. save checkpoint, and resume from it. perform more smooth, and readable logging. use the popular visualization library: tensorboard. For old hands, we can talk about whether the structure of CPU is elegant and reasonable. I have thought a lot about this framework, combining the advantages of several popular frameworks and discarding their shortcomings. Welcome to use it!

#reddit#elegant#strong

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SemperZero•This week

[D] What are some good advanced platforms?

Hey. I'm 27 and I think I got most of the basics for ML. I'm very good at math, I understand statistics and probability quite deep, worked on research projects by myself, for which I had to build models on my own. Not really complex, but still requiring creativity and a good understanding of basic concepts. I will soon start a data science job at a FAANG company and I want to further improve my skills and use their resources to the fullest, but I'm not really sure where to go from here in terms of learning. Could you help me with some more advanced materials/forums for ML research/place with good papers/place with good articles? I'd also like to study the very best and see the way they code and explain advanced concepts (like Andrej Karpathy) where can I find them?? is there a Twitch for challenger level AI researchers streaming live processes? Or videos showing the entire project flow (how they do data visualizations, mining, choosing models, tuning, etc) like top digital artists show the highlights or the entire speed-up of their painting processes? Here's a list all of my projects to get a general idea of my level and where I'm at: calculating the distance between hundreds of 42.000 feature objects (containing categorical, strings, numbers, hashes, booleans as variables) and then clustering. with some vector processing and a neural network implemented from scratch in C some models like ARIMA (together with linear regression) combining a FFT with a neural network for a 42d wave classification T-SNE to split dataset into 2d grids -> Kullback–Leibler on grids for distance -> DBSCAN/KMEANS for clustering genetic algorithms for hyperparameter optimizations and reinforcement learning (neuro evolution) DBSCAN -> Levenberg-Marquardt for polynomial coefficients-> neural network predicting the coefficients based on different parameters playing with instance segmentation and some algorithms to synchronize a color and a depth camera simulations/statistics/probabilities for video games a lot of visualizations and data mining for patterns As you can see there is no LLM/ Generative AI/ Computer Vision stuff, which I would like to get into. I'm also not 100% sure what else would be nice to learn in general. I know most of the basic procedures for training, balancing datasets, avoid overfit, computing error plots, comparing models, etc and I'm familiar with most of math (not insanely advanced) used in ML. I didn't read many papers, but holy ... most of them are so unreadable and filled with pompous nonsense that 99% of the effort is de-obfuscating the bs and reading for so long just to figure out how the input is encoded, what's the output, and what's the model. Where can I find good, readable, structured papers which are actually on point? I'm from Eastern Europe and most of my learning has been done by my self after high school, the education quality is close to zero in the universities here and I never had any mentors at the jobs I worked. There's no research in this country, and getting to work on these projects was insanely hard, some of them being done in my free time or for free just to get experience... Fortunately after a lot of hard work I got into FAANG, and I hope things will be better here. Most of what I've learned has been from very fragmented places on the internet, and now I'm looking for centralized places and communities of top quality content. TL;DR: sorry for the long rambling. had to order my thoughts and figure what i actually want: Looking for top tier AI researchers showcasing their work processes, places with clear papers/articles, tips for someone who's no longer a very beginner, and other communities like this.

#reddit#some#good

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Flaky_Suit_8665•This week

[D] The current and future state of AI/ML is shockingly demoralizing with little hope of redemption

I recently encountered the PaLM (Scaling Language Modeling with Pathways) paper from Google Research and it opened up a can of worms of ideas I’ve felt I’ve intuitively had for a while, but have been unable to express – and I know I can’t be the only one. Sometimes I wonder what the original pioneers of AI – Turing, Neumann, McCarthy, etc. – would think if they could see the state of AI that we’ve gotten ourselves into. 67 authors, 83 pages, 540B parameters in a model, the internals of which no one can say they comprehend with a straight face, 6144 TPUs in a commercial lab that no one has access to, on a rig that no one can afford, trained on a volume of data that a human couldn’t process in a lifetime, 1 page on ethics with the same ideas that have been rehashed over and over elsewhere with no attempt at a solution – bias, racism, malicious use, etc. – for purposes that who asked for? When I started my career as an AI/ML research engineer 2016, I was most interested in two types of tasks – 1.) those that most humans could do but that would universally be considered tedious and non-scalable. I’m talking image classification, sentiment analysis, even document summarization, etc. 2.) tasks that humans lack the capacity to perform as well as computers for various reasons – forecasting, risk analysis, game playing, and so forth. I still love my career, and I try to only work on projects in these areas, but it’s getting harder and harder. This is because, somewhere along the way, it became popular and unquestionably acceptable to push AI into domains that were originally uniquely human, those areas that sit at the top of Maslows’s hierarchy of needs in terms of self-actualization – art, music, writing, singing, programming, and so forth. These areas of endeavor have negative logarithmic ability curves – the vast majority of people cannot do them well at all, about 10% can do them decently, and 1% or less can do them extraordinarily. The little discussed problem with AI-generation is that, without extreme deterrence, we will sacrifice human achievement at the top percentile in the name of lowering the bar for a larger volume of people, until the AI ability range is the norm. This is because relative to humans, AI is cheap, fast, and infinite, to the extent that investments in human achievement will be watered down at the societal, educational, and individual level with each passing year. And unlike AI gameplay which superseded humans decades ago, we won’t be able to just disqualify the machines and continue to play as if they didn’t exist. Almost everywhere I go, even this forum, I encounter almost universal deference given to current SOTA AI generation systems like GPT-3, CODEX, DALL-E, etc., with almost no one extending their implications to its logical conclusion, which is long-term convergence to the mean, to mediocrity, in the fields they claim to address or even enhance. If you’re an artist or writer and you’re using DALL-E or GPT-3 to “enhance” your work, or if you’re a programmer saying, “GitHub Co-Pilot makes me a better programmer?”, then how could you possibly know? You’ve disrupted and bypassed your own creative process, which is thoughts -> (optionally words) -> actions -> feedback -> repeat, and instead seeded your canvas with ideas from a machine, the provenance of which you can’t understand, nor can the machine reliably explain. And the more you do this, the more you make your creative processes dependent on said machine, until you must question whether or not you could work at the same level without it. When I was a college student, I often dabbled with weed, LSD, and mushrooms, and for a while, I thought the ideas I was having while under the influence were revolutionary and groundbreaking – that is until took it upon myself to actually start writing down those ideas and then reviewing them while sober, when I realized they weren’t that special at all. What I eventually determined is that, under the influence, it was impossible for me to accurately evaluate the drug-induced ideas I was having because the influencing agent the generates the ideas themselves was disrupting the same frame of reference that is responsible evaluating said ideas. This is the same principle of – if you took a pill and it made you stupider, would even know it? I believe that, especially over the long-term timeframe that crosses generations, there’s significant risk that current AI-generation developments produces a similar effect on humanity, and we mostly won’t even realize it has happened, much like a frog in boiling water. If you have children like I do, how can you be aware of the the current SOTA in these areas, project that 20 to 30 years, and then and tell them with a straight face that it is worth them pursuing their talent in art, writing, or music? How can you be honest and still say that widespread implementation of auto-correction hasn’t made you and others worse and worse at spelling over the years (a task that even I believe most would agree is tedious and worth automating). Furthermore, I’ve yet to set anyone discuss the train – generate – train - generate feedback loop that long-term application of AI-generation systems imply. The first generations of these models were trained on wide swaths of web data generated by humans, but if these systems are permitted to continually spit out content without restriction or verification, especially to the extent that it reduces or eliminates development and investment in human talent over the long term, then what happens to the 4th or 5th generation of models? Eventually we encounter this situation where the AI is being trained almost exclusively on AI-generated content, and therefore with each generation, it settles more and more into the mean and mediocrity with no way out using current methods. By the time that happens, what will we have lost in terms of the creative capacity of people, and will we be able to get it back? By relentlessly pursuing this direction so enthusiastically, I’m convinced that we as AI/ML developers, companies, and nations are past the point of no return, and it mostly comes down the investments in time and money that we’ve made, as well as a prisoner’s dilemma with our competitors. As a society though, this direction we’ve chosen for short-term gains will almost certainly make humanity worse off, mostly for those who are powerless to do anything about it – our children, our grandchildren, and generations to come. If you’re an AI researcher or a data scientist like myself, how do you turn things back for yourself when you’ve spent years on years building your career in this direction? You’re likely making near or north of $200k annually TC and have a family to support, and so it’s too late, no matter how you feel about the direction the field has gone. If you’re a company, how do you standby and let your competitors aggressively push their AutoML solutions into more and more markets without putting out your own? Moreover, if you’re a manager or thought leader in this field like Jeff Dean how do you justify to your own boss and your shareholders your team’s billions of dollars in AI investment while simultaneously balancing ethical concerns? You can’t – the only answer is bigger and bigger models, more and more applications, more and more data, and more and more automation, and then automating that even further. If you’re a country like the US, how do responsibly develop AI while your competitors like China single-mindedly push full steam ahead without an iota of ethical concern to replace you in numerous areas in global power dynamics? Once again, failing to compete would be pre-emptively admitting defeat. Even assuming that none of what I’ve described here happens to such an extent, how are so few people not taking this seriously and discounting this possibility? If everything I’m saying is fear-mongering and non-sense, then I’d be interested in hearing what you think human-AI co-existence looks like in 20 to 30 years and why it isn’t as demoralizing as I’ve made it out to be. &#x200B; EDIT: Day after posting this -- this post took off way more than I expected. Even if I received 20 - 25 comments, I would have considered that a success, but this went much further. Thank you to each one of you that has read this post, even more so if you left a comment, and triply so for those who gave awards! I've read almost every comment that has come in (even the troll ones), and am truly grateful for each one, including those in sharp disagreement. I've learned much more from this discussion with the sub than I could have imagined on this topic, from so many perspectives. While I will try to reply as many comments as I can, the sheer comment volume combined with limited free time between work and family unfortunately means that there are many that I likely won't be able to get to. That will invariably include some that I would love respond to under the assumption of infinite time, but I will do my best, even if the latency stretches into days. Thank you all once again!

#reddit#current#future

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MLstartupLondon•This week

looking for ML aficionado in London for great chats and maybe a startup

TL;DR? Here's the gist: Me: 3 startups under my belt. Started as a programmer, then trainer, then entrepreneur, now CTO & Board member for a leading customer insight company part of large bank. Large system and infrastructure specialist. Extensive & practical experience in raising funds and successfully managing both startup and established businesses. Fascinated by the power of data. Can't imagine myself spending the rest of my life being a cog in the machine. You: Machine learning specialist, programmer, analyst, understands how to navigate and crunch large datasets, from BI to predictive analytics. Interested in implementing applications from fraud detection to margin improvements through better clustering regardless of industry. Fascinated by the power of data. Can't imagine himself spending the rest of his or her life being a cog in the machine. The startup: The core idea it to build platforms and systems around the progressively larger datasets held by various sized companies, helping them solve big issues - cost reduction, profitability and reducing risk. I’m an infrastructure and software specialist and have access to 1) systems, 2) datasets 3) extensive practical in certain industry segments, namely web-scale companies and tier 1 retailers. This project is in the very early planning stages. I'm looking forward to discuss the form it could take with like-minded individuals but with complementary skills sets, namely: predictive analytics & AI as it applies to machine learning on large datasets. Want more specifics ideas? I have plenty of these, but I’m sure you do to, so let’s meet face to face and discuss them. Ultimately the goal is to crystallize on a specific concept, develop together a minimum viable product and get the company bootstrapped or angel-funded (something I also have plenty of experience with), all via a lean startup model. My philosophy on startups: Startups built in one’s free time often fail because they drag on, ending up as little more than side projects you can’t quite get rid of (due to co-founder guilt, or perhaps the little money they bring in every month). The core idea for this project is based on lean, that is, to launch a minimum viable product as early as possible. Getting feedback. Measuring results (important!). Pivot if it’s not working. This helps tremendously in staying motivated, limits the dreaded paralyzing fear of failure, and more importantly, keep the time from inception to first client/funding to a minimum. If it sounds interesting please message me and we can exchange contact details! Worst that can happen is we have a great chat!

#reddit#looking#aficionado

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AnnaSalamon•This week

5-Day Applied Rationality Workshop for Machine Learning Students & Researchers

The Center for Applied Rationality is a Berkeley-based nonprofit that runs immersive workshops for entrepreneurs, researchers, students, and other ambitious, analytical, practically-minded people. The practice of “applied rationality”, which the workshops aim towards, involves noticing what cognitive algorithms you seem to be running, checking whether those algorithms seem to be helping you form accurate beliefs and achieve your goals, and looking for ways to improve them. A typical 4-day CFAR workshop costs $3900 to attend, but thanks to a generous grant from the Future of Life Institute this fall we will be running a free five-day workshop for students and researchers in the fields of machine learning and artificial intelligence. All costs are covered by this grant, including room, board, and flights. The workshop will take place this Aug 30 through Sep 4 in the San Francisco Bay Area and will include: 2 days focused on learning models and skills, such as how habits develop and how to redesign your habits. 2 days focused on practicing skills and applying them to whichever areas of your life you would like to make improvements on, such as how to make faster progress on projects or how to have more productive collaborations with colleagues. 1 day (special to this workshop) focused on discussion of the long-term impact of artificial intelligence, and on what reasoning habits — if spread across the relevant research communities — may increase the probability of positive long-term AI outcomes. Go here to read more or to apply, or ask questions here.

#reddit#5-day#applied

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cdossman•This week

[Research] What is the State of AutoML in 2019?

https://medium.com/ai%C2%B3-theory-practice-business/what-is-the-state-of-automl-in-2019-64167f581dd1 Abstract—Deep learning has penetrated all aspects of our lives and brought us great convenience. However, the process of building a high-quality deep learning system for a specific task is not only time-consuming but also requires lots of resources and relies on human expertise, which hinders the development of deep learning in both industry and academia. To alleviate this problem, a growing number of research projects focus on automated machine learning (AutoML). In this paper, we provide a comprehensive and up-to-date study on the state-of-the-art AutoML. First, we introduce the AutoML techniques in details according to the machine learning pipeline. Then we summarize existing Neural Architecture Search (NAS) research, which is one of the most popular topics in AutoML. We also compare the models generated by NAS algorithms with those human-designed models. Finally, we present several open problems for future research.

#reddit#[research]#state

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AlexSnakeKing•This week

[D] Is the Covid-19 crisis the rock on which the ML hype wave finally crashes?

People have been predicting the end of the ML Hype for a while, but it didn't seem to go away. Andrew Ng's "A.I. is the new electricity" statement looked like it was true, and the number of ML related stuff on resumes, job descriptions and software requirements, not to mention startups, seemed to keep increasing and increasing, and increasing.... Then came a virus, with a billion years of optimization and search efficiency baked into its RNA. Some considerations: Despite all the hype, production grade ML was still a challenge for most companies outside of the big tech shops and some talented startups. With the Covid-19 induced economic meltdown, most companies don't have the money or the resources to fund the projects required to take ML from PoC/Jupyter Notebook status to value generating production applications. Most of the startups that are building ML productionizing tools and platforms will run out of funds, clients, or both. Moreover, the current economic meltdown makes most historical data on business KPIs, Customer behavior, time series forecasting, etc...is no longer useful as training data. The only data sets that are still useful are those for "hard-core" ML problems like computer vision and NLP, for which completely automated APIs have been already developed and Auto-ML works pretty well, so no real ML talent is needed in deploying them. All of this tells me that Q2 2020 will mark the end of the ML and Deep Learning hype, and besides a likely multi-year economic depression in the U.S., we are also headed into another AI winter. I'm not happy about the ML hype dying, it has helped me a lot in my career, and I really really love Deep Learning from a purely conceptual point of view. But one needs to be realistic in such a job market, should we all start reframing our skill sets and our resumes? I'm kind of hoping somebody will prove my above reasoning wrong.

#reddit#covid-19#crisis

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MassivePellfish•This week

[N] Inside DeepMind's secret plot to break away from Google

Article https://www.businessinsider.com/deepmind-secret-plot-break-away-from-google-project-watermelon-mario-2021-9 by Hugh Langley and Martin Coulter For a while, some DeepMind employees referred to it as "Watermelon." Later, executives called it "Mario." Both code names meant the same thing: a secret plan to break away from parent company Google. DeepMind feared Google might one day misuse its technology, and executives worked to distance the artificial-intelligence firm from its owner for years, said nine current and former employees who were directly familiar with the plans. This included plans to pursue an independent legal status that would distance the group's work from Google, said the people, who asked not to be identified discussing private matters. One core tension at DeepMind was that it sold the business to people it didn't trust, said one former employee. "Everything that happened since that point has been about them questioning that decision," the person added. Efforts to separate DeepMind from Google ended in April without a deal, The Wall Street Journal reported. The yearslong negotiations, along with recent shake-ups within Google's AI division, raise questions over whether the search giant can maintain control over a technology so crucial to its future. "DeepMind's close partnership with Google and Alphabet since the acquisition has been extraordinarily successful — with their support, we've delivered research breakthroughs that transformed the AI field and are now unlocking some of the biggest questions in science," a DeepMind spokesperson said in a statement. "Over the years, of course we've discussed and explored different structures within the Alphabet group to find the optimal way to support our long-term research mission. We could not be prouder to be delivering on this incredible mission, while continuing to have both operational autonomy and Alphabet's full support." When Google acquired DeepMind in 2014, the deal was seen as a win-win. Google got a leading AI research organization, and DeepMind, in London, won financial backing for its quest to build AI that can learn different tasks the way humans do, known as artificial general intelligence. But tensions soon emerged. Some employees described a cultural conflict between researchers who saw themselves firstly as academics and the sometimes bloated bureaucracy of Google's colossal business. Others said staff were immediately apprehensive about putting DeepMind's work under the control of a tech giant. For a while, some employees were encouraged to communicate using encrypted messaging apps over the fear of Google spying on their work. At one point, DeepMind's executives discovered that work published by Google's internal AI research group resembled some of DeepMind's codebase without citation, one person familiar with the situation said. "That pissed off Demis," the person added, referring to Demis Hassabis, DeepMind's CEO. "That was one reason DeepMind started to get more protective of their code." After Google restructured as Alphabet in 2015 to give riskier projects more freedom, DeepMind's leadership started to pursue a new status as a separate division under Alphabet, with its own profit and loss statement, The Information reported. DeepMind already enjoyed a high level of operational independence inside Alphabet, but the group wanted legal autonomy too. And it worried about the misuse of its technology, particularly if DeepMind were to ever achieve AGI. Internally, people started referring to the plan to gain more autonomy as "Watermelon," two former employees said. The project was later formally named "Mario" among DeepMind's leadership, these people said. "Their perspective is that their technology would be too powerful to be held by a private company, so it needs to be housed in some other legal entity detached from shareholder interest," one former employee who was close to the Alphabet negotiations said. "They framed it as 'this is better for society.'" In 2017, at a company retreat at the Macdonald Aviemore Resort in Scotland, DeepMind's leadership disclosed to employees its plan to separate from Google, two people who were present said. At the time, leadership said internally that the company planned to become a "global interest company," three people familiar with the matter said. The title, not an official legal status, was meant to reflect the worldwide ramifications DeepMind believed its technology would have. Later, in negotiations with Google, DeepMind pursued a status as a company limited by guarantee, a corporate structure without shareholders that is sometimes used by nonprofits. The agreement was that Alphabet would continue to bankroll the firm and would get an exclusive license to its technology, two people involved in the discussions said. There was a condition: Alphabet could not cross certain ethical redlines, such as using DeepMind technology for military weapons or surveillance. In 2019, DeepMind registered a new company called DeepMind Labs Limited, as well as a new holding company, filings with the UK's Companies House showed. This was done in anticipation of a separation from Google, two former employees involved in those registrations said. Negotiations with Google went through peaks and valleys over the years but gained new momentum in 2020, one person said. A senior team inside DeepMind started to hold meetings with outside lawyers and Google to hash out details of what this theoretical new formation might mean for the two companies' relationship, including specifics such as whether they would share a codebase, internal performance metrics, and software expenses, two people said. From the start, DeepMind was thinking about potential ethical dilemmas from its deal with Google. Before the 2014 acquisition closed, both companies signed an "Ethics and Safety Review Agreement" that would prevent Google from taking control of DeepMind's technology, The Economist reported in 2019. Part of the agreement included the creation of an ethics board that would supervise the research. Despite years of internal discussions about who should sit on this board, and vague promises to the press, this group "never existed, never convened, and never solved any ethics issues," one former employee close to those discussions said. A DeepMind spokesperson declined to comment. DeepMind did pursue a different idea: an independent review board to convene if it were to separate from Google, three people familiar with the plans said. The board would be made up of Google and DeepMind executives, as well as third parties. Former US president Barack Obama was someone DeepMind wanted to approach for this board, said one person who saw a shortlist of candidates. DeepMind also created an ethical charter that included bans on using its technology for military weapons or surveillance, as well as a rule that its technology should be used for ways that benefit society. In 2017, DeepMind started a unit focused on AI ethics research composed of employees and external research fellows. Its stated goal was to "pave the way for truly beneficial and responsible AI." A few months later, a controversial contract between Google and the Pentagon was disclosed, causing an internal uproar in which employees accused Google of getting into "the business of war." Google's Pentagon contract, known as Project Maven, "set alarm bells ringing" inside DeepMind, a former employee said. Afterward, Google published a set of principles to govern its work in AI, guidelines that were similar to the ethical charter that DeepMind had already set out internally, rankling some of DeepMind's senior leadership, two former employees said. In April, Hassabis told employees in an all-hands meeting that negotiations to separate from Google had ended. DeepMind would maintain its existing status inside Alphabet. DeepMind's future work would be overseen by Google's Advanced Technology Review Council, which includes two DeepMind executives, Google's AI chief Jeff Dean, and the legal SVP Kent Walker. But the group's yearslong battle to achieve more independence raises questions about its future within Google. Google's commitment to AI research has also come under question, after the company forced out two of its most senior AI ethics researchers. That led to an industry backlash and sowed doubt over whether it could allow truly independent research. Ali Alkhatib, a fellow at the Center for Applied Data Ethics, told Insider that more public accountability was "desperately needed" to regulate the pursuit of AI by large tech companies. For Google, its investment in DeepMind may be starting to pay off. Late last year, DeepMind announced a breakthrough to help scientists better understand the behavior of microscopic proteins, which has the potential to revolutionize drug discovery. As for DeepMind, Hassabis is holding on to the belief that AI technology should not be controlled by a single corporation. Speaking at Tortoise's Responsible AI Forum in June, he proposed a "world institute" of AI. Such a body might sit under the jurisdiction of the United Nations, Hassabis theorized, and could be filled with top researchers in the field. "It's much stronger if you lead by example," he told the audience, "and I hope DeepMind can be part of that role-modeling for the industry."

#reddit#inside#deepmind's

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good_rice•This week

[D] A Jobless Rant - ML is a Fool's Gold

Aside from the clickbait title, I am earnestly looking for some advice and discussion from people who are actually employed. That being said, here's my gripe: I have been relentlessly inundated by the words "AI, ML, Big Data" throughout my undergrad from other CS majors, business and sales oriented people, media, and .ai type startups. It seems like everyone was peddling ML as the go to solution, the big money earner, and the future of the field. I've heard college freshman ask stuff like, "if I want to do CS, am I going to need to learn ML to be relevant" - if you're on this sub, I probably do not need to continue to elaborate on just how ridiculous the ML craze is. Every single university has opened up ML departments or programs and are pumping out ML graduates at an unprecedented rate. Surely, there'd be a job market to meet the incredible supply of graduates and cultural interest? Swept up in a mixture of genuine interest and hype, I decided to pursue computer vision. I majored in Math-CS at a top-10 CS university (based on at least one arbitrary ranking). I had three computer vision internships, two at startups, one at NASA JPL, in each doing non-trivial CV work; I (re)implemented and integrated CV systems from mixtures of recently published papers. I have a bunch of projects showing both CV and CS fundamentals (OS, networking, data structures, algorithms, etc) knowledge. I have taken graduate level ML coursework. I was accepted to Carnegie Mellon for an MS in Computer Vision, but I deferred to 2021 - all in all, I worked my ass off to try to simultaneously get a solid background in math AND computer science AND computer vision. That brings me to where I am now, which is unemployed and looking for jobs. Almost every single position I have seen requires a PhD and/or 5+ years of experience, and whatever I have applied for has ghosted me so far. The notion that ML is a high paying in-demand field seems to only be true if your name is Andrej Karpathy - and I'm only sort of joking. It seems like unless you have a PhD from one of the big 4 in CS and multiple publications in top tier journals you're out of luck, or at least vying for one of the few remaining positions at small companies. This seems normalized in ML, but this is not the case for quite literally every other subfield or even generalized CS positions. Getting a high paying job at a Big N company is possible as a new grad with just a bachelors and general SWE knowledge, and there are a plethora of positions elsewhere. Getting the equivalent with basically every specialization, whether operating systems, distributed systems, security, networking, etc, is also possible, and doesn't require 5 CVPR publications. TL;DR From my personal perspective, if you want to do ML because of career prospects, salaries, or job security, pick almost any other CS specialization. In ML, you'll find yourself working 2x as hard through difficult theory and math to find yourself competing with more applicants for fewer positions. I am absolutely complaining and would love to hear a more positive perspective, but in the meanwhile I'll be applying to jobs, working on more post-grad projects, and contemplating switching fields.

#reddit#jobless#rant

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Stevens97•This week

[D] LLMs causing more harm than good for the field?

This post might be a bit ranty, but i feel more and more share this sentiment with me as of late. If you bother to read this whole post feel free to share how you feel about this. When OpenAI put the knowledge of AI in the everyday household, I was at first optimistic about it. In smaller countries outside the US, companies were very hesitant before about AI, they thought it felt far away and something only big FANG companies were able to do. Now? Its much better. Everyone is interested in it and wants to know how they can use AI in their business. Which is great! Pre-ChatGPT-times, when people asked me what i worked with and i responded "Machine Learning/AI" they had no clue and pretty much no further interest (Unless they were a tech-person) Post-ChatGPT-times, when I get asked the same questions I get "Oh, you do that thing with the chatbots?" Its a step in the right direction, I guess. I don't really have that much interest in LLMs and have the privilege to work exclusively on vision related tasks unlike some other people who have had to pivot to working full time with LLMs. However, right now I think its almost doing more harm to the field than good. Let me share some of my observations, but before that I want to highlight I'm in no way trying to gatekeep the field of AI in any way. I've gotten job offers to be "ChatGPT expert", What does that even mean? I strongly believe that jobs like these don't really fill a real function and is more of a "hypetrain"-job than a job that fills any function at all. Over the past years I've been going to some conferences around Europe, one being last week, which has usually been great with good technological depth and a place for Data-scientists/ML Engineers to network, share ideas and collaborate. However, now the talks, the depth, the networking has all changed drastically. No longer is it new and exiting ways companies are using AI to do cool things and push the envelope, its all GANs and LLMs with surface level knowledge. The few "old-school" type talks being sent off to a 2nd track in a small room The panel discussions are filled with philosophists with no fundamental knowledge of AI talking about if LLMs will become sentient or not. The spaces for data-scientists/ML engineers are quickly dissapearing outside the academic conferences, being pushed out by the current hypetrain. The hypetrain evangelists also promise miracles and gold with LLMs and GANs, miracles that they will never live up to. When the investors realize that the LLMs cant live up to these miracles they will instantly get more hesitant with funding for future projects within AI, sending us back into an AI-winter once again. EDIT: P.S. I've also seen more people on this reddit appearing claiming to be "Generative AI experts". But when delving deeper it turns out they are just "good prompters" and have no real knowledge, expertice or interest in the actual field of AI or Generative AI.

#reddit#llms#causing

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Downtown_Bag8166•This week

[Dataset][R] 19,762 Garbage Images for Building AI Recycling Solutions

Hi ML community! I’m excited to share the Garbage Classification V2 Dataset, featuring 19,762 high-quality images of garbage categorized into 10 distinct classes (e.g., metal, plastic, clothes, and paper). Why this matters: Train AI models for automated waste sorting and recycling. Develop waste segregation apps or sustainability-focused tools. Create innovative computer vision projects for environmental impact. 🔗 Dataset Link: Garbage Classification V2 This dataset has been used in the research paper, "Managing Household Waste Through Transfer Learning," proving its utility in real-world applications. Looking forward to seeing how you can use it to promote sustainability!

#reddit#[dataset][r]#19,762

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milaworld•This week

[D] Misuse of Deep Learning in Nature Journal’s Earthquake Aftershock Paper

Recently, I saw a post by Rajiv Shah, Chicago-based data-scientist, regarding an article published in Nature last year called Deep learning of aftershock patterns following large earthquakes, written by scientists at Harvard in collaboration with Google. Below is the article: Stand Up for Best Practices: Misuse of Deep Learning in Nature’s Earthquake Aftershock Paper The Dangers of Machine Learning Hype Practitioners of AI, machine learning, predictive modeling, and data science have grown enormously over the last few years. What was once a niche field defined by its blend of knowledge is becoming a rapidly growing profession. As the excitement around AI continues to grow, the new wave of ML augmentation, automation, and GUI tools will lead to even more growth in the number of people trying to build predictive models. But here’s the rub: While it becomes easier to use the tools of predictive modeling, predictive modeling knowledge is not yet a widespread commodity. Errors can be counterintuitive and subtle, and they can easily lead you to the wrong conclusions if you’re not careful. I’m a data scientist who works with dozens of expert data science teams for a living. In my day job, I see these teams striving to build high-quality models. The best teams work together to review their models to detect problems. There are many hard-to-detect-ways that lead to problematic models (say, by allowing target leakage into their training data). Identifying issues is not fun. This requires admitting that exciting results are “too good to be true” or that their methods were not the right approach. In other words, it’s less about the sexy data science hype that gets headlines and more about a rigorous scientific discipline. Bad Methods Create Bad Results Almost a year ago, I read an article in Nature that claimed unprecedented accuracy in predicting earthquake aftershocks by using deep learning. Reading the article, my internal radar became deeply suspicious of their results. Their methods simply didn’t carry many of the hallmarks of careful predicting modeling. I started to dig deeper. In the meantime, this article blew up and became widely recognized! It was even included in the release notes for Tensorflow as an example of what deep learning could do. However, in my digging, I found major flaws in the paper. Namely, data leakage which leads to unrealistic accuracy scores and a lack of attention to model selection (you don’t build a 6 layer neural network when a simpler model provides the same level of accuracy). To my earlier point: these are subtle, but incredibly basic predictive modeling errors that can invalidate the entire results of an experiment. Data scientists are trained to recognize and avoid these issues in their work. I assumed that this was simply overlooked by the author, so I contacted her and let her know so that she could improve her analysis. Although we had previously communicated, she did not respond to my email over concerns with the paper. Falling On Deaf Ears So, what was I to do? My coworkers told me to just tweet it and let it go, but I wanted to stand up for good modeling practices. I thought reason and best practices would prevail, so I started a 6-month process of writing up my results and shared them with Nature. Upon sharing my results, I received a note from Nature in January 2019 that despite serious concerns about data leakage and model selection that invalidate their experiment, they saw no need to correct the errors, because “Devries et al. are concerned primarily with using machine learning as [a] tool to extract insight into the natural world, and not with details of the algorithm design.” The authors provided a much harsher response. You can read the entire exchange on my github. It’s not enough to say that I was disappointed. This was a major paper (it’s Nature!) that bought into AI hype and published a paper despite it using flawed methods. Then, just this week, I ran across articles by Arnaud Mignan and Marco Broccardo on shortcomings that they found in the aftershocks article. Here are two more data scientists with expertise in earthquake analysis who also noticed flaws in the paper. I also have placed my analysis and reproducible code on github. Standing Up For Predictive Modeling Methods I want to make it clear: my goal is not to villainize the authors of the aftershocks paper. I don’t believe that they were malicious, and I think that they would argue their goal was to just show how machine learning could be applied to aftershocks. Devries is an accomplished earthquake scientist who wanted to use the latest methods for her field of study and found exciting results from it. But here’s the problem: their insights and results were based on fundamentally flawed methods. It’s not enough to say, “This isn’t a machine learning paper, it’s an earthquake paper.” If you use predictive modeling, then the quality of your results are determined by the quality of your modeling. Your work becomes data science work, and you are on the hook for your scientific rigor. There is a huge appetite for papers that use the latest technologies and approaches. It becomes very difficult to push back on these papers. But if we allow papers or projects with fundamental issues to advance, it hurts all of us. It undermines the field of predictive modeling. Please push back on bad data science. Report bad findings to papers. And if they don’t take action, go to twitter, post about it, share your results and make noise. This type of collective action worked to raise awareness of p-values and combat the epidemic of p-hacking. We need good machine learning practices if we want our field to continue to grow and maintain credibility. Link to Rajiv's Article Original Nature Publication (note: paywalled) GitHub repo contains an attempt to reproduce Nature's paper Confrontational correspondence with authors

#reddit#misuse#deep

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Current-Payment-5403•This week

Should I start an AI no-code agency?

This past year I learned no-code to develop a SaaS called AIrMedia. Here's what happened: Developed our platform no-code on Bubble Realised for our exact use-case we needed custom-code Redevelop an hybrid version with custom-code with my partner While doing some marketing to grow our waitlist to 300+ companies, I had to get some cash to be able to sustain both the company and rent. I thought why not selling my no-code skills and I secured 2 clients. For both, the use-case was similar: they have an idea on how to leverage AI to simplify their exact use-case (AI for farmers, AI Marketplace). Finished the first platform in a week and the second in 2 weeks. Now I have a few more projects in the pipeline while I only intended to have 1 client initially. AirMedia comes first with no doubt but I'm wondering if I should start vetting some developers and properly develop this activity while overviewing the projects and reinvest what's left on the main company. It seems like there's strong demand of individuals wanting to get their idea built quickly - could position it to build AI apps in 2 weeks and start marketing the services. What would you do? Also if some talented no-code (preferably Bubble) developers see this - feel free to comment Same for people looking to get their idea built.

#reddit#should#start

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johnrushx•This week

If only someone told me this before my first startup

If only someone told me this before my first startup: Validate idea first. I wasted a decade building stuff nobody needed. Incubators and VCs served to me as a validation, but I was so wrong. Kill my EGO. It’s not about me, but the user. I must want what the user wants, not what I want. My taste isn't important. The user has expectations, and I must fulfill them. Don’t chaise investors. Chase users, and then investors will be chasing me. I've never had more incoming interest from VC than now when I'm the least interested in them. Never hire managers. Only hire doers until PMF. So many people know how to manage people and so few can actually get sh\*t done barehand. Landing page is the least important thing in a startup. Pick a simple template, edit texts with a no-code website builder in less than an hour and that's it! At the early stage, I win traffic outside of my website, people are already interested, so don't make them search for the signup button among the texts! Focus on conversion optimization only when the traffic is consistent. Keep it to one page. Nobody gonna browse this website. Hire only fullstack devs. There is nothing less productive in this world than a team of developers for an early-stage product. One full stack dev building the whole product. That’s it. Chase global market from day 1. If the product and marketing are good, it will work on the global market too, if it’s bad, it won’t work on the local market too. So better go global from day 1, so that if it works, the upside is 100x bigger. I launched all startups for the Norwegian market, hoping we will scale to international at some point. I wish I launched to international from day 1 as I do now. The size of the market is 10000x bigger. I can validate and grow products in days, not in years as it used to be. Do SEO from day 2. As early as I can. I ignored this for 14 years. It’s my biggest regret. It takes just 5 minutes to get it done on my landing page. I go to Google Keyword Planner, enter a few keywords around my product, sort them by traffic, filter out high competition kws, pick the top 10, and place them natively on my home page and meta tags. Add one blog article every week. Either manually or by paying for an AI blogging tool. Sell features, before building them. Ask existing users if they want this feature. I run DMs with 10-20 users every day, where I chat about all my ideas and features I wanna add. I clearly see what resonates with me most and only go build those. If I don't have followers, try HN, Reddit, or just search on X for posts and ask it in the replies. People are helpful, they will reply if the question is easy to understand. Hire only people I would wanna hug. My cofounder, an old Danish man said this to me in 2015. And it was a big shift. I realized that if I don’t wanna hug the person, it means I dislike them on a chemical/animal level. Even if I can’t say why, but that’s the fact. Sooner or later, we would have a conflict and eventually break up. It takes up to 10 years to build a startup, make sure I do it with people I have this connection with. Invest all money into my startups and friends. Not crypt0, not stockmarket, not properties. I did some math, if I kept investing all my money into all my friends’ startups, that would be about 70 investments. 3 of them turned into unicorns eventually. Even 1 would have made the bank. Since 2022, I have invested all my money into my products, friends, and network. If I don't have friends who do startups, invest it in myself. Post on Twitter daily. I started posting here in March last year. It’s my primary source of new connections and growth. I could have started it earlier, I don't know why I didn't. Don’t work/partner with corporates. Corporations always seem like an amazing opportunity. They’re big and rich, they promise huge stuff, millions of users, etc. But every single time none of this happens. Because I talk to a regular employees there. They waste my time, destroy focus, shift priorities, and eventually bring in no users/money. Don’t get ever distracted by hype e.g. crypt0. I lost 1.5 years of my life this way. I met the worst people along the way. Fricks, scammers, thieves. Some of my close friends turned into thieves along the way, just because it was so common in that space. I wish this didn’t happen to me. I wish I was stronger and stayed on my mission. Don’t build consumer apps. Only b2b. Consumer apps are so hard, like a lottery. It’s just 0.00001% who make it big. The rest don’t. Even if I got many users, then there is a monetization challenge. I’ve spent 4 years in consumer apps and regret it. Don’t hold on bad project for too long, max 1 year. Some projects just don’t work. In most cases, it’s either the idea that’s so wrong that I can’t even pivot it or it’s a team that is good one by one but can’t make it as a team. Don’t drag this out for years. Tech conferences are a waste of time. They cost money, take energy, and time and I never really meet anyone there. Most people there are the “good” employees of corporations who were sent there as a perk for being loyal to the corporation. Very few fellow makers. Scrum is a Scam. For small teams and bootstrapped teams. If I had a team that had to be nagged every morning with questions as if they were children in kindergarten, then things would eventually fail. The only good stuff I managed to do happened with people who were grownups and could manage their stuff on their own. We would just do everything over chat as a sync on goals and plans. Outsource nothing at all until PMF. In a startup, almost everything needs to be done in a slightly different way, more creative, and more integrated into the vision. When outsourcing, the external members get no love and no case for the product. It’s just yet another assignment in their boring job. Instead of coming up with great ideas for my project they will be just focusing on ramping up their skills to get a promotion or a better job offer. Bootstrap. I spent way too much time raising money. I raised more than 10 times, preseed, seeded, and series A. But each time it was a 3-9 month project, meetings every week, and lots of destruction. I could afford to bootstrap, but I still went the VC-funded way, I don’t know why. To be honest, I didn’t know bootstrapping was a thing I could do or anyone does. It may take a decade. When I was 20, I was convinced it takes a few years to build and succeed with a startup. So I kept pushing my plans forward, to do it once I exited. Family, kids. I wish I married earlier. I wish I had kids earlier. No Free Tier. I'd launch a tool with a free tier, and it'd get sign-ups, but very few would convert. I'd treat free sign-ups as KPIs and run on it for years. I'd brag about signups and visitors. I'd even raise VC money with these stats. But eventually, I would fail to reach PMF. Because my main feedback would come from free users and the product turned into a perfect free product. Once I switched to "paid only" until I validated the product, things went really well. Free and paid users often need different products. Don't fall into this trap as I did. Being To Cheap. I always started by checking all competitors and setting the lowest price. I thought this would be one of the key advantages of my product. But no, I was wrong. The audience on $5 and $50 are totally different. $5: pain in the \*ss, never happy, never recommend me to a friend, leave in 4 months. $50: polite, give genuine feedback, happy, share with friends, become my big fan if I solve their request. I will fail. When I started my first startup. I thought if I did everything right, it would work out. But it turned out that almost every startup fails. I wish I knew that and I tried to fail faster, to get to the second iteration, then to the third, and keep going on, until I either find out nothing works or make it work. Use boilerplates. I wasted years of dev time and millions of VC money to pay for basic things. To build yet another sidebar, yet another dashboard, and payment integration... I had too much pride, I couldn't see myself taking someone else code as a basis for my product. I wanted it to be 100% mine, original, from scratch. Because my product seems special to me. Spend more time with Family & Friends. I missed the weddings of all my best friends and family. I was so busy. I thought if I didn't do it on time, the world would end. Looking back today, it was so wrong. I meet my friends and can't share those memories with them, which makes me very sad. I realized now, that spending 10% of my time with family and friends would practically make no negative impact on my startups. Build Products For Audiences I Love. I never thought of this. I'd often build products either for corporates, consumers, or for developers. It turns out I have no love for all 3. But I deeply love indie founders. Because they are risk-takers and partly kids in their hearts. Once I switched the focus to indie makers on my products, my level of joy increased by 100x for me. Ignore Badges and Awards I was chasing those awards just like everyone else. Going to ceremonies, signing up for events and stuff. I've won tons of awards, but none of those were eventually useful to my business. I better focused on my business and users. Write Every Single Day. When I was a kid, I loved writing stories. In school, they would give an assignment, and I'd often write a long story for it, however, the teacher would put an F on it. The reason was simple, I had an issue with the direction of the letters and the sequence of letters in the words. I still have it, it's just the Grammarly app helping me to correct these issues. So the teacher would fail my stories because almost every sentence had a spelling mistake that I couldn't even see. It made me think I'm made at writing. So I stopped, for 15 years. But I kept telling stories all these years. Recently I realized that in any group, the setup ends up turning into me telling stories to everyone. So I tried it all again, here on X 10 months ago. I love it, the process, the feedback from people. I write every day. I wish I had done it all these years. The End. \ this is an updated version of my post on the same topic from 2 months ago. I've edited some of the points and added 9 new ones.* \\ This is not advice, it's my self-reflection that might help you avoid same mistakes if you think those were mistakes

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AI_Scout_Official•This week

I run an AI automation agency (AAA). My honest overview and review of this new business model

I started an AI tools directory in February, and then branched off that to start an AI automation agency (AAA) in June. So far I've come across a lot of unsustainable "ideas" to make money with AI, but at the same time a few diamonds in the rough that aren't fully tapped into yet- especially the AAA model. Thought I'd share this post to shine light into this new business model and share some ways you could potentially start your own agency, or at the very least know who you are dealing with and how to pick and choose when you (inevitably) get bombarded with cold emails from them down the line. Foreword Running an AAA does NOT involve using AI tools directly to generate and sell content directly. That ship has sailed, and unless you are happy with $5 from Fiverr every month or so, it is not a real business model. Cry me a river but generating generic art with AI and slapping it onto a T-shirt to sell on Etsy won't make you a dime. At the same time, the AAA model will NOT require you to have a deep theoretical knowledge of AI, or any academic degree, as we are more so dealing with the practical applications of generative AI and how we can implement these into different workflows and tech-stacks, rather than building AI models from the ground up. Regardless of all that, common sense and a willingness to learn will help (a shit ton), as with anything. Keep in mind - this WILL involve work and motivation as well. The mindset that AI somehow means everything can be done for you on autopilot is not the right way to approach things. The common theme of businesses I've seen who have successfully implemented AI into their operations is the willingess to work with AI in a way that augments their existing operations, rather than flat out replace a worker or team. And this is exactly the train of thought you need when working with AI as a business model. However, as the field is relatively unsaturated and hype surrounding AI is still fresh for enterprises, right now is the prime time to start something new if generative AI interests you at all. With that being said, I'll be going over three of the most successful AI-adjacent businesses I've seen over this past year, in addition to some tips and resources to point you in the right direction. so.. WTF is an AI Automation Agency? The AI automation agency (or as some YouTubers have coined it, the AAA model) at its core involves creating custom AI solutions for businesses. I have over 1500 AI tools listed in my directory, however the feedback I've received from some enterprise users is that ready-made SaaS tools are too generic to meet their specific needs. Combine this with the fact virtually no smaller companies have the time or skills required to develop custom solutions right off the bat, and you have yourself real demand. I would say in practice, the AAA model is quite similar to Wordpress and even web dev agencies, with the major difference being all solutions you develop will incorporate key aspects of AI AND automation. Which brings me to my second point- JUST AI IS NOT ENOUGH. Rather than reducing the amount of time required to complete certain tasks, I've seen many AI agencies make the mistake of recommending and (trying to) sell solutions that more likely than not increase the workload of their clients. For example, if you were to make an internal tool that has AI answer questions based on their knowledge base, but this knowledge base has to be updated manually, this is creating unnecessary work. As such I think one of the key components of building successful AI solutions is incorporating the new (Generative AI/LLMs) with the old (programmtic automation- think Zapier, APIs, etc.). Finally, for this business model to be successful, ideally you should target a niche in which you have already worked and understand pain points and needs. Not only does this make it much easier to get calls booked with prospects, the solutions you build will have much greater value to your clients (meaning you get paid more). A mistake I've seen many AAA operators make (and I blame this on the "Get Rich Quick" YouTubers) is focusing too much on a specific productized service, rather than really understanding the needs of businesses. The former is much done via a SaaS model, but when going the agency route the only thing that makes sense is building custom solutions. This is why I always take a consultant-first approach. You can only build once you understand what they actually need and how certain solutions may impact their operations, workflows, and bottom-line. Basics of How to Get Started Pick a niche. As I mentioned previously, preferably one that you've worked in before. Niches I know of that are actively being bombarded with cold emails include real estate, e-commerce, auto-dealerships, lawyers, and medical offices. There is a reason for this, but I will tell you straight up this business model works well if you target any white-collar service business (internal tools approach) or high volume businesses (customer facing tools approach). Setup your toolbox. If you wanted to start a pressure washing business, you would need a pressure-washer. This is no different. For those without programming knowledge, I've seen two common ways AAA get setup to build- one is having a network of on-call web developers, whether its personal contacts or simply going to Upwork or any talent sourcing agency. The second is having an arsenal of no-code tools. I'll get to this more in a second, but this works beecause at its core, when we are dealing with the practical applications of AI, the code is quite simple, simply put. Start cold sales. Unless you have a network already, this is not a step you can skip. You've already picked a niche, so all you have to do is find the right message. Keep cold emails short, sweet, but enticing- and it will help a lot if you did step 1 correctly and intimately understand who your audience is. I'll be touching base later about how you can leverage AI yourself to help you with outreach and closing. The beauty of gen AI and the AAA model You don't need to be a seasoned web developer to make this business model work. The large majority of solutions that SME clients want is best done using an API for an LLM for the actual AI aspect. The value we create with the solutions we build comes with the conceptual framework and design that not only does what they need it to but integrates smoothly with their existing tech-stack and workflow. The actual implementation is quite straightforward once you understand the high level design and know which tools you are going to use. To give you a sense, even if you plan to build out these apps yourself (say in Python) the large majority of the nitty gritty technical work has already been done for you, especially if you leverage Python libraries and packages that offer high level abstraction for LLM-related functions. For instance, calling GPT can be as little as a single line of code. (And there are no-code tools where these functions are simply an icon on a GUI). Aside from understanding the capabilities and limitations of these tools and frameworks, the only thing that matters is being able to put them in a way that makes sense for what you want to build. Which is why outsourcing and no-code tools both work in our case. Okay... but how TF am I suppposed to actually build out these solutions? Now the fun part. I highly recommend getting familiar with Langchain and LlamaIndex. Both are Python libraires that help a lot with the high-level LLM abstraction I mentioned previously. The two most important aspects include being able to integrate internal data sources/knowledge bases with LLMs, and have LLMs perform autonomous actions. The two most common methods respectively are RAG and output parsing. RAG (retrieval augmented Generation) If you've ever seen a tool that seemingly "trains" GPT on your own data, and wonder how it all works- well I have an answer from you. At a high level, the user query is first being fed to what's called a vector database to run vector search. Vector search basically lets you do semantic search where you are searching data based on meaning. The vector databases then retrieves the most relevant sections of text as it relates to the user query, and this text gets APPENDED to your GPT prompt to provide extra context to the AI. Further, with prompt engineering, you can limit GPT to only generate an answer if it can be found within this extra context, greatly limiting the chance of hallucination (this is where AI makes random shit up). Aside from vector databases, we can also implement RAG with other data sources and retrieval methods, for example SQL databses (via parsing the outputs of LLM's- more on this later). Autonomous Agents via Output Parsing A common need of clients has been having AI actually perform tasks, rather than simply spitting out text. For example, with autonomous agents, we can have an e-commerce chatbot do the work of a basic customer service rep (i.e. look into orders, refunds, shipping). At a high level, what's going on is that the response of the LLM is being used programmtically to determine which API to call. Keeping on with the e-commerce example, if I wanted a chatbot to check shipping status, I could have a LLM response within my app (not shown to the user) with a prompt that outputs a random hash or string, and programmatically I can determine which API call to make based on this hash/string. And using the same fundamental concept as with RAG, I can append the the API response to a final prompt that would spit out the answer for the user. How No Code Tools Can Fit In (With some example solutions you can build) With that being said, you don't necessarily need to do all of the above by coding yourself, with Python libraries or otherwise. However, I will say that having that high level overview will help IMMENSELY when it comes to using no-code tools to do the actual work for you. Regardless, here are a few common solutions you might build for clients as well as some no-code tools you can use to build them out. Ex. Solution 1: AI Chatbots for SMEs (Small and Medium Enterprises) This involves creating chatbots that handle user queries, lead gen, and so forth with AI, and will use the principles of RAG at heart. After getting the required data from your client (i.e. product catalogues, previous support tickets, FAQ, internal documentation), you upload this into your knowledge base and write a prompt that makes sense for your use case. One no-code tool that does this well is MyAskAI. The beauty of it especially for building external chatbots is the ability to quickly ingest entire websites into your knowledge base via a sitemap, and bulk uploading files. Essentially, they've covered the entire grunt work required to do this manually. Finally, you can create a inline or chat widget on your client's website with a few lines of HTML, or altneratively integrate it with a Slack/Teams chatbot (if you are going for an internal Q&A chatbot approach). Other tools you could use include Botpress and Voiceflow, however these are less for RAG and more for building out complete chatbot flows that may or may not incorporate LLMs. Both apps are essentially GUIs that eliminate the pain and tears and trying to implement complex flows manually, and both natively incoporate AI intents and a knowledge base feature. Ex. Solution 2: Internal Apps Similar to the first example, except we go beyond making just chatbots but tools such as report generation and really any sort of internal tool or automations that may incorporate LLM's. For instance, you can have a tool that automatically generates replies to inbound emails based on your client's knowledge base. Or an automation that does the same thing but for replies to Instagram comments. Another example could be a tool that generates a description and screeenshot based on a URL (useful for directory sites, made one for my own :P). Getting into more advanced implementations of LLMs, we can have tools that can generate entire drafts of reports (think 80+ pages), based not only on data from a knowledge base but also the writing style, format, and author voice of previous reports. One good tool to create content generation panels for your clients would be MindStudio. You can train LLM's via prompt engineering in a structured way with your own data to essentially fine tune them for whatever text you need it to generate. Furthermore, it has a GUI where you can dictate the entire AI flow. You can also upload data sources via multiple formats, including PDF, CSV, and Docx. For automations that require interactions between multiple apps, I recommend the OG zapier/make.com if you want a no-code solution. For instance, for the automatic email reply generator, I can have a trigger such that when an email is received, a custom AI reply is generated by MyAskAI, and finally a draft is created in my email client. Or, for an automation where I can create a social media posts on multiple platforms based on a RSS feed (news feed), I can implement this directly in Zapier with their native GPT action (see screenshot) As for more complex LLM flows that may require multiple layers of LLMs, data sources, and APIs working together to generate a single response i.e. a long form 100 page report, I would recommend tools such as Stack AI or Flowise (open-source alternative) to build these solutions out. Essentially, you get most of the functions and features of Python packages such as Langchain and LlamaIndex in a GUI. See screenshot for an example of a flow How the hell are you supposed to find clients? With all that being said, none of this matters if you can't find anyone to sell to. You will have to do cold sales, one way or the other, especially if you are brand new to the game. And what better way to sell your AI services than with AI itself? If we want to integrate AI into the cold outreach process, first we must identify what it's good at doing, and that's obviously writing a bunch of text, in a short amount of time. Similar to the solutions that an AAA can build for its clients, we can take advantage of the same principles in our own sales processes. How to do outreach Once you've identified your niche and their pain points/opportunities for automation, you want to craft a compelling message in which you can send via cold email and cold calls to get prospects booked on demos/consultations. I won't get into too much detail in terms of exactly how to write emails or calling scripts, as there are millions of resources to help with this, but I will tell you a few key points you want to keep in mind when doing outreach for your AAA. First, you want to keep in mind that many businesses are still hesitant about AI and may not understand what it really is or how it can benefit their operations. However, we can take advantage of how mass media has been reporting on AI this past year- at the very least people are AWARE that sooner or later they may have to implement AI into their businesses to stay competitive. We want to frame our message in a way that introduces generative AI as a technology that can have a direct, tangible, and positive impact on their business. Although it may be hard to quantify, I like to include estimates of man-hours saved or costs saved at least in my final proposals to prospects. Times are TOUGH right now, and money is expensive, so you need to have a compelling reason for businesses to get on board. Once you've gotten your messaging down, you will want to create a list of prospects to contact. Tools you can use to find prospects include Apollo.io, reply.io, zoominfo (expensive af), and Linkedin Sales Navigator. What specific job titles, etc. to target will depend on your niche but for smaller companies this will tend to be the owner. For white collar niches, i.e. law, the professional that will be directly benefiting from the tool (i.e. partners) may be better to contact. And for larger organizations you may want to target business improvement and digital transformation leads/directors- these are the people directly in charge of projects like what you may be proposing. Okay- so you have your message, and your list, and now all it comes down to is getting the good word out. I won't be going into the details of how to send these out, a quick Google search will give you hundreds of resources for cold outreach methods. However, personalization is key and beyond simple dynamic variables you want to make sure you can either personalize your email campaigns directly with AI (SmartWriter.ai is an example of a tool that can do this), or at the very least have the ability to import email messages programmatically. Alternatively, ask ChatGPT to make you a Python Script that can take in a list of emails, scrape info based on their linkedin URL or website, and all pass this onto a GPT prompt that specifies your messaging to generate an email. From there, send away. How tf do I close? Once you've got some prospects booked in on your meetings, you will need to close deals with them to turn them into clients. Call #1: Consultation Tying back to when I mentioned you want to take a consultant-first appraoch, you will want to listen closely to their goals and needs and understand their pain points. This would be the first call, and typically I would provide a high level overview of different solutions we could build to tacke these. It really helps to have a presentation available, so you can graphically demonstrate key points and key technologies. I like to use Plus AI for this, it's basically a Google Slides add-on that can generate slide decks for you. I copy and paste my default company messaging, add some key points for the presentation, and it comes out with pretty decent slides. Call #2: Demo The second call would involve a demo of one of these solutions, and typically I'll quickly prototype it with boilerplate code I already have, otherwise I'll cook something up in a no-code tool. If you have a niche where one type of solution is commonly demanded, it helps to have a general demo set up to be able to handle a larger volume of calls, so you aren't burning yourself out. I'll also elaborate on how the final product would look like in comparison to the demo. Call #3 and Beyond: Once the initial consultation and demo is complete, you will want to alleviate any remaining concerns from your prospects and work with them to reach a final work proposal. It's crucial you lay out exactly what you will be building (in writing) and ensure the prospect understands this. Furthermore, be clear and transparent with timelines and communication methods for the project. In terms of pricing, you want to take this from a value-based approach. The same solution may be worth a lot more to client A than client B. Furthermore, you can create "add-ons" such as monthly maintenance/upgrade packages, training sessions for employeees, and so forth, separate from the initial setup fee you would charge. How you can incorporate AI into marketing your businesses Beyond cold sales, I highly recommend creating a funnel to capture warm leads. For instance, I do this currently with my AI tools directory, which links directly to my AI agency and has consistent branding throughout. Warm leads are much more likely to close (and honestly, much nicer to deal with). However, even without an AI-related website, at the very least you will want to create a presence on social media and the web in general. As with any agency, you will want basic a professional presence. A professional virtual address helps, in addition to a Google Business Profile (GBP) and TrustPilot. a GBP (especially for local SEO) and Trustpilot page also helps improve the looks of your search results immensely. For GBP, I recommend using ProfilePro, which is a chrome extension you can use to automate SEO work for your GBP. Aside from SEO optimzied business descriptions based on your business, it can handle Q/A answers, responses, updates, and service descriptions based on local keywords. Privacy and Legal Concerns of the AAA Model Aside from typical concerns for agencies relating to service contracts, there are a few issues (especially when using no-code tools) that will need to be addressed to run a successful AAA. Most of these surround privacy concerns when working with proprietary data. In your terms with your client, you will want to clearly define hosting providers and any third party tools you will be using to build their solution, and a DPA with these third parties listed as subprocessors if necessary. In addition, you will want to implement best practices like redacting private information from data being used for building solutions. In terms of addressing concerns directly from clients, it helps if you host your solutions on their own servers (not possible with AI tools), and address the fact only ChatGPT queries in the web app, not OpenAI API calls, will be used to train OpenAI's models (as reported by mainstream media). The key here is to be open and transparent with your clients about ALL the tools you are using, where there data will be going, and make sure to get this all in writing. have fun, and keep an open mind Before I finish this post, I just want to reiterate the fact that this is NOT an easy way to make money. Running an AI agency will require hours and hours of dedication and work, and constantly rearranging your schedule to meet prospect and client needs. However, if you are looking for a new business to run, and have a knack for understanding business operations and are genuinely interested in the pracitcal applications of generative AI, then I say go for it. The time is ticking before AAA becomes the new dropshipping or SMMA, and I've a firm believer that those who set foot first and establish themselves in this field will come out top. And remember, while 100 thousand people may read this post, only 2 may actually take initiative and start.

#reddit#automation#agency

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hopefully_useful•This week

We made $325k in 2023 from AI products, starting from 0, with no-code, no funding and no audience

I met my co-founder in late 2022 after an introduction from a mutual friend to talk about how to find contract Product Management roles. I was sporadically contracting at start-up at the time and he had just come out of another start-up that was wiped out by the pandemic. We hit it off, talking about ideas, sharing what other indie-hackers were doing, and given GPT-3’s prominence at the time, we started throwing around ideas about things we could build with it, if nothing else, just to learn. I should caveat, neither of us were AI experts when starting out, everything we learned has been through Twitter and blogs, my background is as an accountant, and his a consultant. Here’s how it went since then: &#x200B; Nov 2022 (+$50) \- We built a simple tool in around a week using GPT-3 fine-tuning and a no-code tool (Bubble) that helped UK university students write their personal statements for their applications \- We set some Google Ads going and managed to make a few sales (\~$50) in the first week \- OpenAI were still approving applications at the time and said this went against their “ethics” so we had to take it down &#x200B; Dec 2022 (+$200) \- We couldn’t stop coming up with ideas related to AI fine-tuning, but realised it was almost impossible to decide which to pursue \- We needed a deadline to force us so we signed up for the Ben’s Bites hackathon in late December \- In a week, we built and launched a no-code fine-tuning platform, allowing people to create fine-tuned models by dragging and dropping an Excel file onto it \- We launched it on Product Hunt, having no idea how to price it, and somehow managed to get \~2,000 visitors on the site and make 2 sales at $99 &#x200B; Jan 2023 (+$3,000) \- We doubled down on the fine-tuning idea and managed to get up to \~$300 MRR, plus a bunch of one-time sales and a few paid calls to help people get the most out of their models \- We quickly realised that people didn’t want to curate models themselves, they just wanted to dump data and get magic out \- That was when we saw people building “Talk with x book/podcast” on Twitter as side projects and realised that was the missing piece, we needed to turn it into a tool \- We started working on the new product in late January &#x200B; Feb 2023 (+$9,000) \- We started pre-selling access to an MVP for the new product, which allowed people to “chat with their data/content”, we got $5,000 in pre-sales, more than we made from the previous product in total \- By mid-February, after 3 weeks of building we were able to launch and immediately managed to get traction, getting to $1k MRR in < 1 week, building on the hype of ChatGPT and AI (we were very lucky here) &#x200B; Mar - Jul 2023 (+$98,000) \- We worked all the waking hours to keep up with customer demand, bugs, OpenAI issues \- We built integrations for a bunch of services like Slack, Teams, Wordpress etc, added tons of new functionality and continue talking to customers every day \- We managed to grow to $17k MRR (just about enough to cover our living expenses and costs in London) through building in public on Twitter, newsletters and AI directories (and a million other little things) \- We sold our fine-tuning platform for \~$20k and our university project for \~$3k on Acquire &#x200B; Aug 2023 (+$100,000) \- We did some custom development work based on our own product for a customer that proved pretty lucrative &#x200B; Sep - Oct 2023 (+$62,000) \- After 8 months of building constantly, we started digging more seriously into our usage and saw subscriptions plateauing \- We talked to and analysed all our paying users to identify the main use cases and found 75% were for SaaS customer support \- We took the leap to completely rebuild a version of our product around this use case, our biggest to date (especially given most features with no-code took us <1 day) &#x200B; Nov - Dec 2023 (+$53,000) \- We picked up some small custom development work that utilised our own tech \- We’re sitting at around $22k MRR now with a few bigger clients signed up and coming soon \- After 2 months of building and talking to users, we managed to finish our “v2” of our product, focussed squarely on SaaS customer support and launched it today. &#x200B; We have no idea what the response will be to this new version, but we’re pretty happy with it, but couldn’t have planned anything that happened to us in 2023 so who knows what will come of 2024, we just know that we are going to be learning a ton more. &#x200B; Overall, it is probably the most I have had to think in my life - other jobs you can zone out from time to time or rely on someone else if you aren’t feeling it - not when you are doing this, case and point, I am writing this with a banging head-cold right now, but wanted to get this done. A few more things we have learned along the way - context switching is unreal, as is keeping up with, learning and reacting to AI. There isn’t a moment of the day I am not thinking about what we do next. But while in some way we now have hundreds of bosses (our customers) I still haven’t felt this free and can’t imagine ever going back to work for someone else. Next year we’re really hoping to figure out some repeatable distribution channels and personally, I want to get a lot better at creating content/writing, this is a first step! Hope this helps someone else reading this to just try starting something and see what happens.

#reddit#made#$325k

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FounderFolks•This week

Turning a Social Media Agency into $1.5 Million in Revenue

Steffie here from Founder Folks, with a recent interview I did with Jason Yormark from Socialistics. Here is his story how he started and grew his social media agency. Name: Jason Yormark Company: Socialistics Employee Size: 10 Revenue: $1,500,000/year Year Founded: 2018 Website: www.socialistics.com Technology Tools: ClickUp, Slack, KumoSpace, Google Workspace, Shift, Zapier, Klayvio, Zoom, Gusto, Calendly, Pipedrive Introduction: I am the founder of Socialistics (www.socialistics.com), a leading social media agency that helps businesses turn their social media efforts into real measurable results. I am a 20+ year marketing veteran whose prior work has included launching and managing social media efforts for Microsoft Advertising, Office for Mac, the Air Force, and Habitat for Humanity. I have been recognized as a top B2B social media influencer and thought leader on multiple lists and publications including Forbes, ranking #30 on their 2012 list. I've recently published the book Anti-Agency: A Realistic Path to a $1,000,000 Business, and host the Anti Agency podcast where I share stories of doing business differently. You can learn more about me at www.jasonyormark.com. The Inspiration To Become An Entrepreneur: I’ve been involved with social media marketing since 2007, and have pretty much carved my career out of that. It was a natural progression for me to transition into starting a social media agency. From Idea to Reality: For me realistically, I had to side hustle something long enough to build it up to a point that I could take the leap and risks going full time on my own. For these reasons, I built the company and brand on the side putting out content regularly, and taking on side hustle projects to build out my portfolio and reputation. This went on for about 18 months at which point I had reached the breaking point of my frustrations of working for someone else, and felt I was ready to take the leap since I had the wheels in motion. While balancing a full-time job, I made sure not to overdo it. My main focus was on building out the website/brand and putting out content regularly to gain some traction and work towards some search visibility. I only took on 1-2 clients at a time to make sure I could still meet their needs while balancing a full time job. Attracting Customers: Initially I tapped into my existing network to get my first few clients. Then it was a mix of trade shows, networking events, and throwing a bit of money at paid directories and paid media. This is really a long game. You have to plant seeds over time with people and nurture those relationships over time. A combination of being helpful, likable and a good resource for folks will position you to make asks in the future. If people respect and like you, it makes it much easier to approach for opportunities when the time comes. Overcoming Challenges in Starting the Business: Plenty. Learning when to say no, only hiring the very best, and ultimately the realization that owning a marketing agency is going to have hills and valleys no matter what you do. Costs and Revenue: My largest expense by FAR is personnel, comprising between 50-60% of the business’ expenses, and justifiably so. It’s a people business. Our revenue doubled from the years 2018 through 2021, and we’ve seen between 10-20% growth year over year. A Day in the Life: I’ve successfully removed myself from the day to day of the business and that’s by design. I have a tremendous team, and a rock start Director of Operations who runs the agency day to day. It frees me up to pursue other opportunities, and to mentor, speak and write more. It also allows me to evangelize the book I wrote detailing my journey to a $1M business titled: Anti-Agency: A Realistic Path To A $1,000,000 Business (www.antiagencybook.com). Staying Ahead in a Changing Landscape: You really have to stay on top of technology trends. AI is a huge impact on marketing these days, so making sure we are up to speed on that, and not abusing it or relying on it too much. You also have to embrace that technology and not hide the fact that it’s used. Non-marketers still don’t and can’t do the work regardless of how much AI can help, so we just need to be transparent and smart on how we integrate it, but the fact is, technology will never replace creativity. As an agency, it’s imperative that we operationally allow our account managers to have bandwidth to be creative for clients all the time. It’s how we keep clients and buck the trend of companies changing agencies every year or two. The Vision for Socialistics: Continuing to evolve to cater to our clients through learning, education, and staying on top of the latest tools and technologies. Attracting bigger and more exciting clients, and providing life changing employment opportunities.

#reddit#turning#social

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0.895

matthewbarby•This week

The delicate balance of building an online community business

Hey /r/Entrepreneur 👋 Just under two years ago I launched an online community business called Traffic Think Tank with two other co-founders, Nick Eubanks and Ian Howells. As a Traffic Think Tank customer you (currently) pay $119 a month to get access to our online community, which is run through Slack. The community is focused on helping you learn various aspects of marketing, with a particular focus on search engine optimization (SEO). Alongside access to the Slack community, we publish new educational video content from outside experts every week that all customers have access to. At the time of writing, Traffic Think Tank has around 650 members spanning across 17 of the 24 different global time zones. I was on a business trip over in Sydney recently, and during my time there I met up with some of our Australia-based community members. During dinner I was asked by several of them how the idea for Traffic Think Tank came about and what steps we took to validate that the idea was worth pursuing.  This is what I told them… How it all began It all started with a personal need. Nick, an already successful entrepreneur and owner of a marketing agency, had tested out an early version Traffic Think Tank in early 2017. He offered real-time consulting for around ten customers that he ran from Slack. He would publish some educational videos and offer his advice on projects that the members were running. The initial test went well, but it was tough to maintain on his own and he had to charge a fairly high price to make it worth his time. That’s when he spoke to me and Ian about turning this idea into something much bigger. Both Ian and I offered something slightly different to Nick. We’ve both spent time in senior positions at marketing agencies, but currently hold senior director positions in 2,000+ public employee companies (HubSpot and LendingTree). Alongside this, as a trio we could really ramp up the quality and quantity of content within the community, spread out the administrative workload and just generally have more resources to throw at getting this thing off the ground. Admittedly, Nick was much more optimistic about the potential of Traffic Think Tank – something I’m very thankful for now – whereas Ian and I were in the camp of “you’re out of your mind if you think hundreds of people are going to pay us to be a part of a Slack channel”. To validate the idea at scale, we decided that we’d get an initial MVP of the community up and running with a goal of reaching 100 paying customers in the first six months. If we achieved that, we’d validated that it was a viable business and we would continue to pursue it. If not, we’d kill it. We spent the next month building out the initial tech stack that enabled us to accept payments, do basic user management to the Slack channel, and get a one-page website up and running with information on what Traffic Think Tank was all about.  After this was ready, we doubled down on getting some initial content created for members – I mean, we couldn’t have people just land in an empty Slack channel, could we? We created around ten initial videos, 20 or so articles and then some long threads full of useful information within the Slack channel so that members would have some content to pour into right from the beginning.  Then, it was time to go live. The first 100 customers Fortunately, both Nick and I had built a somewhat substantial following in the SEO space over the previous 5-10 years, so we at least had a large email list to tap into (a total of around 40,000 people). We queued up some launch emails, set an initial price of $99 per month and pressed send. [\[LINK\] The launch email I sent to my subscribers announcing Traffic Think Tank](https://mailchi.mp/matthewbarby/future-of-marketing-1128181) What we didn’t expect was to sell all of the initial 100 membership spots in the first 72 hours. “Shit. What do we do now? Are we ready for this many people? Are we providing them with enough value? What if something breaks in our tech stack? What if they don’t like the content? What if everyone hates Slack?” All of these were thoughts running through my head. This brings me to the first great decision we made: we closed down new membership intake for 3 months so that we could focus completely on adding value to the first cohort of users. The right thing at the right time SEO is somewhat of a dark art to many people that are trying to learn about it for the first time. There’s hundreds of thousands (possibly millions) of articles and videos online that talk about how to do SEO.  Some of it’s good advice; a lot of it is very bad advice.  Add to this that the barrier to entry of claiming to be an “expert” in SEO is practically non-existent and you have a recipe for disaster. This is why, for a long time, individuals involved in SEO have flocked in their masses to online communities for information and to bounce ideas off of others in the space. Forums like SEObook, Black Hat World, WickedFire, Inbound.org, /r/BigSEO, and many more have, at one time, been called home by many SEOs.  In recent times, these communities have either been closed down or just simply haven’t adapted to the changing needs of the community – one of those needs being real-time feedback on real-world problems.  The other big need that we all spotted and personally had was the ability to openly share the things that are working – and the things that aren’t – in SEO within a private forum. Not everyone wanted to share their secret sauce with the world. One of the main reasons we chose Slack as the platform to run our community on was the fact that it solved these two core needs. It gave the ability to communicate in real-time across multiple devices, and all of the information shared within it was outside of the public domain. The other problem that plagued a lot of these early communities was spam. Most of them were web-based forums that were free to access. That meant they became a breeding ground for people trying to either sell their services or promote their own content – neither of which is conducive to building a thriving community. This was our main motivation for charging a monthly fee to access Traffic Think Tank. We spent a lot of time thinking through pricing. It needed to be enough money that people would be motivated to really make use of their membership and act in a way that’s beneficial to the community, but not too much money that it became cost prohibitive to the people that would benefit from it the most. Considering that most of our members would typically spend between $200-800 per month on SEO software, $99 initially felt like the perfect balance. Growing pains The first three months of running the community went by without any major hiccups. Members were incredibly patient with us, gave us great feedback and were incredibly helpful and accommodating to other members. Messages were being posted every day, with Nick, Ian and myself seeding most of the engagement at this stage.  With everything going smoothly, we decided that it was time to open the doors to another intake of new members. At this point we’d accumulated a backlog of people on our waiting list, so we knew that simply opening our doors would result in another large intake. Adding more members to a community has a direct impact on the value that each member receives. For Traffic Think Tank in particular, the value for members comes from three areas: The ability to have your questions answered by me, Nick and Ian, as well as other members of the community. The access to a large library of exclusive content. The ability to build connections with the wider community. In the early stages of membership growth, there was a big emphasis on the first of those three points. We didn’t have an enormous content library, nor did we have a particularly large community of members, so a lot of the value came from getting a lot of one-to-one time with the community founders. [\[IMAGE\] Screenshot of engagement within the Traffic Think Tank Slack community](https://cdn.shortpixel.ai/client/qglossy,retimg,w_1322/https://www.matthewbarby.com/wp-content/uploads/2019/08/Community-Engagement-in-Traffic-Think-Tank.png) The good thing about having 100 members was that it was just about feasible to give each and every member some one-to-one time within the month, which really helped us to deliver those moments of delight that the community needed early on. Two-and-a-half months after we launched Traffic Think Tank, we opened the doors to another 250 people, taking our total number of members to 350. This is where we experienced our first growing pains.  Our original members had become used to being able to drop us direct messages and expect an almost instant response, but this wasn’t feasible anymore. There were too many people, and we needed to create a shift in behavior. We needed more value to come from the community engaging with one another or we’d never be able to scale beyond this level. We started to really pay attention to engagement metrics; how many people were logging in every day, and of those, how many were actually posting messages within public channels.  We asked members that were logging in a lot but weren’t posting (the “lurkers”) why that was the case. We also asked the members that engaged in the community the most what motivated them to post regularly. We learned a lot from doing this. We found that the large majority of highly-engaged members had much more experience in SEO, whereas most of the “lurkers” were beginners. This meant that most of the information being shared in the community was very advanced, with a lot of feedback from the beginners in the group being that they “didn’t want to ask a stupid question”.  As managers of the community, we needed to facilitate conversations that catered to all of our members, not just those at a certain level of skill. To tackle this problem, we created a number of new channels that had a much deeper focus on beginner topics so novice members had a safe place to ask questions without judgment.  We also started running live video Q&As each month where we’d answer questions submitted by the community. This gave our members one-on-one time with me, Nick and Ian, but spread the value of these conversations across the whole community rather than them being hidden within private messages. As a result of these changes, we found that the more experienced members in the community were really enjoying sharing their knowledge with those with less experience. The number of replies within each question thread was really starting to increase, and the community started to shift away from just being a bunch of threads created by me, Nick and Ian to a thriving forum of diverse topics compiled by a diverse set of individuals. This is what we’d always wanted. A true community. It was starting to happen. [\[IMAGE\] Chart showing community engagement vs individual member value](https://cdn.shortpixel.ai/client/qglossy,retimg,w_1602/https://www.matthewbarby.com/wp-content/uploads/2019/08/Community-Engagement-Balance-Graph.jpg) At the same time, we started to realize that we’ll eventually reach a tipping point where there’ll be too much content for us to manage and our members to engage with. When we reach this point, the community will be tough to follow and the quality of any given post will go down. Not only that, but the community will become increasingly difficult to moderate. We’re not there yet, but we recognize that this will come, and we’ll have to adjust our model again. Advocating advocacy As we started to feel more comfortable about the value that members were receiving, we made the decision to indefinitely open for new members. At the same time, we increased the price of membership (from $99 a month to $119) in a bid to strike the right balance between profitability as a business and to slow down the rate at which we were reaching the tipping point of community size. We also made the decision to repay all of our early adopters by grandfathering them in to the original pricing – and committing to always do this in the future. Despite the price increase, we saw a continued flow of new members come into the community. The craziest part about this was that we were doing practically no marketing activities to encourage new members– this was all coming from word of mouth. Our members were getting enough value from the community that they were recommending it to their friends, colleagues and business partners.  The scale at which this was happening really took us by surprise and it told us one thing very clearly: delivering more value to members resulted in more value being delivered to the business. This is a wonderful dynamic to have because it perfectly aligns the incentives on both sides. We’d said from the start that we wouldn’t sacrifice value to members for more revenue – this is something that all three of us felt very strongly about. First and foremost, we wanted to create a community that delivered value to its members and was run in a way that aligned with our values as people. If we could find a way to stimulate brand advocacy, while also tightening the bonds between all of our individual community members, we’d be boosting both customer retention and customer acquisition in the same motion. This became our next big focus. [\[TWEET\] Adam, one of our members wore his Traffic Think Tank t-shirt in the Sahara desert](https://twitter.com/AdamGSteele/status/1130892481099382784) We started with some simple things: We shipped out Traffic Think Tank branded T-shirts to all new members. We’d call out each of the individuals that would submit questions to our live Q&A sessions and thank them live on air. We set up a new channel that was dedicated to sharing a quick introduction to who you are, what you do and where you’re based for all new members. We’d created a jobs channel and a marketplace for selling, buying and trading services with other members. Our monthly “blind dates” calls were started where you’d be randomly grouped with 3-4 other community members so that you could hop on a call to get to know each other better. The Traffic Think Tank In Real Life (IRL)* channel was born, which enabled members to facilitate in-person meetups with each other. In particular, we saw that as members started to meet in person or via calls the community itself was feeling more and more like a family. It became much closer knit and some members started to build up a really positive reputation for being particularly helpful to other members, or for having really strong knowledge in a specific area. [\[TWEET\] Dinner with some of the Traffic Think Tank members in Brighton, UK](https://twitter.com/matthewbarby/status/1117175584080134149) Nick, Ian and I would go out of our way to try and meet with members in real life wherever we could. I was taken aback by how appreciative people were for us doing this, and it also served as an invaluable way to gain honest feedback from members. There was another trend that we’d observed that we didn’t really expect to happen. More and more members were doing business with each another. We’ve had people find new jobs through the community, sell businesses to other members, launch joint ventures together and bring members in as consultants to their business. This has probably been the most rewarding thing to watch, and it was clear that the deeper relationships that our members were forming were resulting in an increased level of trust to work with each other. We wanted to harness this and take it to a new level. This brought us to arguably the best decision we’ve made so far running Traffic Think Tank… we were going to run a big live event for our members. I have no idea what I’m doing It’s the first week of January 2019 and we’re less than three weeks away from Traffic Think Tank LIVE, our first ever in-person event hosting 150 people, most of which are Traffic Think Tank members. It's like an ongoing nightmare I can’t wake up from. That was Nick’s response in our private admin channel to myself and Ian when I asked if they were finding the run-up to the event as stressful as I was. I think that all three of us were riding on such a high from how the community was growing that we felt like we could do anything. Running an event? How hard can it be? Well, turns out it’s really hard. We had seven different speakers flying over from around the world to speak at the event, there was a pre- and after event party, and we’d planned a charity dinner where we would take ten attendees (picked at random via a raffle) out for a fancy meal. Oh, and Nick, Ian and I were hosting a live Q&A session on stage. It wasn’t until precisely 48 hours before the event that we’d realized we didn’t have any microphones, nor had a large amount of the swag we’d ordered arrived. Plus, a giant storm had hit Philly causing a TON of flight cancellations. Perfect. Just perfect. This was honestly the tip of the iceberg. We hadn’t thought about who was going to run the registration desk, who would be taking photos during the event and who would actually field questions from the audience while all three of us sat on stage for our live Q&A panel. Turns out that the answer to all of those questions were my wife, Laura, and Nick’s wife, Kelley. Thankfully, they were on hand to save our asses. The weeks running up to the event were honestly some of the most stressful of my life. We sold around 50% of our ticket allocation within the final two weeks before the event. All of the event organizers told us this would happen, but did we believe them? Hell no!  Imagine having two weeks until the big day and as it stood half of the room would be completely empty. I was ready to fly most of my extended family over just to make it look remotely busy. [\[IMAGE\] One of our speakers, Ryan Stewart, presenting at Traffic Think Tank LIVE](https://cdn.shortpixel.ai/client/qglossy,retimg,w_1920/https://www.matthewbarby.com/wp-content/uploads/2019/08/Traffic-Think-Tank-LIVE-Ryan-Presenting.jpg) Thankfully, if all came together. We managed to acquire some microphones, the swag arrived on the morning of the event, all of our speakers were able to make it on time and the weather just about held up so that our entire allocation of ticket holders was able to make it to the event. We pooled together and I’m proud to say that the event was a huge success. While we made a substantial financial loss on the event itself, January saw a huge spike in new members, which more than recouped our losses. Not only that, but we got to hang out with a load of our members all day while they said really nice things about the thing we’d built. It was both exhausting and incredibly rewarding. Bring on Traffic Think Tank LIVE 2020! (This time we’re hiring an event manager...)   The road ahead Fast forward to today (August 2019) and Traffic Think Tank has over 650 members. The biggest challenges that we’re tackling right now include making sure the most interesting conversations and best content surfaces to the top of the community, making Slack more searchable (this is ultimately one of its flaws as a platform) and giving members a quicker way to find the exclusive content that we create. You’ll notice there’s a pretty clear theme here. In the past 30 days, 4,566 messages were posted in public channels inside Traffic Think Tank. If you add on any messages posted inside private direct messages, this number rises to 21,612. That’s a lot of messages. To solve these challenges and enable further scale in the future, we’ve invested a bunch of cash and our time into building out a full learning management system (LMS) that all members will get access to alongside the Slack community. The LMS will be a web-based portal that houses all of the video content we produce. It will also  provide an account admin section where users can update or change their billing information (they have to email us to do this right now, which isn’t ideal), a list of membership perks and discounts with our partners, and a list of links to some of the best threads within Slack – when clicked, these will drop you directly into Slack. [\[IMAGE\] Designs for the new learning management system (LMS)](https://cdn.shortpixel.ai/client/qglossy,retimg,w_2378/https://www.matthewbarby.com/wp-content/uploads/2019/08/Traffic-Think-Tank-LMS.png) It’s not been easy, but we’re 95% of the way through this and I’m certain that it will have a hugely positive impact on the experience for our members. Alongside this we hired a community manager, Liz, who supports with any questions that our members have, coordinates with external experts to arrange webinars for the community, helps with new member onboarding, and has tightened up some of our processes around billing and general accounts admin. This was a great decision. Finally, we’ve started planning next year’s live event, which we plan to more than double in size to 350 attendees, and we decided to pick a slightly warmer location in Miami this time out. Stay tuned for me to have a complete meltdown 3 weeks from the event. Final thoughts When I look back on the journey we’ve had so far building Traffic Think Tank, there’s one very important piece to this puzzle that’s made all of this work that I’ve failed to mention so far: co-founder alignment. Building a community is a balancing act that relies heavily on those in charge being completely aligned. Nick, Ian and I completely trust each other and more importantly, are philosophically aligned on how we want to run and grow the community. If we didn’t have this, the friction between us could tear apart the entire community. Picking the right people to work with is important in any company, but when your business is literally about bringing people together, there’s no margin for error here.  While I’m sure there will be many more challenges ahead, knowing that we all trust each other to make decisions that fall in line with each of our core values makes these challenges dramatically easier to overcome. Finally, I’d like to thank all of our members for making the community what it is today – it’d be nothing without you and I promise that we’ll never take that for granted. &#x200B; I originally posted this on my blog here. Welcoming all of your thoughts, comments, questions and I'll do my best to answer them :)

#reddit#delicate#balance

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jamesackerman1234•This week

[CASE STUDY] From 217/m to $2,836/m in 9 months - Sold for $59,000; I grow and monetise web traffic of 5, 6, 7 figures USD valued passive income content sites [AMA]

Hello Everyone (VERY LONG CASE STUDY AHEAD) - 355% return in 9 months Note: I own a 7-figures USD valued portfolio of 41+ content sites that generates 5-6 figures USD a month in passive income. This is my first time posting in this sub and my goal is to NOT share generic advice but precise numbers, data and highly refined processes so you can also get started with this business yourself or if you already have an existing business, drive huge traffic to it and scale it substantially (get more customers). I will use a case study to explain the whole process. As most of us are entrepreneurs here, explaining an actual project would be more meaningful. In this case study I used AI assisted content to grow an existing site from $217/m to $2,836/m in 9 months (NO BACKLINKS) and sold it for $59,000. ROI of 3 months: 355% Previous case studies (before I give an overview of the model) Amazon Affiliate Content Site: $371/m to $19,263/m in 14 MONTHS - $900K CASE STUDY \[AMA\] Affiliate Website from $267/m to $21,853/m in 19 months (CASE STUDY - Amazon?) \[AMA\] Amazon Affiliate Website from $0 to $7,786/month in 11 months Amazon Affiliate Site from $118/m to $3,103/m in 8 MONTHS (SOLD it for $62,000+) Note: You can check pinned posts on my profile. Do go through the comments as well as a lot of questions are answered in those. However, if you still have any questions, feel free to reach out. This is an \[AMA\]. Quick Overview of the Model Approach: High traffic, niche specific, informative content websites that monetise its traffic through highly automated methods like display ads and affiliate. The same model can be applied to existing businesses to drive traffic and get customers. Main idea: Make passive income in a highly automated way Easy to understand analogy You have real estate (here you have digital asset like a website) You get rental income (here you get ads and affiliate income with no physical hassle, in case you have a business like service, product etc. then you can get customers for that too but if not, it's alright) Real estate has value (this digital asset also has value that can be appreciated with less effort) Real estate can be sold (this can be sold too but faster) IMPORTANT NOTE: Search traffic is the BEST way to reach HUGE target audience and it's important when it comes to scaling. This essentially means that you can either monetise that via affiliate, display etc. or if you have a business then you can reach a bigger audience to scale. Overview of this website's valuation (then and now: Oct. 2022 and June 2023) Oct 2022: $217/m Valuation: $5,750.5 (26.5x) - set it the same as the multiple it was sold for June 2023: $2,836/m Traffic and revenue trend: growing fast Last 3 months avg: $2,223 Valuation now: $59,000 (26.5x) Description: The domain was registered in 2016, it grew and then the project was left unattended. I decided to grow it again using properly planned AI assisted content. Backlink profile: 500+ Referring domains (Ahrefs). Backlinks mean the sites linking back to you. This is important when it comes to ranking. Summary of Results of This Website - Before and After Note: If the terms seem technical, do not worry. I will explain them in detail later. Still if you have any questions. Feel free to comment or reach out. |Metric|Oct 2022|June 2023|Difference|Comments| |:-|:-|:-|:-|:-| |Articles|314|804|\+490|AI Assisted content published in 3 months| |Traffic|9,394|31,972|\+22,578|Organic| |Revenue|$217|$2,836|\+$2,619|Multiple sources| |RPM (revenue/1000 web traffic)|23.09|$88.7|\+$65.61|Result of Conversion rate optimisation (CRO). You make changes to the site for better conversions| |EEAT (expertise, experience, authority and trust of website)|2 main authors|8 authors|6|Tables, video ads and 11 other fixations| |CRO|Nothing|Tables, video ads |Tables, video ads and 11 other fixations || &#x200B; Month by Month Growth |Month|Revenue|Steps| |:-|:-|:-| |Sept 2022|NA|Content Plan| |Oct 2022|$217|Content Production| |Nov 2022|$243|Content production + EEAT authors| |Dec 2022|$320|Content production + EEAT authors| |Jan 2023|$400|Monitoring| |Feb 2023|$223|Content production + EEAT authors| |Mar 2023|$2,128|CRO & Fixations| |April 2023|$1,609|CRO & Fixations| |May 2023|$2,223|Content production + EEAT authors| |June 2023|$2,836|CRO and Fixations| |Total|$10,199|| &#x200B; What will I share Content plan and Website structure Content Writing Content Uploading, formatting and onsite SEO Faster indexing Conversion rate optimisation Guest Posting EEAT (Experience, Expertise, Authority, Trust) Costing ROI The plans moving forward with these sites &#x200B; Website Structure and Content Plan This is probably the most important important part of the whole process. The team spends around a month just to get this right. It's like defining the direction of the project. Description: Complete blueprint of the site's structure in terms of organisation of categories, subcategories and sorting of articles in each one of them. It also includes the essential pages. The sorted articles target main keyword, relevant entities and similar keywords. This has to be highly data driven and we look at over 100 variables just to get it right. It's like beating Google's algorithm to ensure you have a blueprint for a site that will rank. It needs to be done right. If there is a mistake, then even if you do everything right - it's not going to work out and after 8-16 months you will realise that everything went to waste. Process For this project, we had a niche selected already so we didn't need to do a lot of research pertaining to that. We also knew the topic since the website was already getting good traffic on that. We just validated from Ahrefs, SEMRUSH and manual analysis if it would be worth it to move forward with that topic. &#x200B; Find entities related to the topic: We used Ahrefs and InLinks to get an idea about the related entities (topics) to create a proper topical relevance. In order to be certain and have a better idea, we used ChatGPT to find relevant entities as well \> Ahrefs (tool): Enter main keyword in keywords explorer. Check the left pain for popular topics \> Inlinks (tool): Enter the main keyword, check the entity maps \> ChatGPT (tool): Ask it to list down the most important and relevant entities in order of their priority Based on this info, you can map out the most relevant topics that are semantically associated to your main topic Sorting the entities in topics (categories) and subtopics (subcategories): Based on the information above, cluster them properly. The most relevant ones must be grouped together. Each group must be sorted into its relevant category. \> Example: Site about cycling. \> Categories/entities: bicycles, gear and equipment, techniques, safety, routes etc. \> The subcategories/subentities for let's say "techniques" would be: Bike handling, pedaling, drafting etc. Extract keywords for each subcategory/subentity: You can do this using Ahrefs or Semrush. Each keyword would be an article. Ensure that you target the similar keywords in one article. For example: how to ride a bicycle and how can I ride a bicycle will be targeted by one article. Make the more important keyword in terms of volume and difficulty as the main keyword and the other one(s) as secondary Define main focus vs secondary focus: Out of all these categories/entities - there will be one that you would want to dominate in every way. So, focus on just that in the start. This will be your main focus. Try to answer ALL the questions pertaining to that. You can extract the questions using Ahrefs. \> Ahrefs > keywords explorer \> enter keyword \> Questions \> Download the list and cluster the similar ones. This will populate your main focus category/entity and will drive most of the traffic. Now, you need to write in other categories/subentities as well. This is not just important, but crucial to complete the topical map loop. In simple words, if you do this Google sees you as a comprehensive source on the topic - otherwise, it ignores you and you don't get ranked Define the URLs End result: List of all the entities and sub-entities about the main site topic in the form of categories and subcategories respectively. A complete list of ALL the questions about the main focus and at around 10 questions for each one of the subcategories/subentities that are the secondary focus Content Writing So, now that there's a plan. Content needs to be produced. Pick out a keyword (which is going to be a question) and... Answer the question Write about 5 relevant entities Answer 10 relevant questions Write a conclusion Keep the format the same for all the articles. Content Uploading, formatting and onsite SEO Ensure the following is taken care of: H1 Permalink H2s H3s Lists Tables Meta description Socials description Featured image 2 images in text \\Schema Relevant YouTube video (if there is) Note: There are other pointers link internal linking in a semantically relevant way but this should be good to start with. Faster Indexing Indexing means Google has read your page. Ranking only after this step has been done. Otherwise, you can't rank if Google hasn't read the page. Naturally, this is a slow process. But, we expedite it in multiple ways. You can use RankMath to quickly index the content. Since, there are a lot of bulk pages you need a reliable method. Now, this method isn't perfect. But, it's better than most. Use Google Indexing API and developers tools to get indexed. Rank Math plugin is used. I don't want to bore you and write the process here. But, a simple Google search can help you set everything up. Additionally, whenever you post something - there will be an option to INDEX NOW. Just press that and it would be indexed quite fast. Conversion rate optimisation Once you get traffic, try adding tables right after the introduction of an article. These tables would feature a relevant product on Amazon. This step alone increased our earnings significantly. Even though the content is informational and NOT review. This still worked like a charm. Try checking out the top pages every single day in Google analytics and add the table to each one of them. Moreover, we used EZOIC video ads as well. That increased the RPM significantly as well. Both of these steps are highly recommended. Overall, we implemented over 11 fixations but these two contribute the most towards increasing the RPM so I would suggest you stick to these two in the start. Guest Posting We made additional income by selling links on the site as well. However, we were VERY careful about who we offered a backlink to. We didn't entertain any objectionable links. Moreover, we didn't actively reach out to anyone. We had a professional email clearly stated on the website and a particularly designated page for "editorial guidelines" A lot of people reached out to us because of that. As a matter of fact, the guy who bought the website is in the link selling business and plans to use the site primarily for selling links. According to him, he can easily make $4000+ from that alone. Just by replying to the prospects who reached out to us. We didn't allow a lot of people to be published on the site due to strict quality control. However, the new owner is willing to be lenient and cash it out. EEAT (Experience, Expertise, Authority, Trust) This is an important ranking factor. You need to prove on the site that your site has authors that are experienced, have expertise, authority and trust. A lot of people were reaching out to publish on our site and among them were a few established authors as well. We let them publish on our site for free, added them on our official team, connected their socials and shared them on all our socials. In return, we wanted them to write 3 articles each for us and share everything on all the social profiles. You can refer to the tables I shared above to check out the months it was implemented. We added a total of 6 writers (credible authors). Their articles were featured on the homepage and so were their profiles. Costing Well, we already had the site and the backlinks on it. Referring domains (backlinks) were already 500+. We just needed to focus on smart content and content. Here is the summary of the costs involved. Articles: 490 Avg word count per article: 1500 Total words: 735,000 (approximately) Cost per word: 2 cents (includes research, entities, production, quality assurance, uploading, formatting, adding images, featured image, alt texts, onsite SEO, publishing/scheduling etc.) Total: $14,700 ROI (Return on investment) Earning: Oct 22 - June 23 Earnings: $10,199 Sold for: $59,000 Total: $69,199 Expenses: Content: $14,700 Misc (hosting and others): $500 Total: $15,200 ROI over a 9 months period: 355.25% The plans moving forward This website was a part of a research and development experiment we did. With AI, we wanted to test new waters and transition more towards automation. Ideally, we want to use ChatGPT or some other API to produce these articles and bulk publish on the site. The costs with this approach are going to be much lower and the ROI is much more impressive. It's not the the 7-figures projects I created earlier (as you may have checked the older case studies on my profile), but it's highly scalable. We plan to refine this model even further, test more and automate everything completely to bring down our costs significantly. Once we have a model, we are going to scale it to 100s of sites. The process of my existing 7-figures websites portfolio was quite similar. I tested out a few sites, refined the model and scaled it to over 41 sites. Now, the fundamentals are the same however, we are using AI in a smarter way to do the same but at a lower cost, with a smaller team and much better returns. The best thing in my opinion is to run numerous experiments now. Our experimentation was slowed down a lot in the past since we couldn't write using AI but now it's much faster. The costs are 3-6 times lower so when it used to take $50-100k to start, grow and sell a site. Now you can pump 3-6 more sites for the same budget. This is a good news for existing business owners as well who want to grow their brand. Anyway, I am excited to see the results of more sites. In the meantime, if you have any questions - feel free to let me know. Best of luck for everything. Feel free to ask questions. I'd be happy to help. This is an AMA.

#reddit#[case#study]

reddit

0

1

hopefully_useful•This week

We made $325k in 2023 from AI products, starting from 0, with no-code, no funding and no audience

I met my co-founder in late 2022 after an introduction from a mutual friend to talk about how to find contract Product Management roles. I was sporadically contracting at start-up at the time and he had just come out of another start-up that was wiped out by the pandemic. We hit it off, talking about ideas, sharing what other indie-hackers were doing, and given GPT-3’s prominence at the time, we started throwing around ideas about things we could build with it, if nothing else, just to learn. I should caveat, neither of us were AI experts when starting out, everything we learned has been through Twitter and blogs, my background is as an accountant, and his a consultant. Here’s how it went since then: &#x200B; Nov 2022 (+$50) \- We built a simple tool in around a week using GPT-3 fine-tuning and a no-code tool (Bubble) that helped UK university students write their personal statements for their applications \- We set some Google Ads going and managed to make a few sales (\~$50) in the first week \- OpenAI were still approving applications at the time and said this went against their “ethics” so we had to take it down &#x200B; Dec 2022 (+$200) \- We couldn’t stop coming up with ideas related to AI fine-tuning, but realised it was almost impossible to decide which to pursue \- We needed a deadline to force us so we signed up for the Ben’s Bites hackathon in late December \- In a week, we built and launched a no-code fine-tuning platform, allowing people to create fine-tuned models by dragging and dropping an Excel file onto it \- We launched it on Product Hunt, having no idea how to price it, and somehow managed to get \~2,000 visitors on the site and make 2 sales at $99 &#x200B; Jan 2023 (+$3,000) \- We doubled down on the fine-tuning idea and managed to get up to \~$300 MRR, plus a bunch of one-time sales and a few paid calls to help people get the most out of their models \- We quickly realised that people didn’t want to curate models themselves, they just wanted to dump data and get magic out \- That was when we saw people building “Talk with x book/podcast” on Twitter as side projects and realised that was the missing piece, we needed to turn it into a tool \- We started working on the new product in late January &#x200B; Feb 2023 (+$9,000) \- We started pre-selling access to an MVP for the new product, which allowed people to “chat with their data/content”, we got $5,000 in pre-sales, more than we made from the previous product in total \- By mid-February, after 3 weeks of building we were able to launch and immediately managed to get traction, getting to $1k MRR in < 1 week, building on the hype of ChatGPT and AI (we were very lucky here) &#x200B; Mar - Jul 2023 (+$98,000) \- We worked all the waking hours to keep up with customer demand, bugs, OpenAI issues \- We built integrations for a bunch of services like Slack, Teams, Wordpress etc, added tons of new functionality and continue talking to customers every day \- We managed to grow to $17k MRR (just about enough to cover our living expenses and costs in London) through building in public on Twitter, newsletters and AI directories (and a million other little things) \- We sold our fine-tuning platform for \~$20k and our university project for \~$3k on Acquire &#x200B; Aug 2023 (+$100,000) \- We did some custom development work based on our own product for a customer that proved pretty lucrative &#x200B; Sep - Oct 2023 (+$62,000) \- After 8 months of building constantly, we started digging more seriously into our usage and saw subscriptions plateauing \- We talked to and analysed all our paying users to identify the main use cases and found 75% were for SaaS customer support \- We took the leap to completely rebuild a version of our product around this use case, our biggest to date (especially given most features with no-code took us <1 day) &#x200B; Nov - Dec 2023 (+$53,000) \- We picked up some small custom development work that utilised our own tech \- We’re sitting at around $22k MRR now with a few bigger clients signed up and coming soon \- After 2 months of building and talking to users, we managed to finish our “v2” of our product, focussed squarely on SaaS customer support and launched it today. &#x200B; We have no idea what the response will be to this new version, but we’re pretty happy with it, but couldn’t have planned anything that happened to us in 2023 so who knows what will come of 2024, we just know that we are going to be learning a ton more. &#x200B; Overall, it is probably the most I have had to think in my life - other jobs you can zone out from time to time or rely on someone else if you aren’t feeling it - not when you are doing this, case and point, I am writing this with a banging head-cold right now, but wanted to get this done. A few more things we have learned along the way - context switching is unreal, as is keeping up with, learning and reacting to AI. There isn’t a moment of the day I am not thinking about what we do next. But while in some way we now have hundreds of bosses (our customers) I still haven’t felt this free and can’t imagine ever going back to work for someone else. Next year we’re really hoping to figure out some repeatable distribution channels and personally, I want to get a lot better at creating content/writing, this is a first step! Hope this helps someone else reading this to just try starting something and see what happens.

#reddit#made#$325k

reddit

0

1

mr_t_forhire•This week

Started a content marketing agency 6 years ago - $0 to $5,974,324 (2023 update)

Hey friends, My name is Tyler and for the past 6 years, I’ve been documenting my experience building a content marketing agency called Optimist. Year 1 - 0 to $500k ARR Year 2 - $500k to $1MM ARR Year 3 - $1MM ARR to $1.5MM(ish) ARR Year 4 - $3,333,686 Revenue Year 5 - $4,539,659 Revenue How Optimist Works First, an overview/recap of the Optimist business model: We operate as a “collective” of full time/professional freelancers Everyone aside from me is a contractor Entirely remote/distributed team Each freelancer earns $65-85/hour Clients pay us a flat monthly fee for full-service content marketing (research, strategy, writing, editing, design/photography, reporting and analytics, targeted linkbuilding, and more) We recently introduced hourly engagements for clients who fit our model but have some existing in-house support Packages range in price from $10-20k/mo We offer profit share to everyone on our core team as a way to give everyone ownership in the company In 2022, we posted $1,434,665 in revenue. It was our highest revenue year to date and brings our lifetime total to $5,974,324. Here’s our monthly revenue from January 2017 to December of 2022. But, like every year, it was a mix of ups and downs. Here’s my dispatch for 2023. — Running a business is like spilling a drink. It starts as a small and simple thing. But, if you don’t clean it up, the spill will spread and grow — taking up more space, seeping into every crack. There’s always something you could be doing. Marketing you could be working on. Pitches you could be making. Networking you could be doing. Client work you could help with. It can be all-consuming. And it will be — if you don’t clean up the spill. I realized this year that I had no containment for the spill that I created. Running an agency was spilling over into nearly every moment of my life. When I wasn’t working, I was thinking about work. When I wasn’t thinking about work, I was dreaming about it. Over the years, I’ve shared about a lot of my personal feelings and experience as an entrepreneur. And I also discussed my reckoning with the limitations of running the business we’ve built. My acceptance that it was an airplane but not a rocket. And my plan to try to compartmentalize the agency to make room in my life for other things — new business ideas, new revenue streams, and maybe some non-income-producing activity. 🤷 What I found in 2022 was that the business wasn’t quite ready for me to make that move. It was still sucking up too much of my time and attention. There were still too many gaps to fill and I was the one who was often filling them. So what do you do? Ultimately you have two choices on the table anytime you run a business and it’s not going the way you want it: Walk away Turn the ship — slowly For a huge number of reasons (personal, professional, financial, etc), walking away from Optimist was not really even an option or the right move for me. But it did feel like things needed to change. I needed to keep turning the ship to get it to the place where it fit into my life — instead of my life fitting around the business. This means 2022 was a year of transition for the agency. (Again?) Refocusing on Profit Some money is better than no money. Right? Oddly, this was one of the questions I found myself asking in 2022. Over the years, we’ve been fortunate to have many clients who have stuck with us a long time. In some cases, we’ve had clients work with us for 2, 3, or even 4 years. (That’s over half of our existence!) But, things have gotten more expensive — we’ve all felt it. We’ve had to increase pay to remain competitive for top talent. Software costs have gone up. It’s eaten into our margin. Because of our increasing costs and evolving scope, many of our best, most loyal clients were our least profitable. In fact, many were barely profitable — if at all. We’ve tried to combat that by increasing rates on new, incoming clients to reflect our new costs and try to make up for shrinking margin on long-term clients. But we didn’t have a good strategy in place for updating pricing for current clients. And it bit us in the ass. Subsidizing lower-profit, long-term clients with new, higher-margin clients ultimately didn’t work out. Our margins continued to dwindle and some months we were barely breaking even while posting six-figures of monthly revenue. 2022 was our highest revenue year but one of our least profitable. It only left one option. We had to raise rates on some of our long-term clients. But, of course, raising rates on a great, long-term client can be delicate. You’ve built a relationship with these people over the years and you’re setting yourself up for an ultimatum — are you more valuable to the client or is the client more valuable to you? Who will blink first? We offered all of these clients the opportunity to move to updated pricing. Unfortunately, some of them weren’t on board. Again, we had 2 options: Keep them at a low/no profit rate Let them churn It seems intuitive that having a low-profit client is better than having no client. But we’ve learned an important lesson many times over the years. Our business doesn’t scale infinitely and we can only handle so many clients at a time. That means that low-profit clients are actually costing us money in some cases. Say our average client generates $2,500 per month in profit — $30,000 per year. If one of our clients is only generating $500/mo in profit, working with them means missing out on bringing on a more profitable client (assuming our team is currently at capacity). Instead of $30,000/year, we’re only making $6,000. Keeping that client costs us $24,000. That’s called opportunity cost. So it’s clear: We had to let these clients churn. We decided to churn about 25% of our existing clients. On paper, the math made sense. And we had a pretty consistent flow of new opportunities coming our way. At the time, it felt like a no-brainer decision. And I felt confident that we could quickly replace these low-profit clients with higher-margin ones. I was wrong. Eating Shit Right after we initiated proactively churning some of our clients, other clients — ones we planned to keep — gave us notice that they were planning to end the engagement. Ouch. Fuck. We went from a 25% planned drop in revenue to a nearly 40% cliff staring us right in the face. Then things got even worse. Around Q3 of this year, talk of recession and layoffs really started to intensify. We work primarily with tech companies and startups. And these were the areas most heavily impacted by the economic news. Venture funding was drying up. Our leads started to slow down. This put us in a tough position. Looking back now, I think it’s clear that I made the wrong decision. We went about this process in the wrong way. The reality sinks in when you consider the imbalance between losing a client and gaining a client. It takes 30 days for someone to fire us. It’s a light switch. But it could take 1-3 months to qualify, close, and onboard a new client. We have lots of upfront work, research, and planning that goes into the process. We have to learn a new brand voice, tone, and style. It’s a marathon. So, for every client we “trade”, there’s a lapse in revenue and work. This means that, in retrospect, I would probably have made this transition using some kind of staggered schedule rather than a cut-and-dry approach. We could have gradually off-boarded clients when we had more definitive work to replace them. I was too confident. But that’s a lesson I had to learn the hard way. Rebuilding & Resetting Most of the voluntary and involuntary churn happened toward the end of 2022. So we’re still dealing with the fall out. Right now, it feels like a period of rebuilding. We didn’t quite lose 50% of our revenue, but we definitely saw a big hit heading into 2023. To be transparent: It sucks. It feels like a gigantic mistake that I made which set us back significantly from our previous high point. I acted rashly and it cost us a lot of money — at least on the surface. But I remind myself of the situation we were in previously. Nearly twice the revenue but struggling to maintain profitability. Would it have been better to try to slowly fix that situation and battle through months of loss or barely-break-even profits? Or was ripping off the bandaid the right move after all? I’m an optimist. (Heh, heh) Plus, I know that spiraling over past decisions won’t change them or help me move forward. So I’m choosing to look at this as an opportunity — to rebuild, reset, and refocus the company. I get to take all of the tough lessons I’ve learned over the last 6 years and apply them to build the company in a way that better aligns with our new and current goals. It’s not quite a fresh, clean start, but by parting ways with some of our oldest clients, we’ve eliminated some of the “debt” that’s accumulated over the years. We get a chance to fully realize the new positioning that we rolled out last year. Many of those long-term clients who churned had a scope of work or engagement structure that didn’t fit with our new positioning and focus. So, by losing them, we’re able to completely close up shop on the SOWs that no longer align with the future version of Optimist. Our smaller roster of clients is a better fit for that future. My job is to protect that positioning by ensuring that while we’re rebuilding our new roster of clients we don’t get desperate. We maintain the qualifications we set out for future clients and only take on work that fits. How’s that for seeing the upside? Some other upside from the situation is that we got an opportunity to ask for candid feedback from clients who were leaving. We asked for insight about their decision, what factors they considered, how they perceived us, and the value of our work. Some of the reasons clients left were obvious and possibly unavoidable. Things like budget cuts, insourcing, and uncertainty about the economy all played at least some part of these decisions. But, reading between the lines, where was one key insight that really struck me. It’s one of those, “oh, yeah — duh — I already knew that,” things that can be difficult to learn and easy to forget…. We’re in the Relationship Business (Plan Accordingly) For all of our focus on things like rankings, keywords, content, conversions, and a buffet of relevant metrics, it can be easy to lose the forest for the trees. Yes, the work itself matters. Yes, the outcomes — the metrics — matter. But sometimes the relationship matters more. When you’re running an agency, you can live or die by someone just liking you. Admittedly, this feels totally unfair. It opens up all kinds of dilemmas, frustration, opportunity for bias and prejudice, and other general messiness. But it’s the real world. If a client doesn’t enjoy working with us — even if for purely personal reasons — they could easily have the power to end of engagement, regardless of how well we did our actual job. We found some evidence of this in the offboarding conversations we had with clients. In some cases, we had clients who we had driven triple- and quadruple-digital growth. Our work was clearly moving the needle and generating positive ROI and we had the data to prove it. But they decided to “take things in another direction” regardless. And when we asked about why they made the decision, it was clear that it was more about the working relationship than anything we could have improved about the service itself. The inverse is also often true. Our best clients have lasting relationships with our team. The work is important — and they want results. But even if things aren’t quite going according to plan, they’re patient and quick to forgive. Those relationships feel solid — unshakeable. Many of these folks move onto new roles or new companies and quickly look for an opportunity to work with us again. On both sides, relationships are often more important than the work itself. We’ve already established that we’re not building a business that will scale in a massive way. Optimist will always be a small, boutique service firm. We don’t need 100 new leads per month We need a small, steady roster of clients who are a great fit for the work we do and the value we create. We want them to stick around. We want to be their long-term partner. I’m not built for churn-and-burn agency life. And neither is the business. When I look at things through this lens, I realize how much I can cut from our overall business strategy. We don’t need an ultra-sophisticated, multi-channel marketing strategy. We just need strong relationships — enough of them to make our business work. There are a few key things we can take away from this as a matter of business strategy: Put most of our effort into building and strengthening relationships with our existing clients Be intentional about establishing a strong relationship with new clients as part of onboarding Focus on relationships as the main driver of future business development Embracing Reality: Theory vs Practice Okay, so with the big learnings out the way, I want to pivot into another key lesson from 2022. It’s the importance of understanding theory vs practice — specifically when it comes to thinking about time, work, and life. It all started when I was considering how to best structure my days and weeks around running Optimist, my other ventures, and my life goals outside of work. Over the years, I’ve dabbled in many different ways to block time and find focus — to compartmentalize all of the things that are spinning and need my attention. As I mapped this out, I realized that I often tried to spread myself too thin throughout the week. Not just that I was trying to do too much but that I was spreading that work into too many small chunks rather than carving out time for focus. In theory, 5 hours is 5 hours. If you have 5 hours of work to get done, you just fit into your schedule whenever you have an open time slot. In reality, a single 5-hour block of work is 10x more productive and satisfying than 10, 30-minute blocks of work spread out across the week. In part, this is because of context switching. Turning your focus from one thing to another thing takes time. Achieving flow and focus takes time. And the more you jump from one project to another, the more time you “lose” to switching. This is insightful for me both in the context of work and planning my day, but also thinking about my life outside of Optimist. One of my personal goals is to put a finite limit on my work time and give myself more freedom. I can structure that in many different ways. Is it better to work 5 days a week but log off 1 hour early each day? Or should I try to fit more hours into each workday so I can take a full day off? Of course, it’s the latter. Both because of the cost of context switching and spreading work into more, smaller chunks — but also because of the remainder that I end up with when I’m done working. A single extra hour in my day probably means nothing. Maybe I can binge-watch one more episode of a new show or do a few extra chores around the house. But it doesn’t significantly improve my life or help me find greater balance. Most things I want to do outside of work can’t fit into a single extra hour. A full day off from work unlocks many more options. I can take the day to go hiking or biking. I can spend the day with my wife, planning or playing a game. Or I can push it up against the weekend and take a 3-day trip. It gives me more of the freedom and balance that I ultimately want. So this has become a guiding principle for how I structure my schedule. I want to: Minimize context switching Maximize focused time for work and for non-work The idea of embracing reality also bleeds into some of the shifts in business strategy that I mentioned above. In theory, any time spent on marketing will have a positive impact on the company. In reality, focusing more on relationships than blasting tweets into the ether is much more likely to drive the kind of growth and stability that we’re seeking. As I think about 2023, I think this is a recurring theme. It manifests in many ways. Companies are making budget cuts and tough decisions about focus and strategy. Most of us are looking for ways to rein in the excess and have greater impact with a bit less time and money. We can’t do everything. We can’t even do most things. So our #1 priority should be to understand the reality of our time and our effort to make the most of every moment (in both work and leisure). That means thinking deeply about our strengths and our limitations. Being practical, even if it feels like sacrifice. Update on Other Businesses Finally, I want to close up by sharing a bit about my ventures outside of Optimist. I shared last year how I planned to shift some of my (finite) time and attention to new ventures and opportunities. And, while I didn’t get to devote as much as I hoped to these new pursuits, they weren’t totally in vain. I made progress across the board on all of the items I laid out in my post. Here’s what happened: Juice: The first Optimist spin-out agency At the end of 2021, we launched our first new service business based on demand from Optimist clients. Focused entirely on building links for SEO, we called the agency Juice. Overall, we made strong progress toward turning this into a legitimate standalone business in 2022. Relying mostly on existing Optimist clients and a few word-of-mouth opportunities (no other marketing), we built a team and set up a decent workflow and operations. There’s still many kinks and challenges that we’re working through on this front. All told, Juice posted almost $100,000 in revenue in our first full year. Monetizing the community I started 2022 with a focus on figuring out how to monetize our free community, Top of the Funnel. Originally, my plan was to sell sponsorships as the main revenue driver. And that option is still on the table. But, this year, I pivoted to selling paid content and subscriptions. We launched a paid tier for content and SEO entrepreneurs where I share more of my lessons, workflows, and ideas for building and running a freelance or agency business. It’s gained some initial traction — we reached \~$1,000 MRR from paid subscriptions. In total, our community revenue for 2022 was about $2,500. In 2023, I’m hoping to turn this into a $30,000 - $50,000 revenue opportunity. Right now, we’re on track for \~$15,000. Agency partnerships and referrals In 2022, we also got more serious about referring leads to other agencies. Any opportunity that was not a fit for Optimist or we didn’t have capacity to take on, we’d try to connect with another partner. Transparently, we struggled to operationalize this as effectively as I would have liked. In part, this was driven by my lack of focus here. With the other challenges throughout the year, I wasn’t able to dedicate as much time as I’d like to setting goals and putting workflows into place. But it wasn’t a total bust. We referred out several dozen potential clients to partner agencies. Of those, a handful ended up converting into sales — and referral commission. In total, we generated about $10,000 in revenue from referrals. I still see this as a huge opportunity for us to unlock in 2023. Affiliate websites Lastly, I mentioned spending some time on my new and existing affiliate sites as another big business opportunity in 2022. This ultimately fell to the bottom of my list and didn’t get nearly the attention I wanted. But I did get a chance to spend a few weeks throughout the year building this income stream. For 2022, I generated just under $2,000 in revenue from affiliate content. My wife has graciously agreed to dedicate some of her time and talent to these projects. So, for 2023, I think this will become a bit of a family venture. I’m hoping to build a solid and consistent workflow, expand the team, and develop a more solid business strategy. Postscript — AI, SEO, OMG As I’m writing this, much of my world is in upheaval. If you’re not in this space (and/or have possibly been living under a rock), the release of ChatGPT in late 2022 has sparked an arms race between Google, Bing, OpenAI, and many other players. The short overview: AI is likely to fundamentally change the way internet search works. This has huge impact on almost all of the work that I do and the businesses that I run. Much of our focus is on SEO and understanding the current Google algorithm, how to generate traffic for clients, and how to drive traffic to our sites and projects. That may all change — very rapidly. This means we’re standing at a very interesting point in time. On the one hand, it’s scary as hell. There’s a non-zero chance that this will fundamentally shift — possibly upturn — our core business model at Optimist. It could dramatically change how we work and/or reduce demand for our core services. No bueno. But it’s also an opportunity (there’s the optimist in me, again). I certainly see a world where we can become leaders in this new frontier. We can pivot, adjust, and capitalize on a now-unknown version of SEO that’s focused on understanding and optimizing for AI-as-search. With that, we may also be able to help others — say, those in our community? — also navigate this tumultuous time. See? It’s an opportunity. I wish I had the answers right now. But, it’s still a time of uncertainty. I just know that there’s a lot of change happening and I want to be in front of it rather than trying to play catch up. Wish me luck. — Alright friends — that's my update for 2023! I’ve always appreciated sharing these updates with the Reddit community, getting feedback, being asked tough questions, and even battling it out with some of my haters (hey!! 👋) As usual, I’m going to pop in throughout the next few days to respond to comments or answer questions. Feel free to share thoughts, ideas, and brutal takedowns in the comments. If you're interested in following the Optimist journey and the other projects I'm working on in 2023, you can follow me on Twitter. Cheers, Tyler P.S. - If you're running or launching a freelance or agency business and looking for help figuring it out, please DM me. Our subscription community, Middle of the Funnel, was created to provide feedback, lessons, and resources for other entrepreneurs in this space.

#reddit#started#content

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Careless_Fill1407•This week

AI for Business- Sharing Strategies, Knowledge and Tools

Hi All, As a small business owner running a tech company that focuses on process automation, I understand the importance of staying up-to-date with the latest advancements in AI and other emerging technologies. I have found the amount of new tools and tech overwhelming to keep track of as of recent. I would like to start a group of entrepreneurs and tech professionals who share a common interest in the latest tools relating to AI, but also tech in general. My goal is to create an environment for members to discuss business strategies, share projects involving how they are introducing AI, new tools... I hope that by pooling knowledge everyone involved can benefit. To ensure the community remains focused and valuable I am thinking that It will be a requirement for all members to share a tool, project ideas, strategies... related to business on a bi-weekly basis. Potentially bi-weekly calls as well, but Ill figure that out based on the interest. We currently have about 15 members and looking to add maybe 10-15 more. If this sounds like something you'd be interested in post below or send me a DM. Add a brief description of what you are interested in / working on. Looking forward to connecting

#reddit#business-#sharing

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0.882

laddermanUS•This week

How To Learn About AI Agents (A Road Map From Someone Who's Done It)

If you are a newb to AI Agents, welcome, I love newbies and this fledgling industry needs you! You've hear all about AI Agents and you want some of that action right?  You might even feel like this is a watershed moment in tech, remember how it felt when the internet became 'a thing'?  When apps were all the rage?  You missed that boat right?   Well you may have missed that boat, but I can promise you one thing..... THIS BOAT IS BIGGER !  So if you are reading this you are getting in just at the right time.  Let me answer some quick questions before we go much further: Q: Am I too late already to learn about AI agents? A: Heck no, you are literally getting in at the beginning, call yourself and 'early adopter' and pin a badge on your chest! Q: Don't I need a degree or a college education to learn this stuff?  I can only just about work out how my smart TV works! A: NO you do not.  Of course if you have a degree in a computer science area then it does help because you have covered all of the fundamentals in depth... However 100000% you do not need a degree or college education to learn AI Agents.  Q: Where the heck do I even start though?  Its like sooooooo confusing A: You start right here my friend, and yeh I know its confusing, but chill, im going to try and guide you as best i can. Q: Wait i can't code, I can barely write my name, can I still do this? A: The simple answer is YES you can. However it is great to learn some basics of python.  I say his because there are some fabulous nocode tools like n8n that allow you to build agents without having to learn how to code...... Having said that, at the very least understanding the basics is highly preferable. That being said, if you can't be bothered or are totally freaked about by looking at some code, the simple answer is YES YOU CAN DO THIS. Q: I got like no money, can I still learn? A: YES 100% absolutely.  There are free options to learn about AI agents and there are paid options to fast track you.  But defiantly you do not need to spend crap loads of cash on learning this.  So who am I anyway? (lets get some context)  I am an AI Engineer and I own and run my own AI Consultancy business where I design, build and deploy AI agents and AI automations.  I do also run a small academy where I teach this stuff, but I am not self promoting or posting links in this post because im not spamming this group.  If you want links send me a DM or something and I can forward them to you.  Alright so on to the good stuff, you're a newb, you've already read a 100 posts and are now totally confused and every day you consume about 26 hours of youtube videos on AI agents.....I get you, we've all been there.  So here is my 'Worth Its Weight In Gold' road map on what to do: \[1\]  First of all you need learn some fundamental concepts.  Whilst you can defiantly jump right in start building, I strongly recommend you learn some of the basics.  Like HOW to LLMs work, what is a system prompt, what is long term memory, what is Python, who the heck is this guy named Json that everyone goes on about?  Google is your old friend who used to know everything, but you've also got your new buddy who can help you if you want to learn for FREE.  Chat GPT is an awesome resource to create your own mini learning courses to understand the basics. Start with a prompt such as: "I want to learn about AI agents but this dude on reddit said I need to know the fundamentals to this ai tech, write for me a short course on Json so I can learn all about it. Im a beginner so keep the content easy for me to understand. I want to also learn some code so give me code samples and explain it like a 10 year old" If you want some actual structured course material on the fundamentals, like what the Terminal is and how to use it, and how LLMs work, just hit me, Im not going to spam this post with a hundred links. \[2\] Alright so let's assume you got some of the fundamentals down.  Now what? Well now you really have 2 options.  You either start to pick up some proper learning content (short courses) to deep dive further and really learn about agents or you can skip that sh\*t and start building!  Honestly my advice is to seek out some short courses on agents, Hugging Face have an awesome free course on agents and DeepLearningAI also have numerous free courses. Both are really excellent places to start.  If you want a proper list of these with links, let me know.  If you want to jump in because you already know it all, then learn the n8n platform!   And no im not a share holder and n8n are not paying me to say this.  I can code, im an AI Engineer and I use n8n sometimes.   N8N is a nocode platform that gives you a drag and drop interface to build automations and agents.  Its very versatile and you can self host it.  Its also reasonably easy to actually deploy a workflow in the cloud so it can be used by an actual paying customer.  Please understand that i literally get hate mail from devs and experienced AI enthusiasts for recommending no code platforms like n8n.  So im risking my mental wellbeing for you!!!    \[3\] Keep building!   ((WTF THAT'S IT?????))  Yep. the more you build the more you will learn.  Learn by doing my young Jedi learner.  I would call myself pretty experienced in building AI Agents, and I only know a tiny proportion of this tech.  But I learn but building projects and writing about AI Agents.  The more you build the more you will learn.  There are more intermediate courses you can take at this point as well if you really want to deep dive (I was forced to - send help) and I would recommend you do if you like short courses because if you want to do well then you do need to understand not just the underlying tech but also more advanced concepts like Vector Databases and how to implement long term memory.  Where to next? Well if you want to get some recommended links just DM me or leave a comment and I will DM you, as i said im not writing this with the intention of spamming the crap out of the group. So its up to you.  Im also happy to chew the fat if you wanna chat, so hit me up.  I can't always reply immediately because im in a weird time zone, but I promise I will reply if you have any questions. THE LAST WORD (Warning - Im going to motivate the crap out of you now) Please listen to me:  YOU CAN DO THIS.  I don't care what background you have, what education you have, what language you speak or what country you are from..... I believe in you and anyway can do this.  All you need is determination, some motivation to want to learn and a computer (last one is essential really, the other 2 are optional!) But seriously you can do it and its totally worth it.  You are getting in right at the beginning of the gold rush, and yeh I believe that.   AI Agents are going to be HUGE. I believe this will be the new internet gold rush.

#reddit#learn#about

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WebsterPepster•This week

I got fired due to automation — lessons learned. Two-month overview.

UPD: Guys, I'm not promoting myself as some of the redditors decided. That's why to deal with contradictions I'll do next things: make additional post with short review and description of the general tools and processes you could apply. help only those who have already written me. So I won't answer on new offers or DMs. As mentioned, damn robots have taken my job. PRE-HISTORY During Covid times, I found myself without my offline job, and since I was interested in marketing and SMM, I began searching for a job there. Completed free Google and Udemy courses and finally landed my first SMM manager position with a business owner. He had several projects so, finally, I started managing three Twitter accounts, two Facebook accs, two IGs, and one TikTok. I handled posting, content editing and responding routine, while freelancers usually took care of video creation for IG and TT. THE STORY ITSELF Things took a turn for the worse in April when my employer introduced ChatGPT and Midjourney, tools I was already using. The owner insisted on integrating them into the workflow, and my wages took a 20% hit. I thought I could roll with it, but it was just the beginning. By midsummer, the owner implemented second-layer AI tools like Visla, Pictory, and Woxo for video (bye freelancers, lol), as well as TweetHunter, Jasper, and Perplexity for content. Midjourney and Firefly joined for image generation. All together, my paycheck was slashed by 50%. Finally, at the end of October, my boss told me he automated stuff with Zapier, cutting costs that way. Additionally, he adopted MarketOwl, autoposting tool for Twitter, and SocialBee for Facebook. He stated that he didn’t need me, as by now he could manage the social media accounts himself. I feel so pissed then and even thought that there's no point in searching for similar jobs. HOW I SPENT TWO MONTHS Well, for the first two weeks, I did nothing but being miserable, drinking and staring at the wall. My gf said it's unbearable and threatened to leave if I not pull myself together. It was not the final push, but definitely made me rethink things. So I decided to learn more about the capabilities of these automation covers and eventually became an AI adviser for small businesses. It's ironic that now I sometimes earn money advising on how to optimize marketing, possibly contributing to other people's job loss. FINAL THOUGHTS I am fully aware of the instability of such a job and have invested my last savings in taking an online marketing course at Columbia to gain more marketing experience and got something more stable afterwards. Message for mods: I'm not promoting myself or anything mentioned here; just sharing the experience that someone might find helpful.

#reddit#fired#automation

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0.882

mr_t_forhire•This week

Started a content marketing agency 8 years ago - $0 to $7,863,052 (2025 update)

Hey friends, My name is Tyler and for the past 8 years, I’ve been documenting my experience building a content marketing agency called Optimist. Year 1 — 0 to $500k ARR Year 2 — $500k to $1MM ARR Year 3 — $1MM ARR to $1.5MM(ish) ARR Year 4 — $3,333,686 Revenue Year 5 — $4,539,659 Revenue Year 6 — $5,974,324 Revenue Year 7 - $6,815,503 Revenue (Edit: Seems like links are banned now. You can check my post history for all of my previous updates with lessons and learnings.) How Optimist Works First, an overview/recap of the Optimist business model: We operate as a “collective” of full time/professional freelancers Everyone aside from me is a contractor Entirely remote/distributed team We pay freelancers a flat fee for most work, working out to roughly $65-100/hour. Clients pay us a flat monthly fee for full-service content marketing (research, strategy, writing, editing, design/photography, reporting and analytics, targeted linkbuilding, and more)\ Packages range in price from \~$10-20k/mo \This is something we are revisiting now* The Financials In 2024, we posted $1,032,035.34 in revenue. This brings our lifetime revenue to $7,863,052. Here’s our monthly revenue from January 2017 to December of 2024. (Edit: Seems like I'm not allowed to link to the chart.) The good news: Revenue is up 23% YoY. EBITDA in Q4 trending up 1-2 points. We hosted our first retreat in 4 years, going to Ireland with about half the team. The bad news: Our revenue is still historically low. At $1MM for the year, we’re down about 33% from our previous years over $1.5MM. Revenue has been rocky. It doesn’t feel like we’ve really “recovered” from the bumps last year. The trend doesn’t really look great. Even though, anecdotally, it feels like we are moving in a good direction. EBITDA is still hovering at around 7%. Would love to get that closer to 20%. (For those who may ask: I’m calculating EBITDA after paying taxes and W2 portion of my income.) — Almost every year, my update starts the same way: This has been a year of growth and change. Both for my business—and me personally. 2024 was no different. I guess that tells you something about entrepreneurship. It’s a lot more like sailing a ship than driving a car. You’re constantly adapting, tides are shifting, and any blip of calm is usually just a moment before the next storm. As with past years, there’s a lot to unpack from the last 12 months. Here we go again. Everything is Burning In the last 2 years, everything has turned upside down in the world of content and SEO. Back in 2020, we made a big decision to re-position the agency. (See post history) We decided to narrow our focus to our most successful, profitable, and consistent segment of clients and re-work our entire operation to focus on serving them. We defined our ICP as: \~Series A ($10mm+ funding) with 6-12 months runway to scale organic as a channel Product-led company with “simple” sales cycle involving fewer stakeholders Demonstrable opportunity to use SEO to drive business growth Our services: Content focused on growing organic search (SEO) Full-service engagements that included research, planning, writing, design, reporting And our engagement structure: Engaged directly with an executive; ownership over strategy and day-to-day execution 1-2 points of contact or stakeholders Strategic partner that drives business growth (not a service vendor who makes content) Most importantly, we decided that we were no longer going to offer a broader range of content that we used to sell. That included everything from thought leadership content to case studies and ebooks. We doubled-down on “SEO content” for product-led SaaS companies. And this worked phenomenally for us. We started bringing on more clients than ever. We developed a lot of internal system and processes that helped us scale and take on more work than we’ve ever had and drive great outcomes for our ideal clients. But in 2023 and 2024, things started going awry. One big change, of course, was the rise of AI. Many companies and executives (and writers) feel that AI can write content just as well as an agency like ours. That made it a lot harder to sell a $10,000 per month engagement when they feel like the bulk of the work could be “done for free.” (Lots of thoughts on this if you want my opinions.) But it wasn’t just that. Google also started tinkering with their algorithm, introducing new features like AI Overviews, and generally changing the rules of the game. This created 3 big shifts in our world: The perceived value of content (especially “SEO content”) dropped dramatically in many people’s minds because of AI’s writing capabilities SEO became less predictable as a source of traffic and revenue It’s harder than ever for startups and smaller companies to rank for valuable keywords (let alone generate any meaningful traffic or revenue from them) The effect? The middle of the content market has hollowed out. People—like us—providing good, human-crafted content aimed on driving SEO growth saw a dramatic decline in demand. We felt it all year. Fewer and fewer leads. The leads we did see usually scoffed at our prices. They were indexing us against the cost of content mills and mass-produced AI articles. It was a time of soul-searching and looking for a way forward. I spent the first half of the year convinced that the only way to survive was to run toward the fire. We have to build our own AI workflows. We have to cut our rates internally. We have to get faster and cheaper to stay competitive with the agencies offering the same number of deliverables for a fraction of our rates. It’s the only way forward. But then I asked myself a question… Is this the game I actually want to play? As an entrepreneur, do I want to run a business where I’m competing mostly on price and efficiency rather than quality and value? Do I want to hop into a race toward cheaper and cheaper content? Do I want to help people chase a dwindling amount of organic traffic that’s shrinking in value? No. That’s not the game I want to play. That’s not a business I want to run. I don’t want to be in the content mill business. So I decided to turn the wheel—again. Repositioning Part II: Electric Boogaloo What do you do when the whole world shifts around you and the things that used to work aren’t working anymore? You pivot. You re-position the business and move in another direction. So that’s what we decided to do. Again. There was only one problem: I honestly wasn’t sure what opportunities existed in the content marketing industry outside of what we were already doing. We lived in a little echo chamber of startups and SEO. It felt like the whole market was on fire and I had fight through the smoke to find an escape hatch. So I started making calls. Good ol’ fashioned market research. I reached out to a few dozen marketing and content leaders at a bunch of different companies. I got on the phone and just asked lots of questions about their content programs, their goals, and their pain points. I wanted to understand what was happening in the market and how we could be valuable. And, luckily, this process really paid off. I learned a lot about the fragmentation happening across content and how views were shifting. I noticed key trends and how our old target market really wasn’t buying what we were selling. Startups and small companies are no longer willing to invest in an agency like ours. If they were doing content and SEO at all, they were focused entirely on using AI to scale output and minimize costs. VC money is still scarce and venture-backed companies are more focused on profitability than pure growth and raising another round. Larger companies (\~500+ employees) are doing more content than ever and drowning in content production. They want to focus on strategy but can barely tread water keeping up with content requests from sales, demand gen, the CEO, and everyone else. Many of the companies still investing in content are looking at channels and formats outside of SEO. Things like thought leadership, data reports, interview-driven content, and more. They see it as a way to stand out from the crowd of “bland SEO content.” Content needs are constantly in flux. They range from data reports and blog posts to product one-pagers. The idea of a fixed-scope retainer is a total mismatch for the needs of most companies. All of this led to the logical conclusion: We were talking to the wrong people about the wrong things\.\ Many companies came to one of two logical conclusions: SEO is a risky bet, so it’s gotta be a moonshot—super-low cost with a possibility for a big upside (i.e., use AI to crank out lots of content. If it works, great. If it doesn’t, then at least we aren’t out much money.) SEO is a risky bet, so we should diversify into other strategies and channels to drive growth (i.e., shift our budget from SEO and keyword-focused content to video, podcasts, thought leadership, social, etc) Unless we were going to lean into AI and dramatically cut our costs and rates, our old buyers weren’t interested. And the segment of the market that needs our help most are looking primarily for production support across a big range of content types. They’re not looking for a team to run a full-blown program focused entirely on SEO. So we had to go back to the drawing board. I’ve written before about our basic approach to repositioning the business. But, ultimately it comes down to identifying our unique strengths as a team and then connecting them to needs in the market. After reviewing the insights from my discussions and taking another hard look at our business and our strengths, I decided on a new direction: Move upmarket: Serve mid-size to enterprise businesses with \~500-5,000 employees instead of startups Focus on content that supports a broader range of business goals instead of solely on SEO and organic growth (e.g., sales, demand gen, brand, etc) Shift back to our broader playbook of content deliverables, including thought leadership, data studies, and more Focus on content execution and production to support an internally-directed content strategy across multiple functions In a way, it’s sort of a reverse-niche move. Rather than zooming in specifically on driving organic growth for startups, we want to be more of an end-to-end content production partner that solves issues of execution and operations for all kinds of content teams. It’s early days, but the response here has been promising. We’ve seen an uptick in leads through Q4. And more companies in our pipeline fit the new ICP. They’re bigger, often have more budget. (But they move more slowly). We should know by the end of the quarter if this maneuver is truly paying off. Hopefully, this will work out. Hopefully our research and strategy are right and we’ll find a soft landing serving a different type of client. If it doesn’t? Then it will be time to make some harder decisions. As I already mentioned, I’m not interested in the race to the bottom of AI content. And if that’s the only game left in town, then it might be time to think hard about a much bigger change. — To be done: Build new content playbooks for expanded deliverables Build new showcase page for expanded deliverables Retooling the Operation It’s easy to say we’re doing something new. It’s a lot harder to actually do it—and do it well. Beyond just changing our positioning, we have to do open-heart surgery on the entire content operation behind the scenes. We need to create new systems that work for a broader range of content types, formats, and goals. Here’s the first rub: All of our workflows are tooled specifically for SEO-focused content. Every template, worksheet, and process that we’ve built and scaled in the last 5 years assumes that the primary goal of every piece of content is SEO. Even something as simple as requiring a target keyword is a blocker in a world where we’re not entirely focused on SEO. This is relatively easy to fix, but it requires several key changes: Update content calendars to make keywords optional Update workflows to determine whether we need an optimization report for each deliverable Next, we need to break down the deliverables into parts rather than a single line item. In our old system, we would plan content as a single row in a Content Calendar spreadsheet. It was a really wide sheet with lots of fields where we’d define the dimensions of each individual article. This was very efficient and simple to follow. But every article had the same overall scope when it came to the workflow. In Asana (our project management tool), all of the steps in the creation were strung together in a single task. We would create a few basic templates for each client, and then each piece would flow through the same steps: Briefing Writing Editing Design etc. If we had anything that didn’t fit into the “standard” workflow, we’d just tag it in the calendar with an unofficial notation \[USING BRACKETS\]. It worked. But it wasn’t ideal. Now we need the steps to be more modular. Imagine, for example, a client asks us to create a mix of deliverables: 1 article with writing + design 1 content brief 1 long-form ebook with an interview + writing + design Each of these would require its own steps and its own workflow. We need to break down the work to accommodate for a wider variety of workflows and variables. This means we need to update the fields and structure of our calendar to accommodate for the new dimensions—while also keeping the planning process simple and manageable. This leads to the next challenge: The number of “products” that we’re offering could be almost infinite. Just looking at the example scope above, you can mix and match all of these different building blocks to create a huge variety of different types of work, each requiring its own workflow. This is part of the reason we pivoted away from this model to focus on a productized, SEO-focused content service back in 2020. Take something as simple as a case study. On the surface, it seems like one deliverable that can be easily scoped and priced, right? Well, unpack what goes into a case study: Is there already source material from the customer or do we need to conduct an interview? How long is it? Is it a short overview case study or a long-form narrative? Does it need images and graphics? How many? Each of these variables opens up 2-3 possibilities. And when you combine them, we end up with something like 10 possible permutations for this single type of deliverable. It gets a bit messy. But not only do we have to figure out how to scope and price all for all of these variables, we also have to figure out how to account for these variables in the execution. We have to specify—for every deliverable—what type it is, how long, which steps are involved and not involved, the timeline for delivery, and all of the other factors. We’re approaching infinite complexity, here. We have to figure out a system that allows for a high level of flexibility to serve the diverse needs of our clients but is also productized enough that we can build workflows, process, and templates to deliver the work. I’ve spent the last few months designing that system. Failed Attempt #1: Ultra-Productization In my first pass, I tried to make it as straight forward as possible. Just sit down, make a list of all of the possible deliverables we could provide and then assign them specific scopes and services. Want a case study? Okay that’ll include an interview, up to 2,000 words of content, and 5 custom graphics. It costs $X. But this solution quickly fell apart when we started testing it against real-world scenarios. What if the client provided the brief instead of us creating one? What if they didn’t want graphics? What if this particular case study really needs to be 3,000 words but all of the others should be 2,000? In order for this system to work, we’d need to individual scope and price all of these permutations of each productized service. Then we’d need to somehow keep track of all of these and make sure that we accurately scope, price, and deliver them across dozens of clients. It’s sort of like a restaurant handling food allergies by creating separate versions of every single dish to account for every individual type of allergy. Most restaurants have figured out that it makes way more sense to have a “standard” and an “allergy-free” version. Then you only need 2 options to cover 100% of the cases. Onto the next option. Failed Attempt #2: Deliverable-Agnostic Services Next, I sat down with my head of Ops, Katy, to try to map it out. We took a big step back and said: Why does the deliverable itself even matter? At the end of the day, what we’re selling is just a few types of work (research, writing, editing, design, etc) that can be packaged up in an infinite number of ways. Rather than try to define deliverables, shouldn’t we leave it open ended for maximum flexibility? From there, we decided to break down everything into ultra-modular building blocks. We started working on this super complex system of modular deliverables where we would have services like writing, design, editing, etc—plus a sliding scale for different scopes like the length of writing or the number of images. In theory, it would allow us to mix and match any combination of services to create custom deliverables for the client. In fact, we wanted the work to be deliverable-agnostic. That way we could mold it to fit any client’s needs and deliver any type of content, regardless of the format or goal. Want a 5,000-word case study with 15 custom graphics? That’ll be $X. Want a 2,000-word blog post with an interview and no visuals? $Y. Just want us to create 10 briefs, you handle the writing, and we do design? It’s $Z. Again, this feels like a reasonable solution. But it quickly spiraled out of amuck. (That’s an Office reference.) For this to work, we need to have incredibly precise scoping process for every single deliverable. Before we can begin work (or even quote a price), we need to know pretty much the exact word count of the final article, for example. In the real world? This almost never happens. The content is as long as the content needs to be. Clients rarely know if the blog post should be 2,000 words or 3,000 words. They just want good content. We have a general ballpark, but we can rarely dial it in within just 1,000 words until we’ve done enough research to create the brief. Plus, from a packaging and pricing perspective, it introduces all kind of weird scenarios where clients will owe exactly $10,321 for this ultra-specific combination of services. We were building an open system that could accommodate any and all types of potential deliverables. On the face that seems great because it makes us incredibly flexible. In reality, the ambiguity actually works against us. It makes it harder for us to communicate to clients clearly about what they’ll get, how much it will cost, and how long it will take. That, of course, also means that it hurts our client relationships. (This actually kind of goes back to my personal learnings, which I’ll mention in a bit. I tend to be a “let’s leave things vague so we don’t have to limit our options” kind of person. But I’m working on fixing this to be more precise, specific, and clear in everything that we do.) Dialing It In: Building a Closed System We were trying to build an open system. We need to build a closed system. We need to force clarity and get specific about what we do, what we don’t do, and how much it all costs. Then we need a system to expand on that closed system—add new types of deliverables, new content playbooks, and new workflows if and when the need arises. With that in mind, we can start by mapping out the key dimensions of any type of deliverable that we would ever want to deliver. These are the universal dimensions that determine the scope, workflow, and price of any deliverable—regardless of the specific type output. Dimensions are: Brief scope Writing + editing scope Design scope Interview scope Revision (rounds) Scope, essentially, just tells us how many words, graphics, interviews, etc are required for the content we’re creating. In our first crack at the system, we got super granular with these scopes. But to help force a more manageable system, we realized that we didn’t need tiny increments for most of this work. Instead, we just need boundaries—you pay $X for up to Y words. We still need some variability around the scope of these articles. Obviously, most clients won’t be willing to pay the same price for a 1,000-word article as a 10,000-word article. But we can be smarter about the realistic break points. We boiled it down to the most common ranges: (Up to) 250 words 1,000 words 3,000 words 6,000 words 10,000 words This gives us a much more manageable number of variables. But we still haven’t exactly closed the system. We need one final dimension: Deliverable type. This tells us what we’re actually building with these building blocks. This is how we’ll put a cap on the potentially infinite number of combinations we could offer. The deliverable type will define what the final product should look like (e.g., blog post, case study, ebook, etc). And it will also give us a way to put standards and expectations around different types of deliverables that we want to offer. Then we can expand on this list of deliverables to offer new services. In the mean time, only the deliverables that we have already defined are, “on the menu,” so to speak. If a client comes to us and asks for something like a podcast summary article (which we don’t currently offer), we’ll have to either say we can’t provide that work or create a new deliverable type and define the dimensions of that specific piece. But here’s the kicker: No matter the deliverable type, it has to still fit within the scopes we’ve already defined. And the pricing will be the same. This means that if you’re looking for our team to write up to 1,000 words of content, it costs the same amount—whether it’s a blog post, an ebook, a LinkedIn post, or anything else. Rather than trying to retool our entire system to offer this new podcast summary article deliverable, we’ll just create the new deliverable type, add it to the list of options, and it’s ready to sell with the pre-defined dimensions we’ve already identified. To do: Update onboarding workflow Update contracts and scope documents Dial in new briefing process Know Thyself For the last year, I’ve been going through personal therapy. (Huge shout out to my wife, Laura, for her support and encouragement throughout the process.) It’s taught me a lot about myself and my tendencies. It’s helped me find some of my weaknesses and think about how I can improve as a person, as a partner, and as an entrepreneur. And it’s forced me to face a lot of hard truths. For example, consider some of the critical decisions I’ve made for my business: Unconventional freelance “collective” model No formal management structure Open-ended retainers with near-infinite flexibility General contracts without defined scope “Take it or leave it” approach to sales and marketing Over the years, I’ve talked about almost everything on this list as a huge advantage. I saw these things as a reflection of how I wanted to do things differently and better than other companies. But now, I see them more as a reflection of my fears and insecurities. Why did I design my business like this? Why do I want so much “flexibility” and why do I want things left open-ended rather than clearly defined? One reason that could clearly explain it: I’m avoidant. If you’re not steeped in the world of therapy, this basically means that my fight or flight response gets turned all the way to “flight.” If I’m unhappy or uncomfortable, my gut reaction is usually to withdraw from the situation. I see commitment and specificity as a prelude to future conflict. And I avoid conflict whenever possible. So I built my business to minimize it. If I don’t have a specific schedule of work that I’m accountable for delivering, then we can fudge the numbers a bit and hope they even out in the end. If I don’t set a specific standard for the length of an article, then I don’t have to let the client know when their request exceeds that limit. Conflict….avoided? Now, that’s not to say that everything I’ve built was wrong or bad. There is a lot of value in having flexibility in your business. For example, I would say that our flexible retainers are, overall, an advantage. Clients have changing needs. Having flexibility to quickly adapt to those needs can be a huge value add. And not everything can be clearly defined upfront (at least not without a massive amount of time and work just to decide how long to write an article). Overly-rigid structures and processes can be just as problematic as loosey-goosey ones. But, on the whole, I realized that my avoidant tendencies and laissez faire approach to management have left a vacuum in many areas. The places where I avoided specificity were often the places where there was the most confusion, uncertainty, and frustration from the team and from clients. People simply didn’t know what to expect or what was expected of them. Ironically, this often creates the conflict I’m trying to avoid. For example, if I don’t give feedback to people on my team, then they feel uneasy about their work. Or they make assumptions about expectations that don’t match what I’m actually expecting. Then the client might get upset, I might get upset, and our team members may be upset. Conflict definitely not avoided. This happens on the client side, too. If we don’t define a specific timeline when something will be delivered, the client might expect it sooner than we can deliver—creating frustration when we don’t meet their expectation. This conflict actually would have been avoided if we set clearer expectations upfront. But we didn’t do that. I didn’t do that. So it’s time to step up and close the gaps. Stepping Up and Closing the Gaps If I’m going to address these gaps and create more clarity and stability, I have to step up. Both personally and professionally. I have to actually face the fear and uncertainty that drives me to be avoidant. And then apply that to my business in meaningful ways that aren’t cop-out ways of kinda-sorta providing structure without really doing it. I’ve gotta be all in. This means: Fill the gaps where I rely on other people to do things that aren’t really their job but I haven’t put someone in place to do it Set and maintain expectations about our internal work processes, policies, and standards Define clear boundaries on things like roles, timelines, budgets, and scopes Now, this isn’t going to happen overnight. And just because I say that I need to step up to close these gaps doesn’t mean that I need to be the one who’s responsible for them (at least not forever). It just means that, as the business leader, I need to make sure the gaps get filled—by me or by someone else who has been specifically charged with owning that part of the operation. So, this is probably my #1 focus over the coming quarter. And it starts by identifying the gaps that exist. Then, step into those gaps myself, pay someone else to fill that role, or figure out how to eliminate the gap another way. This means going all the way back to the most basic decisions in our business. One of the foundational things about Optimist is being a “different kind” of agency. I always wanted to build something that solved for the bureaucracy, hierarchy, and siloed structure of agencies. If a client has feedback, they should be able to talk directly to the person doing the work rather than going through 3 layers of account management and creative directors. So I tried to be clever. I tried to design all kinds of systems and processes that eliminated these middle rungs. (In retrospect, what I was actually doing was designing a system that played into my avoidant tendencies and made it easy to abdicate responsibility for lots of things.) Since we didn’t want to create hierarchy, we never implemented things like Junior and Senior roles. We never hired someone to manage or direct the individual creatives. We didn’t have Directors or VPs. (Hell, we barely had a project manager for the first several years of existence.) This aversion to hierarchy aligned with our values around elevating ownership and collective contribution. I still believe in the value a flat structure. But a flat structure doesn’t eliminate the complexity of a growing business. No one to review writers and give them 1:1 feedback? I guess I’ll just have to do that….when I have some spare time. No Content Director? Okay, well someone needs to manage our content playbooks and roll out new ones. Just add it to my task list. Our flat structure didn’t eliminate the need for these roles. It just eliminated the people to do them. All of those unfilled roles ultimately fell back on me or our ops person, Katy. Of course, this isn’t the first time we’ve recognized this. We’ve known there were growing holes in our business as it’s gotten bigger and more complex. Over the years, we’ve experimented with different ways to solve for it. The Old Solution: Distributed Ops One system we designed was a “distributed ops” framework. Basically, we had one person who was the head of ops (at the time, we considered anything that was non-client-facing to be “ops”). They’d plan and organize all of the various things that needed to happen around Optimist. Then they’d assign out the work to whoever was able to help. We had a whole system for tying this into the our profit share and even gave people “Partner” status based on their contributions to ops. It worked—kinda. One big downfall is that all of the tasks and projects were ad hoc. People would pick up jobs, but they didn’t have much context or expertise to apply. So the output often varied. Since we were trying to maintain a flat structure, there was minimal oversight or management of the work. In other words, we didn’t always get the best results. But, more importantly, we still didn’t close all of the gaps entirely. Because everything was an ad-hoc list of tasks and projects, we never really had the “big picture” view of everything that needed to be done across the business. This also meant we rarely had clarity on what was important, what was trivial, and what was critical. We need a better system. Stop Reinventing the Wheel (And Create a Damn Org Chart) It’s time to get serious about filling the gaps in our business. It can’t be a half-fix or an ad hoc set of projects and tasks. We need clarity on the roles that need to be filled and then fill them. The first step here is to create an org chart. A real one. Map out all of the jobs that need to be done for Optimist to be successful besides just writers and designers. Roles like: Content director Design director SEO manager Reporting Finance Account management Business development Sales Marketing Project management It feels a bit laughable listing all of these roles. Because most are either empty or have my name attached to them. And that’s the problem. I can’t do everything. And all of the empty roles are gaps in our structure—places where people aren’t getting the direction, feedback, or guidance they need to do their best work. Or where things just aren’t being done consistently. Content director, for example, should be responsible for steering the output of our content strategists, writers, and editors. They’re not micromanaging every deliverable. But they give feedback, set overall policy, and help our team identify opportunities to get better. Right now we don’t have anyone in that role. Which means it’s my job—when I have time. Looking at the org chart (a real org chart that I actually built to help with this), it’s plain as day how many roles look like this. Even if we aren’t going to implement a traditional agency structure and a strict hierarchy, we still need to address these gaps. And the only way for that to happen is face the reality and then create a plan to close the gaps. Now that we have a list of theoretical roles, we need to clearly define the responsibilities and boundaries of those roles to make sure they cover everything that actually needs to happen. Then we can begin the process of delegating, assigning, hiring, and otherwise addressing each one. So that’s what I need to do. To be done: Create job descriptions for all of the roles we need to fill Hire Biz Dev role Hire Account Lead role(s) Hire Head of Content Playing Offense As we move into Q1 of 2025 and I reflect on the tumultuous few years we’ve had, one thought keeps running through my head. We need to play offense. Most of the last 1-2 years was reacting to changes that were happening around us. Trying to make sense and chart a new path forward. Reeling. But what I really want—as a person and as an entrepreneur—is to be proactive. I want to think and plan ahead. Figure out where we want to go before we’re forced to change course by something that’s out of our control. So my overarching focus for Q1 is playing offense. Thinking longer term. Getting ahead of the daily deluge and creating space to be more proactive, innovative, and forward thinking. To do: Pilot new content formats Audit and update our own content strategy Improve feedback workflows Build out long-term roadmap for 1-2 years for Optimist Final Note on Follow-Through and Cadence In my reflection this year, one of the things I’ve realized is how helpful these posts are for me. I process by writing. So I actually end up making a lot of decisions and seeing things more clearly each time I sit down to reflect and write my yearly recap. It also gives me a space to hold myself accountable for the things I said I would do. So, I’m doing two things a bit differently from here on out. First: I’m identifying clear action items that I’m holding myself accountable for getting done in the next 3 months (listed in the above sections). In each future update, I’ll do an accounting of what I got done and what wasn’t finished (and why). Second: I’m going to start writing shorter quarterly updates. This will gives me more chances each year to reflect, process, and make decisions. Plus it gives me a shorter feedback loop for the action items that I identified above. (See—playing offense.) — Okay friends, enemies, and frenemies. This is my first update for 2025. Glad to share with y’all. And thanks to everyone who’s read, commented, reached out, and shared their own experiences over the years. We are all the accumulation of our connections and our experiences. As always, I will pop in to respond to comments and answer questions. Feel free to share your thoughts, questions, and general disdain down below. Cheers, Tyler

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Alarming_Management3•This week

Switching Gears: Implementing AI for My Agency’s Marketing After a Decade

Hi there, I’ve been running a software development and design agency for the last 10 years, mainly focusing on building custom solutions for businesses and SaaS. For the last 2 years, I’ve consistently recommended that clients use AI technologies, especially for social media and content creation to generate traffic. Funny enough, I wasn’t practicing what I preached. Most of my client projects came from platforms like Upwork and word-of-mouth referrals from clients or people from networking events. Background I started my journey in 2014, switching from an employee to a freelancer. Within the first 10 months, my initial projects grew beyond what I could handle alone, prompting me to hire additional developers. This shift turned my role from a full-stack developer to a team lead and developer. Over the years, my focus has been a blend of tech and product. About five years ago, I realized the importance of design, leading me to adding designers to the agency to provide full-cycle service development—from product ideation and design to development, testing, launch, and support. I still continue to set up dedicated teams for some clients, maintaining a strong technical role as a tech lead, solution architect, and head product designer. To enhance my skills, I even completed UI/UX design courses to offer better product solutions. Despite these changes, building products has always been the easy part. The challenge was ensuring these client products didn’t end up in the graveyard due to poor product-market fit, often caused by inadequate marketing and sales strategies but more often just absence of them. (we are talking about startup and first time founders here 🙂 ) My Journey and Observations Advising Clients: I often found myself advising clients on increasing traffic for their SaaS products and crafting strategic marketing plans. Learning: I’ve gained most of my knowledge from consuming internet materials, courses, and blog posts and learning from successful client project launches. Realization: Despite giving this advice, I wasn’t applying these strategies to my own business, leading to low visits to my agency’s website. Initial Solution: Hiring a Marketer Hiring: I brought in a marketer with a solid background in content creating and interview video editing from an educational organization. Goal: The aim was to increase website visits through a comprehensive marketing strategy. Outcome: Although the content produced was high-quality and useful for pitching services, it didn’t lead to significant traffic increases. Issue: The marketer focused more on content creation rather than distribution channels, which limited effectiveness. Shift to AI-Driven Strategy Experiment: I decided to try using AI for content creation and distribution, which aligns with my agency’s specialization in design-driven development and AI integrations. Implementation plan: I will be generating all content with minimal edits using AI and implementing a strategic backlinking approach. Backlinking Strategy Initial Plan: I initially thought of hiring a specialist for backlinks. Realization: The costs and profiles of freelancers didn’t seem promising. Solution: I found AI-driven services for backlinks, which seem more efficient and cost-effective. Plan: My plan is to use these tools for programmatic SEO-driven AI-generated articles and third-party backlinking services over the next two to three months. Current Approach Management: This approach can be managed and executed by 1 person and monitored weekly, reducing human error and optimizing efficiency. I will start it myself and then replace myself with an editor with managing skills. Reflection: It’s a bit ironic and funny that it took me 10 years to start implementing these strategies in my own agency business, but I now feel more confident with AI and automation in place. Why Increase Website Visitors? You might ask, why do I want to increase the number of visitors to the site, and how can I ensure these visitors will be qualified? Hands-On Experience: To gain hands-on experience and perform this exercise effectively. Introduce Packaged Services: I want to introduce a set of low-cost packaged services tailored for non-technical people who want to build things for themselves - the DIY kits for non-technical folks. These services will provide a foundational template for them to build upon on top of existing established solutions such as Wix, Square Why am I Posting and Sharing Here? You might also wonder, why am I posting it here and sharing this? Well, I'm doing this more for myself. Most of my career, the things I’ve done have been behind the curtains. With this small project, I want to make it public to see the reaction of the community. Perhaps there will be good and smart suggestions offered, and maybe some insights or highlights of tools I wasn’t aware of or didn’t consider. I’ll keep sharing updates on this journey of website promotion, marketing, and SEO. My current goal is to reach 2,000 visits per month, which is a modest start. Looking forward to any thoughts or advice from this community! Disclaimer: This content was not generated by AI, but it was edited by it 😛

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AI_Scout_Official•This week

I run an AI automation agency (AAA). My honest overview and review of this new business model

I started an AI tools directory in February, and then branched off that to start an AI automation agency (AAA) in June. So far I've come across a lot of unsustainable "ideas" to make money with AI, but at the same time a few diamonds in the rough that aren't fully tapped into yet- especially the AAA model. Thought I'd share this post to shine light into this new business model and share some ways you could potentially start your own agency, or at the very least know who you are dealing with and how to pick and choose when you (inevitably) get bombarded with cold emails from them down the line. Foreword Running an AAA does NOT involve using AI tools directly to generate and sell content directly. That ship has sailed, and unless you are happy with $5 from Fiverr every month or so, it is not a real business model. Cry me a river but generating generic art with AI and slapping it onto a T-shirt to sell on Etsy won't make you a dime. At the same time, the AAA model will NOT require you to have a deep theoretical knowledge of AI, or any academic degree, as we are more so dealing with the practical applications of generative AI and how we can implement these into different workflows and tech-stacks, rather than building AI models from the ground up. Regardless of all that, common sense and a willingness to learn will help (a shit ton), as with anything. Keep in mind - this WILL involve work and motivation as well. The mindset that AI somehow means everything can be done for you on autopilot is not the right way to approach things. The common theme of businesses I've seen who have successfully implemented AI into their operations is the willingess to work with AI in a way that augments their existing operations, rather than flat out replace a worker or team. And this is exactly the train of thought you need when working with AI as a business model. However, as the field is relatively unsaturated and hype surrounding AI is still fresh for enterprises, right now is the prime time to start something new if generative AI interests you at all. With that being said, I'll be going over three of the most successful AI-adjacent businesses I've seen over this past year, in addition to some tips and resources to point you in the right direction. so.. WTF is an AI Automation Agency? The AI automation agency (or as some YouTubers have coined it, the AAA model) at its core involves creating custom AI solutions for businesses. I have over 1500 AI tools listed in my directory, however the feedback I've received from some enterprise users is that ready-made SaaS tools are too generic to meet their specific needs. Combine this with the fact virtually no smaller companies have the time or skills required to develop custom solutions right off the bat, and you have yourself real demand. I would say in practice, the AAA model is quite similar to Wordpress and even web dev agencies, with the major difference being all solutions you develop will incorporate key aspects of AI AND automation. Which brings me to my second point- JUST AI IS NOT ENOUGH. Rather than reducing the amount of time required to complete certain tasks, I've seen many AI agencies make the mistake of recommending and (trying to) sell solutions that more likely than not increase the workload of their clients. For example, if you were to make an internal tool that has AI answer questions based on their knowledge base, but this knowledge base has to be updated manually, this is creating unnecessary work. As such I think one of the key components of building successful AI solutions is incorporating the new (Generative AI/LLMs) with the old (programmtic automation- think Zapier, APIs, etc.). Finally, for this business model to be successful, ideally you should target a niche in which you have already worked and understand pain points and needs. Not only does this make it much easier to get calls booked with prospects, the solutions you build will have much greater value to your clients (meaning you get paid more). A mistake I've seen many AAA operators make (and I blame this on the "Get Rich Quick" YouTubers) is focusing too much on a specific productized service, rather than really understanding the needs of businesses. The former is much done via a SaaS model, but when going the agency route the only thing that makes sense is building custom solutions. This is why I always take a consultant-first approach. You can only build once you understand what they actually need and how certain solutions may impact their operations, workflows, and bottom-line. Basics of How to Get Started Pick a niche. As I mentioned previously, preferably one that you've worked in before. Niches I know of that are actively being bombarded with cold emails include real estate, e-commerce, auto-dealerships, lawyers, and medical offices. There is a reason for this, but I will tell you straight up this business model works well if you target any white-collar service business (internal tools approach) or high volume businesses (customer facing tools approach). Setup your toolbox. If you wanted to start a pressure washing business, you would need a pressure-washer. This is no different. For those without programming knowledge, I've seen two common ways AAA get setup to build- one is having a network of on-call web developers, whether its personal contacts or simply going to Upwork or any talent sourcing agency. The second is having an arsenal of no-code tools. I'll get to this more in a second, but this works beecause at its core, when we are dealing with the practical applications of AI, the code is quite simple, simply put. Start cold sales. Unless you have a network already, this is not a step you can skip. You've already picked a niche, so all you have to do is find the right message. Keep cold emails short, sweet, but enticing- and it will help a lot if you did step 1 correctly and intimately understand who your audience is. I'll be touching base later about how you can leverage AI yourself to help you with outreach and closing. The beauty of gen AI and the AAA model You don't need to be a seasoned web developer to make this business model work. The large majority of solutions that SME clients want is best done using an API for an LLM for the actual AI aspect. The value we create with the solutions we build comes with the conceptual framework and design that not only does what they need it to but integrates smoothly with their existing tech-stack and workflow. The actual implementation is quite straightforward once you understand the high level design and know which tools you are going to use. To give you a sense, even if you plan to build out these apps yourself (say in Python) the large majority of the nitty gritty technical work has already been done for you, especially if you leverage Python libraries and packages that offer high level abstraction for LLM-related functions. For instance, calling GPT can be as little as a single line of code. (And there are no-code tools where these functions are simply an icon on a GUI). Aside from understanding the capabilities and limitations of these tools and frameworks, the only thing that matters is being able to put them in a way that makes sense for what you want to build. Which is why outsourcing and no-code tools both work in our case. Okay... but how TF am I suppposed to actually build out these solutions? Now the fun part. I highly recommend getting familiar with Langchain and LlamaIndex. Both are Python libraires that help a lot with the high-level LLM abstraction I mentioned previously. The two most important aspects include being able to integrate internal data sources/knowledge bases with LLMs, and have LLMs perform autonomous actions. The two most common methods respectively are RAG and output parsing. RAG (retrieval augmented Generation) If you've ever seen a tool that seemingly "trains" GPT on your own data, and wonder how it all works- well I have an answer from you. At a high level, the user query is first being fed to what's called a vector database to run vector search. Vector search basically lets you do semantic search where you are searching data based on meaning. The vector databases then retrieves the most relevant sections of text as it relates to the user query, and this text gets APPENDED to your GPT prompt to provide extra context to the AI. Further, with prompt engineering, you can limit GPT to only generate an answer if it can be found within this extra context, greatly limiting the chance of hallucination (this is where AI makes random shit up). Aside from vector databases, we can also implement RAG with other data sources and retrieval methods, for example SQL databses (via parsing the outputs of LLM's- more on this later). Autonomous Agents via Output Parsing A common need of clients has been having AI actually perform tasks, rather than simply spitting out text. For example, with autonomous agents, we can have an e-commerce chatbot do the work of a basic customer service rep (i.e. look into orders, refunds, shipping). At a high level, what's going on is that the response of the LLM is being used programmtically to determine which API to call. Keeping on with the e-commerce example, if I wanted a chatbot to check shipping status, I could have a LLM response within my app (not shown to the user) with a prompt that outputs a random hash or string, and programmatically I can determine which API call to make based on this hash/string. And using the same fundamental concept as with RAG, I can append the the API response to a final prompt that would spit out the answer for the user. How No Code Tools Can Fit In (With some example solutions you can build) With that being said, you don't necessarily need to do all of the above by coding yourself, with Python libraries or otherwise. However, I will say that having that high level overview will help IMMENSELY when it comes to using no-code tools to do the actual work for you. Regardless, here are a few common solutions you might build for clients as well as some no-code tools you can use to build them out. Ex. Solution 1: AI Chatbots for SMEs (Small and Medium Enterprises) This involves creating chatbots that handle user queries, lead gen, and so forth with AI, and will use the principles of RAG at heart. After getting the required data from your client (i.e. product catalogues, previous support tickets, FAQ, internal documentation), you upload this into your knowledge base and write a prompt that makes sense for your use case. One no-code tool that does this well is MyAskAI. The beauty of it especially for building external chatbots is the ability to quickly ingest entire websites into your knowledge base via a sitemap, and bulk uploading files. Essentially, they've covered the entire grunt work required to do this manually. Finally, you can create a inline or chat widget on your client's website with a few lines of HTML, or altneratively integrate it with a Slack/Teams chatbot (if you are going for an internal Q&A chatbot approach). Other tools you could use include Botpress and Voiceflow, however these are less for RAG and more for building out complete chatbot flows that may or may not incorporate LLMs. Both apps are essentially GUIs that eliminate the pain and tears and trying to implement complex flows manually, and both natively incoporate AI intents and a knowledge base feature. Ex. Solution 2: Internal Apps Similar to the first example, except we go beyond making just chatbots but tools such as report generation and really any sort of internal tool or automations that may incorporate LLM's. For instance, you can have a tool that automatically generates replies to inbound emails based on your client's knowledge base. Or an automation that does the same thing but for replies to Instagram comments. Another example could be a tool that generates a description and screeenshot based on a URL (useful for directory sites, made one for my own :P). Getting into more advanced implementations of LLMs, we can have tools that can generate entire drafts of reports (think 80+ pages), based not only on data from a knowledge base but also the writing style, format, and author voice of previous reports. One good tool to create content generation panels for your clients would be MindStudio. You can train LLM's via prompt engineering in a structured way with your own data to essentially fine tune them for whatever text you need it to generate. Furthermore, it has a GUI where you can dictate the entire AI flow. You can also upload data sources via multiple formats, including PDF, CSV, and Docx. For automations that require interactions between multiple apps, I recommend the OG zapier/make.com if you want a no-code solution. For instance, for the automatic email reply generator, I can have a trigger such that when an email is received, a custom AI reply is generated by MyAskAI, and finally a draft is created in my email client. Or, for an automation where I can create a social media posts on multiple platforms based on a RSS feed (news feed), I can implement this directly in Zapier with their native GPT action (see screenshot) As for more complex LLM flows that may require multiple layers of LLMs, data sources, and APIs working together to generate a single response i.e. a long form 100 page report, I would recommend tools such as Stack AI or Flowise (open-source alternative) to build these solutions out. Essentially, you get most of the functions and features of Python packages such as Langchain and LlamaIndex in a GUI. See screenshot for an example of a flow How the hell are you supposed to find clients? With all that being said, none of this matters if you can't find anyone to sell to. You will have to do cold sales, one way or the other, especially if you are brand new to the game. And what better way to sell your AI services than with AI itself? If we want to integrate AI into the cold outreach process, first we must identify what it's good at doing, and that's obviously writing a bunch of text, in a short amount of time. Similar to the solutions that an AAA can build for its clients, we can take advantage of the same principles in our own sales processes. How to do outreach Once you've identified your niche and their pain points/opportunities for automation, you want to craft a compelling message in which you can send via cold email and cold calls to get prospects booked on demos/consultations. I won't get into too much detail in terms of exactly how to write emails or calling scripts, as there are millions of resources to help with this, but I will tell you a few key points you want to keep in mind when doing outreach for your AAA. First, you want to keep in mind that many businesses are still hesitant about AI and may not understand what it really is or how it can benefit their operations. However, we can take advantage of how mass media has been reporting on AI this past year- at the very least people are AWARE that sooner or later they may have to implement AI into their businesses to stay competitive. We want to frame our message in a way that introduces generative AI as a technology that can have a direct, tangible, and positive impact on their business. Although it may be hard to quantify, I like to include estimates of man-hours saved or costs saved at least in my final proposals to prospects. Times are TOUGH right now, and money is expensive, so you need to have a compelling reason for businesses to get on board. Once you've gotten your messaging down, you will want to create a list of prospects to contact. Tools you can use to find prospects include Apollo.io, reply.io, zoominfo (expensive af), and Linkedin Sales Navigator. What specific job titles, etc. to target will depend on your niche but for smaller companies this will tend to be the owner. For white collar niches, i.e. law, the professional that will be directly benefiting from the tool (i.e. partners) may be better to contact. And for larger organizations you may want to target business improvement and digital transformation leads/directors- these are the people directly in charge of projects like what you may be proposing. Okay- so you have your message, and your list, and now all it comes down to is getting the good word out. I won't be going into the details of how to send these out, a quick Google search will give you hundreds of resources for cold outreach methods. However, personalization is key and beyond simple dynamic variables you want to make sure you can either personalize your email campaigns directly with AI (SmartWriter.ai is an example of a tool that can do this), or at the very least have the ability to import email messages programmatically. Alternatively, ask ChatGPT to make you a Python Script that can take in a list of emails, scrape info based on their linkedin URL or website, and all pass this onto a GPT prompt that specifies your messaging to generate an email. From there, send away. How tf do I close? Once you've got some prospects booked in on your meetings, you will need to close deals with them to turn them into clients. Call #1: Consultation Tying back to when I mentioned you want to take a consultant-first appraoch, you will want to listen closely to their goals and needs and understand their pain points. This would be the first call, and typically I would provide a high level overview of different solutions we could build to tacke these. It really helps to have a presentation available, so you can graphically demonstrate key points and key technologies. I like to use Plus AI for this, it's basically a Google Slides add-on that can generate slide decks for you. I copy and paste my default company messaging, add some key points for the presentation, and it comes out with pretty decent slides. Call #2: Demo The second call would involve a demo of one of these solutions, and typically I'll quickly prototype it with boilerplate code I already have, otherwise I'll cook something up in a no-code tool. If you have a niche where one type of solution is commonly demanded, it helps to have a general demo set up to be able to handle a larger volume of calls, so you aren't burning yourself out. I'll also elaborate on how the final product would look like in comparison to the demo. Call #3 and Beyond: Once the initial consultation and demo is complete, you will want to alleviate any remaining concerns from your prospects and work with them to reach a final work proposal. It's crucial you lay out exactly what you will be building (in writing) and ensure the prospect understands this. Furthermore, be clear and transparent with timelines and communication methods for the project. In terms of pricing, you want to take this from a value-based approach. The same solution may be worth a lot more to client A than client B. Furthermore, you can create "add-ons" such as monthly maintenance/upgrade packages, training sessions for employeees, and so forth, separate from the initial setup fee you would charge. How you can incorporate AI into marketing your businesses Beyond cold sales, I highly recommend creating a funnel to capture warm leads. For instance, I do this currently with my AI tools directory, which links directly to my AI agency and has consistent branding throughout. Warm leads are much more likely to close (and honestly, much nicer to deal with). However, even without an AI-related website, at the very least you will want to create a presence on social media and the web in general. As with any agency, you will want basic a professional presence. A professional virtual address helps, in addition to a Google Business Profile (GBP) and TrustPilot. a GBP (especially for local SEO) and Trustpilot page also helps improve the looks of your search results immensely. For GBP, I recommend using ProfilePro, which is a chrome extension you can use to automate SEO work for your GBP. Aside from SEO optimzied business descriptions based on your business, it can handle Q/A answers, responses, updates, and service descriptions based on local keywords. Privacy and Legal Concerns of the AAA Model Aside from typical concerns for agencies relating to service contracts, there are a few issues (especially when using no-code tools) that will need to be addressed to run a successful AAA. Most of these surround privacy concerns when working with proprietary data. In your terms with your client, you will want to clearly define hosting providers and any third party tools you will be using to build their solution, and a DPA with these third parties listed as subprocessors if necessary. In addition, you will want to implement best practices like redacting private information from data being used for building solutions. In terms of addressing concerns directly from clients, it helps if you host your solutions on their own servers (not possible with AI tools), and address the fact only ChatGPT queries in the web app, not OpenAI API calls, will be used to train OpenAI's models (as reported by mainstream media). The key here is to be open and transparent with your clients about ALL the tools you are using, where there data will be going, and make sure to get this all in writing. have fun, and keep an open mind Before I finish this post, I just want to reiterate the fact that this is NOT an easy way to make money. Running an AI agency will require hours and hours of dedication and work, and constantly rearranging your schedule to meet prospect and client needs. However, if you are looking for a new business to run, and have a knack for understanding business operations and are genuinely interested in the pracitcal applications of generative AI, then I say go for it. The time is ticking before AAA becomes the new dropshipping or SMMA, and I've a firm believer that those who set foot first and establish themselves in this field will come out top. And remember, while 100 thousand people may read this post, only 2 may actually take initiative and start.

#reddit#automation#agency

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johnrushx•This week

26 Ways to Make Money as a Startup Founder (for coders & noncoders)

I've launched 24 projects (here is the proof johnrush.me). None of my projects is making millions a month, but many of them make over $1k a month, some do over $10k, and few do even more. I'd not recommend anyone to start by trying to build a unicorn. Better start simple. Aim for $2-4k a month first. Once you get there, either scale it or start a new project with large TAM. From my own experience, the 26 Ways to Make Money as a Startup Founder: One-Feature SaaS. Extract a feature from a popular tool and build a micro SaaS around it. Idea: A SaaS that only offers automated email follow-ups. Launchpads. Develop a launch platform for a specific industry. Idea: A launchpad for growth tools. SEO Tools. Create a tool that focuses on a single aspect of SEO. Idea: A tool that generates alt texts for images. Productized Services. Offer standardized services that are repeatable. Idea: design, coding or social media management. Marketplace Platforms. Create a platform that connects buyers and sellers, earning transaction fees. Idea: An online marketplace for domains. Membership Sites. A subscription-based site with exclusive content. Idea: A founder 0-to-1 site. White Labeling. A product that other businesses can rebrand as their own. Idea: A white-labeled website builder. Selling Data. Provide anonymized data insights to companies. Idea: Selling user behavior data. Affiliate Marketing. Promote products/services and earn commissions on sales. Idea: Recommending hosting services on a tech blog. Selling Leads. Generate and sell business leads. Idea: Selling leads who raised a fresh seed round. Niche Social Networks. Create a paid community around a specific interest. Idea: A network for SEO experts. Sell Domains. Buy and sell domain names for profit. Virtual Products. Sell digital products like templates or graphics. Idea: Website themes for nextjs or boilerplates. On-Demand Services. Build a platform for gigs like delivery or tutoring. Idea: An app for freelance tutors. Niche Job Boards. Start a job board focused on a specific industry. Idea: A job board for remote tech jobs. Crowdsourced Content. Create a user-generated content platform and monetize through ads. Idea: Site to share startup hacks. Buy and Flip Businesses. Purchase underperforming businesses, improve them, and sell for profit. Idea: Acquiring a low-traffic blog, optimizing it, and selling. AI-Powered agents. Develop AI tools that solve specific business problems. Idea: An AI tool that automates customer support. Microservices. Offer small, specialized tools, sdks or APIs. Idea: An api for currency conversion. Influencer Platforms. Create a platform connecting influencers with brands. Idea: Connect AI influencers with AI founders. Niche Directories. Build a paid directory for a specific industry. Idea: A directory of developers who can train models. E-Learning Platforms. Build a platform for educators to sell courses. Idea: A site where AI experts sell AI courses. Virtual assistants. Hire them and sell on subscription. No-Code Tools. Create tools that allow non-technical users to build things. Idea: A no-code website builder for bakeries. Labor arbitrage. Idea: Connect support agents from Portugal with US clients and charge commission.

#reddit#ways#make

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jeannen•This week

I just sold my startup for $200,000 after 11 months. AMA

Last August, I was looking for a startup idea I could grow and made a MVP in a week then launched it. I received the $200,000 wire from the buyer a couple of days ago I found tons of useful info online for free, so I hope this can be my way of giving back :) Here is some background: Idea I got the idea when trying to write a tweet using Google Doc's transcription tool, which was terrible. I was pretty sure I wasn't the only one too lazy to type, I made my own solution using AI to transcribe and reformat voice notes into any kind of content. I called it Talknotes, mainly because it was the only domain available lol Validation: My rule is to only reinvest what the project generates. After listing on startup directories and posting on Twitter, I generated $700 in 10 days. It wasn't much, but enough to show interest and keep me motivated. I added user-requested features, but the launch effect wore off, and daily revenues dropped to $0 after a few weeks. I almost gave up, but friends encouraged me to continue. In October, I launched on ProductHunt and it blew up. It became Product of the Day and reached $1500 MRR thanks to media coverage. I initially built everything using vanilla JS/CSS/HTML + Node for backend. But it's pretty limited for apps with lots of interactivity so, I rebuilt the app using Nuxt.js to make it easier to ship new features. Then, I launched ads on Facebook and I implemented a feedback loop: Get new users Learn about them through onboarding Make more ads based on onboarding data This doubled MRR in about 2 months. Burnout and Sale: In May, I had a bad burnout after emergency bug fixes. This made it hard to work on the app after. At this point MRR was around $7000 and total revenues around $70,0000 I listed it on Acquire.com for $200,000, a very good price for the buyer considering revenues and growth. I could've gotten $300,000 with buyer financing or earn-outs, but I wanted cash, $200,000 today is better than $300,000 in a year. Everything was smooth until we tried using Escrow, which almost fucked up the deal (details here). Long story short, had to threaten them to make a sponsored post on Twitter explaining what they did + legal action. They sent the refund the very next day, and we completed the transfer directly. Now, this isn't an overnight success. It's the result of 7 years of grind. I launched over 40 projects since I started, and most of them failed. I often worked 100 hours per week, and I rarely go out or meet many people. It's not for everyone, but I'm fine with it With the profit from the app + sale, and other projects, I have close to 1/3 of a million dollar. I could retire in Asia if I wanted Just mind blowing to think I wrote funny characters in a code editor and sold it for the price of a house lol Edit 1: A few people got confused. I said it's 7 years of grind and most of my projects failed, not that I was not making money. I also said I OFTEN worked 100h/week, not every week :) Since I learned to code 2 years ago I've made close to $400k from my app's profit + exit (this one + another one for $65k last year). And before that I was making money as a marketing freelancer. Also, I dropped after high-school, so, I had to learn everything from scratch, it takes time! Edit 2: Lots of people asked how/where I learned to code in 2 months. I wrote a blog/journal about it back then with links to resources, you can find it here if you're interested

#reddit#just#sold

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Alarming_Management3•This week

Switching Gears: Implementing AI for My Agency’s Marketing After a Decade

Hi there, I’ve been running a software development and design agency for the last 10 years, mainly focusing on building custom solutions for businesses and SaaS. For the last 2 years, I’ve consistently recommended that clients use AI technologies, especially for social media and content creation to generate traffic. Funny enough, I wasn’t practicing what I preached. Most of my client projects came from platforms like Upwork and word-of-mouth referrals from clients or people from networking events. Background I started my journey in 2014, switching from an employee to a freelancer. Within the first 10 months, my initial projects grew beyond what I could handle alone, prompting me to hire additional developers. This shift turned my role from a full-stack developer to a team lead and developer. Over the years, my focus has been a blend of tech and product. About five years ago, I realized the importance of design, leading me to adding designers to the agency to provide full-cycle service development—from product ideation and design to development, testing, launch, and support. I still continue to set up dedicated teams for some clients, maintaining a strong technical role as a tech lead, solution architect, and head product designer. To enhance my skills, I even completed UI/UX design courses to offer better product solutions. Despite these changes, building products has always been the easy part. The challenge was ensuring these client products didn’t end up in the graveyard due to poor product-market fit, often caused by inadequate marketing and sales strategies but more often just absence of them. (we are talking about startup and first time founders here 🙂 ) My Journey and Observations Advising Clients: I often found myself advising clients on increasing traffic for their SaaS products and crafting strategic marketing plans. Learning: I’ve gained most of my knowledge from consuming internet materials, courses, and blog posts and learning from successful client project launches. Realization: Despite giving this advice, I wasn’t applying these strategies to my own business, leading to low visits to my agency’s website. Initial Solution: Hiring a Marketer Hiring: I brought in a marketer with a solid background in content creating and interview video editing from an educational organization. Goal: The aim was to increase website visits through a comprehensive marketing strategy. Outcome: Although the content produced was high-quality and useful for pitching services, it didn’t lead to significant traffic increases. Issue: The marketer focused more on content creation rather than distribution channels, which limited effectiveness. Shift to AI-Driven Strategy Experiment: I decided to try using AI for content creation and distribution, which aligns with my agency’s specialization in design-driven development and AI integrations. Implementation plan: I will be generating all content with minimal edits using AI and implementing a strategic backlinking approach. Backlinking Strategy Initial Plan: I initially thought of hiring a specialist for backlinks. Realization: The costs and profiles of freelancers didn’t seem promising. Solution: I found AI-driven services for backlinks, which seem more efficient and cost-effective. Plan: My plan is to use these tools for programmatic SEO-driven AI-generated articles and third-party backlinking services over the next two to three months. Current Approach Management: This approach can be managed and executed by 1 person and monitored weekly, reducing human error and optimizing efficiency. I will start it myself and then replace myself with an editor with managing skills. Reflection: It’s a bit ironic and funny that it took me 10 years to start implementing these strategies in my own agency business, but I now feel more confident with AI and automation in place. Why Increase Website Visitors? You might ask, why do I want to increase the number of visitors to the site, and how can I ensure these visitors will be qualified? Hands-On Experience: To gain hands-on experience and perform this exercise effectively. Introduce Packaged Services: I want to introduce a set of low-cost packaged services tailored for non-technical people who want to build things for themselves - the DIY kits for non-technical folks. These services will provide a foundational template for them to build upon on top of existing established solutions such as Wix, Square Why am I Posting and Sharing Here? You might also wonder, why am I posting it here and sharing this? Well, I'm doing this more for myself. Most of my career, the things I’ve done have been behind the curtains. With this small project, I want to make it public to see the reaction of the community. Perhaps there will be good and smart suggestions offered, and maybe some insights or highlights of tools I wasn’t aware of or didn’t consider. I’ll keep sharing updates on this journey of website promotion, marketing, and SEO. My current goal is to reach 2,000 visits per month, which is a modest start. Looking forward to any thoughts or advice from this community! Disclaimer: This content was not generated by AI, but it was edited by it 😛

#reddit#switching#gears:

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madscholar•This week

Where Do I Find Like-Minded, Unorthodox Co-founders? [Tech]

After more than 20 years in the tech industry I'm pretty fed up. I've been at it non-stop, so the burnout was building up for a while. Eventually, it's gotten so bad that it was no longer a question whether I need to take a break; I knew that I had to, for the sake of myself and loved ones. A few months ago I quit my well-paying, mid-level mgmt job to have some much-needed respite. I can't say that I've fully recovered, but I'm doing a bit better, so I'm starting to think about what's next. That said, the thoughts of going back into the rat race fill me with dread and anxiety. I've had an interesting career - I spent most of it in startups doing various roles from an SWE to a VP Eng, including having my own startup adventures for a couple of years. The last 4.5 years of my career have been in one of the fastest growing tech companies - it was a great learning experience, but also incredibly stressful, toxic and demoralizing. It's clear to me that I'm not cut out for the corporate world -- the ethos contradicts with my personality and beliefs -- but it's not just. I've accumulated "emotional scars" from practically every place I worked at and it made me loathe the industry to the degree that if I ever have another startup, it'd have to be by my own -- unorthodox -- ideals, even if it means a premature death due to lack of funding. I was young, stupid and overly confident when I had my first startup. I tried to do it "by the book" and dance to the tune of investors. While my startup failed for other, unrelated reasons, it gave me an opportunity to peak behind the curtain, experience the power dynamics, and get a better understanding to how the game is played - VCs and other person of interest have popularized the misconception that if a company doesn't scale, it would stagnate and eventually regress and die. This is nonsense. This narrative was created because it would make the capitalist pigs obsolete - they need companies to go through the entire alphabet before forcing them to sell or IPO. The sad reality is that the most entrepreneurs still believe in this paradigm and fall into the VC's honeypot traps. It's true that many businesses cannot bootstrap or scale without VC money, but it's equally true that far too many companies pivot/scale prematurely (and enshitify their product in the process) due to external pressures fueled by pure greed. This has a top-bottom effect - enshitification doesn't only effect users, but it also heavily effects the processes and structrures of companies, which can explain why the average tenure in tech is only \~2 years. I think that we live in an age where self-starting startups are more feasible than ever. It's not just the rise of AI and automation, but also the plethora of tools, services, and open-source projects that are available to all for free. On the one hand, this is fantastic, but on the other, the low barrier-to-entry creates oversaturation of companies which makes research & discovery incredibly hard - it is overwhelming to keep up with the pace and distill the signal from the noise, and there's a LOT of noise - there's not enough metaphorical real-estate for the graveyard of startups that will be defunct in the very near future. I'd like to experiment with startups again, but I don't want to navigate through this complex mine field all by myself - I want to find a like-minded co-founder who shares the same ideals as I do. It goes without saying that being on the same page isn't enough - I also want someone who's experienced, intelligent, creative, productive, well-rounded, etc. At the moment, I don't have anyone in my professional network who has/wants what it takes. I can look into startup bootcamps/accelerators like YC et al., and sure enough, I'll find talented individuals, but it'd be a mismatch from the get-go. For shits and giggles, this is (very roughly) how I envision the ideal company: Excellent work life balance: the goal is not to make a quick exit, become filthy rich, and turn into a self-absorbed asshole bragging about how they got so succesful. The goal is to generate a steady revenue stream while not succumbing to social norms that encourage greed. The entire purpose is to reach humble financial indepedence while maintaining a stress-free (as one possibly can) work environment. QOL should always be considered before ARR. Bootstraping: no external money. Not now, not later. No quid pro quo. No shady professionals or advisors. Company makes it or dies trying. Finances: very conservative to begin with - the idea is to play it safe and build a long fucking runaway before hiring. Spend every penny mindfully and frugally. Growth shouldn't be too quick & reckless. The business will be extremely efficient in spending. The only exception to the rule is crucial infrastructure and wages to hire top talent and keep salaries competitive and fair. Hiring: fully remote. Global presence, where applicable. Headcount will be limited to the absolute bare minimum. The goal is to run with a skeleton crew of the best generalists out there - bright, self-sufficient, highly motivated, autodidact, and creative individuals. Hiring the right people is everything and should be the company's top priority. Compensation & Perks: transperent and fair, incentivizing exceptional performance with revenue sharing bonuses. The rest is your typical best-in-class perks: top tier health/dental/vision insurance, generous PTO with mandatory required minimum, parental leave, mental wellness, etc. Process: processes will be extremely efficient, automated to the max, documented, unbloated, and data-driven through and through. Internal knowledge & data metrics will be accessible and transparent to all. Employees get full autonomy of their respective areas and are fully in charge of how they spend their days as long as they have agreed-upon, coherent, measurable metrics of success. Meetings will be reduced to the absolute minimum and would have to be justified and actionable - the ideal is that most communications will be done in written form, while face-to-face will be reserved for presentations/socializing. I like the Kaizen philosophy to continuously improve and optimize processes. Product: As previously stated, "data-driven through and through". Mindful approach to understand cost/benefit. Deliberate and measured atomic improvements to avoid feature creep and slow down the inevitable entropy. Most importantly, client input should be treated with the utmost attention but should never be the main driver for the product roadmap. This is a very controversial take, but sometimes it's better to lose a paying customer than to cave to their distracting/unreasonable/time-consuming demands. People Culture: ironicaly, this would be what most companies claim to have, but for realsies. Collaborative, open, blameless environment. People are treated like actual grown ups with flat structure, full autonomy, and unwavering trust. Socializing and bonding is highly encourged, but never required. Creativity and ingenuity is highly valued - people are encouraged to work on side projects one day of the week. Values: I can write a lot about it, but it really boils down to being kind and humble. We all know what happened with "don't be evil". It's incredibly hard to retain values over time, esp. when there are opposing views within a company. I don't know how to solve it, but I believe that there should be some (tried and true) internal checks & balances from the get go to ensure things are on track. I never mentioned what this hypothetical startup does. Sure, there's another very relevant layer of domain experience fit, but this mindset allows one to be a bit more fluid because the goal is not to disrupt an industry or "make the world a better place"; it's to see work for what it truly is - a mean to an end. It's far more important for me to align with a co-founder on these topics than on an actual idea or technical details. Pivoting and rebranding are so common that many VCs outweigh the make up and chemistry of the founding team (and their ability to execute) over the feasibility of their ideas.  To wrap this long-winded post, I'm not naive or disillusioned - utopias aren't real and profitable companies who operate at a 70-80% rate of what I propose are the real unicorns, but despite them being a tiny minority, I think they are the real forward thinkers of the industry. I might be wrong, but I hope that I'm right and that more and more startups will opt towards long-term sustainability over the promise of short-term gains because the status quo really stinks for most people. What do you folks think? Does anyone relate? Where can I find others like me? P.S I thought about starting a blog writing about these topics in length (everything that is wrong with tech & what can be done to improve it), but I have the Impostor Syndrom and I'm too self-conscious about how I come off. If you somehow enjoyed reading through that and would love to hear more of my thoughts and experiences in greater detail, please let me know. P.P.S If you have a company that is close to what I'm describing and you're hiring, let me know!

#reddit#find#like-minded,

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TornikePkh•This week

A Free Course for Entrepreneurs to use AI text-to-image for real-life use cases

Hi there👋 We are flooded by text-to-image generators and how cool they are to generate cute photos of cats. Around a month ago I asked myself, how can I, an entrepreneur (kinda?), use this tool for my real-life professional use? I am very actively involved in running my stuff, including an e-commerce shop and an ed-tech startup. At the same time, I am a co-founder at an AI venture studio, thus spending a lot of time in that field, especially for the last 6-8 months. I decided to share my journey of figuring out how to use generative Art for my projects. In this 2-hour exploration-type course, I share tactics and use cases that I found to be very useful, and hope to be useful for some of you as well. Here are the main takeaways that I have regarding text-to-image generators: There is a learning curve for diving deep into different parameters, but once you do, you can create very useful visual assets for your projects It is an amazingly well fit for visual explorations \- creating draft mood boards, crafting visual identity for new projects and marketing materials If your project/startup can not afford top designers, generative Art is the only option to level up your project's visual communication capabilities. I would love to hear about your experience with using text-to-image generators for your real-life cases.

#reddit#free#course

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pknerd•This week

Hey r/Entrepreneur, backend dev with a passion for AI and automation here!

Been freelancing for a while, but lately, I've been diving headfirst into building innovative solutions like chatbots, custom GPT integrations for websites (think monetizing your own GPT or reaching users beyond the platform!), and automating content creation workflows. These have been exciting projects so far, but I'm reaching out to the entrepreneurial minds here: Is there potential to turn these AI/automation skills into a business that could help to generate monthly revenue? I'm eager to learn from experienced entrepreneurs and explore different possibilities. Any insights or advice you have would be greatly appreciated! P.S. I've included some demo videos of my work in the comments below if you'd like to see what I've been building.

#reddit#r/entrepreneur,#backend

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guidum80•This week

Feedback appreciated 🙏🏻 : a tool for solo entrepreneurs and small startups to help with marketing | app.maestrix.ai

Hey friends, I've run a boutique marketing agency for almost 8 years and since chatGPT has arrived a year ago, the game has really changed. I'm sure there are here a handful of you who have tried AI in some fashion for a marketing-related task; My realization has quickly been that this could (and will) make my (and a ton of ) marketing jobs obsolete. That made me start to go very serious about writing extremely specific instructions for marketing tasks. I started to map out all my knowledge and operating frameworks that I knew with 20+ years of experience. I ended up organizing these core marketing activities with AI: positioning and value proposition canvases, buyer's journey analysis target audience analysis SEO strategy campaign messaging blog and social copywriting landing pages — Fast forward 6 months today, I''ve built the no-code tool that runs with OpenAi's latest model (LLM) available via API (GPT-4-Turbo-preview for the geeks) : app.maestrix.ai I'd love to get your feedback on the following items: \- the quality of the outputs generated for your product/service \- the tasks that are missing for your current projects. \- how I can make it more useful down the road Your feedbacks are very precious 🙏🏻 Guillaume — P.S: DM me if you have tested it so I can give you thank-you task credits 🎁

#reddit#feedback#appreciated

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jamesackerman1234•This week

[CASE STUDY] From 217/m to $2,836/m in 9 months - Sold for $59,000; I grow and monetise web traffic of 5, 6, 7 figures USD valued passive income content sites [AMA]

Hello Everyone (VERY LONG CASE STUDY AHEAD) - 355% return in 9 months Note: I own a 7-figures USD valued portfolio of 41+ content sites that generates 5-6 figures USD a month in passive income. This is my first time posting in this sub and my goal is to NOT share generic advice but precise numbers, data and highly refined processes so you can also get started with this business yourself or if you already have an existing business, drive huge traffic to it and scale it substantially (get more customers). I will use a case study to explain the whole process. As most of us are entrepreneurs here, explaining an actual project would be more meaningful. In this case study I used AI assisted content to grow an existing site from $217/m to $2,836/m in 9 months (NO BACKLINKS) and sold it for $59,000. ROI of 3 months: 355% Previous case studies (before I give an overview of the model) Amazon Affiliate Content Site: $371/m to $19,263/m in 14 MONTHS - $900K CASE STUDY \[AMA\] Affiliate Website from $267/m to $21,853/m in 19 months (CASE STUDY - Amazon?) \[AMA\] Amazon Affiliate Website from $0 to $7,786/month in 11 months Amazon Affiliate Site from $118/m to $3,103/m in 8 MONTHS (SOLD it for $62,000+) Note: You can check pinned posts on my profile. Do go through the comments as well as a lot of questions are answered in those. However, if you still have any questions, feel free to reach out. This is an \[AMA\]. Quick Overview of the Model Approach: High traffic, niche specific, informative content websites that monetise its traffic through highly automated methods like display ads and affiliate. The same model can be applied to existing businesses to drive traffic and get customers. Main idea: Make passive income in a highly automated way Easy to understand analogy You have real estate (here you have digital asset like a website) You get rental income (here you get ads and affiliate income with no physical hassle, in case you have a business like service, product etc. then you can get customers for that too but if not, it's alright) Real estate has value (this digital asset also has value that can be appreciated with less effort) Real estate can be sold (this can be sold too but faster) IMPORTANT NOTE: Search traffic is the BEST way to reach HUGE target audience and it's important when it comes to scaling. This essentially means that you can either monetise that via affiliate, display etc. or if you have a business then you can reach a bigger audience to scale. Overview of this website's valuation (then and now: Oct. 2022 and June 2023) Oct 2022: $217/m Valuation: $5,750.5 (26.5x) - set it the same as the multiple it was sold for June 2023: $2,836/m Traffic and revenue trend: growing fast Last 3 months avg: $2,223 Valuation now: $59,000 (26.5x) Description: The domain was registered in 2016, it grew and then the project was left unattended. I decided to grow it again using properly planned AI assisted content. Backlink profile: 500+ Referring domains (Ahrefs). Backlinks mean the sites linking back to you. This is important when it comes to ranking. Summary of Results of This Website - Before and After Note: If the terms seem technical, do not worry. I will explain them in detail later. Still if you have any questions. Feel free to comment or reach out. |Metric|Oct 2022|June 2023|Difference|Comments| |:-|:-|:-|:-|:-| |Articles|314|804|\+490|AI Assisted content published in 3 months| |Traffic|9,394|31,972|\+22,578|Organic| |Revenue|$217|$2,836|\+$2,619|Multiple sources| |RPM (revenue/1000 web traffic)|23.09|$88.7|\+$65.61|Result of Conversion rate optimisation (CRO). You make changes to the site for better conversions| |EEAT (expertise, experience, authority and trust of website)|2 main authors|8 authors|6|Tables, video ads and 11 other fixations| |CRO|Nothing|Tables, video ads |Tables, video ads and 11 other fixations || &#x200B; Month by Month Growth |Month|Revenue|Steps| |:-|:-|:-| |Sept 2022|NA|Content Plan| |Oct 2022|$217|Content Production| |Nov 2022|$243|Content production + EEAT authors| |Dec 2022|$320|Content production + EEAT authors| |Jan 2023|$400|Monitoring| |Feb 2023|$223|Content production + EEAT authors| |Mar 2023|$2,128|CRO & Fixations| |April 2023|$1,609|CRO & Fixations| |May 2023|$2,223|Content production + EEAT authors| |June 2023|$2,836|CRO and Fixations| |Total|$10,199|| &#x200B; What will I share Content plan and Website structure Content Writing Content Uploading, formatting and onsite SEO Faster indexing Conversion rate optimisation Guest Posting EEAT (Experience, Expertise, Authority, Trust) Costing ROI The plans moving forward with these sites &#x200B; Website Structure and Content Plan This is probably the most important important part of the whole process. The team spends around a month just to get this right. It's like defining the direction of the project. Description: Complete blueprint of the site's structure in terms of organisation of categories, subcategories and sorting of articles in each one of them. It also includes the essential pages. The sorted articles target main keyword, relevant entities and similar keywords. This has to be highly data driven and we look at over 100 variables just to get it right. It's like beating Google's algorithm to ensure you have a blueprint for a site that will rank. It needs to be done right. If there is a mistake, then even if you do everything right - it's not going to work out and after 8-16 months you will realise that everything went to waste. Process For this project, we had a niche selected already so we didn't need to do a lot of research pertaining to that. We also knew the topic since the website was already getting good traffic on that. We just validated from Ahrefs, SEMRUSH and manual analysis if it would be worth it to move forward with that topic. &#x200B; Find entities related to the topic: We used Ahrefs and InLinks to get an idea about the related entities (topics) to create a proper topical relevance. In order to be certain and have a better idea, we used ChatGPT to find relevant entities as well \> Ahrefs (tool): Enter main keyword in keywords explorer. Check the left pain for popular topics \> Inlinks (tool): Enter the main keyword, check the entity maps \> ChatGPT (tool): Ask it to list down the most important and relevant entities in order of their priority Based on this info, you can map out the most relevant topics that are semantically associated to your main topic Sorting the entities in topics (categories) and subtopics (subcategories): Based on the information above, cluster them properly. The most relevant ones must be grouped together. Each group must be sorted into its relevant category. \> Example: Site about cycling. \> Categories/entities: bicycles, gear and equipment, techniques, safety, routes etc. \> The subcategories/subentities for let's say "techniques" would be: Bike handling, pedaling, drafting etc. Extract keywords for each subcategory/subentity: You can do this using Ahrefs or Semrush. Each keyword would be an article. Ensure that you target the similar keywords in one article. For example: how to ride a bicycle and how can I ride a bicycle will be targeted by one article. Make the more important keyword in terms of volume and difficulty as the main keyword and the other one(s) as secondary Define main focus vs secondary focus: Out of all these categories/entities - there will be one that you would want to dominate in every way. So, focus on just that in the start. This will be your main focus. Try to answer ALL the questions pertaining to that. You can extract the questions using Ahrefs. \> Ahrefs > keywords explorer \> enter keyword \> Questions \> Download the list and cluster the similar ones. This will populate your main focus category/entity and will drive most of the traffic. Now, you need to write in other categories/subentities as well. This is not just important, but crucial to complete the topical map loop. In simple words, if you do this Google sees you as a comprehensive source on the topic - otherwise, it ignores you and you don't get ranked Define the URLs End result: List of all the entities and sub-entities about the main site topic in the form of categories and subcategories respectively. A complete list of ALL the questions about the main focus and at around 10 questions for each one of the subcategories/subentities that are the secondary focus Content Writing So, now that there's a plan. Content needs to be produced. Pick out a keyword (which is going to be a question) and... Answer the question Write about 5 relevant entities Answer 10 relevant questions Write a conclusion Keep the format the same for all the articles. Content Uploading, formatting and onsite SEO Ensure the following is taken care of: H1 Permalink H2s H3s Lists Tables Meta description Socials description Featured image 2 images in text \\Schema Relevant YouTube video (if there is) Note: There are other pointers link internal linking in a semantically relevant way but this should be good to start with. Faster Indexing Indexing means Google has read your page. Ranking only after this step has been done. Otherwise, you can't rank if Google hasn't read the page. Naturally, this is a slow process. But, we expedite it in multiple ways. You can use RankMath to quickly index the content. Since, there are a lot of bulk pages you need a reliable method. Now, this method isn't perfect. But, it's better than most. Use Google Indexing API and developers tools to get indexed. Rank Math plugin is used. I don't want to bore you and write the process here. But, a simple Google search can help you set everything up. Additionally, whenever you post something - there will be an option to INDEX NOW. Just press that and it would be indexed quite fast. Conversion rate optimisation Once you get traffic, try adding tables right after the introduction of an article. These tables would feature a relevant product on Amazon. This step alone increased our earnings significantly. Even though the content is informational and NOT review. This still worked like a charm. Try checking out the top pages every single day in Google analytics and add the table to each one of them. Moreover, we used EZOIC video ads as well. That increased the RPM significantly as well. Both of these steps are highly recommended. Overall, we implemented over 11 fixations but these two contribute the most towards increasing the RPM so I would suggest you stick to these two in the start. Guest Posting We made additional income by selling links on the site as well. However, we were VERY careful about who we offered a backlink to. We didn't entertain any objectionable links. Moreover, we didn't actively reach out to anyone. We had a professional email clearly stated on the website and a particularly designated page for "editorial guidelines" A lot of people reached out to us because of that. As a matter of fact, the guy who bought the website is in the link selling business and plans to use the site primarily for selling links. According to him, he can easily make $4000+ from that alone. Just by replying to the prospects who reached out to us. We didn't allow a lot of people to be published on the site due to strict quality control. However, the new owner is willing to be lenient and cash it out. EEAT (Experience, Expertise, Authority, Trust) This is an important ranking factor. You need to prove on the site that your site has authors that are experienced, have expertise, authority and trust. A lot of people were reaching out to publish on our site and among them were a few established authors as well. We let them publish on our site for free, added them on our official team, connected their socials and shared them on all our socials. In return, we wanted them to write 3 articles each for us and share everything on all the social profiles. You can refer to the tables I shared above to check out the months it was implemented. We added a total of 6 writers (credible authors). Their articles were featured on the homepage and so were their profiles. Costing Well, we already had the site and the backlinks on it. Referring domains (backlinks) were already 500+. We just needed to focus on smart content and content. Here is the summary of the costs involved. Articles: 490 Avg word count per article: 1500 Total words: 735,000 (approximately) Cost per word: 2 cents (includes research, entities, production, quality assurance, uploading, formatting, adding images, featured image, alt texts, onsite SEO, publishing/scheduling etc.) Total: $14,700 ROI (Return on investment) Earning: Oct 22 - June 23 Earnings: $10,199 Sold for: $59,000 Total: $69,199 Expenses: Content: $14,700 Misc (hosting and others): $500 Total: $15,200 ROI over a 9 months period: 355.25% The plans moving forward This website was a part of a research and development experiment we did. With AI, we wanted to test new waters and transition more towards automation. Ideally, we want to use ChatGPT or some other API to produce these articles and bulk publish on the site. The costs with this approach are going to be much lower and the ROI is much more impressive. It's not the the 7-figures projects I created earlier (as you may have checked the older case studies on my profile), but it's highly scalable. We plan to refine this model even further, test more and automate everything completely to bring down our costs significantly. Once we have a model, we are going to scale it to 100s of sites. The process of my existing 7-figures websites portfolio was quite similar. I tested out a few sites, refined the model and scaled it to over 41 sites. Now, the fundamentals are the same however, we are using AI in a smarter way to do the same but at a lower cost, with a smaller team and much better returns. The best thing in my opinion is to run numerous experiments now. Our experimentation was slowed down a lot in the past since we couldn't write using AI but now it's much faster. The costs are 3-6 times lower so when it used to take $50-100k to start, grow and sell a site. Now you can pump 3-6 more sites for the same budget. This is a good news for existing business owners as well who want to grow their brand. Anyway, I am excited to see the results of more sites. In the meantime, if you have any questions - feel free to let me know. Best of luck for everything. Feel free to ask questions. I'd be happy to help. This is an AMA.

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IntellectualAINC•This week

We create AI software and provide AI automation for companies. Here is a list of the best AI tools for sales IMHO

Here are some AI tools that are useful for sales. I tried to touch as many different parts of the sales process so the tools are all quite different but all useful for sales. I tried to include some of the best and underrated AI tools. Most of them are free so check them out if you want. I did not include ChatGPT as it can basically be used for anything with the right prompts. So these tools will be more research-oriented. A quick disclaimer – I work for the company Idealink where we create custom ChatGPT for businesses and other AI products. Apollo AI Seamless AI CoPilot AI Lavender AI Regie AI Gemini Plusdocs Make Midjourney Fireflies AI Apollo AI - Find potential customers Apollo is a platform for sales and business development. It offers a range of tools to find and engage with ideal customers. The platform has an extensive B2B database and features that streamline the sales process from prospecting to closing deals. Key Features: Extensive B2B Database: Apollo boasts a large, accurate database of over 275 million contacts, providing a wealth of potential leads and opportunities for sales teams. Data Enrichment and Lead Insights: The platform offers data enrichment capabilities, ensuring CRM systems are continuously updated with detailed and actionable lead information. AI-Driven Sales Engagement: Apollo's AI technology assists in crafting effective communication and prioritizing high-value leads, enhancing the overall sales engagement process. Comprehensive Sales Tools: The platform provides an integrated suite of tools for email, call, and social media engagement, combined with analytics and automation features to streamline the sales cycle. Tailored Solutions for Teams: Apollo offers customized solutions for different team types, including sales and business development, founders, and marketing teams, addressing specific needs and goals. Seamless AI - Sale process made easier Seamless.AI is an innovative B2B sales lead generation solution that allows sales teams to efficiently connect with their ideal customers. The platform's features provide accurate and up-to-date contact information and integrate easily with existing sales and marketing tools. Key Features: Real-Time Search Engine: Seamless.AI uses AI to scour the web in real time, ensuring the contact information for sales leads is current and accurate. Comprehensive Integration: Easily integrates with popular CRMs and sales tools like Salesforce, HubSpot, and LinkedIn Sales Navigator, enhancing productivity and eliminating manual data entry. Chrome Extension: Enhances web browsing experience for sales teams, allowing them to build lead lists directly from their browser. Pitch Intelligence and Writer: Tools for crafting effective sales messages and marketing content, personalized for each potential customer. Data Enrichment and Autopilot: Keeps customer data current and automates lead-building, supporting consistent lead generation. Buyer Intent Data and Job Changes: Offers insights into potential customers' buying intentions and keeps track of significant job changes within key accounts. CoPilot AI - Helps sales reps manage leads CoPilot AI is an advanced AI-powered sales support platform designed for B2B sales teams and agencies to drive consistent revenue growth. The tool focuses on using LinkedIn for sales prospecting, engagement, and conversion. Key Features: LinkedIn Lead Generation: Targets and automates outreach to high-intent LinkedIn leads, enhancing efficiency and scalability in lead generation. Personalized Messaging Automation: Facilitates sending of personalized, one-click messages at scale, maintaining a human touch in digital interactions. Sales Conversion Insights: Offers tools to understand and adapt to prospects' communication styles, improving the likelihood of conversion. Sales Process Optimization: Provides analytics to evaluate and refine sales strategies, identifying opportunities for improvement in the sales funnel. Industry Versatility: Adapts to diverse industries, offering tailored solutions for B2B sales, marketing, HR, and financial services sectors. Collaborative Team Tools: Enables team synchronization and collaboration, boosting productivity and synergy in sales teams Lavender AI - Email AI assistant Lavender AI is an AI-powered email tool that helps users write better emails. It provides real-time feedback and personalized suggestions to optimize email communication efficiency. Key Features: Email Coaching and Scoring: Lavender evaluates emails using AI and a vast database of email interactions, offering a score and tips for improvement. It identifies factors that might reduce the likelihood of receiving a reply, helping users refine their email content. Personalization Assistant: This feature integrates prospect data directly into the user's email platform, suggesting personalization strategies based on recipient data and personality insights to foster deeper connections. Adaptive Improvement: Lavender's scoring and recommendations evolve in real-time with changing email behaviors and practices, thanks to its generative AI and extensive data analysis, ensuring users always follow the best practices. Data-Driven Managerial Insights: The platform provides managers with valuable insights derived from actual email interactions, aiding them in coaching their teams more effectively based on real performance and communication trends. Broad Integration Capability: Lavender integrates with various email and sales platforms including Gmail, Outlook, and others, making it versatile for different user preferences and workflows. Regie AI - Great for business intelligence Regie.ai simplifies the sales prospecting process for businesses, using GenAI and automation to improve interactions with prospects. The platform offers tools like Auto-Pilot for automatic prospecting and meeting scheduling, Co-Pilot for sales rep support, and integrations with various CRM and sales engagement platforms. It also includes a Chrome Extension and CMS for content management and customization. Key Features: Automated Prospecting with Auto-Pilot: Regie.ai's Auto-Pilot feature autonomously prospects and schedules meetings, using Generative AI for Sales Agents to enhance outbound sales efforts. Audience Discovery and Content Generation: The platform identifies target accounts not in the CRM, generating relevant, on-brand content for each message, thus ensuring efficiency in list building and message personalization. Outbound Prioritization and Dynamic Engagement: It utilizes engagement and intent data to prioritize outreach to in-market prospects and adjust engagement strategies based on buyer responsiveness. Full Funnel Brand Protection and Analytics: Regie.ai ensures consistent use of marketing-approved language in all sales outreach and provides insights into campaign and document performance, thereby safeguarding brand integrity throughout the sales funnel. Gemini - AI powered conversational platform Gemini is a large language model chatbot developed by Google AI. It can generate text, translate languages, write different creative text formats, and answer your questions in an informative way. It is still under development but has learned to perform many kinds of tasks. Key features: Generate different creative text formats of text content (poems, code, scripts, musical pieces, email, letters, etc.) Answer your questions in an informative way, even if they are open ended, challenging, or strange. Translate languages Follow your instructions and complete your requests thoughtfully. Plusdocs (Plus AI) - AI tool for presentations Plus AI is a versatile tool that helps improve presentations and integrates with Slides in a simple and intuitive way. It simplifies slide creation and customization by converting text into slides and utilizing AI for various languages. Key Features: Text-to-Slide Conversion: Plus AI excels in transforming textual content into visually appealing slides, streamlining the presentation creation process. Multilingual AI Support: The tool is equipped to handle various languages, making it adaptable for a global user base. Professional Design Options: Users have access to professionally designed slide layouts, enabling the creation of polished presentations with ease. Customization and AI Design: Plus AI allows for extensive customization, including the use of AI for designing and editing slides, ensuring unique and personalized presentations. Live Snapshots and Templates: The tool offers live snapshots for real-time updates and a wide range of templates for quick and effective slide creation. Make - AI automation Make is a powerful visual platform that allows users to build and automate tasks, workflows, apps, and systems. It offers an intuitive, no-code interface that empowers users across various business functions to design and implement complex processes without the need for developer resources. Key Features: No-Code Visual Workflow Builder: Make's core feature is its user-friendly interface that allows for the creation of intricate workflows without coding expertise, making it accessible to a wide range of users. Extensive App Integration: The platform boasts compatibility with over 1000 apps, facilitating seamless connections and data sharing across diverse tools and systems. Custom Automation Solutions: Make enables personalized automation strategies, fitting various business needs from marketing automation to IT workflow control. Template Library: Users can jumpstart their automation projects with a vast collection of pre-built templates, which are customizable to fit specific workflow requirements. Enterprise-Level Solutions: Make offers advanced options for larger organizations, including enhanced security, single sign-on, custom functions, and dedicated support. Midjourney - Making sales content Midjourney is an AI-based image generation tool that changes the way we visualise and create digital art. It offers a lot of artistic possibilities, allowing users to create stunning images from text prompts. This innovative service caters to artists, designers, and anyone seeking to bring their creative visions to life. Key Features: Advanced AI Image Generation: Midjourney's core strength lies in its powerful AI algorithms, which interpret text prompts to generate detailed, high-quality images. This feature allows users to explore an endless array of visual concepts and styles. User-driven Customization: The tool offers significant control over the image creation process, enabling users to guide the AI with specific instructions, ensuring that the final output aligns closely with their vision. Diverse Artistic Styles: Midjourney can mimic various artistic styles, from classical to contemporary, providing users with a wide range of aesthetic options for their creations. Collaboration and Community Features: The platform fosters a community of users who can share, critique, and collaborate on artistic projects, enriching the creative experience. Fireflies AI - Sales meeting assistant Fireflies.ai is a powerful tool for improving team productivity and efficiency in managing meetings and voice conversations. It offers a range of features to simplify the process of capturing, organizing, and analyzing meeting content. Key Features: Automatic Meeting Transcription: Fireflies.ai can transcribe meetings held on various video-conferencing platforms and dialers. The tool captures both video and audio, providing transcripts quickly and efficiently. AI-Powered Search and Summarization: It allows users to review long meetings in a fraction of the time, highlighting key action items, tasks, and questions. Users can filter and focus on specific topics discussed in meetings. Improved Collaboration: The tool enables adding comments, pins, and reactions to specific conversation parts. Users can create and share soundbites and integrate meeting notes with popular collaboration apps such as Slack, Notion, and Asana. Conversation Intelligence: Fireflies.ai offers insights into meetings by tracking metrics like speaker talk time and sentiment. It helps in coaching team members and improving performance in sales, recruiting, and other internal processes. Workflow Automation: The AI assistant from Fireflies.ai can log call notes and activities in CRMs, create tasks through voice commands, and share meeting recaps instantly across various platforms. Comprehensive Knowledge Base: It compiles all voice conversations into an easily accessible and updatable knowledge base, with features to organize meetings into channels and set custom privacy controls. I’ll keep updating this little guide, so add your comments and I’ll try to add more tools. This is all just a personal opinion, so it’s completely cool if you disagree with it. Btw here is the link to the full blog post about all the AI tools in a bit more depth.

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AI_Scout_Official•This week

I run an AI automation agency (AAA). My honest overview and review of this new business model

I started an AI tools directory in February, and then branched off that to start an AI automation agency (AAA) in June. So far I've come across a lot of unsustainable "ideas" to make money with AI, but at the same time a few diamonds in the rough that aren't fully tapped into yet- especially the AAA model. Thought I'd share this post to shine light into this new business model and share some ways you could potentially start your own agency, or at the very least know who you are dealing with and how to pick and choose when you (inevitably) get bombarded with cold emails from them down the line. Foreword Running an AAA does NOT involve using AI tools directly to generate and sell content directly. That ship has sailed, and unless you are happy with $5 from Fiverr every month or so, it is not a real business model. Cry me a river but generating generic art with AI and slapping it onto a T-shirt to sell on Etsy won't make you a dime. At the same time, the AAA model will NOT require you to have a deep theoretical knowledge of AI, or any academic degree, as we are more so dealing with the practical applications of generative AI and how we can implement these into different workflows and tech-stacks, rather than building AI models from the ground up. Regardless of all that, common sense and a willingness to learn will help (a shit ton), as with anything. Keep in mind - this WILL involve work and motivation as well. The mindset that AI somehow means everything can be done for you on autopilot is not the right way to approach things. The common theme of businesses I've seen who have successfully implemented AI into their operations is the willingess to work with AI in a way that augments their existing operations, rather than flat out replace a worker or team. And this is exactly the train of thought you need when working with AI as a business model. However, as the field is relatively unsaturated and hype surrounding AI is still fresh for enterprises, right now is the prime time to start something new if generative AI interests you at all. With that being said, I'll be going over three of the most successful AI-adjacent businesses I've seen over this past year, in addition to some tips and resources to point you in the right direction. so.. WTF is an AI Automation Agency? The AI automation agency (or as some YouTubers have coined it, the AAA model) at its core involves creating custom AI solutions for businesses. I have over 1500 AI tools listed in my directory, however the feedback I've received from some enterprise users is that ready-made SaaS tools are too generic to meet their specific needs. Combine this with the fact virtually no smaller companies have the time or skills required to develop custom solutions right off the bat, and you have yourself real demand. I would say in practice, the AAA model is quite similar to Wordpress and even web dev agencies, with the major difference being all solutions you develop will incorporate key aspects of AI AND automation. Which brings me to my second point- JUST AI IS NOT ENOUGH. Rather than reducing the amount of time required to complete certain tasks, I've seen many AI agencies make the mistake of recommending and (trying to) sell solutions that more likely than not increase the workload of their clients. For example, if you were to make an internal tool that has AI answer questions based on their knowledge base, but this knowledge base has to be updated manually, this is creating unnecessary work. As such I think one of the key components of building successful AI solutions is incorporating the new (Generative AI/LLMs) with the old (programmtic automation- think Zapier, APIs, etc.). Finally, for this business model to be successful, ideally you should target a niche in which you have already worked and understand pain points and needs. Not only does this make it much easier to get calls booked with prospects, the solutions you build will have much greater value to your clients (meaning you get paid more). A mistake I've seen many AAA operators make (and I blame this on the "Get Rich Quick" YouTubers) is focusing too much on a specific productized service, rather than really understanding the needs of businesses. The former is much done via a SaaS model, but when going the agency route the only thing that makes sense is building custom solutions. This is why I always take a consultant-first approach. You can only build once you understand what they actually need and how certain solutions may impact their operations, workflows, and bottom-line. Basics of How to Get Started Pick a niche. As I mentioned previously, preferably one that you've worked in before. Niches I know of that are actively being bombarded with cold emails include real estate, e-commerce, auto-dealerships, lawyers, and medical offices. There is a reason for this, but I will tell you straight up this business model works well if you target any white-collar service business (internal tools approach) or high volume businesses (customer facing tools approach). Setup your toolbox. If you wanted to start a pressure washing business, you would need a pressure-washer. This is no different. For those without programming knowledge, I've seen two common ways AAA get setup to build- one is having a network of on-call web developers, whether its personal contacts or simply going to Upwork or any talent sourcing agency. The second is having an arsenal of no-code tools. I'll get to this more in a second, but this works beecause at its core, when we are dealing with the practical applications of AI, the code is quite simple, simply put. Start cold sales. Unless you have a network already, this is not a step you can skip. You've already picked a niche, so all you have to do is find the right message. Keep cold emails short, sweet, but enticing- and it will help a lot if you did step 1 correctly and intimately understand who your audience is. I'll be touching base later about how you can leverage AI yourself to help you with outreach and closing. The beauty of gen AI and the AAA model You don't need to be a seasoned web developer to make this business model work. The large majority of solutions that SME clients want is best done using an API for an LLM for the actual AI aspect. The value we create with the solutions we build comes with the conceptual framework and design that not only does what they need it to but integrates smoothly with their existing tech-stack and workflow. The actual implementation is quite straightforward once you understand the high level design and know which tools you are going to use. To give you a sense, even if you plan to build out these apps yourself (say in Python) the large majority of the nitty gritty technical work has already been done for you, especially if you leverage Python libraries and packages that offer high level abstraction for LLM-related functions. For instance, calling GPT can be as little as a single line of code. (And there are no-code tools where these functions are simply an icon on a GUI). Aside from understanding the capabilities and limitations of these tools and frameworks, the only thing that matters is being able to put them in a way that makes sense for what you want to build. Which is why outsourcing and no-code tools both work in our case. Okay... but how TF am I suppposed to actually build out these solutions? Now the fun part. I highly recommend getting familiar with Langchain and LlamaIndex. Both are Python libraires that help a lot with the high-level LLM abstraction I mentioned previously. The two most important aspects include being able to integrate internal data sources/knowledge bases with LLMs, and have LLMs perform autonomous actions. The two most common methods respectively are RAG and output parsing. RAG (retrieval augmented Generation) If you've ever seen a tool that seemingly "trains" GPT on your own data, and wonder how it all works- well I have an answer from you. At a high level, the user query is first being fed to what's called a vector database to run vector search. Vector search basically lets you do semantic search where you are searching data based on meaning. The vector databases then retrieves the most relevant sections of text as it relates to the user query, and this text gets APPENDED to your GPT prompt to provide extra context to the AI. Further, with prompt engineering, you can limit GPT to only generate an answer if it can be found within this extra context, greatly limiting the chance of hallucination (this is where AI makes random shit up). Aside from vector databases, we can also implement RAG with other data sources and retrieval methods, for example SQL databses (via parsing the outputs of LLM's- more on this later). Autonomous Agents via Output Parsing A common need of clients has been having AI actually perform tasks, rather than simply spitting out text. For example, with autonomous agents, we can have an e-commerce chatbot do the work of a basic customer service rep (i.e. look into orders, refunds, shipping). At a high level, what's going on is that the response of the LLM is being used programmtically to determine which API to call. Keeping on with the e-commerce example, if I wanted a chatbot to check shipping status, I could have a LLM response within my app (not shown to the user) with a prompt that outputs a random hash or string, and programmatically I can determine which API call to make based on this hash/string. And using the same fundamental concept as with RAG, I can append the the API response to a final prompt that would spit out the answer for the user. How No Code Tools Can Fit In (With some example solutions you can build) With that being said, you don't necessarily need to do all of the above by coding yourself, with Python libraries or otherwise. However, I will say that having that high level overview will help IMMENSELY when it comes to using no-code tools to do the actual work for you. Regardless, here are a few common solutions you might build for clients as well as some no-code tools you can use to build them out. Ex. Solution 1: AI Chatbots for SMEs (Small and Medium Enterprises) This involves creating chatbots that handle user queries, lead gen, and so forth with AI, and will use the principles of RAG at heart. After getting the required data from your client (i.e. product catalogues, previous support tickets, FAQ, internal documentation), you upload this into your knowledge base and write a prompt that makes sense for your use case. One no-code tool that does this well is MyAskAI. The beauty of it especially for building external chatbots is the ability to quickly ingest entire websites into your knowledge base via a sitemap, and bulk uploading files. Essentially, they've covered the entire grunt work required to do this manually. Finally, you can create a inline or chat widget on your client's website with a few lines of HTML, or altneratively integrate it with a Slack/Teams chatbot (if you are going for an internal Q&A chatbot approach). Other tools you could use include Botpress and Voiceflow, however these are less for RAG and more for building out complete chatbot flows that may or may not incorporate LLMs. Both apps are essentially GUIs that eliminate the pain and tears and trying to implement complex flows manually, and both natively incoporate AI intents and a knowledge base feature. Ex. Solution 2: Internal Apps Similar to the first example, except we go beyond making just chatbots but tools such as report generation and really any sort of internal tool or automations that may incorporate LLM's. For instance, you can have a tool that automatically generates replies to inbound emails based on your client's knowledge base. Or an automation that does the same thing but for replies to Instagram comments. Another example could be a tool that generates a description and screeenshot based on a URL (useful for directory sites, made one for my own :P). Getting into more advanced implementations of LLMs, we can have tools that can generate entire drafts of reports (think 80+ pages), based not only on data from a knowledge base but also the writing style, format, and author voice of previous reports. One good tool to create content generation panels for your clients would be MindStudio. You can train LLM's via prompt engineering in a structured way with your own data to essentially fine tune them for whatever text you need it to generate. Furthermore, it has a GUI where you can dictate the entire AI flow. You can also upload data sources via multiple formats, including PDF, CSV, and Docx. For automations that require interactions between multiple apps, I recommend the OG zapier/make.com if you want a no-code solution. For instance, for the automatic email reply generator, I can have a trigger such that when an email is received, a custom AI reply is generated by MyAskAI, and finally a draft is created in my email client. Or, for an automation where I can create a social media posts on multiple platforms based on a RSS feed (news feed), I can implement this directly in Zapier with their native GPT action (see screenshot) As for more complex LLM flows that may require multiple layers of LLMs, data sources, and APIs working together to generate a single response i.e. a long form 100 page report, I would recommend tools such as Stack AI or Flowise (open-source alternative) to build these solutions out. Essentially, you get most of the functions and features of Python packages such as Langchain and LlamaIndex in a GUI. See screenshot for an example of a flow How the hell are you supposed to find clients? With all that being said, none of this matters if you can't find anyone to sell to. You will have to do cold sales, one way or the other, especially if you are brand new to the game. And what better way to sell your AI services than with AI itself? If we want to integrate AI into the cold outreach process, first we must identify what it's good at doing, and that's obviously writing a bunch of text, in a short amount of time. Similar to the solutions that an AAA can build for its clients, we can take advantage of the same principles in our own sales processes. How to do outreach Once you've identified your niche and their pain points/opportunities for automation, you want to craft a compelling message in which you can send via cold email and cold calls to get prospects booked on demos/consultations. I won't get into too much detail in terms of exactly how to write emails or calling scripts, as there are millions of resources to help with this, but I will tell you a few key points you want to keep in mind when doing outreach for your AAA. First, you want to keep in mind that many businesses are still hesitant about AI and may not understand what it really is or how it can benefit their operations. However, we can take advantage of how mass media has been reporting on AI this past year- at the very least people are AWARE that sooner or later they may have to implement AI into their businesses to stay competitive. We want to frame our message in a way that introduces generative AI as a technology that can have a direct, tangible, and positive impact on their business. Although it may be hard to quantify, I like to include estimates of man-hours saved or costs saved at least in my final proposals to prospects. Times are TOUGH right now, and money is expensive, so you need to have a compelling reason for businesses to get on board. Once you've gotten your messaging down, you will want to create a list of prospects to contact. Tools you can use to find prospects include Apollo.io, reply.io, zoominfo (expensive af), and Linkedin Sales Navigator. What specific job titles, etc. to target will depend on your niche but for smaller companies this will tend to be the owner. For white collar niches, i.e. law, the professional that will be directly benefiting from the tool (i.e. partners) may be better to contact. And for larger organizations you may want to target business improvement and digital transformation leads/directors- these are the people directly in charge of projects like what you may be proposing. Okay- so you have your message, and your list, and now all it comes down to is getting the good word out. I won't be going into the details of how to send these out, a quick Google search will give you hundreds of resources for cold outreach methods. However, personalization is key and beyond simple dynamic variables you want to make sure you can either personalize your email campaigns directly with AI (SmartWriter.ai is an example of a tool that can do this), or at the very least have the ability to import email messages programmatically. Alternatively, ask ChatGPT to make you a Python Script that can take in a list of emails, scrape info based on their linkedin URL or website, and all pass this onto a GPT prompt that specifies your messaging to generate an email. From there, send away. How tf do I close? Once you've got some prospects booked in on your meetings, you will need to close deals with them to turn them into clients. Call #1: Consultation Tying back to when I mentioned you want to take a consultant-first appraoch, you will want to listen closely to their goals and needs and understand their pain points. This would be the first call, and typically I would provide a high level overview of different solutions we could build to tacke these. It really helps to have a presentation available, so you can graphically demonstrate key points and key technologies. I like to use Plus AI for this, it's basically a Google Slides add-on that can generate slide decks for you. I copy and paste my default company messaging, add some key points for the presentation, and it comes out with pretty decent slides. Call #2: Demo The second call would involve a demo of one of these solutions, and typically I'll quickly prototype it with boilerplate code I already have, otherwise I'll cook something up in a no-code tool. If you have a niche where one type of solution is commonly demanded, it helps to have a general demo set up to be able to handle a larger volume of calls, so you aren't burning yourself out. I'll also elaborate on how the final product would look like in comparison to the demo. Call #3 and Beyond: Once the initial consultation and demo is complete, you will want to alleviate any remaining concerns from your prospects and work with them to reach a final work proposal. It's crucial you lay out exactly what you will be building (in writing) and ensure the prospect understands this. Furthermore, be clear and transparent with timelines and communication methods for the project. In terms of pricing, you want to take this from a value-based approach. The same solution may be worth a lot more to client A than client B. Furthermore, you can create "add-ons" such as monthly maintenance/upgrade packages, training sessions for employeees, and so forth, separate from the initial setup fee you would charge. How you can incorporate AI into marketing your businesses Beyond cold sales, I highly recommend creating a funnel to capture warm leads. For instance, I do this currently with my AI tools directory, which links directly to my AI agency and has consistent branding throughout. Warm leads are much more likely to close (and honestly, much nicer to deal with). However, even without an AI-related website, at the very least you will want to create a presence on social media and the web in general. As with any agency, you will want basic a professional presence. A professional virtual address helps, in addition to a Google Business Profile (GBP) and TrustPilot. a GBP (especially for local SEO) and Trustpilot page also helps improve the looks of your search results immensely. For GBP, I recommend using ProfilePro, which is a chrome extension you can use to automate SEO work for your GBP. Aside from SEO optimzied business descriptions based on your business, it can handle Q/A answers, responses, updates, and service descriptions based on local keywords. Privacy and Legal Concerns of the AAA Model Aside from typical concerns for agencies relating to service contracts, there are a few issues (especially when using no-code tools) that will need to be addressed to run a successful AAA. Most of these surround privacy concerns when working with proprietary data. In your terms with your client, you will want to clearly define hosting providers and any third party tools you will be using to build their solution, and a DPA with these third parties listed as subprocessors if necessary. In addition, you will want to implement best practices like redacting private information from data being used for building solutions. In terms of addressing concerns directly from clients, it helps if you host your solutions on their own servers (not possible with AI tools), and address the fact only ChatGPT queries in the web app, not OpenAI API calls, will be used to train OpenAI's models (as reported by mainstream media). The key here is to be open and transparent with your clients about ALL the tools you are using, where there data will be going, and make sure to get this all in writing. have fun, and keep an open mind Before I finish this post, I just want to reiterate the fact that this is NOT an easy way to make money. Running an AI agency will require hours and hours of dedication and work, and constantly rearranging your schedule to meet prospect and client needs. However, if you are looking for a new business to run, and have a knack for understanding business operations and are genuinely interested in the pracitcal applications of generative AI, then I say go for it. The time is ticking before AAA becomes the new dropshipping or SMMA, and I've a firm believer that those who set foot first and establish themselves in this field will come out top. And remember, while 100 thousand people may read this post, only 2 may actually take initiative and start.

#reddit#automation#agency

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AI_Scout_Official•This week

I run an AI automation agency (AAA). My honest overview and review of this new business model

I started an AI tools directory in February, and then branched off that to start an AI automation agency (AAA) in June. So far I've come across a lot of unsustainable "ideas" to make money with AI, but at the same time a few diamonds in the rough that aren't fully tapped into yet- especially the AAA model. Thought I'd share this post to shine light into this new business model and share some ways you could potentially start your own agency, or at the very least know who you are dealing with and how to pick and choose when you (inevitably) get bombarded with cold emails from them down the line. Foreword Running an AAA does NOT involve using AI tools directly to generate and sell content directly. That ship has sailed, and unless you are happy with $5 from Fiverr every month or so, it is not a real business model. Cry me a river but generating generic art with AI and slapping it onto a T-shirt to sell on Etsy won't make you a dime. At the same time, the AAA model will NOT require you to have a deep theoretical knowledge of AI, or any academic degree, as we are more so dealing with the practical applications of generative AI and how we can implement these into different workflows and tech-stacks, rather than building AI models from the ground up. Regardless of all that, common sense and a willingness to learn will help (a shit ton), as with anything. Keep in mind - this WILL involve work and motivation as well. The mindset that AI somehow means everything can be done for you on autopilot is not the right way to approach things. The common theme of businesses I've seen who have successfully implemented AI into their operations is the willingess to work with AI in a way that augments their existing operations, rather than flat out replace a worker or team. And this is exactly the train of thought you need when working with AI as a business model. However, as the field is relatively unsaturated and hype surrounding AI is still fresh for enterprises, right now is the prime time to start something new if generative AI interests you at all. With that being said, I'll be going over three of the most successful AI-adjacent businesses I've seen over this past year, in addition to some tips and resources to point you in the right direction. so.. WTF is an AI Automation Agency? The AI automation agency (or as some YouTubers have coined it, the AAA model) at its core involves creating custom AI solutions for businesses. I have over 1500 AI tools listed in my directory, however the feedback I've received from some enterprise users is that ready-made SaaS tools are too generic to meet their specific needs. Combine this with the fact virtually no smaller companies have the time or skills required to develop custom solutions right off the bat, and you have yourself real demand. I would say in practice, the AAA model is quite similar to Wordpress and even web dev agencies, with the major difference being all solutions you develop will incorporate key aspects of AI AND automation. Which brings me to my second point- JUST AI IS NOT ENOUGH. Rather than reducing the amount of time required to complete certain tasks, I've seen many AI agencies make the mistake of recommending and (trying to) sell solutions that more likely than not increase the workload of their clients. For example, if you were to make an internal tool that has AI answer questions based on their knowledge base, but this knowledge base has to be updated manually, this is creating unnecessary work. As such I think one of the key components of building successful AI solutions is incorporating the new (Generative AI/LLMs) with the old (programmtic automation- think Zapier, APIs, etc.). Finally, for this business model to be successful, ideally you should target a niche in which you have already worked and understand pain points and needs. Not only does this make it much easier to get calls booked with prospects, the solutions you build will have much greater value to your clients (meaning you get paid more). A mistake I've seen many AAA operators make (and I blame this on the "Get Rich Quick" YouTubers) is focusing too much on a specific productized service, rather than really understanding the needs of businesses. The former is much done via a SaaS model, but when going the agency route the only thing that makes sense is building custom solutions. This is why I always take a consultant-first approach. You can only build once you understand what they actually need and how certain solutions may impact their operations, workflows, and bottom-line. Basics of How to Get Started Pick a niche. As I mentioned previously, preferably one that you've worked in before. Niches I know of that are actively being bombarded with cold emails include real estate, e-commerce, auto-dealerships, lawyers, and medical offices. There is a reason for this, but I will tell you straight up this business model works well if you target any white-collar service business (internal tools approach) or high volume businesses (customer facing tools approach). Setup your toolbox. If you wanted to start a pressure washing business, you would need a pressure-washer. This is no different. For those without programming knowledge, I've seen two common ways AAA get setup to build- one is having a network of on-call web developers, whether its personal contacts or simply going to Upwork or any talent sourcing agency. The second is having an arsenal of no-code tools. I'll get to this more in a second, but this works beecause at its core, when we are dealing with the practical applications of AI, the code is quite simple, simply put. Start cold sales. Unless you have a network already, this is not a step you can skip. You've already picked a niche, so all you have to do is find the right message. Keep cold emails short, sweet, but enticing- and it will help a lot if you did step 1 correctly and intimately understand who your audience is. I'll be touching base later about how you can leverage AI yourself to help you with outreach and closing. The beauty of gen AI and the AAA model You don't need to be a seasoned web developer to make this business model work. The large majority of solutions that SME clients want is best done using an API for an LLM for the actual AI aspect. The value we create with the solutions we build comes with the conceptual framework and design that not only does what they need it to but integrates smoothly with their existing tech-stack and workflow. The actual implementation is quite straightforward once you understand the high level design and know which tools you are going to use. To give you a sense, even if you plan to build out these apps yourself (say in Python) the large majority of the nitty gritty technical work has already been done for you, especially if you leverage Python libraries and packages that offer high level abstraction for LLM-related functions. For instance, calling GPT can be as little as a single line of code. (And there are no-code tools where these functions are simply an icon on a GUI). Aside from understanding the capabilities and limitations of these tools and frameworks, the only thing that matters is being able to put them in a way that makes sense for what you want to build. Which is why outsourcing and no-code tools both work in our case. Okay... but how TF am I suppposed to actually build out these solutions? Now the fun part. I highly recommend getting familiar with Langchain and LlamaIndex. Both are Python libraires that help a lot with the high-level LLM abstraction I mentioned previously. The two most important aspects include being able to integrate internal data sources/knowledge bases with LLMs, and have LLMs perform autonomous actions. The two most common methods respectively are RAG and output parsing. RAG (retrieval augmented Generation) If you've ever seen a tool that seemingly "trains" GPT on your own data, and wonder how it all works- well I have an answer from you. At a high level, the user query is first being fed to what's called a vector database to run vector search. Vector search basically lets you do semantic search where you are searching data based on meaning. The vector databases then retrieves the most relevant sections of text as it relates to the user query, and this text gets APPENDED to your GPT prompt to provide extra context to the AI. Further, with prompt engineering, you can limit GPT to only generate an answer if it can be found within this extra context, greatly limiting the chance of hallucination (this is where AI makes random shit up). Aside from vector databases, we can also implement RAG with other data sources and retrieval methods, for example SQL databses (via parsing the outputs of LLM's- more on this later). Autonomous Agents via Output Parsing A common need of clients has been having AI actually perform tasks, rather than simply spitting out text. For example, with autonomous agents, we can have an e-commerce chatbot do the work of a basic customer service rep (i.e. look into orders, refunds, shipping). At a high level, what's going on is that the response of the LLM is being used programmtically to determine which API to call. Keeping on with the e-commerce example, if I wanted a chatbot to check shipping status, I could have a LLM response within my app (not shown to the user) with a prompt that outputs a random hash or string, and programmatically I can determine which API call to make based on this hash/string. And using the same fundamental concept as with RAG, I can append the the API response to a final prompt that would spit out the answer for the user. How No Code Tools Can Fit In (With some example solutions you can build) With that being said, you don't necessarily need to do all of the above by coding yourself, with Python libraries or otherwise. However, I will say that having that high level overview will help IMMENSELY when it comes to using no-code tools to do the actual work for you. Regardless, here are a few common solutions you might build for clients as well as some no-code tools you can use to build them out. Ex. Solution 1: AI Chatbots for SMEs (Small and Medium Enterprises) This involves creating chatbots that handle user queries, lead gen, and so forth with AI, and will use the principles of RAG at heart. After getting the required data from your client (i.e. product catalogues, previous support tickets, FAQ, internal documentation), you upload this into your knowledge base and write a prompt that makes sense for your use case. One no-code tool that does this well is MyAskAI. The beauty of it especially for building external chatbots is the ability to quickly ingest entire websites into your knowledge base via a sitemap, and bulk uploading files. Essentially, they've covered the entire grunt work required to do this manually. Finally, you can create a inline or chat widget on your client's website with a few lines of HTML, or altneratively integrate it with a Slack/Teams chatbot (if you are going for an internal Q&A chatbot approach). Other tools you could use include Botpress and Voiceflow, however these are less for RAG and more for building out complete chatbot flows that may or may not incorporate LLMs. Both apps are essentially GUIs that eliminate the pain and tears and trying to implement complex flows manually, and both natively incoporate AI intents and a knowledge base feature. Ex. Solution 2: Internal Apps Similar to the first example, except we go beyond making just chatbots but tools such as report generation and really any sort of internal tool or automations that may incorporate LLM's. For instance, you can have a tool that automatically generates replies to inbound emails based on your client's knowledge base. Or an automation that does the same thing but for replies to Instagram comments. Another example could be a tool that generates a description and screeenshot based on a URL (useful for directory sites, made one for my own :P). Getting into more advanced implementations of LLMs, we can have tools that can generate entire drafts of reports (think 80+ pages), based not only on data from a knowledge base but also the writing style, format, and author voice of previous reports. One good tool to create content generation panels for your clients would be MindStudio. You can train LLM's via prompt engineering in a structured way with your own data to essentially fine tune them for whatever text you need it to generate. Furthermore, it has a GUI where you can dictate the entire AI flow. You can also upload data sources via multiple formats, including PDF, CSV, and Docx. For automations that require interactions between multiple apps, I recommend the OG zapier/make.com if you want a no-code solution. For instance, for the automatic email reply generator, I can have a trigger such that when an email is received, a custom AI reply is generated by MyAskAI, and finally a draft is created in my email client. Or, for an automation where I can create a social media posts on multiple platforms based on a RSS feed (news feed), I can implement this directly in Zapier with their native GPT action (see screenshot) As for more complex LLM flows that may require multiple layers of LLMs, data sources, and APIs working together to generate a single response i.e. a long form 100 page report, I would recommend tools such as Stack AI or Flowise (open-source alternative) to build these solutions out. Essentially, you get most of the functions and features of Python packages such as Langchain and LlamaIndex in a GUI. See screenshot for an example of a flow How the hell are you supposed to find clients? With all that being said, none of this matters if you can't find anyone to sell to. You will have to do cold sales, one way or the other, especially if you are brand new to the game. And what better way to sell your AI services than with AI itself? If we want to integrate AI into the cold outreach process, first we must identify what it's good at doing, and that's obviously writing a bunch of text, in a short amount of time. Similar to the solutions that an AAA can build for its clients, we can take advantage of the same principles in our own sales processes. How to do outreach Once you've identified your niche and their pain points/opportunities for automation, you want to craft a compelling message in which you can send via cold email and cold calls to get prospects booked on demos/consultations. I won't get into too much detail in terms of exactly how to write emails or calling scripts, as there are millions of resources to help with this, but I will tell you a few key points you want to keep in mind when doing outreach for your AAA. First, you want to keep in mind that many businesses are still hesitant about AI and may not understand what it really is or how it can benefit their operations. However, we can take advantage of how mass media has been reporting on AI this past year- at the very least people are AWARE that sooner or later they may have to implement AI into their businesses to stay competitive. We want to frame our message in a way that introduces generative AI as a technology that can have a direct, tangible, and positive impact on their business. Although it may be hard to quantify, I like to include estimates of man-hours saved or costs saved at least in my final proposals to prospects. Times are TOUGH right now, and money is expensive, so you need to have a compelling reason for businesses to get on board. Once you've gotten your messaging down, you will want to create a list of prospects to contact. Tools you can use to find prospects include Apollo.io, reply.io, zoominfo (expensive af), and Linkedin Sales Navigator. What specific job titles, etc. to target will depend on your niche but for smaller companies this will tend to be the owner. For white collar niches, i.e. law, the professional that will be directly benefiting from the tool (i.e. partners) may be better to contact. And for larger organizations you may want to target business improvement and digital transformation leads/directors- these are the people directly in charge of projects like what you may be proposing. Okay- so you have your message, and your list, and now all it comes down to is getting the good word out. I won't be going into the details of how to send these out, a quick Google search will give you hundreds of resources for cold outreach methods. However, personalization is key and beyond simple dynamic variables you want to make sure you can either personalize your email campaigns directly with AI (SmartWriter.ai is an example of a tool that can do this), or at the very least have the ability to import email messages programmatically. Alternatively, ask ChatGPT to make you a Python Script that can take in a list of emails, scrape info based on their linkedin URL or website, and all pass this onto a GPT prompt that specifies your messaging to generate an email. From there, send away. How tf do I close? Once you've got some prospects booked in on your meetings, you will need to close deals with them to turn them into clients. Call #1: Consultation Tying back to when I mentioned you want to take a consultant-first appraoch, you will want to listen closely to their goals and needs and understand their pain points. This would be the first call, and typically I would provide a high level overview of different solutions we could build to tacke these. It really helps to have a presentation available, so you can graphically demonstrate key points and key technologies. I like to use Plus AI for this, it's basically a Google Slides add-on that can generate slide decks for you. I copy and paste my default company messaging, add some key points for the presentation, and it comes out with pretty decent slides. Call #2: Demo The second call would involve a demo of one of these solutions, and typically I'll quickly prototype it with boilerplate code I already have, otherwise I'll cook something up in a no-code tool. If you have a niche where one type of solution is commonly demanded, it helps to have a general demo set up to be able to handle a larger volume of calls, so you aren't burning yourself out. I'll also elaborate on how the final product would look like in comparison to the demo. Call #3 and Beyond: Once the initial consultation and demo is complete, you will want to alleviate any remaining concerns from your prospects and work with them to reach a final work proposal. It's crucial you lay out exactly what you will be building (in writing) and ensure the prospect understands this. Furthermore, be clear and transparent with timelines and communication methods for the project. In terms of pricing, you want to take this from a value-based approach. The same solution may be worth a lot more to client A than client B. Furthermore, you can create "add-ons" such as monthly maintenance/upgrade packages, training sessions for employeees, and so forth, separate from the initial setup fee you would charge. How you can incorporate AI into marketing your businesses Beyond cold sales, I highly recommend creating a funnel to capture warm leads. For instance, I do this currently with my AI tools directory, which links directly to my AI agency and has consistent branding throughout. Warm leads are much more likely to close (and honestly, much nicer to deal with). However, even without an AI-related website, at the very least you will want to create a presence on social media and the web in general. As with any agency, you will want basic a professional presence. A professional virtual address helps, in addition to a Google Business Profile (GBP) and TrustPilot. a GBP (especially for local SEO) and Trustpilot page also helps improve the looks of your search results immensely. For GBP, I recommend using ProfilePro, which is a chrome extension you can use to automate SEO work for your GBP. Aside from SEO optimzied business descriptions based on your business, it can handle Q/A answers, responses, updates, and service descriptions based on local keywords. Privacy and Legal Concerns of the AAA Model Aside from typical concerns for agencies relating to service contracts, there are a few issues (especially when using no-code tools) that will need to be addressed to run a successful AAA. Most of these surround privacy concerns when working with proprietary data. In your terms with your client, you will want to clearly define hosting providers and any third party tools you will be using to build their solution, and a DPA with these third parties listed as subprocessors if necessary. In addition, you will want to implement best practices like redacting private information from data being used for building solutions. In terms of addressing concerns directly from clients, it helps if you host your solutions on their own servers (not possible with AI tools), and address the fact only ChatGPT queries in the web app, not OpenAI API calls, will be used to train OpenAI's models (as reported by mainstream media). The key here is to be open and transparent with your clients about ALL the tools you are using, where there data will be going, and make sure to get this all in writing. have fun, and keep an open mind Before I finish this post, I just want to reiterate the fact that this is NOT an easy way to make money. Running an AI agency will require hours and hours of dedication and work, and constantly rearranging your schedule to meet prospect and client needs. However, if you are looking for a new business to run, and have a knack for understanding business operations and are genuinely interested in the pracitcal applications of generative AI, then I say go for it. The time is ticking before AAA becomes the new dropshipping or SMMA, and I've a firm believer that those who set foot first and establish themselves in this field will come out top. And remember, while 100 thousand people may read this post, only 2 may actually take initiative and start.

#reddit#automation#agency

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Boring_Elephant2767•This week

Looking for a Developer Co-Founder to Build an AI-Powered Film Budgeting Tool

Hey everyone, I’m a seasoned producer/line producer with over 10 years in the film industry, specializing in budgeting and production strategy for films, commercials, and music videos. I’ve built over 150 budgets for projects ranging from indie features to large-scale commercials and have worked with major artists, brands, and studios. I’m looking for a developer or AI/ML engineer interested in co-founding a startup with me to build an AI-powered budgeting tool for the film industry. The Problem Creating a budget for a film, music video, or commercial is time-consuming and expensive (typically $3K–$5K per budget for films). Filmmakers, studios, agencies, and managers need a faster, more cost-effective way to estimate production costs without hiring a full-time producer for every project. The Solution The goal is to develop an AI-assisted budgeting tool that takes in scripts, creative decks, or project briefs and outputs a preliminary budget & production schedule. The vision is a hybrid service: • AI-powered script/deck breakdown to extract production elements • Smart reasoning based on real industry budgets • Producer oversight for accuracy before sending budgets to users • Flexible pricing model (lower cost than hiring a full-time producer) What I Bring to the Table Deep industry knowledge – I know how to build accurate budgets & schedules for any type of project. Proven demand – I already have early adopters in indie film, production companies, and agencies. Strong network – I work with studios, reps, and filmmakers who would use this tool. A unique approach – I haven’t seen an AI budgeting tool that truly understands production costs based on creative elements. What I’m Looking For I need a developer partner with experience in AI, automation, and/or SaaS development who can help build this. Ideally, someone interested in co-founding (equity-based, not just a freelance gig). If you have experience with GPT, machine learning, NLP, or building interactive SaaS products, that’s a plus. I’m keeping this low-key for now while I figure out the best path forward. If you’re interested, let’s chat! Even if you’re not a developer but have advice or ideas, I’d love to hear your thoughts. Drop a comment or DM me if this sounds interesting!

#reddit#looking#developer

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Significant_Stable_7•This week

Best AI tools to help company productivity?

Hey guys! I recently did a big restructuring of my production company and moving away from smaller businesses ad’s and moving up to working with larger marketing agencies. My partner and I are brainstorming ways to automate or at least improve certain parts of our business as we also start to expand our team & to improve ease of labour as our turn around times tend to have to be pretty quick. The main things we’re looking to improve is in: • Sales/out reach strategy: we are constantly reaching out to new agencies in different parts of the world. I am already used to manually making a plan for each company we reach out to but it can be very time consuming. I don’t know if there is even a tool that could help with this haha. Even if it helps with pointers! • Organizing/visualizing spreadsheets: we deal with spreadsheets on what we spend per production and how we distribute our total budget per department. If there is anyway to ease the workflow for our managers and on top of that also allow us to expand easier without having to look for someone who is very efficient on excel or spending more time and money on the training. • Scheduling: We already have so much to organize day per day, im not sure if there is any tool or ai system that could help in regards to scheduling meetings, organizing priorities or even just deadlines for certain projects. Example: we need to schedule everything from pre production deadlines (meetings with talent, agency, and crew) production deadlines, & post production deadlines. I’m sure there is other small things I am missing but those are the three main things! There is just so many things i saw on the internet that are “ai powered” or “ai improved workflow” that all claim are the best or some just use chat gpt so its essentially all the same thing. I thought id ask on here to see if anyone has actually tried and could recommend some ai tools out there! Cheers,

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Tapedulema919•This week

The 15 Best (Free to Use) AI Tools for Creating Websites, Presentations, Graphics, UIs, Photos, and more

While we wait for ChatGPT to roll out its own official image input+output tool, I wanted to put together a list of the best AI design tools I've seen so far. Obviously text-based tasks like writing and coding get the bulk of the attention, but I wanted to see how it’s being used in design and more visual tasks. From UI and full-on website design, to graphics and photo generation, there are a ton of interesting and free tools coming out that are worth trying and using as inspiration for your own projects. These tools cover a bunch of different use cases and can hopefully help some of you, whether you’re a professional designer looking to automate parts of your work or just someone who wants to find ways to speed up the design work for your business/side projects. All of them are free to try, but most have some kind of paid plan or limit on the number of free generations. Fair enough given it costs money to run the models, but I've tried to include notes on any that don't have permanent free plans. Let me know if you know of any tools I’ve missed so I can add them to the list! I’ve grouped them by categories, to make it easier to see what each tool is capable of, then given a bit more detail under each specific tool. AI Website, Graphic and UI Generators: Framer: Describe the website you want, and Framer will create it for you. Edit and instantly publish your site from their platform. Ironically my favorite thing about Framer isn’t its AI tool. Its real advantage is its website editor which is the best I’ve seen on any platform (and usable for free). It’s like Figma if Figma let you publish directly to the web. Microsoft Designer: Generates designs based on user input for social media posts, logos, and business graphics. It’s free to use with a Microsoft account, and fairly impressive if not always consistent. If you pay a lot or spend a ton of time on design/social media content, Designer is definitely worth checking out. UIzard: Transforms text and images into design mockups, wireframes, and full user interfaces. It’s an ambitious concept, but very cool. While Framer was better for generating websites from text prompts, UIZard offers something none of the others did: taking a sketch drawing and turning it into a UI and/or wireframing. Visualizations, Graphics and Illustrations: Taskade: AI powered productivity tool to visualize your notes, projects, and tasks. Taskade lets you easily generate mind maps and other visualizations of your work, and makes use of AI in a bunch of cool ways. For example, you can generate a mind map to help you brainstorm and then ask it to expand on a certain point or even research it for you with the internet. Bing Image Creator: Generate images from natural text descriptions, powered by DALL-E. Whether you’re looking for blog illustrations, images for your site’s pages or any other purpose, it’s worth trying. AutoDraw: Autodraw is a Google Project that lets you draw something freehand with your cursor, and AutoDraw uses AI to transform it into a refined image with icons and predrawn designs, all for free in your browser. AI Presentations and Slides: Plus AI for Google Slides: AI generated slides and full-on presentations, all within Google Slides. I liked how Plus AI worked within Google Slides and made it easy to make changes to the presentation (as lets be real, no AI tool is going to generate exactly* the content and formatting you need for a serious presentation). SlidesGo: Generate slides with illustrations, images, and icons chosen by AI. SlidesGo also has their own editor to let you edit and refine the AI generated presentation. Tome: Tell Tome what you want to say to your audience, and it will create a presentation that effectively communicates it clearly and effectively. Tome actually goes beyond just presentations and has a few cool formats worth checking out that I could see being useful for salespeople and anyone who needs to pitch an idea or product at work or to clients. Product Photography: These are all fairly similar so I’ve kept the descriptions short, but it’s genuinely a pretty useful category if you run any kind of business or side hustle that needs product photos. These photos establish the professionalism of your store/brand, and all the ones I tried had genuinely impressive results that seemed much better than what I could do myself. Pebblely: AI image generator for product images in various styles and settings. 40 free images, paid after that. Booth.ai: Generates professional-quality product photos using AI, focused on furniture, fashion, and packaged goods. Stylized.ai: Generates product photos integrated into ecommerce platforms like Shopify. Miscellaneous Tools: Fronty: Converts uploaded images or drawings into HTML and CSS code using AI. It’s a bit clunky, but a cool concept nonetheless. LetsEnhance: Uses AI to enhance the resolution of images and photographs. Generally works pretty well from my experience, and gives you 10 free credits with signup. Unfortunately beyond that it is a paid product. Remove.bg: Specializes in recognizing and removing image backgrounds effectively. Doesn’t promise much, but it does the job and doesn’t require you to sign up. TL;DR/Overall favorites: These are the ones I've found the most use for in my day-to-day work. Framer: responsive website design with a full-featured editor to edit and publish your site all in one place. Free + paid plans. Taskade: visualize and automate your workflows, projects, mind maps, and more with AI powered templates. Free + paid plans. Microsoft Designer: generate social media and other marketing graphics with AI. Free to use. Plus AI: plugin for Google Slides to generate slide content, designs, and make tweaks with AI. Free + paid plans. Pebblely: professional-quality product photos in various settings and backgrounds, free to generate up to 40 images* (through you can always sign up for another account…)

#reddit#best#(free

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0.917

Tapedulema919•This week

The best (actually free to use) AI tools for day-to-day work + productivity

I've spent an ungodly amount of time ~~procrastinating~~ trying tons of new/free AI tools from Reddit and various lists of the best AI tools for different use cases. Frankly, most free AI tools (and even paid ones) are gimmicky ChatGPT wrappers with questionable utility in everyday tasks or overpriced enterprise software that don't use AI as anything more than a marketing buzzword. My last list of free AI tools got a good response here, and I wanted to make another with the best AI tools that I actually use day-to-day now that I've spent more time with them. All these tools can be used for free, though most of them have some kind of premium offering if you need more advanced stuff or a ton of queries. To make it easy to sort through, I've also added whether each tool requires signup. ChatPDF: Free Tool to Use ChatGPT on Your Own Documents/PDFs (free no signup) Put simply, ChatPDF lets you upload any PDF and interact with it like ChatGPT. I heard about this one from my nephew who used it to automatically generate flashcards and explain concepts based on class notes and readings. There are a few similar services out there, but I found ChatPDF the easiest to use of those that don't require payment/signup. If you're a student or someone who needs to read through long PDFs regularly, the possibilities to use this are endless. It's also completely free and doesn't require signup. Key Features: Free to upload up to 3 PDFs daily, with up to 120 pages in each PDF Can be used without signing up at all Taskade: AI Task Management, Scheduling, and Notetaking Tool with GPT-4 Built-In (free with signup) Taskade is an all-in-one notetaking, task management, and scheduling platform with built-in AI workflows and templates. Like Notion, Taskade lets you easily create workspaces, documents, and templates for your workflows. Unlike Notion’s GPT-3 based AI, Taskade has built-in GPT-4 based AI that’s trained to structure your documents, create content, and otherwise help you improve your productivity. Key Features: GPT-4 is built in to their free plan and trained to help with document formatting, scheduling, content creation and answering questions through a chat interface. Its AI seems specifically trained to work seamlessly with your documents and workspaces, and understands queries specific to their interface like asking it to turn (text) notes into a mind map. One of the highest usage limits of the free tools: Taskade’s free plan comes with 1000 monthly requests, which is one of the highest I’ve seen for a tool with built-in GPT-4. Because it’s built into a document editor with database, scheduling and chat capabilities, you can use it for pretty much anything you’d use ChatGPT for but without* paying for ChatGPT Premium. Free templates to get you started with actually integrating AI into your workflows: there are a huge number of genuinely useful free templates for workflows, task management, mind mapping, etc. For example, you can add a project and have Taskade automatically map out and schedule a breakdown of the tasks that make up that overall deliverable. Plus AI for Google Slides: AI-generated (and improved) slide decks (free with signup, addon for Google Slides) I've tried out a bunch of AI presentation/slide generating tools. To be honest, most of them leave a lot to be desired and aren't genuinely useful unless you're literally paid to generate a presentation vaguely related to some topic. Plus AI is a (free!) Google Slides addon that lets you describe the kind of slide deck you're making, then generate and fine-tune it based on your exact needs. It's still not at the point where you can literally just tell it one prompt and get the entire finished product, but it saves a bunch of time getting an initial structure together that you can then perfect. Similarly, if you have existing slides made you can tell it (in natural language) how you want it changed. For example, asking it to change up the layout of text on a page, improve the writing style, or even use external data sources. Key Features: Integrates seamlessly into Google Slides: if you’re already using Slides, using Plus AI is as simple as installing the plugin. Their tutorials are easy to follow and it doesn’t require learning some new slideshow software or interface like some other options. Create and* tweak slides using natural language: Plus AI lets you create whole slideshows, adjust text, or change layouts using natural language. It’s all fairly intuitive and the best of the AI slide tools I’ve tried. FlowGPT: Database of AI prompts and workflows (free without signup-though it pushes you to signup!) FlowGPT collects prompts and collections of prompts to do various tasks, from marketing, productivity, and coding to random stuff people find interesting. It uses an upvote system similar to Reddit that makes it easy to find interesting ways to use ChatGPT. It also lets you search for prompts if you have something in mind and want to see what others have done. It's free and has a lot of cool features like showing you previews of how ChatGPT responds to the prompts. Unfortunately, it's also a bit pushy with getting you to signup, and the design leaves something to be desired, but it's the best of these tools I've found. Key Features: Lots of users that share genuinely useful and interesting prompts Upvote system similar to Reddit’s that allows you to find interesting prompts within the categories you’re interested in Summarize.Tech: AI summaries of YouTube Videos (free no signup) Summarize generates AI summaries of YouTube videos, condensing them into relatively short written notes with timestamps. All the summaries I've seen have been accurate and save significant time. I find it especially useful when looking at longer tutorials where I want to find if: &#x200B; The tutorial actually tells me what I'm looking for, and See where in the video I can find that specific part. The one downside I've seen is that it doesn't work for videos that don't have subtitles, but hopefully, someone can build something with Whisper or a similar audio transcription API to solve that. Claude: ChatGPT Alternative with ~75k Word Limit (free with signup) If you've used ChatGPT, you've probably run into the issue of its (relatively low) token limit. Put simply, it can't handle text longer than a few thousand words. It's the same reason why ChatGPT "forgets" instructions you gave it earlier on in a conversation. Claude solves that, with a \~75,000 word limit that lets you input literal novels and do pretty much everything you can do with ChatGPT. Unfortunately, Claude is currently only free in the US or UK. Claude pitches itself as the "safer" AI, which can make it a pain to use for many use cases, but it's worth trying out and better than ChatGPT for certain tasks. Currently, I'm mainly using it to summarize long documents that ChatGPT literally cannot process as a single prompt. Key Features: Much longer word limit than even ChatGPT’s highest token models Stronger guardrails than ChatGPT: if you're into this, Claude focuses a lot more on "trust and safety" than even ChatGPT does. While an AI telling me what information I can and can't have is more of an annoyance for my use cases, it can be useful if you're building apps like customer support or other use cases where it's a top priority to keep the AI from writing something "surprising." Phind: AI Search Engine That Combines Google with ChatGPT (free no signup) Like a combination of Google and ChatGPT. Like ChatGPT, it can understand complex prompts and give you detailed answers condensing multiple sources. Like Google, it shows you the most up-to-date sources answering your question and has access to everything on the internet in real time (vs. ChatGPT's September 2021 cutoff). Unlike Google, it avoids spammy links that seem to dominate Google nowadays and actually answers your question. Key Features: Accesses the internet to get you real-time information vs. ChatGPT’s 2021 cutoff. While ChatGPT is great for content generation and other tasks that you don’t really need live information for, it can’t get you any information from past its cutoff point. Provides actual sources for its claims, helping you dive deeper into any specific points and avoid hallucinations. Phind was the first to combine the best of both worlds between Google and ChatGPT, giving you easy access to actual sources the way Google does while summarizing relevant results the way ChatGPT does. It’s still one of the best places for that, especially if you have technical questions. Bing AI: ChatGPT Alternative Based on GPT-4 (with internet access!) (free no signup) For all the hate Bing gets, they've done the best job of all the major search engines of integrating AI chat to answer questions. Bing's Chat AI is very similar to ChatGPT (it's based on GPT-4). Unlike ChatGPT's base model without plugins, it has access to the internet. It also doesn't require signing in, which is nice. At the risk of sounding like a broken record, Google has really dropped the ball lately in delivering non-spammy search results that actually answer the query, and it's nice to see other search engines like Bing and Phind providing alternatives. Key Features: Similar to Phind, though arguably a bit better for non-technical questions: Bing similarly provides sourced summaries, generates content and otherwise integrates AI and search nicely. Built on top of GPT-4: like Taskade, Bing has confirmed they use GPT-4. That makes it another nice option to get around paying for GPT-4 while still getting much of the same capabilities as ChatGPT. Seamless integration with a standard search engine that’s much better than I remember it being (when it was more of a joke than anything) Honorable Mentions: These are the “rest of the best” free AI tools I've found that are simpler/don't need a whole entry to explain: PdfGPT: Alternative to ChatPDF that also uses AI to summarize and let you interact with PDF documents. Nice to have options if you run into one site’s PDF or page limit and don’t want to pay to do so. Remove.bg: One of the few image AI tools I use regularly. Remove.bg uses simple AI to remove backgrounds from your images. It's very simple, but something I end up doing surprisingly often editing product images, etc. CopyAI and Jasper: both are AI writing tools primarily built for website marketing/blog content. I've tried both but don't use them enough regularly to be able to recommend one over the other. Worth trying if you do a lot of content writing and want to automate parts of it. Let me know if you guys recommend any other free AI tools that you use day-to-day and I can add them to the list. I’m also interested in any requests you guys have for AI tools that don’t exist yet, as I’m looking for new projects to work on at the moment! TL;DR: ChatPDF: Interact with any PDF using ChatGPT without signing up, great for students and anyone who needs to filter through long PDFs. Taskade: All-in-one task management, scheduling, and notetaking with built-in GPT-4 Chat + AI assistant for improving productivity. Plus AI for Google Slides: Addon for Google Slides that generates and fine-tunes slide decks based on your description(s) in natural language. FlowGPT: Database of AI prompts and workflows. Nice resource to find interesting ChatGPT prompts. Summarize.Tech: AI summaries of YouTube videos with timestamps that makes it easier to find relevant information in longer videos. Claude: ChatGPT alternative with a \~75k word limit, ideal for handling long documents and tasks that go above ChatGPT's token limit. Phind: AI search engine similar to a combination of Google and ChatGPT. Built in internet access and links/citations for its claims. Bing AI: Bing's ChatGPT alternative based on GPT-4. Has real-time internet access + integrates nicely with their normal search engine.

#reddit#best#(actually

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AI_Scout_Official•This week

I run an AI automation agency (AAA). My honest overview and review of this new business model

I started an AI tools directory in February, and then branched off that to start an AI automation agency (AAA) in June. So far I've come across a lot of unsustainable "ideas" to make money with AI, but at the same time a few diamonds in the rough that aren't fully tapped into yet- especially the AAA model. Thought I'd share this post to shine light into this new business model and share some ways you could potentially start your own agency, or at the very least know who you are dealing with and how to pick and choose when you (inevitably) get bombarded with cold emails from them down the line. Foreword Running an AAA does NOT involve using AI tools directly to generate and sell content directly. That ship has sailed, and unless you are happy with $5 from Fiverr every month or so, it is not a real business model. Cry me a river but generating generic art with AI and slapping it onto a T-shirt to sell on Etsy won't make you a dime. At the same time, the AAA model will NOT require you to have a deep theoretical knowledge of AI, or any academic degree, as we are more so dealing with the practical applications of generative AI and how we can implement these into different workflows and tech-stacks, rather than building AI models from the ground up. Regardless of all that, common sense and a willingness to learn will help (a shit ton), as with anything. Keep in mind - this WILL involve work and motivation as well. The mindset that AI somehow means everything can be done for you on autopilot is not the right way to approach things. The common theme of businesses I've seen who have successfully implemented AI into their operations is the willingess to work with AI in a way that augments their existing operations, rather than flat out replace a worker or team. And this is exactly the train of thought you need when working with AI as a business model. However, as the field is relatively unsaturated and hype surrounding AI is still fresh for enterprises, right now is the prime time to start something new if generative AI interests you at all. With that being said, I'll be going over three of the most successful AI-adjacent businesses I've seen over this past year, in addition to some tips and resources to point you in the right direction. so.. WTF is an AI Automation Agency? The AI automation agency (or as some YouTubers have coined it, the AAA model) at its core involves creating custom AI solutions for businesses. I have over 1500 AI tools listed in my directory, however the feedback I've received from some enterprise users is that ready-made SaaS tools are too generic to meet their specific needs. Combine this with the fact virtually no smaller companies have the time or skills required to develop custom solutions right off the bat, and you have yourself real demand. I would say in practice, the AAA model is quite similar to Wordpress and even web dev agencies, with the major difference being all solutions you develop will incorporate key aspects of AI AND automation. Which brings me to my second point- JUST AI IS NOT ENOUGH. Rather than reducing the amount of time required to complete certain tasks, I've seen many AI agencies make the mistake of recommending and (trying to) sell solutions that more likely than not increase the workload of their clients. For example, if you were to make an internal tool that has AI answer questions based on their knowledge base, but this knowledge base has to be updated manually, this is creating unnecessary work. As such I think one of the key components of building successful AI solutions is incorporating the new (Generative AI/LLMs) with the old (programmtic automation- think Zapier, APIs, etc.). Finally, for this business model to be successful, ideally you should target a niche in which you have already worked and understand pain points and needs. Not only does this make it much easier to get calls booked with prospects, the solutions you build will have much greater value to your clients (meaning you get paid more). A mistake I've seen many AAA operators make (and I blame this on the "Get Rich Quick" YouTubers) is focusing too much on a specific productized service, rather than really understanding the needs of businesses. The former is much done via a SaaS model, but when going the agency route the only thing that makes sense is building custom solutions. This is why I always take a consultant-first approach. You can only build once you understand what they actually need and how certain solutions may impact their operations, workflows, and bottom-line. Basics of How to Get Started Pick a niche. As I mentioned previously, preferably one that you've worked in before. Niches I know of that are actively being bombarded with cold emails include real estate, e-commerce, auto-dealerships, lawyers, and medical offices. There is a reason for this, but I will tell you straight up this business model works well if you target any white-collar service business (internal tools approach) or high volume businesses (customer facing tools approach). Setup your toolbox. If you wanted to start a pressure washing business, you would need a pressure-washer. This is no different. For those without programming knowledge, I've seen two common ways AAA get setup to build- one is having a network of on-call web developers, whether its personal contacts or simply going to Upwork or any talent sourcing agency. The second is having an arsenal of no-code tools. I'll get to this more in a second, but this works beecause at its core, when we are dealing with the practical applications of AI, the code is quite simple, simply put. Start cold sales. Unless you have a network already, this is not a step you can skip. You've already picked a niche, so all you have to do is find the right message. Keep cold emails short, sweet, but enticing- and it will help a lot if you did step 1 correctly and intimately understand who your audience is. I'll be touching base later about how you can leverage AI yourself to help you with outreach and closing. The beauty of gen AI and the AAA model You don't need to be a seasoned web developer to make this business model work. The large majority of solutions that SME clients want is best done using an API for an LLM for the actual AI aspect. The value we create with the solutions we build comes with the conceptual framework and design that not only does what they need it to but integrates smoothly with their existing tech-stack and workflow. The actual implementation is quite straightforward once you understand the high level design and know which tools you are going to use. To give you a sense, even if you plan to build out these apps yourself (say in Python) the large majority of the nitty gritty technical work has already been done for you, especially if you leverage Python libraries and packages that offer high level abstraction for LLM-related functions. For instance, calling GPT can be as little as a single line of code. (And there are no-code tools where these functions are simply an icon on a GUI). Aside from understanding the capabilities and limitations of these tools and frameworks, the only thing that matters is being able to put them in a way that makes sense for what you want to build. Which is why outsourcing and no-code tools both work in our case. Okay... but how TF am I suppposed to actually build out these solutions? Now the fun part. I highly recommend getting familiar with Langchain and LlamaIndex. Both are Python libraires that help a lot with the high-level LLM abstraction I mentioned previously. The two most important aspects include being able to integrate internal data sources/knowledge bases with LLMs, and have LLMs perform autonomous actions. The two most common methods respectively are RAG and output parsing. RAG (retrieval augmented Generation) If you've ever seen a tool that seemingly "trains" GPT on your own data, and wonder how it all works- well I have an answer from you. At a high level, the user query is first being fed to what's called a vector database to run vector search. Vector search basically lets you do semantic search where you are searching data based on meaning. The vector databases then retrieves the most relevant sections of text as it relates to the user query, and this text gets APPENDED to your GPT prompt to provide extra context to the AI. Further, with prompt engineering, you can limit GPT to only generate an answer if it can be found within this extra context, greatly limiting the chance of hallucination (this is where AI makes random shit up). Aside from vector databases, we can also implement RAG with other data sources and retrieval methods, for example SQL databses (via parsing the outputs of LLM's- more on this later). Autonomous Agents via Output Parsing A common need of clients has been having AI actually perform tasks, rather than simply spitting out text. For example, with autonomous agents, we can have an e-commerce chatbot do the work of a basic customer service rep (i.e. look into orders, refunds, shipping). At a high level, what's going on is that the response of the LLM is being used programmtically to determine which API to call. Keeping on with the e-commerce example, if I wanted a chatbot to check shipping status, I could have a LLM response within my app (not shown to the user) with a prompt that outputs a random hash or string, and programmatically I can determine which API call to make based on this hash/string. And using the same fundamental concept as with RAG, I can append the the API response to a final prompt that would spit out the answer for the user. How No Code Tools Can Fit In (With some example solutions you can build) With that being said, you don't necessarily need to do all of the above by coding yourself, with Python libraries or otherwise. However, I will say that having that high level overview will help IMMENSELY when it comes to using no-code tools to do the actual work for you. Regardless, here are a few common solutions you might build for clients as well as some no-code tools you can use to build them out. Ex. Solution 1: AI Chatbots for SMEs (Small and Medium Enterprises) This involves creating chatbots that handle user queries, lead gen, and so forth with AI, and will use the principles of RAG at heart. After getting the required data from your client (i.e. product catalogues, previous support tickets, FAQ, internal documentation), you upload this into your knowledge base and write a prompt that makes sense for your use case. One no-code tool that does this well is MyAskAI. The beauty of it especially for building external chatbots is the ability to quickly ingest entire websites into your knowledge base via a sitemap, and bulk uploading files. Essentially, they've covered the entire grunt work required to do this manually. Finally, you can create a inline or chat widget on your client's website with a few lines of HTML, or altneratively integrate it with a Slack/Teams chatbot (if you are going for an internal Q&A chatbot approach). Other tools you could use include Botpress and Voiceflow, however these are less for RAG and more for building out complete chatbot flows that may or may not incorporate LLMs. Both apps are essentially GUIs that eliminate the pain and tears and trying to implement complex flows manually, and both natively incoporate AI intents and a knowledge base feature. Ex. Solution 2: Internal Apps Similar to the first example, except we go beyond making just chatbots but tools such as report generation and really any sort of internal tool or automations that may incorporate LLM's. For instance, you can have a tool that automatically generates replies to inbound emails based on your client's knowledge base. Or an automation that does the same thing but for replies to Instagram comments. Another example could be a tool that generates a description and screeenshot based on a URL (useful for directory sites, made one for my own :P). Getting into more advanced implementations of LLMs, we can have tools that can generate entire drafts of reports (think 80+ pages), based not only on data from a knowledge base but also the writing style, format, and author voice of previous reports. One good tool to create content generation panels for your clients would be MindStudio. You can train LLM's via prompt engineering in a structured way with your own data to essentially fine tune them for whatever text you need it to generate. Furthermore, it has a GUI where you can dictate the entire AI flow. You can also upload data sources via multiple formats, including PDF, CSV, and Docx. For automations that require interactions between multiple apps, I recommend the OG zapier/make.com if you want a no-code solution. For instance, for the automatic email reply generator, I can have a trigger such that when an email is received, a custom AI reply is generated by MyAskAI, and finally a draft is created in my email client. Or, for an automation where I can create a social media posts on multiple platforms based on a RSS feed (news feed), I can implement this directly in Zapier with their native GPT action (see screenshot) As for more complex LLM flows that may require multiple layers of LLMs, data sources, and APIs working together to generate a single response i.e. a long form 100 page report, I would recommend tools such as Stack AI or Flowise (open-source alternative) to build these solutions out. Essentially, you get most of the functions and features of Python packages such as Langchain and LlamaIndex in a GUI. See screenshot for an example of a flow How the hell are you supposed to find clients? With all that being said, none of this matters if you can't find anyone to sell to. You will have to do cold sales, one way or the other, especially if you are brand new to the game. And what better way to sell your AI services than with AI itself? If we want to integrate AI into the cold outreach process, first we must identify what it's good at doing, and that's obviously writing a bunch of text, in a short amount of time. Similar to the solutions that an AAA can build for its clients, we can take advantage of the same principles in our own sales processes. How to do outreach Once you've identified your niche and their pain points/opportunities for automation, you want to craft a compelling message in which you can send via cold email and cold calls to get prospects booked on demos/consultations. I won't get into too much detail in terms of exactly how to write emails or calling scripts, as there are millions of resources to help with this, but I will tell you a few key points you want to keep in mind when doing outreach for your AAA. First, you want to keep in mind that many businesses are still hesitant about AI and may not understand what it really is or how it can benefit their operations. However, we can take advantage of how mass media has been reporting on AI this past year- at the very least people are AWARE that sooner or later they may have to implement AI into their businesses to stay competitive. We want to frame our message in a way that introduces generative AI as a technology that can have a direct, tangible, and positive impact on their business. Although it may be hard to quantify, I like to include estimates of man-hours saved or costs saved at least in my final proposals to prospects. Times are TOUGH right now, and money is expensive, so you need to have a compelling reason for businesses to get on board. Once you've gotten your messaging down, you will want to create a list of prospects to contact. Tools you can use to find prospects include Apollo.io, reply.io, zoominfo (expensive af), and Linkedin Sales Navigator. What specific job titles, etc. to target will depend on your niche but for smaller companies this will tend to be the owner. For white collar niches, i.e. law, the professional that will be directly benefiting from the tool (i.e. partners) may be better to contact. And for larger organizations you may want to target business improvement and digital transformation leads/directors- these are the people directly in charge of projects like what you may be proposing. Okay- so you have your message, and your list, and now all it comes down to is getting the good word out. I won't be going into the details of how to send these out, a quick Google search will give you hundreds of resources for cold outreach methods. However, personalization is key and beyond simple dynamic variables you want to make sure you can either personalize your email campaigns directly with AI (SmartWriter.ai is an example of a tool that can do this), or at the very least have the ability to import email messages programmatically. Alternatively, ask ChatGPT to make you a Python Script that can take in a list of emails, scrape info based on their linkedin URL or website, and all pass this onto a GPT prompt that specifies your messaging to generate an email. From there, send away. How tf do I close? Once you've got some prospects booked in on your meetings, you will need to close deals with them to turn them into clients. Call #1: Consultation Tying back to when I mentioned you want to take a consultant-first appraoch, you will want to listen closely to their goals and needs and understand their pain points. This would be the first call, and typically I would provide a high level overview of different solutions we could build to tacke these. It really helps to have a presentation available, so you can graphically demonstrate key points and key technologies. I like to use Plus AI for this, it's basically a Google Slides add-on that can generate slide decks for you. I copy and paste my default company messaging, add some key points for the presentation, and it comes out with pretty decent slides. Call #2: Demo The second call would involve a demo of one of these solutions, and typically I'll quickly prototype it with boilerplate code I already have, otherwise I'll cook something up in a no-code tool. If you have a niche where one type of solution is commonly demanded, it helps to have a general demo set up to be able to handle a larger volume of calls, so you aren't burning yourself out. I'll also elaborate on how the final product would look like in comparison to the demo. Call #3 and Beyond: Once the initial consultation and demo is complete, you will want to alleviate any remaining concerns from your prospects and work with them to reach a final work proposal. It's crucial you lay out exactly what you will be building (in writing) and ensure the prospect understands this. Furthermore, be clear and transparent with timelines and communication methods for the project. In terms of pricing, you want to take this from a value-based approach. The same solution may be worth a lot more to client A than client B. Furthermore, you can create "add-ons" such as monthly maintenance/upgrade packages, training sessions for employeees, and so forth, separate from the initial setup fee you would charge. How you can incorporate AI into marketing your businesses Beyond cold sales, I highly recommend creating a funnel to capture warm leads. For instance, I do this currently with my AI tools directory, which links directly to my AI agency and has consistent branding throughout. Warm leads are much more likely to close (and honestly, much nicer to deal with). However, even without an AI-related website, at the very least you will want to create a presence on social media and the web in general. As with any agency, you will want basic a professional presence. A professional virtual address helps, in addition to a Google Business Profile (GBP) and TrustPilot. a GBP (especially for local SEO) and Trustpilot page also helps improve the looks of your search results immensely. For GBP, I recommend using ProfilePro, which is a chrome extension you can use to automate SEO work for your GBP. Aside from SEO optimzied business descriptions based on your business, it can handle Q/A answers, responses, updates, and service descriptions based on local keywords. Privacy and Legal Concerns of the AAA Model Aside from typical concerns for agencies relating to service contracts, there are a few issues (especially when using no-code tools) that will need to be addressed to run a successful AAA. Most of these surround privacy concerns when working with proprietary data. In your terms with your client, you will want to clearly define hosting providers and any third party tools you will be using to build their solution, and a DPA with these third parties listed as subprocessors if necessary. In addition, you will want to implement best practices like redacting private information from data being used for building solutions. In terms of addressing concerns directly from clients, it helps if you host your solutions on their own servers (not possible with AI tools), and address the fact only ChatGPT queries in the web app, not OpenAI API calls, will be used to train OpenAI's models (as reported by mainstream media). The key here is to be open and transparent with your clients about ALL the tools you are using, where there data will be going, and make sure to get this all in writing. have fun, and keep an open mind Before I finish this post, I just want to reiterate the fact that this is NOT an easy way to make money. Running an AI agency will require hours and hours of dedication and work, and constantly rearranging your schedule to meet prospect and client needs. However, if you are looking for a new business to run, and have a knack for understanding business operations and are genuinely interested in the pracitcal applications of generative AI, then I say go for it. The time is ticking before AAA becomes the new dropshipping or SMMA, and I've a firm believer that those who set foot first and establish themselves in this field will come out top. And remember, while 100 thousand people may read this post, only 2 may actually take initiative and start.

#reddit#automation#agency

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1

danonino80•This week

How to increase the sales of my book

In just 3 months, it generated over $100 in revenue. I wanted to share my journey for two reasons: to potentially assist others in self-publishing their own books and to receive feedback to enhance my marketing strategy. I envision that there are others facing similar challenges. Let's dive into the financials, time spent, Key takeaways and the Challenges to address behind this product. Finances First, let's take a look at the financial overview. 💳 Expenses 🔹 E-book creation: · Book cover: $ 0. I used Adobe Express with 30 days of free trial. · ChatGPT: 20 $ a month. I leveraged AI to generate the chapters of the book, ensuring that no critical topics were overlooked during the content creation process and to refine the English, as it's not my native language. I also used to help me with copywriting of the web. If anyone is interested, I can share my Python code for outlining the chapters calling the API, but you can also directly ask chatgpt. · Kindle KDP (Kindle Direct Publishing): order author copies: 10 $. 🔹 Web creation: Domain: I got a com) / .org /.net domain for just 1 $ the first year. Carrd.co subscription: 19 $ (1 year) 🔹 Marketing: Promoted post on reddit: $30 Paid ads with google ads: $30 💰 Revenue 🔸 Sales: $102 💸 Net Profit: \~- $ 18 I initially thought the sales for this e-book would be quite modest, maybe only 3 or 4 books. However, the fact that I've sold more than that so far is a pleasant surprise. Even though the overall numbers may still be considered "peanuts" in the grand scheme of book sales, it suggests there could be more demand for content on digital asset custody than I had originally anticipated. This is a good learning experience, and I'll look to refine my marketing approach to see if I can reach a wider audience interested in this topic 🔹 Time Spent Next, let's review the time invested. 📖 Writing the e-book: 40 hours 🌍 Website + Stripe integration: 10 hours 📣 Creating promotional content: 10 hours ⏱️ Additional marketing efforts: 5 hours Total time spent: 65 hours As you can see, I dedicated more time to writing the e-book itself than to marketing and distribution. I spent relevant time to marketing because I though that a successful product launch requires a robust marketing effort. Many e-book authors overlook this crucial aspect! I utilized three sales channels: · Amazon: I found that there were no books specifically about digital asset custody, resulting in strong positioning in Amazon searches. Additionally, my book immediately secured the top position in Google searches for "digital asset custody book." However, despite achieving 50% of sales in the UK, I have not received any reviews globally. Sales distribution for this channel: 20% physical book, 80% ebook. · Twitter: Daniel\_ZZ80. With only 46 followers, the performance on this platform has not been optimal. I am beginning to write posts related to digital assets to increase visibility. · Gumroad: Lockeyyy.gumroad.com. I offered a discounted version of the ebook, but have not yet made any sales through this channel. Key takeaways: · The process of creating this e-book was extremely fulfilling, and while it has garnered overwhelmingly positive feedback from friends and colleagues (not considered as sales), it has yet to receive any Amazon reviews ☹. · Kindle KDP proved to be ideal for a rapid go-to-market strategy. · AI is an excellent tool for generating ideas and providing access to global audiences with perfect grammar. Otherwise, I would need to hire a translator, which can be very expensive. · Despite offering a full 30-day money-back guarantee, leading me to believe that the quality of the content is indeed good. · I have gained valuable insights for future technical books. · Although the current financial balance may be negative, I anticipate reaching the break-even point within one month, and this has now become a passive income stream. However, I recognize the need to regularly update the content due to the rapidly changing nature of this field. Challenges to address: · Is the timing for launching this book appropriate? In other words, is the world of digital asset custody a trendy and interesting topic for the audience? · What is causing the lack of sales through Gumroad? · Should I seek assistance as my marketing efforts have not yielded results? · Why are there no reviews on Amazon? · Why are sales primarily concentrated in the EU with only one sale in the US, which is my main target market? Feedback is appreciated. If you're interested in learning more about my approach, feel free to send me a direct message. A bit about my background: After dedicating my entire career to the banking industry, I explored various side projects. As an IT professional, I have now transitioned into the digital asset realm. After three years of intensive study, I recently published my first book on digital asset custody. I hope you found this post informative. Cheers! P.S.: I'm currently in the process of launching two more books using this system. 😊

#reddit#increase#sales

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1

someone-shoot-me•This week

Hear me out, you are annoying

I am a full stack web developer capable of realizing most of the people’s ideas here. Few things to mention out: Developers are not Google itself. You can’t randomly tell them to implement AI, blockchain, NFT, stocks etc. cus developers are not wizards. Stuff like that requires knowledge, quite a lot of it, and if you want someone to spend shit ton of time implementing smtn, you better prepare to pay for it, cus that knowledge is valuable. Most of the time it requires whole teams to do the work you imagined. If someone came to me and told me yeah, I want a copy of upwork. Sure bud, and where are the other 50 developers? (there are exceptions here but still, people are talking whole platforms while I am the only dev there, mate imma need a whole year for this one) be ready to pay. Sure, your idea is cool, but I can’t wait another 2 years untill it starts making money, plus if it’s fully online business, why would i want to share 50/50 with you? No one is dumb enough to do 3 months od work for free just to share 50/50 in best case, with someone who “had an idea”, I could delete the files any second I want to it’s my code! No one is doing big projects for free, be ready to pay and know the value of other’s work. Otherwise have fun scrolling through indian freelancers! At least try to manage something! I am right now looking for projects that I could join, who needs a web dev and all of them are like okay do the work, don’t expect any money cus we aee starting with no money at all and we won’t bother any investors cus you aee the dumb enough developer do male our business for 25% share

#reddit#hear#out,

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SnooPears4795•This week

Unbiased opinion - Ideas

Hi, I’m currently looking to set up along site my full time job. I’m working away so have spare time mid week evenings to get cracking! If anyone has any other ideas which would link up with my interests please let me know. Note: I set up an airconditioning company which didn’t go to plan as I was just not passionate enough to chase sales/grow the company. Details Capital: I could invest upto 1k a month would prefer less Location: would prefer remote but the below ideas are all possible from my hotel room. Strengths: work well under pressure, technical minded, problem solving Weaknesses: can be lazy if not passionate, organisation, confidence Interests: Music, guitars, tech, coding, beer, motorbikes Experience: 12 years in railway electrical roles, coding bootcamp Ideas Idea: Guitar Electronics (pedals) Pros: cheap to start Enjoy building Creative Design work Cool field Cons: Time consuming Not much profit Scalability Competition is cheap Idea: Project management app/document selection Pros: Experienced in field Relatively quick if excel based Could charge subscription Contacts in industry Expensive if app based Make once sell multiple Remote Small overheads Cons: Not as fun as others learn new language? Limited market Other competition already good (apps) Idea: YouTube - mysteries, interesting topics Pros: Free to startup Enjoy researching Build community leading to other online projects Can voice over/AI No need to have cam Improve confidence Cons: Returns will take a while Get better at video editing Overcome speaking No overheads (have equipment) Time/money slow at start Idea: Railway Electrical Book/Course Pros: Throughly experienced Small market Niche - good money if can get sales Have to learn course software Contacts in field Create once Cons: Not as passionate as other ideas Amount of interest (possibly get other fields electricians involved?) Expensive to make?

#reddit#unbiased#opinion

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Competitive_Salad709•This week

Is SaaS Done?

Other day I was talking to one of the leaders in Office, He said "SAAS IS DY!NG THANKS TO AI". I found this fascinating & started digging on this, I was already part of communities like Build in Public, NoCode Builders & Others. I think he was right. I saw a significant raise in the AI Tools, what other call it 'AI Wrapper Startups' I explored many tools, then I realise why don't we capitalise this opportunity. I found out it is the marketers who needs to be aware of these & if you don't embrace these tools you will end up losing to someone with minimum experience with marketing but good hands on experience with the tools. If these tools keep up the same phase then you have both challenges & opportunities which I've listed it down in the post pros & cons. I think we need to embrace these tools are else we will be left behind. All these things are about marketers but what about the people who want to become solopreneurs or people like Pieter Levels who just want to create something useful get money either by selling or running multiple projects at once. Whatever I've studied & learnt. I came up with something called "The SaaS Marketing Innovation Cycle". The SaaS Marketing Innovation Cycle : Will have six easy steps. Empowering with No-code : Decide what is the problem you are planing to solve & understand which is No-code tool can help you with solution. Some tools will have steep learning curve, become expert on those tools. Integrate Automation AI : This is very crucial for your tool & make sure you have build a tool which will integrates easily with most of the platforms. Build Custom Solution : Right now the whole industry of Micro SaaS stands on building custom solutions, catering your audience is the best way to go for it. Launching MVPs : Because you have no-code tools it is easier to deploy MVPs than ever before & you can build multiple tools at once. Adapt & Grow : This is about the business take feedback from customer add new feature remove few yada yada. Leverage the Growth : Here it is important you have learn to build communities out these tools. if you come up with any new ideas there is always a group of people, who will be able adapt & give you the feedback to improve. Conclusion : Either build something or adapt something quicker when that has built. What do you think Folks ??

#reddit#saas#done?

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shidored•This week

Great business idea but no devs.

I have an idea for the Agile and Project management space. It deals with a tad bit of AI but can work completely without it too. Having worked as both a PM and SM I see some gaps in some of the industries that require them. Currently no tools offer this even big names like Microsoft Projects or Atlassian Jira. I'm like 99% sure if marketed correctly this thing will take off. But I have two problems 1) I am tech savy but cannot code well enough. So I need a full stack dev or a devops team. 2) I do not have the money to pay so the only way I can think of is a commission structure so that when the amount agreed is reached you paid in that manner or become a partner and get paid for life. I prefer second option as that means there will be a vested interest in seeing this succeed

#reddit#great#business

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Whole_Ad_9002•This week

Please rate my business idea aimed at emerging markets

Imagine a single platform that transforms your business operations effortlessly. Think of it like a Swiss Army knife for your business. First Steps (Every Business Needs These): Our website builder is your first step online. Just pick what you want, drag it where you need it, and you've got a professional website or online store. No tech skills needed. Perfect for getting your business visible to customers. Our security tool comes next because every business needs protection. It's like a security guard for your files and data, keeping hackers out and making sure you never lose important work. Think of it as insurance for your digital business. Cybersecurity and backups combined. Growing Business Needs: As your team grows, our digital office keeps everyone connected. Email, chat, share files, and track projects in one place. It's like having everyone in the same room, even when working remotely. This becomes essential once you have more than a few people. When paperwork starts piling up, our AI helper steps in. It's like having a smart assistant who learns your business and helps everyone get more done - handling routine tasks, summarizing meetings, and suggesting better ways to work. Scaling Up (For Established Businesses): Once you're handling lots of data, our eco-friendly cloud storage becomes crucial. Your business gets fast, reliable storage while helping the environment. We use servers powered by renewable energy, so you can grow sustainably. Our data transfer tool becomes important when you're working with multiple systems and need to move files around. It's like having a digital moving service that safely carries your files between different cloud services. With 20+ services to connect to cloud to cloud or on prem to cloud, its easy and fast. Advanced Needs: When your business processes get complex, our workflow automation tool helps you set up automatic systems for repetitive tasks. It's like training a robot to handle your routine work while your team focuses on growth. Simple no-code automation when you need it. Everything works on its own, but they're designed to work even better together. And since it's all in one place, you only need one password and one monthly bill. Most businesses save about half their tech costs by switching to our platform. Cost Benefits: Single subscription replaces multiple vendor contracts Reduces IT overhead by 40-60% on average No need for expensive technical specialists Predictable monthly costs Scales pricing with your usage

#reddit#please#rate

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Due_Cryptographer461•This week

Interview with founder of ReadyPlayerMe (raised $70M+ from a16z)

Thanks to everyone who replied to my previous post with the questions you had for Rainer, I added some of them into this interview. I’m Nikita of Databas3 , and that’s my first interview in a series where I’m learning more about the journey of the best tech and web3 founders. Would appreciate your feedback and suggestions for the next guest! Nikita: Let’s begin with a brief introduction. Can you share a bit about yourself and how the business started? Rainer: I’m Rainer, the CTO of ReadyPlayerMe. Our journey began in 2013 with four co-founders. Over the years, our focus has shifted mainly around our product’s evolution, but our core idea always revolved around virtual actors or virtual people. Our initial venture was into hardware. We created the first full-body scanner in the Nordics, a significant step in photogrammetry. This led us to develop the Luna Scanner, a three-meter tall structure designed to capture facial features and likenesses. When Facebook acquired Oculus in 2014, we foresaw the potential of VR and virtual worlds, especially in social experiences. Nikita: Interesting. How did you move on from there? Rainer: Recognizing the limitations of hardware, we transitioned into software. Our early scanner designs had limitations in scalability. For example, our three-meter tall scanner wasn’t a feasible solution for scanning millions of people. So, we leveraged the datasets from our initial projects and designed a mobile version, making facial scanning as easy as using your phone. Around 2015, this was a new territory, as facial scanning wasn’t a mainstream application. Nikita: What were the early applications of these scanned models? Rainer: In the beginning, we focused on 3D printed figurines from full-body scans. However, as we shifted to facial scanning, we licensed our technology to gaming companies, collaborating with giants like Wargaming and Tencent. We even ventured into virtual fittings with H&M. Each collaboration was custom-tailored, blending our technology with their systems. This model made us cash flow positive. Nikita: So this was the beginning of your foray into the gaming industry? Rainer: Precisely. The demand from gaming companies was substantial. As we built custom solutions for these enterprises, we saw a bigger potential. While our cash flow was positive, we realized the challenge of scaling through exclusive enterprise deals. We envisioned our avatar creation tech reaching indie games and beyond. Nikita: And that led to the birth of ReadyPlayerMe? Rainer: Exactly. Once we understood our market direction, we quickly developed the first iteration of ReadyPlayerMe as a web-based experience, emphasizing easy integration for game developers. The initial version was a character builder, allowing users to personalize their avatars, which many adopted for their social media profiles. Our goal was to create avatars that users could connect with and use across various platforms. Instead of licensing our technology, we offered it for free to everyone. As ReadyPlayerMe gained traction, especially in VR applications, we secured funding to further our mission. Nikita: Your growth seems swift and organic. Were there any challenges? Rainer: Our focus on easy integration significantly fueled our adoption. Pairing that with personalized avatars resonated well with our audience. But like any venture, we’ve faced our share of challenges and have always aimed to evolve and better our offerings. The rapid growth in Web3 projects and virtual worlds made personalization and customization more important. With the NFT boom, you could add utility by allowing access to selected collections. This played into web-based games and metaverse applications. The shift towards Web3 and personalization provided a significant tailwind for us. Many used our characters as profile pictures on social media. Nikita: I’ve heard from other founders that a16z really values viral marketing. Was this one reason they wanted to invest in your project? How was the process with them? Rainer: When a16z reached out, it felt like a natural fit. We wanted investors who understood the gaming space. Our main market is Web3, but we’re exploring the top games market. Their expertise in gaming was invaluable. They’ve been very supportive throughout. We were fortunate to be on their radar. Nikita: So your early growth and organic traction played a role in attracting investors? Rainer: Definitely. Early product growth and the potential future trajectory were essential in our discussions. Nikita: As the CTO, you must have faced challenges. Can you speak about the tech side and its evolution? Rainer: The early version of our platform was built by in-house engineers. As we grew, we had to adapt to increasing complexities and ensure we had the right team to execute our vision. My role often shifted between product management and tech, depending on the need. Nikita: It sounds like the startup environment remains strong within your company. Rainer: Absolutely. We’re all committed, hands-on, and working towards building the best product. Nikita: You mentioned the team earlier. How many people are in your team now? Rainer: We have 70 people, with about half in product and engineering. Nikita: And did you hire the tech team? Rainer: We brought on a head of engineering at the beginning of this year. He’s been instrumental in scaling the engineering organization, from increasing the headcount to refining engineering processes. We’ve recently reorganized into domain-specific teams. As the team grows, regular reorganization ensures we focus on delivering specific customer value. Every stage requires attention to the team’s composition to ensure efficient delivery. Nikita: Any advice for founders just starting with their first startup? Rainer: Focus on customer value, no matter how niche it might seem initially. Begin with a specific problem and solution, then expand from there. You don’t need a massive project right away. Begin small, prove the concept, and scale from there. Nikita: You’ve mentioned your love for books and podcasts. Any recommendations? Rainer: For startups, “High Growth Handbook” and “Lean Startup” are must-reads. “Working Backwards” offers insights into Amazon’s customer-centric approach. For podcasts, I listen to “Rework,” “Lenny’s Podcast,” and “Huberman Lab.” Nikita: All of us have some side project ideas from time to time. How do you handle these when managing a big project? Rainer: Over the years, I’ve built various side projects. Some are small applications to solve immediate problems, like a menu bar app for AirPods which made it to No. 1 on Product Hunt, and was nominated for Golden Kitty Award. I sometimes delve into 3D and AI, merging them for technical demos. I keep a list of ideas and pick from them as the urge arises. Nikita: Any final thoughts or advice? Rainer: As you scale, do so with clarity. Avoid scaling just for external appeal. Always hire when there’s genuine need, not just for the sake of expansion. It helps in staying lean and focused.

#reddit#interview#founder

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1

danonino80•This week

How to increase the sales of my book

In just 3 months, it generated over $100 in revenue. I wanted to share my journey for two reasons: to potentially assist others in self-publishing their own books and to receive feedback to enhance my marketing strategy. I envision that there are others facing similar challenges. Let's dive into the financials, time spent, Key takeaways and the Challenges to address behind this product. Finances First, let's take a look at the financial overview. 💳 Expenses 🔹 E-book creation: · Book cover: $ 0. I used Adobe Express with 30 days of free trial. · ChatGPT: 20 $ a month. I leveraged AI to generate the chapters of the book, ensuring that no critical topics were overlooked during the content creation process and to refine the English, as it's not my native language. I also used to help me with copywriting of the web. If anyone is interested, I can share my Python code for outlining the chapters calling the API, but you can also directly ask chatgpt. · Kindle KDP (Kindle Direct Publishing): order author copies: 10 $. 🔹 Web creation: Domain: I got a com) / .org /.net domain for just 1 $ the first year. Carrd.co subscription: 19 $ (1 year) 🔹 Marketing: Promoted post on reddit: $30 Paid ads with google ads: $30 💰 Revenue 🔸 Sales: $102 💸 Net Profit: \~- $ 18 I initially thought the sales for this e-book would be quite modest, maybe only 3 or 4 books. However, the fact that I've sold more than that so far is a pleasant surprise. Even though the overall numbers may still be considered "peanuts" in the grand scheme of book sales, it suggests there could be more demand for content on digital asset custody than I had originally anticipated. This is a good learning experience, and I'll look to refine my marketing approach to see if I can reach a wider audience interested in this topic 🔹 Time Spent Next, let's review the time invested. 📖 Writing the e-book: 40 hours 🌍 Website + Stripe integration: 10 hours 📣 Creating promotional content: 10 hours ⏱️ Additional marketing efforts: 5 hours Total time spent: 65 hours As you can see, I dedicated more time to writing the e-book itself than to marketing and distribution. I spent relevant time to marketing because I though that a successful product launch requires a robust marketing effort. Many e-book authors overlook this crucial aspect! I utilized three sales channels: · Amazon: I found that there were no books specifically about digital asset custody, resulting in strong positioning in Amazon searches. Additionally, my book immediately secured the top position in Google searches for "digital asset custody book." However, despite achieving 50% of sales in the UK, I have not received any reviews globally. Sales distribution for this channel: 20% physical book, 80% ebook. · Twitter: Daniel\_ZZ80. With only 46 followers, the performance on this platform has not been optimal. I am beginning to write posts related to digital assets to increase visibility. · Gumroad: Lockeyyy.gumroad.com. I offered a discounted version of the ebook, but have not yet made any sales through this channel. Key takeaways: · The process of creating this e-book was extremely fulfilling, and while it has garnered overwhelmingly positive feedback from friends and colleagues (not considered as sales), it has yet to receive any Amazon reviews ☹. · Kindle KDP proved to be ideal for a rapid go-to-market strategy. · AI is an excellent tool for generating ideas and providing access to global audiences with perfect grammar. Otherwise, I would need to hire a translator, which can be very expensive. · Despite offering a full 30-day money-back guarantee, leading me to believe that the quality of the content is indeed good. · I have gained valuable insights for future technical books. · Although the current financial balance may be negative, I anticipate reaching the break-even point within one month, and this has now become a passive income stream. However, I recognize the need to regularly update the content due to the rapidly changing nature of this field. Challenges to address: · Is the timing for launching this book appropriate? In other words, is the world of digital asset custody a trendy and interesting topic for the audience? · What is causing the lack of sales through Gumroad? · Should I seek assistance as my marketing efforts have not yielded results? · Why are there no reviews on Amazon? · Why are sales primarily concentrated in the EU with only one sale in the US, which is my main target market? Feedback is appreciated. If you're interested in learning more about my approach, feel free to send me a direct message. A bit about my background: After dedicating my entire career to the banking industry, I explored various side projects. As an IT professional, I have now transitioned into the digital asset realm. After three years of intensive study, I recently published my first book on digital asset custody. I hope you found this post informative. Cheers! P.S.: I'm currently in the process of launching two more books using this system. 😊

#reddit#increase#sales

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-1

SpiritedSecond4791•This week

Please, help me to narrow down the list of ideas to pursuit

Hi guys, I need help to narrow down the possible problems to solve. How do you do it? What do you think about these ideas? All came from real-life problems. Break-It-Down Problem-Solving Assistant Problem: Large, complex projects can feel overwhelming and difficult to tackle. Solution: An AI-guided assistant that analyzes your project goals and automatically breaks them into smaller, manageable tasks. It provides suggested resources and real-time collaboration with team members for smoother task delegation. Personalized Sleep Solutions Problem: Poor sleep quality affects health, productivity, and overall well-being. Solution: An adaptive app that tracks sleep patterns through wearable data and adjusts sleep routines, room settings, and audio cues based on real-time sleep stages for optimal rest. Skill Analysis & Development Tool Problem: It’s challenging to identify valuable skills for career growth and keep up with future demands. Solution: AI-driven skill analysis with a personalized career roadmap that maps out high-demand skills for your specific industry, combined with real-time market trend analysis to suggest learning resources and certifications. Innovator’s Problem Discovery Platform Problem: Innovators struggle to identify real industry problems that need innovative solutions. Solution: An AI-powered platform that gathers and analyzes challenges from different industries, crowdsources ideas, and uses machine learning to highlight innovation opportunities tailored to your skills and interests. High-Earning Career Strategy Platform Problem: Many professionals face challenges in maximizing their earning potential and advancing their careers. Solution: A dynamic career advancement platform that analyzes your skill set, tracks job market trends, and offers personalized mentorship sessions with high-earning professionals in your field, along with salary benchmarking and negotiation tips.

#reddit#please,#help

reddit

0

1

someone-shoot-me•This week

Hear me out, you are annoying

I am a full stack web developer capable of realizing most of the people’s ideas here. Few things to mention out: Developers are not Google itself. You can’t randomly tell them to implement AI, blockchain, NFT, stocks etc. cus developers are not wizards. Stuff like that requires knowledge, quite a lot of it, and if you want someone to spend shit ton of time implementing smtn, you better prepare to pay for it, cus that knowledge is valuable. Most of the time it requires whole teams to do the work you imagined. If someone came to me and told me yeah, I want a copy of upwork. Sure bud, and where are the other 50 developers? (there are exceptions here but still, people are talking whole platforms while I am the only dev there, mate imma need a whole year for this one) be ready to pay. Sure, your idea is cool, but I can’t wait another 2 years untill it starts making money, plus if it’s fully online business, why would i want to share 50/50 with you? No one is dumb enough to do 3 months od work for free just to share 50/50 in best case, with someone who “had an idea”, I could delete the files any second I want to it’s my code! No one is doing big projects for free, be ready to pay and know the value of other’s work. Otherwise have fun scrolling through indian freelancers! At least try to manage something! I am right now looking for projects that I could join, who needs a web dev and all of them are like okay do the work, don’t expect any money cus we aee starting with no money at all and we won’t bother any investors cus you aee the dumb enough developer do male our business for 25% share

#reddit#hear#out,

reddit

0

1

Good0times•This week

Presenting my fresh new ideas for Google! Warning: These might make TOO much money

It's well known that Google likes its safe avenues, such as email, word processing, and file storage. One would think that it has done everything it can. I disagree! There are so many untapped markets out there to compete in and employ its digital ~~monopoly~~ advantage. behold the future of Google projects and even more wealth at your fingertips: Google Street Crime: An app that uses AI/ML to identify easy places to rob or even just people to hit. Google Jail: The perfect way to communicate with your family, lawyers, and drug dealers. Google Prostitution: Similar to the 'Uber' style of business, albeit big pimping. Google Human Trafficking: Sell your victims on a competitive global marketplace! Or organs. Google Slavery: Kind of like the same but probably with different taxes. Google Death Sentence: Automated appeal process. Free cookies with all fails! Google Cartel: Another kind of marketplace. Premium gives you a free chainsaw. Google Misery: A VR-powered world which portrays hell. It's an improvement. Google Domination: The blissful removal of this tedious experiment of democracy. Google Religion: The worship of Google as a lovecraftian but benevolent creator of all existence. All of these shall be accompanied by chirpy music, minimalist graphics, and deliriously happy animated cartoons. Well now you've got the ideas so chase that money you freaks! It's right there. Smell of money! Smell of money!

#reddit#presenting#fresh

reddit

0

1

Whole_Ad_9002•This week

Please rate my business idea aimed at emerging markets

Imagine a single platform that transforms your business operations effortlessly. Think of it like a Swiss Army knife for your business. First Steps (Every Business Needs These): Our website builder is your first step online. Just pick what you want, drag it where you need it, and you've got a professional website or online store. No tech skills needed. Perfect for getting your business visible to customers. Our security tool comes next because every business needs protection. It's like a security guard for your files and data, keeping hackers out and making sure you never lose important work. Think of it as insurance for your digital business. Cybersecurity and backups combined. Growing Business Needs: As your team grows, our digital office keeps everyone connected. Email, chat, share files, and track projects in one place. It's like having everyone in the same room, even when working remotely. This becomes essential once you have more than a few people. When paperwork starts piling up, our AI helper steps in. It's like having a smart assistant who learns your business and helps everyone get more done - handling routine tasks, summarizing meetings, and suggesting better ways to work. Scaling Up (For Established Businesses): Once you're handling lots of data, our eco-friendly cloud storage becomes crucial. Your business gets fast, reliable storage while helping the environment. We use servers powered by renewable energy, so you can grow sustainably. Our data transfer tool becomes important when you're working with multiple systems and need to move files around. It's like having a digital moving service that safely carries your files between different cloud services. With 20+ services to connect to cloud to cloud or on prem to cloud, its easy and fast. Advanced Needs: When your business processes get complex, our workflow automation tool helps you set up automatic systems for repetitive tasks. It's like training a robot to handle your routine work while your team focuses on growth. Simple no-code automation when you need it. Everything works on its own, but they're designed to work even better together. And since it's all in one place, you only need one password and one monthly bill. Most businesses save about half their tech costs by switching to our platform. Cost Benefits: Single subscription replaces multiple vendor contracts Reduces IT overhead by 40-60% on average No need for expensive technical specialists Predictable monthly costs Scales pricing with your usage

#reddit#please#rate

reddit

0

1

Competitive_Salad709•This week

Is SaaS Done?

Other day I was talking to one of the leaders in Office, He said "SAAS IS DY!NG THANKS TO AI". I found this fascinating & started digging on this, I was already part of communities like Build in Public, NoCode Builders & Others. I think he was right. I saw a significant raise in the AI Tools, what other call it 'AI Wrapper Startups' I explored many tools, then I realise why don't we capitalise this opportunity. I found out it is the marketers who needs to be aware of these & if you don't embrace these tools you will end up losing to someone with minimum experience with marketing but good hands on experience with the tools. If these tools keep up the same phase then you have both challenges & opportunities which I've listed it down in the post pros & cons. I think we need to embrace these tools are else we will be left behind. All these things are about marketers but what about the people who want to become solopreneurs or people like Pieter Levels who just want to create something useful get money either by selling or running multiple projects at once. Whatever I've studied & learnt. I came up with something called "The SaaS Marketing Innovation Cycle". The SaaS Marketing Innovation Cycle : Will have six easy steps. Empowering with No-code : Decide what is the problem you are planing to solve & understand which is No-code tool can help you with solution. Some tools will have steep learning curve, become expert on those tools. Integrate Automation AI : This is very crucial for your tool & make sure you have build a tool which will integrates easily with most of the platforms. Build Custom Solution : Right now the whole industry of Micro SaaS stands on building custom solutions, catering your audience is the best way to go for it. Launching MVPs : Because you have no-code tools it is easier to deploy MVPs than ever before & you can build multiple tools at once. Adapt & Grow : This is about the business take feedback from customer add new feature remove few yada yada. Leverage the Growth : Here it is important you have learn to build communities out these tools. if you come up with any new ideas there is always a group of people, who will be able adapt & give you the feedback to improve. Conclusion : Either build something or adapt something quicker when that has built. What do you think Folks ??

#reddit#saas#done?

reddit

0

1

Whole_Ad_9002•This week

Please rate my business idea aimed at emerging markets

Imagine a single platform that transforms your business operations effortlessly. Think of it like a Swiss Army knife for your business. First Steps (Every Business Needs These): Our website builder is your first step online. Just pick what you want, drag it where you need it, and you've got a professional website or online store. No tech skills needed. Perfect for getting your business visible to customers. Our security tool comes next because every business needs protection. It's like a security guard for your files and data, keeping hackers out and making sure you never lose important work. Think of it as insurance for your digital business. Cybersecurity and backups combined. Growing Business Needs: As your team grows, our digital office keeps everyone connected. Email, chat, share files, and track projects in one place. It's like having everyone in the same room, even when working remotely. This becomes essential once you have more than a few people. When paperwork starts piling up, our AI helper steps in. It's like having a smart assistant who learns your business and helps everyone get more done - handling routine tasks, summarizing meetings, and suggesting better ways to work. Scaling Up (For Established Businesses): Once you're handling lots of data, our eco-friendly cloud storage becomes crucial. Your business gets fast, reliable storage while helping the environment. We use servers powered by renewable energy, so you can grow sustainably. Our data transfer tool becomes important when you're working with multiple systems and need to move files around. It's like having a digital moving service that safely carries your files between different cloud services. With 20+ services to connect to cloud to cloud or on prem to cloud, its easy and fast. Advanced Needs: When your business processes get complex, our workflow automation tool helps you set up automatic systems for repetitive tasks. It's like training a robot to handle your routine work while your team focuses on growth. Simple no-code automation when you need it. Everything works on its own, but they're designed to work even better together. And since it's all in one place, you only need one password and one monthly bill. Most businesses save about half their tech costs by switching to our platform. Cost Benefits: Single subscription replaces multiple vendor contracts Reduces IT overhead by 40-60% on average No need for expensive technical specialists Predictable monthly costs Scales pricing with your usage

#reddit#please#rate

reddit

0

1

NearestNeighbr•This week

Ideas or niche for AI business?

Hey everyone! I'm a mathematician specialized in AI and I'm currently looking to start an innovative business or project. I was wondering if anyone here has experience with processes, machines, sensors, or any other systems that generate some amount of data that, in your opinion, isn't being fully utilized or could benefit from AI. I’m particularly interested in niche and specific cases that might not be widely known by the general public. I'm asking you because I believe that the diverse professional experiences within this community may reveal hidden opportunities. To give you some context: I have experience working on AI projects across different fields like healthcare, robotics, finance, and more. My specialization lies in forecasting. Mi current role is basically to make (or assist in making) strategic decisions based on the results of forecasting different events, metrics, indicators, ... I have no prior businesses, but I’m currently enrolled in a startup and business incubator program to help develop and refine my ideas. My plan is to apply to top incubators and accelerators if I can develop a decent business concept. I’m looking for an online business or project that requires relatively little capital to start, as I’m a "recent" graduate. I’m based in Spain, near the Mediterranean, though I'm not looking to center my business idea specifically around this. Any specific suggestions or insights based on your professional experiences would be incredibly valuable. If you have experience with underutilized data-generating processes, machines, or sensors, or know of a niche application where AI could be transformative, I’d love to hear your thoughts! Thanks so much!

#reddit#ideas#niche

reddit

0

0

East-Suggestion-8249•This week

I am a software engineer looking for partners to work on any kind of project

I am a software developer, I am sick of working 9 to 5, looking for someone who have a business a startup idea or other developers that plan to make something useful and can benefit from my experience, I can spin up full stack solutions including AI (no frontend) I made so many projects that didn’t make any profits but I think it just needs a group of serious people to start a business.

#reddit#software#engineer

reddit

0

1

Competitive_Salad709•This week

Is SaaS Done?

Other day I was talking to one of the leaders in Office, He said "SAAS IS DY!NG THANKS TO AI". I found this fascinating & started digging on this, I was already part of communities like Build in Public, NoCode Builders & Others. I think he was right. I saw a significant raise in the AI Tools, what other call it 'AI Wrapper Startups' I explored many tools, then I realise why don't we capitalise this opportunity. I found out it is the marketers who needs to be aware of these & if you don't embrace these tools you will end up losing to someone with minimum experience with marketing but good hands on experience with the tools. If these tools keep up the same phase then you have both challenges & opportunities which I've listed it down in the post pros & cons. I think we need to embrace these tools are else we will be left behind. All these things are about marketers but what about the people who want to become solopreneurs or people like Pieter Levels who just want to create something useful get money either by selling or running multiple projects at once. Whatever I've studied & learnt. I came up with something called "The SaaS Marketing Innovation Cycle". The SaaS Marketing Innovation Cycle : Will have six easy steps. Empowering with No-code : Decide what is the problem you are planing to solve & understand which is No-code tool can help you with solution. Some tools will have steep learning curve, become expert on those tools. Integrate Automation AI : This is very crucial for your tool & make sure you have build a tool which will integrates easily with most of the platforms. Build Custom Solution : Right now the whole industry of Micro SaaS stands on building custom solutions, catering your audience is the best way to go for it. Launching MVPs : Because you have no-code tools it is easier to deploy MVPs than ever before & you can build multiple tools at once. Adapt & Grow : This is about the business take feedback from customer add new feature remove few yada yada. Leverage the Growth : Here it is important you have learn to build communities out these tools. if you come up with any new ideas there is always a group of people, who will be able adapt & give you the feedback to improve. Conclusion : Either build something or adapt something quicker when that has built. What do you think Folks ??

#reddit#saas#done?

reddit

0

1

Whole_Ad_9002•This week

Please rate my business idea aimed at emerging markets

Imagine a single platform that transforms your business operations effortlessly. Think of it like a Swiss Army knife for your business. First Steps (Every Business Needs These): Our website builder is your first step online. Just pick what you want, drag it where you need it, and you've got a professional website or online store. No tech skills needed. Perfect for getting your business visible to customers. Our security tool comes next because every business needs protection. It's like a security guard for your files and data, keeping hackers out and making sure you never lose important work. Think of it as insurance for your digital business. Cybersecurity and backups combined. Growing Business Needs: As your team grows, our digital office keeps everyone connected. Email, chat, share files, and track projects in one place. It's like having everyone in the same room, even when working remotely. This becomes essential once you have more than a few people. When paperwork starts piling up, our AI helper steps in. It's like having a smart assistant who learns your business and helps everyone get more done - handling routine tasks, summarizing meetings, and suggesting better ways to work. Scaling Up (For Established Businesses): Once you're handling lots of data, our eco-friendly cloud storage becomes crucial. Your business gets fast, reliable storage while helping the environment. We use servers powered by renewable energy, so you can grow sustainably. Our data transfer tool becomes important when you're working with multiple systems and need to move files around. It's like having a digital moving service that safely carries your files between different cloud services. With 20+ services to connect to cloud to cloud or on prem to cloud, its easy and fast. Advanced Needs: When your business processes get complex, our workflow automation tool helps you set up automatic systems for repetitive tasks. It's like training a robot to handle your routine work while your team focuses on growth. Simple no-code automation when you need it. Everything works on its own, but they're designed to work even better together. And since it's all in one place, you only need one password and one monthly bill. Most businesses save about half their tech costs by switching to our platform. Cost Benefits: Single subscription replaces multiple vendor contracts Reduces IT overhead by 40-60% on average No need for expensive technical specialists Predictable monthly costs Scales pricing with your usage

#reddit#please#rate

reddit

0

1

nocturnal_1_1995•This week

Not sure if this is the right sub for it, but could you guys please roast my CV?

A brief about myself, I am an MSc from a top European University where I focused on NLP mostly hence most of my projects are just in NLP. I do have an experience of 3 years as a SE, did a 6 month stint as a consultant that I did not like, and finally got hired by a company I was doing my university project under to built their first products. The last 2 employments were part-time as I was also completing my masters at the same time. I am looking to apply in India mostly now. What do you think I can do differently, I just feel like something is missing here. Would be very thankful to anyone who can give me some constructive criticism on what to change here. Thanks again!

#reddit#sure#right

reddit

0

1

SxxVe•This week

I applied for many companies and can’t get a job

Just like the title said, I applied but still cant get a job, what’s the issue am 17 years old really love machine learning/AI. As you can see from the CV i built many projects, whats the issue is it because of the Age or my projects are not impressive?

#reddit#applied#many

reddit

0

1

MohammadBais•This week

TiCs -where innovation meets intelligence

Be Part of India’s AI Revolution – Join the TiCs Movement! We are TiCs (Tuba International Cooperative Society)—India’s first global AI powerhouse. We’re not just building a company; we’re launching a movement that will redefine AI-driven healthcare, fitness, and well-being. Through our brands WellNest (AI-powered health ecosystem) and Zenova (next-gen smart wearables), we are pioneering a future where technology truly understands and enhances human health. Why Are We Calling You? We’re assembling a community of passionate minds—AI enthusiasts, developers, designers, innovators, and problem-solvers—who want to be part of something bigger. This is NOT an internship. This is NOT a job. This is a mission to build the future of health-tech. What’s in It for You? ✅ Work on groundbreaking AI & LLM projects that solve real-world healthcare problems ✅ Hands-on experience in AI, ML, IoT, and smart wearables ✅ Mentorship & learning opportunities from top AI leaders ✅ Exclusive perks like health, wellness, and gym packages ✅ Recognition & growth opportunities—top contributors will be given leadership roles as we scale ✅ Certificates & endorsements to showcase your contributions ✅ Opportunity to be part of a global AI-led revolution in healthcare & fitness ✅ Network with like-minded innovators, entrepreneurs, and industry pioneers ✅ Early access to WellNest & Zenova products and AI-driven health plans ✅ Possibility of paid roles & equity-based opportunities for the most dedicated members Who Should Join? Students & fresh graduates eager to apply their skills AI & tech enthusiasts passionate about real-world innovation Developers, designers, and creators who want to build something impactful Anyone who believes in the power of AI for good and wants to contribute This is More Than Just a Tech Project We’re building an AI-powered health revolution. If you want to be part of something that changes lives, breaks barriers, and creates real impact, this is your chance. Movements aren’t built by employees—they are led by believers. If you believe in the power of AI to transform health, join us and let’s build the future together!

#reddit#tics#-where

reddit

0

1

LivingMars•This week

New Vs code extension trial

Hi guys Recently built a Vs code extension called Code-Canvas. Reason behind this idea is i am a beginner in tech, and love to learn programming through working on projects. So for almost every line of code i had to note down the meaning of the keywords or the workflows or the use cases by commenting. By the end more than code lines there were comments. So implemented this to have a note icon for a clean look. Would be really helpful if you guys went through the README where i have mentioned all about the extension, please try it out and provide ratings and reviews!

#reddit#code#extension

youtube

0.369

0.26

Simplilearn•Mar 28, 2025

ChatGPT Full Course For 2025 | ChatGPT Tutorial For Beginnners | ChatGPT Course | Simplilearn

🔥Purdue - Applied Generative AI Specialization - https://www.simplilearn.com/applied-ai-course?utmcampaign=C4lBsBlloL0&utmmedium=Lives&utm_source=Youtube 🔥Professional Certificate Program in Generative AI and Machine Learning - IITG (India Only) - https://www.simplilearn.com/iitg-generative-ai-machine-learning-program?utmcampaign=C4lBsBlloL0&utmmedium=Lives&utm_source=Youtube 🔥Advanced Executive Program In Applied Generative AI - https://www.simplilearn.com/applied-generative-ai-course?utmcampaign=C4lBsBlloL0&utmmedium=Lives&utm_source=Youtube This ChatGPT Full Course 2025 by Simplilearn provides a comprehensive learning journey, starting with an introduction to ChatGPT and Generative AI, followed by insights into AI job opportunities and a comparison between ChatGPT 4.0 and 4.0 Turbo. The tutorial covers prompt engineering techniques, machine learning fundamentals, and running Llama models privately. Learners will explore ChatGPT-powered application development, its role in programming, and Excel automation. The course also dives into blogging, PowerPoint automation, customer support, and finance applications. Advanced topics like RAG vs. Prompt Tuning, prompt injection, and LangChain are included, along with discussions on OpenAI's latest innovations, including Sora and Strawberry. By the end, participants will gain a strong understanding of ChatGPT’s capabilities and monetization strategies. 🚀 Following are the topics covered in the ChatGPT Full Course 2025: 00:00:00 - Introduction to ChatGPT Full Course 2025 00:09:26 - What is ChatGPT 00:10:11 - What is Gen AI 00:26:29 - How to get Job in AI 00:27:06 - ChatGPT 40 vs ChatGPT 4 01:03:14 - Chatgpt analyse 02:13:57 - Prompt Engineering Tutorial 03:10:34 - What is Machine Learning 04:07:06 - Machine Learning Tutorial 04:08:13 - Run Lama Privately 04:23:50 - Search GPT 04:25:31 - Build App Using ChatGPT 06:31:11 - ChatGPT for Programming 06:46:08 - Prompt Formulae Chatgpt 07:58:38 - Automate Excel using Chatgpt 08:00:06 - Blogging with ChatGpt 08:27:25 - Powerpoint using Chatgpt 08:28:31 - Rag Vs Prompt Tuning 09:37:43 - Chatgpt for Customer Support 11:11:06 - ChatGPT for finance 11:17:38 - Prompt injection 11:18:38 - How to Earn Money using ChatGPT 11:41:46 - Open AI Strawberry 11:52:42 - Openai sora 11:54:57 - Langchain 12:22:19 - Open ai chatgpt o1 model ✅ Subscribe to our Channel to learn more about the top Technologies: https://bit.ly/2VT4WtH ⏩ Check out the Artificial Intelligence training videos: https://youtube.com/playlist?list=PLEiEAq2VkUULa5aOQmO_al2VVmhC-eqeI #gpt #chatgpt #chatgptforbeginners #chatgptcourse #genai #generativeai #artificialintelligence #ai #machinelearning #llm #simplilearn #2025 ➡️ About Professional Certificate Program in Generative AI and Machine Learning Dive into the future of AI with our Generative AI & Machine Learning course, in collaboration with E&ICT Academy, IIT Guwahati. Learn tools like ChatGPT, OpenAI, Hugging Face, Python, and more. Join masterclasses led by IITG faculty, engage in hands-on projects, and earn Executive Alumni Status. Key Features: ✅ Program completion certificate from E&ICT Academy, IIT Guwahati ✅ Curriculum delivered in live virtual classes by seasoned industry experts ✅ Exposure to the latest AI advancements, such as generative AI, LLMs, and prompt engineering ✅ Interactive live-virtual masterclasses delivered by esteemed IIT Guwahati faculty ✅ Opportunity to earn an 'Executive Alumni Status' from E&ICT Academy, IIT Guwahati ✅ Eligibility for a campus immersion program organized at IIT Guwahati ✅ Exclusive hackathons and “ask-me-anything” sessions by IBM ✅ Certificates for IBM courses and industry masterclasses by IBM experts ✅ Practical learning through 25+ hands-on projects and 3 industry-oriented capstone projects ✅ Access to a wide array of AI tools such as ChatGPT, Hugging Face, DALL-E 2, Midjourney and more ✅ Simplilearn's JobAssist helps you get noticed by top hiring companies Skills Covered: ✅ Generative AI ✅ Prompt Engineering ✅ Chatbot Development ✅ Supervised and Unsupervised Learning ✅ Model Training and Optimization ✅ Model Evaluation and Validation ✅ Ensemble Methods ✅ Deep Learning ✅ Natural Language Processing ✅ Computer Vision ✅ Reinforcement Learning ✅ Machine Learning Algorithms ✅ Speech Recognition ✅ Statistics Learning Path: ✅ Program Induction ✅ Programming Fundamentals ✅ Python for Data Science (IBM) ✅ Applied Data Science with Python ✅ Machine Learning ✅ Deep Learning with TensorFlow (IBM) ✅ Deep Learning Specialization ✅ Essentials of Generative AI, Prompt Engineering & ChatGPT ✅ Advanced Generative AI ✅ Capstone Electives: ✅ ADL & Computer Vision ✅ NLP and Speech Recognition ✅ Reinforcement Learning ✅ Academic Masterclass ✅ Industry Masterclass 👉 Learn More At: https://www.simplilearn.com/iitg-generative-ai-machine-learning-program?utmcampaign=C4lBsBlloL0&utmmedium=Lives&utm_source=Youtube

#gpt#chatgpt#chatgptforbeginners

github

0.563

0.24210481455988786

NirDiamant•Mar 28, 2025

GenAI_Agents

🌟 Support This Project: Your sponsorship fuels innovation in GenAI agent development. Become a sponsor to help maintain and expand this valuable resource! GenAI Agents: Comprehensive Repository for Development and Implementation 🚀 Welcome to one of the most extensive and dynamic collections of Generative AI (GenAI) agent tutorials and implementations available today. This repository serves as a comprehensive resource for learning, building, and sharing GenAI agents, ranging from simple conversational bots to complex, multi-agent systems. 📫 Stay Updated! 🚀Cutting-edgeUpdates 💡ExpertInsights 🎯Top 0.1%Content Join over 15,000 of AI enthusiasts getting unique cutting-edge insights and free tutorials! Plus, subscribers get exclusive early access and special 33% discounts to my book and the upcoming RAG Techniques course! Introduction Generative AI agents are at the forefront of artificial intelligence, revolutionizing the way we interact with and leverage AI technologies. This repository is designed to guide you through the development journey, from basic agent implementations to advanced, cutting-edge systems. 📚 Learn to Build Your First AI Agent Your First AI Agent: Simpler Than You Think This detailed blog post complements the repository by providing a complete A-Z walkthrough with in-depth explanations of core concepts, step-by-step implementation, and the theory behind AI agents. It's designed to be incredibly simple to follow while covering everything you need to know to build your first working agent from scratch. 💡 Plus: Subscribe to the newsletter for exclusive early access to tutorials and special discounts on upcoming courses and books! Our goal is to provide a valuable resource for everyone - from beginners taking their first steps in AI to seasoned practitioners pushing the boundaries of what's possible. By offering a range of examples from foundational to complex, we aim to facilitate learning, experimentation, and innovation in the rapidly evolving field of GenAI agents. Furthermore, this repository serves as a platform for showcasing innovative agent creations. Whether you've developed a novel agent architecture or found an innovative application for existing techniques, we encourage you to share your work with the community. Related Projects 📚 Dive into my comprehensive guide on RAG techniques to learn about integrating external knowledge into AI systems, enhancing their capabilities with up-to-date and relevant information retrieval. 🖋️ Explore my Prompt Engineering Techniques guide for an extensive collection of prompting strategies, from fundamental concepts to advanced methods, improving your ability to communicate effectively with AI language models. A Community-Driven Knowledge Hub This repository grows stronger with your contributions! Join our vibrant Discord community — the central hub for shaping and advancing this project together 🤝 GenAI Agents Discord Community Whether you're a novice eager to learn or an expert ready to share your knowledge, your insights can shape the future of GenAI agents. Join us to propose ideas, get feedback, and collaborate on innovative implementations. For contribution guidelines, please refer to our CONTRIBUTING.md file. Let's advance GenAI agent technology together! 🔗 For discussions on GenAI, agents, or to explore knowledge-sharing opportunities, feel free to connect on LinkedIn. Key Features 🎓 Learn to build GenAI agents from beginner to advanced levels 🧠 Explore a wide range of agent architectures and applications 📚 Step-by-step tutorials and comprehensive documentation 🛠️ Practical, ready-to-use agent implementations 🌟 Regular updates with the latest advancements in GenAI 🤝 Share your own agent creations with the community GenAI Agent Implementations Explore our extensive list of GenAI agent implementations, sorted by categories: 🌱 Beginner-Friendly Agents Simple Conversational Agent LangChain PydanticAI Overview 🔎 A context-aware conversational AI maintains information across interactions, enabling more natural dialogues. Implementation 🛠️ Integrates a language model, prompt template, and history manager to generate contextual responses and track conversation sessions. Simple Question Answering Agent Overview 🔎 Answering (QA) agent using LangChain and OpenAI's language model understands user queries and provides relevant, concise answers. Implementation 🛠️ Combines OpenAI's GPT model, a prompt template, and an LLMChain to process user questions and generate AI-driven responses in a streamlined manner. Simple Data Analysis Agent LangChain PydanticAI Overview 🔎 An AI-powered data analysis agent interprets and answers questions about datasets using natural language, combining language models with data manipulation tools for intuitive data exploration. Implementation 🛠️ Integrates a language model, data manipulation framework, and agent framework to process natural language queries and perform data analysis on a synthetic dataset, enabling accessible insights for non-technical users. 🔧 Framework Tutorial: LangGraph Introduction to LangGraph: Building Modular AI Workflows Overview 🔎 This tutorial introduces LangGraph, a powerful framework for creating modular, graph-based AI workflows. Learn how to leverage LangGraph to build more complex and flexible AI agents that can handle multi-step processes efficiently. Implementation 🛠️ Step-by-step guide on using LangGraph to create a StateGraph workflow. The tutorial covers key concepts such as state management, node creation, and graph compilation. It demonstrates these principles by constructing a simple text analysis pipeline, serving as a foundation for more advanced agent architectures. Additional Resources 📚 Blog Post 🎓 Educational and Research Agents ATLAS: Academic Task and Learning Agent System Overview 🔎 ATLAS demonstrates how to build an intelligent multi-agent system that transforms academic support through AI-powered assistance. The system leverages LangGraph's workflow framework to coordinate multiple specialized agents that provide personalized academic planning, note-taking, and advisory support. Implementation 🛠️ Implements a state-managed multi-agent architecture using four specialized agents (Coordinator, Planner, Notewriter, and Advisor) working in concert through LangGraph's workflow framework. The system features sophisticated workflows for profile analysis and academic support, with continuous adaptation based on student performance and feedback. Additional Resources 📚 YouTube Explanation Blog Post Scientific Paper Agent - Literature Review Overview 🔎 An intelligent research assistant that helps users navigate, understand, and analyze scientific literature through an orchestrated workflow. The system combines academic APIs with sophisticated paper processing techniques to automate literature review tasks, enabling researchers to efficiently extract insights from academic papers while maintaining research rigor and quality control. Implementation 🛠️ Leverages LangGraph to create a five-node workflow system including decision making, planning, tool execution, and quality validation nodes. The system integrates the CORE API for paper access, PDFplumber for document processing, and advanced language models for analysis. Key features include a retry mechanism for robust paper downloads, structured data handling through Pydantic models, and quality-focused improvement cycles with human-in-the-loop validation options. Additional Resources 📚 YouTube Explanation Blog Post Chiron - A Feynman-Enhanced Learning Agent Overview 🔎 An adaptive learning agent that guides users through educational content using a structured checkpoint system and Feynman-style teaching. The system processes learning materials (either user-provided or web-retrieved), verifies understanding through interactive checkpoints, and provides simplified explanations when needed, creating a personalized learning experience that mimics one-on-one tutoring. Implementation 🛠️ Uses LangGraph to orchestrate a learning workflow that includes checkpoint definition, context building, understanding verification, and Feynman teaching nodes. The system integrates web search for dynamic content retrieval, employs semantic chunking for context processing, and manages embeddings for relevant information retrieval. Key features include a 70% understanding threshold for progression, interactive human-in-the-loop validation, and structured output through Pydantic models for consistent data handling. Additional Resources 📚 YouTube Explanation 💼 Business and Professional Agents Customer Support Agent (LangGraph) Overview 🔎 An intelligent customer support agent using LangGraph categorizes queries, analyzes sentiment, and provides appropriate responses or escalates issues. Implementation 🛠️ Utilizes LangGraph to create a workflow combining state management, query categorization, sentiment analysis, and response generation. Essay Grading Agent (LangGraph) Overview 🔎 An automated essay grading system using LangGraph and an LLM model evaluates essays based on relevance, grammar, structure, and depth of analysis. Implementation 🛠️ Utilizes a state graph to define the grading workflow, incorporating separate grading functions for each criterion. Travel Planning Agent (LangGraph) Overview 🔎 A Travel Planner using LangGraph demonstrates how to build a stateful, multi-step conversational AI application that collects user input and generates personalized travel itineraries. Implementation 🛠️ Utilizes StateGraph to define the application flow, incorporates custom PlannerState for process management. GenAI Career Assistant Agent Overview 🔎 The GenAI Career Assistant demonstrates how to create a multi-agent system that provides personalized guidance for careers in Generative AI. Using LangGraph and Gemini LLM, the system delivers customized learning paths, resume assistance, interview preparation, and job search support. Implementation 🛠️ Leverages a multi-agent architecture using LangGraph to coordinate specialized agents (Learning, Resume, Interview, Job Search) through TypedDict-based state management. The system employs sophisticated query categorization and routing while integrating with external tools like DuckDuckGo for job searches and dynamic content generation. Additional Resources 📚 YouTube Explanation Project Manager Assistant Agent Overview 🔎 An AI agent designed to assist in project management tasks by automating the process of creating actionable tasks from project descriptions, identifying dependencies, scheduling work, and assigning tasks to team members based on expertise. The system includes risk assessment and self-reflection capabilities to optimize project plans through multiple iterations, aiming to minimize overall project risk. Implementation 🛠️ Leverages LangGraph to orchestrate a workflow of specialized nodes including task generation, dependency mapping, scheduling, allocation, and risk assessment. Each node uses GPT-4o-mini for structured outputs following Pydantic models. The system implements a feedback loop for self-improvement, where risk scores trigger reflection cycles that generate insights to optimize the project plan. Visualization tools display Gantt charts of the generated schedules across iterations. Additional Resources 📚 YouTube Explanation Contract Analysis Assistant (ClauseAI) Overview 🔎 ClauseAI demonstrates how to build an AI-powered contract analysis system using a multi-agent approach. The system employs specialized AI agents for different aspects of contract review, from clause analysis to compliance checking, and leverages LangGraph for workflow orchestration and Pinecone for efficient clause retrieval and comparison. Implementation 🛠️ Implements a sophisticated state-based workflow using LangGraph to coordinate multiple AI agents through contract analysis stages. The system features Pydantic models for data validation, vector storage with Pinecone for clause comparison, and LLM-based analysis for generating comprehensive contract reports. The implementation includes parallel processing capabilities and customizable report generation based on user requirements. Additional Resources 📚 YouTube Explanation E2E Testing Agent Overview 🔎 The E2E Testing Agent demonstrates how to build an AI-powered system that converts natural language test instructions into executable end-to-end web tests. Using LangGraph for workflow orchestration and Playwright for browser automation, the system enables users to specify test cases in plain English while handling the complexity of test generation and execution. Implementation 🛠️ Implements a structured workflow using LangGraph to coordinate test generation, validation, and execution. The system features TypedDict state management, integration with Playwright for browser automation, and LLM-based code generation for converting natural language instructions into executable test scripts. The implementation includes DOM state analysis, error handling, and comprehensive test reporting. Additional Resources 📚 YouTube Explanation 🎨 Creative and Content Generation Agents GIF Animation Generator Agent (LangGraph) Overview 🔎 A GIF animation generator that integrates LangGraph for workflow management, GPT-4 for text generation, and DALL-E for image creation, producing custom animations from user prompts. Implementation 🛠️ Utilizes LangGraph to orchestrate a workflow that generates character descriptions, plots, and image prompts using GPT-4, creates images with DALL-E 3, and assembles them into GIFs using PIL. Employs asynchronous programming for efficient parallel processing. TTS Poem Generator Agent (LangGraph) Overview 🔎 An advanced text-to-speech (TTS) agent using LangGraph and OpenAI's APIs classifies input text, processes it based on content type, and generates corresponding speech output. Implementation 🛠️ Utilizes LangGraph to orchestrate a workflow that classifies input text using GPT models, applies content-specific processing, and converts the processed text to speech using OpenAI's TTS API. The system adapts its output based on the identified content type (general, poem, news, or joke). Music Compositor Agent (LangGraph) Overview 🔎 An AI Music Compositor using LangGraph and OpenAI's language models generates custom musical compositions based on user input. The system processes the input through specialized components, each contributing to the final musical piece, which is then converted to a playable MIDI file. Implementation 🛠️ LangGraph orchestrates a workflow that transforms user input into a musical composition, using ChatOpenAI (GPT-4) to generate melody, harmony, and rhythm, which are then style-adapted. The final AI-generated composition is converted to a MIDI file using music21 and can be played back using pygame. Content Intelligence: Multi-Platform Content Generation Agent Overview 🔎 Content Intelligence demonstrates how to build an advanced content generation system that transforms input text into platform-optimized content across multiple social media channels. The system employs LangGraph for workflow orchestration to analyze content, conduct research, and generate tailored content while maintaining brand consistency across different platforms. Implementation 🛠️ Implements a sophisticated workflow using LangGraph to coordinate multiple specialized nodes (Summary, Research, Platform-Specific) through the content generation process. The system features TypedDict and Pydantic models for state management, integration with Tavily Search for research enhancement, and platform-specific content generation using GPT-4. The implementation includes parallel processing for multiple platforms and customizable content templates. Additional Resources 📚 YouTube Explanation Business Meme Generator Using LangGraph and Memegen.link Overview 🔎 The Business Meme Generator demonstrates how to create an AI-powered system that generates contextually relevant memes based on company website analysis. Using LangGraph for workflow orchestration, the system combines Groq's Llama model for text analysis and the Memegen.link API to automatically produce brand-aligned memes for digital marketing. Implementation 🛠️ Implements a state-managed workflow using LangGraph to coordinate website content analysis, meme concept generation, and image creation. The system features Pydantic models for data validation, asynchronous processing with aiohttp, and integration with external APIs (Groq, Memegen.link) to create a complete meme generation pipeline with customizable templates. Additional Resources 📚 YouTube Explanation Murder Mystery Game with LLM Agents Overview 🔎 A text-based detective game that utilizes autonomous LLM agents as interactive characters in a procedurally generated murder mystery. Drawing inspiration from the UNBOUNDED paper, the system creates unique scenarios each time, with players taking on the role of Sherlock Holmes to solve the case through character interviews and deductive reasoning. Implementation 🛠️ Leverages two LangGraph workflows - a main game loop for story/character generation and game progression, and a conversation sub-graph for character interactions. The system uses a combination of LLM-powered narrative generation, character AI, and structured game mechanics to create an immersive investigative experience with replayable storylines. Additional Resources 📚 YouTube Explanation 📊 Analysis and Information Processing Agents Memory-Enhanced Conversational Agent Overview 🔎 A memory-enhanced conversational AI agent incorporates short-term and long-term memory systems to maintain context within conversations and across multiple sessions, improving interaction quality and personalization. Implementation 🛠️ Integrates a language model with separate short-term and long-term memory stores, utilizes a prompt template incorporating both memory types, and employs a memory manager for storage and retrieval. The system includes an interaction loop that updates and utilizes memories for each response. Multi-Agent Collaboration System Overview 🔎 A multi-agent collaboration system combining historical research with data analysis, leveraging large language models to simulate specialized agents working together to answer complex historical questions. Implementation 🛠️ Utilizes a base Agent class to create specialized HistoryResearchAgent and DataAnalysisAgent, orchestrated by a HistoryDataCollaborationSystem. The system follows a five-step process: historical context provision, data needs identification, historical data provision, data analysis, and final synthesis. Self-Improving Agent Overview 🔎 A Self-Improving Agent using LangChain engages in conversations, learns from interactions, and continuously improves its performance over time through reflection and adaptation. Implementation 🛠️ Integrates a language model with chat history management, response generation, and a reflection mechanism. The system employs a learning system that incorporates insights from reflection to enhance future performance, creating a continuous improvement loop. Task-Oriented Agent Overview 🔎 A language model application using LangChain that summarizes text and translates the summary to Spanish, combining custom functions, structured tools, and an agent for efficient text processing. Implementation 🛠️ Utilizes custom functions for summarization and translation, wrapped as structured tools. Employs a prompt template to guide the agent, which orchestrates the use of tools. An agent executor manages the process, taking input text and producing both an English summary and its Spanish translation. Internet Search and Summarize Agent Overview 🔎 An intelligent web research assistant that combines web search capabilities with AI-powered summarization, automating the process of gathering information from the internet and distilling it into concise, relevant summaries. Implementation 🛠️ Integrates a web search module using DuckDuckGo's API, a result parser, and a text summarization engine leveraging OpenAI's language models. The system performs site-specific or general searches, extracts relevant content, generates concise summaries, and compiles attributed results for efficient information retrieval and synthesis. Multi agent research team - Autogen Overview 🔎 This technique explores a multi-agent system for collaborative research using the AutoGen library. It employs agents to solve tasks collaboratively, focusing on efficient execution and quality assurance. The system enhances research by distributing tasks among specialized agents. Implementation 🛠️ Agents are configured with specific roles using the GPT-4 model, including admin, developer, planner, executor, and quality assurance. Interaction management ensures orderly communication with defined transitions. Task execution involves collaborative planning, coding, execution, and quality checking, demonstrating a scalable framework for various domains. Additional Resources 📚 comprehensive solution with UI Blogpost Sales Call Analyzer Overview 🔎 An intelligent system that automates the analysis of sales call recordings by combining audio transcription with advanced natural language processing. The analyzer transcribes audio using OpenAI's Whisper, processes the text using NLP techniques, and generates comprehensive reports including sentiment analysis, key phrases, pain points, and actionable recommendations to improve sales performance. Implementation 🛠️ Utilizes multiple components in a structured workflow: OpenAI Whisper for audio transcription, CrewAI for task automation and agent management, and LangChain for orchestrating the analysis pipeline. The system processes audio through a series of steps from transcription to detailed analysis, leveraging custom agents and tasks to generate structured JSON reports containing insights about customer sentiment, sales opportunities, and recommended improvements. Additional Resources 📚 YouTube Explanation Weather Emergency & Response System Overview 🔎 A comprehensive system demonstrating two agent graph implementations for weather emergency response: a real-time graph processing live weather data, and a hybrid graph combining real and simulated data for testing high-severity scenarios. The system handles complete workflow from data gathering through emergency plan generation, with automated notifications and human verification steps. Implementation 🛠️ Utilizes LangGraph for orchestrating complex workflows with state management, integrating OpenWeatherMap API for real-time data, and Gemini for analysis and response generation. The system incorporates email notifications, social media monitoring simulation, and severity-based routing with configurable human verification for low/medium severity events. Additional Resources 📚 YouTube Explanation Self-Healing Codebase System Overview 🔎 An intelligent system that automatically detects, diagnoses, and fixes runtime code errors using LangGraph workflow orchestration and ChromaDB vector storage. The system maintains a memory of encountered bugs and their fixes through vector embeddings, enabling pattern recognition for similar errors across the codebase. Implementation 🛠️ Utilizes a state-based graph workflow that processes function definitions and runtime arguments through specialized nodes for error detection, code analysis, and fix generation. Incorporates ChromaDB for vector-based storage of bug patterns and fixes, with automated search and retrieval capabilities for similar error patterns, while maintaining code execution safety through structured validation steps. Additional Resources 📚 YouTube Explanation DataScribe: AI-Powered Schema Explorer Overview 🔎 An intelligent agent system that enables intuitive exploration and querying of relational databases through natural language interactions. The system utilizes a fleet of specialized agents, coordinated by a stateful Supervisor, to handle schema discovery, query planning, and data analysis tasks while maintaining contextual understanding through vector-based relationship graphs. Implementation 🛠️ Leverages LangGraph for orchestrating a multi-agent workflow including discovery, inference, and planning agents, with NetworkX for relationship graph visualization and management. The system incorporates dynamic state management through TypedDict classes, maintains database context between sessions using a db_graph attribute, and includes safety measures to prevent unauthorized database modifications. Memory-Enhanced Email Agent (LangGraph & LangMem) Overview 🔎 An intelligent email assistant that combines three types of memory (semantic, episodic, and procedural) to create a system that improves over time. The agent can triage incoming emails, draft contextually appropriate responses using stored knowledge, and enhance its performance based on user feedback. Implementation 🛠️ Leverages LangGraph for workflow orchestration and LangMem for sophisticated memory management across multiple memory types. The system implements a triage workflow with memory-enhanced decision making, specialized tools for email composition and calendar management, and a self-improvement mechanism that updates its own prompts based on feedback and past performance. Additional Resources 📚 Blog Post 📰 News and Information Agents News TL;DR using LangGraph Overview 🔎 A news summarization system that generates concise TL;DR summaries of current events based on user queries. The system leverages large language models for decision making and summarization while integrating with news APIs to access up-to-date content, allowing users to quickly catch up on topics of interest through generated bullet-point summaries. Implementation 🛠️ Utilizes LangGraph to orchestrate a workflow combining multiple components: GPT-4o-mini for generating search terms and article summaries, NewsAPI for retrieving article metadata, BeautifulSoup for web scraping article content, and Asyncio for concurrent processing. The system follows a structured pipeline from query processing through article selection and summarization, managing the flow between components to produce relevant TL;DRs of current news articles. Additional Resources 📚 YouTube Explanation Blog Post AInsight: AI/ML Weekly News Reporter Overview 🔎 AInsight demonstrates how to build an intelligent news aggregation and summarization system using a multi-agent architecture. The system employs three specialized agents (NewsSearcher, Summarizer, Publisher) to automatically collect, process and summarize AI/ML news for general audiences through LangGraph-based workflow orchestration. Implementation 🛠️ Implements a state-managed multi-agent system using LangGraph to coordinate the news collection (Tavily API), technical content summarization (GPT-4), and report generation processes. The system features modular architecture with TypedDict-based state management, external API integration, and markdown report generation with customizable templates. Additional Resources 📚 YouTube Explanation Journalism-Focused AI Assistant Overview 🔎 A specialized AI assistant that helps journalists tackle modern journalistic challenges like misinformation, bias, and information overload. The system integrates fact-checking, tone analysis, summarization, and grammar review tools to enhance the accuracy and efficiency of journalistic work while maintaining ethical reporting standards. Implementation 🛠️ Leverages LangGraph to orchestrate a workflow of specialized components including language models for analysis and generation, web search integration via DuckDuckGo's API, document parsing tools like PyMuPDFLoader and WebBaseLoader, text splitting with RecursiveCharacterTextSplitter, and structured JSON outputs. Each component works together through a unified workflow to analyze content, verify facts, detect bias, extract quotes, and generate comprehensive reports. Blog Writer (Open AI Swarm) Overview 🔎 A multi-agent system for collaborative blog post creation using OpenAI's Swarm package. It leverages specialized agents to perform research, planning, writing, and editing tasks efficiently. Implementation 🛠️ Utilizes OpenAI's Swarm Package to manage agent interactions. Includes an admin, researcher, planner, writer, and editor, each with specific roles. The system follows a structured workflow: topic setting, outlining, research, drafting, and editing. This approach enhances content creation through task distribution, specialization, and collaborative problem-solving. Additional Resources 📚 Swarm Repo Podcast Internet Search and Generate Agent 🎙️ Overview 🔎 A two step agent that first searches the internet for a given topic and then generates a podcast on the topic found. The search step uses a search agent and search function to find the most relevant information. The second step uses a podcast generation agent and generation function to create a podcast on the topic found. Implementation 🛠️ Utilizes LangGraph to orchestrate a two-step workflow. The first step involves a search agent and function to gather information from the internet. The second step uses a podcast generation agent and function to create a podcast based on the gathered information. 🛍️ Shopping and Product Analysis Agents ShopGenie - Redefining Online Shopping Customer Experience Overview 🔎 An AI-powered shopping assistant that helps customers make informed purchasing decisions even without domain expertise. The system analyzes product information from multiple sources, compares specifications and reviews, identifies the best option based on user needs, and delivers recommendations through email with supporting video reviews, creating a comprehensive shopping experience. Implementation 🛠️ Uses LangGraph to orchestrate a workflow combining Tavily for web search, Llama-3.1-70B for structured data analysis and product comparison, and YouTube API for review video retrieval. The system processes search results through multiple nodes including schema mapping, product comparison, review identification, and email generation. Key features include structured Pydantic models for consistent data handling, retry mechanisms for robust API interactions, and email delivery through SMTP for sharing recommendations. Additional Resources 📚 YouTube Explanation Car Buyer AI Agent Overview 🔎 The Smart Product Buyer AI Agent demonstrates how to build an intelligent system that assists users in making informed purchasing decisions. Using LangGraph and LLM-based intelligence, the system processes user requirements, scrapes product listings from websites like AutoTrader, and provides detailed analysis and recommendations for car purchases. Implementation 🛠️ Implements a state-based workflow using LangGraph to coordinate user interaction, web scraping, and decision support. The system features TypedDict state management, async web scraping with Playwright, and integrates with external APIs for comprehensive product analysis. The implementation includes a Gradio interface for real-time chat interaction and modular scraper architecture for easy extension to additional product categories. Additional Resources 📚 YouTube Explanation 🎯 Task Management and Productivity Agents Taskifier - Intelligent Task Allocation & Management Overview 🔎 An intelligent task management system that analyzes user work styles and creates personalized task breakdown strategies, born from the observation that procrastination often stems from task ambiguity among students and early-career professionals. The system evaluates historical work patterns, gathers relevant task information through web search, and generates customized step-by-step approaches to optimize productivity and reduce workflow paralysis. Implementation 🛠️ Leverages LangGraph for orchestrating a multi-step workflow including work style analysis, information gathering via Tavily API, and customized plan generation. The system maintains state through the process, integrating historical work pattern data with fresh task research to output detailed, personalized task execution plans aligned with the user's natural working style. Additional Resources 📚 YouTube Explanation Grocery Management Agents System Overview 🔎 A multi-agent system built with CrewAI that automates grocery management tasks including receipt interpretation, expiration date tracking, inventory management, and recipe recommendations. The system uses specialized agents to extract data from receipts, estimate product shelf life, track consumption, and suggest recipes to minimize food waste. Implementation 🛠️ Implements four specialized agents using CrewAI - a Receipt Interpreter that extracts item details from receipts, an Expiration Date Estimator that determines shelf life using online sources, a Grocery Tracker that maintains inventory based on consumption, and a Recipe Recommender that suggests meals using available ingredients. Each agent has specific tools and tasks orchestrated through a crew workflow. Additional Resources 📚 YouTube Explanation 🔍 Quality Assurance and Testing Agents LangGraph-Based Systems Inspector Overview 🔎 A comprehensive testing and validation tool for LangGraph-based applications that automatically analyzes system architecture, generates test cases, and identifies potential vulnerabilities through multi-agent inspection. The inspector employs specialized AI testers to evaluate different aspects of the system, from basic functionality to security concerns and edge cases. Implementation 🛠️ Integrates LangGraph for workflow orchestration, multiple LLM-powered testing agents, and a structured evaluation pipeline that includes static analysis, test case generation, and results verification. The system uses Pydantic for data validation, NetworkX for graph representation, and implements a modular architecture that allows for parallel test execution and comprehensive result analysis. Additional Resources 📚 YouTube Explanation Blog Post EU Green Deal FAQ Bot Overview 🔎 The EU Green Deal FAQ Bot demonstrates how to build a RAG-based AI agent that helps businesses understand EU green deal policies. The system processes complex regulatory documents into manageable chunks and provides instant, accurate answers to common questions about environmental compliance, emissions reporting, and waste management requirements. Implementation 🛠️ Implements a sophisticated RAG pipeline using FAISS vectorstore for document storage, semantic chunking for preprocessing, and multiple specialized agents (Retriever, Summarizer, Evaluator) for query processing. The system features query rephrasing for improved accuracy, cross-reference with gold Q&A datasets for answer validation, and comprehensive evaluation metrics to ensure response quality and relevance. Additional Resources 📚 YouTube Explanation Systematic Review Automation System + Paper Draft Creation Overview 🔎 A comprehensive system for automating academic systematic reviews using a directed graph architecture and LangChain components. The system generates complete, publication-ready systematic review papers, automatically processing everything from literature search through final draft generation with multiple revision cycles. Implementation 🛠️ Utilizes a state-based graph workflow that handles paper search and selection (up to 3 papers), PDF processing, and generates a complete academic paper with all standard sections (abstract, introduction, methods, results, conclusions, references). The system incorporates multiple revision cycles with automated critique and improvement phases, all orchestrated through LangGraph state management. Additional Resources 📚 YouTube Explanation 🌟 Special Advanced Technique 🌟 Sophisticated Controllable Agent for Complex RAG Tasks 🤖 Overview 🔎 An advanced RAG solution designed to tackle complex questions that simple semantic similarity-based retrieval cannot solve. This approach uses a sophisticated deterministic graph as the "brain" 🧠 of a highly controllable autonomous agent, capable of answering non-trivial questions from your own data. Implementation 🛠️ • Implement a multi-step process involving question anonymization, high-level planning, task breakdown, adaptive information retrieval and question answering, continuous re-planning, and rigorous answer verification to ensure grounded and accurate responses. Getting Started To begin exploring and building GenAI agents: Clone this repository: Navigate to the technique you're interested in: Follow the detailed implementation guide in each technique's notebook. Contributing We welcome contributions from the community! If you have a new technique or improvement to suggest: Fork the repository Create your feature branch: git checkout -b feature/AmazingFeature Commit your changes: git commit -m 'Add some AmazingFeature' Push to the branch: git push origin feature/AmazingFeature Open a pull request Contributors License This project is licensed under a custom non-commercial license - see the LICENSE file for details. ⭐️ If you find this repository helpful, please consider giving it a star! Keywords: GenAI, Generative AI, Agents, NLP, AI, Machine Learning, Natural Language Processing, LLM, Conversational AI, Task-Oriented AI

#github#genai_agents

github

0.611

0.9298414218113789

NirDiamant•Mar 28, 2025

Prompt_Engineering

🌟 Support This Project: Your sponsorship fuels innovation in prompt engineering development. Become a sponsor to help maintain and expand this valuable resource! Prompt Engineering Techniques: Comprehensive Repository for Development and Implementation 🖋️ Welcome to one of the most extensive and dynamic collections of Prompt Engineering tutorials and implementations available today. This repository serves as a comprehensive resource for learning, building, and sharing prompt engineering techniques, ranging from basic concepts to advanced strategies for leveraging large language models. 📫 Stay Updated! 🚀Cutting-edgeUpdates 💡ExpertInsights 🎯Top 0.1%Content Join over 15,000 of AI enthusiasts getting unique cutting-edge insights and free tutorials! Plus, subscribers get exclusive early access and special discounts to our upcoming RAG Techniques course! Introduction Prompt engineering is at the forefront of artificial intelligence, revolutionizing the way we interact with and leverage AI technologies. This repository is designed to guide you through the development journey, from basic prompt structures to advanced, cutting-edge techniques. Our goal is to provide a valuable resource for everyone - from beginners taking their first steps in AI to seasoned practitioners pushing the boundaries of what's possible. By offering a range of examples from foundational to complex, we aim to facilitate learning, experimentation, and innovation in the rapidly evolving field of prompt engineering. Furthermore, this repository serves as a platform for showcasing innovative prompt engineering techniques. Whether you've developed a novel approach or found an innovative application for existing techniques, we encourage you to share your work with the community. 📖 Get the Fully Explained Version of This Repo This repository contains 22 hands-on Jupyter Notebook tutorials covering key prompt engineering techniques. If you want to go deeper with full explanations, intuitive insights, and structured exercises, check out the expanded version in book format: 📚 Prompt Engineering from Zero to Hero 📖 All 22 techniques from this repo, fully explained in depth 🧠 Step-by-step breakdowns of key concepts & best practices 🏋️ Hands-on exercises to sharpen your skills 🎯 Designed for learners who want a structured, guided approach 📄 Instant access to the PDF upon purchase 📱 Readable on any device – computer, tablet, or phone 💡 Subscribers to the DiamantAI newsletter receive an exclusive 33% (!) discount on the book. 👉 Get the full explained version here Related Projects 📚 Explore my comprehensive guide on RAG techniques to learn how to enhance AI systems with external knowledge retrieval, complementing language model capabilities with rich, up-to-date information. 🤖 Dive into my GenAI Agents Repository for a wide range of AI agent implementations and tutorials, from simple conversational bots to complex, multi-agent systems for various applications. A Community-Driven Knowledge Hub This repository grows stronger with your contributions! Join our vibrant Discord community — the central hub for shaping and advancing this project together 🤝 DiamantAI Discord Community Whether you're a novice eager to learn or an expert ready to share your knowledge, your insights can shape the future of prompt engineering. Join us to propose ideas, get feedback, and collaborate on innovative implementations. For contribution guidelines, please refer to our CONTRIBUTING.md file. Let's advance prompt engineering technology together! 🔗 For discussions on GenAI, or to explore knowledge-sharing opportunities, feel free to connect on LinkedIn. Key Features 🎓 Learn prompt engineering techniques from beginner to advanced levels 🧠 Explore a wide range of prompt structures and applications 📚 Step-by-step tutorials and comprehensive documentation 🛠️ Practical, ready-to-use prompt implementations 🌟 Regular updates with the latest advancements in prompt engineering 🤝 Share your own prompt engineering creations with the community Prompt Engineering Techniques Explore our extensive list of prompt engineering techniques, ranging from basic to advanced: 🌱 Fundamental Concepts Introduction to Prompt Engineering Overview 🔎 A comprehensive introduction to the fundamental concepts of prompt engineering in the context of AI and language models. Implementation 🛠️ Combines theoretical explanations with practical demonstrations, covering basic concepts, structured prompts, comparative analysis, and problem-solving applications. Basic Prompt Structures Overview 🔎 Explores two fundamental types of prompt structures: single-turn prompts and multi-turn prompts (conversations). Implementation 🛠️ Uses OpenAI's GPT model and LangChain to demonstrate single-turn and multi-turn prompts, prompt templates, and conversation chains. Prompt Templates and Variables Overview 🔎 Introduces creating and using prompt templates with variables, focusing on Python and the Jinja2 templating engine. Implementation 🛠️ Covers template creation, variable insertion, conditional content, list processing, and integration with the OpenAI API. 🔧 Core Techniques Zero-Shot Prompting Overview 🔎 Explores zero-shot prompting, allowing language models to perform tasks without specific examples or prior training. Implementation 🛠️ Demonstrates direct task specification, role-based prompting, format specification, and multi-step reasoning using OpenAI and LangChain. Few-Shot Learning and In-Context Learning Overview 🔎 Covers Few-Shot Learning and In-Context Learning techniques using OpenAI's GPT models and the LangChain library. Implementation 🛠️ Implements basic and advanced few-shot learning, in-context learning, and best practices for example selection and evaluation. Chain of Thought (CoT) Prompting Overview 🔎 Introduces Chain of Thought (CoT) prompting, encouraging AI models to break down complex problems into step-by-step reasoning processes. Implementation 🛠️ Covers basic and advanced CoT techniques, applying them to various problem-solving scenarios and comparing results with standard prompts. 🔍 Advanced Strategies Self-Consistency and Multiple Paths of Reasoning Overview 🔎 Explores techniques for generating diverse reasoning paths and aggregating results to improve AI-generated answers. Implementation 🛠️ Demonstrates designing diverse reasoning prompts, generating multiple responses, implementing aggregation methods, and applying self-consistency checks. Constrained and Guided Generation Overview 🔎 Focuses on techniques to set up constraints for model outputs and implement rule-based generation. Implementation 🛠️ Uses LangChain's PromptTemplate for structured prompts, implements constraints, and explores rule-based generation techniques. Role Prompting Overview 🔎 Explores assigning specific roles to AI models and crafting effective role descriptions. Implementation 🛠️ Demonstrates creating role-based prompts, assigning roles to AI models, and refining role descriptions for various scenarios. 🚀 Advanced Implementations Task Decomposition in Prompts Overview 🔎 Explores techniques for breaking down complex tasks and chaining subtasks in prompts. Implementation 🛠️ Covers problem analysis, subtask definition, targeted prompt engineering, sequential execution, and result synthesis. Prompt Chaining and Sequencing Overview 🔎 Demonstrates how to connect multiple prompts and build logical flows for complex AI-driven tasks. Implementation 🛠️ Explores basic prompt chaining, sequential prompting, dynamic prompt generation, and error handling within prompt chains. Instruction Engineering Overview 🔎 Focuses on crafting clear and effective instructions for language models, balancing specificity and generality. Implementation 🛠️ Covers creating and refining instructions, experimenting with different structures, and implementing iterative improvement based on model responses. 🎨 Optimization and Refinement Prompt Optimization Techniques Overview 🔎 Explores advanced techniques for optimizing prompts, focusing on A/B testing and iterative refinement. Implementation 🛠️ Demonstrates A/B testing of prompts, iterative refinement processes, and performance evaluation using relevant metrics. Handling Ambiguity and Improving Clarity Overview 🔎 Focuses on identifying and resolving ambiguous prompts and techniques for writing clearer prompts. Implementation 🛠️ Covers analyzing ambiguous prompts, implementing strategies to resolve ambiguity, and exploring techniques for writing clearer prompts. Prompt Length and Complexity Management Overview 🔎 Explores techniques for managing prompt length and complexity when working with large language models. Implementation 🛠️ Demonstrates techniques for balancing detail and conciseness, and strategies for handling long contexts including chunking, summarization, and iterative processing. 🛠️ Specialized Applications Negative Prompting and Avoiding Undesired Outputs Overview 🔎 Explores negative prompting and techniques for avoiding undesired outputs from large language models. Implementation 🛠️ Covers basic negative examples, explicit exclusions, constraint implementation using LangChain, and methods for evaluating and refining negative prompts. Prompt Formatting and Structure Overview 🔎 Explores various prompt formats and structural elements, demonstrating their impact on AI model responses. Implementation 🛠️ Demonstrates creating various prompt formats, incorporating structural elements, and comparing responses from different prompt structures. Prompts for Specific Tasks Overview 🔎 Explores the creation and use of prompts for specific tasks: text summarization, question-answering, code generation, and creative writing. Implementation 🛠️ Covers designing task-specific prompt templates, implementing them using LangChain, executing with sample inputs, and analyzing outputs for each task type. 🌍 Advanced Applications Multilingual and Cross-lingual Prompting Overview 🔎 Explores techniques for designing prompts that work effectively across multiple languages and for language translation tasks. Implementation 🛠️ Covers creating multilingual prompts, implementing language detection and adaptation, designing cross-lingual translation prompts, and handling various writing systems and scripts. Ethical Considerations in Prompt Engineering Overview 🔎 Explores the ethical dimensions of prompt engineering, focusing on avoiding biases and creating inclusive and fair prompts. Implementation 🛠️ Covers identifying biases in prompts, implementing strategies to create inclusive prompts, and methods to evaluate and improve the ethical quality of AI outputs. Prompt Security and Safety Overview 🔎 Focuses on preventing prompt injections and implementing content filters in prompts for safe and secure AI applications. Implementation 🛠️ Covers techniques for prompt injection prevention, content filtering implementation, and testing the effectiveness of security and safety measures. Evaluating Prompt Effectiveness Overview 🔎 Explores methods and techniques for evaluating the effectiveness of prompts in AI language models. Implementation 🛠️ Covers setting up evaluation metrics, implementing manual and automated evaluation techniques, and providing practical examples using OpenAI and LangChain. Getting Started To begin exploring and implementing prompt engineering techniques: Clone this repository: Navigate to the technique you're interested in: Follow the detailed implementation guide in each technique's notebook. Contributing We welcome contributions from the community! If you have a new technique or improvement to suggest: Fork the repository Create your feature branch: git checkout -b feature/AmazingFeature Commit your changes: git commit -m 'Add some AmazingFeature' Push to the branch: git push origin feature/AmazingFeature Open a pull request License This project is licensed under a custom non-commercial license - see the LICENSE file for details. ⭐️ If you find this repository helpful, please consider giving it a star! Keywords: Prompt Engineering, AI, Machine Learning, Natural Language Processing, LLM, Language Models, NLP, Conversational AI, Zero-Shot Learning, Few-Shot Learning, Chain of Thought

#github#prompt_engineering

github

0.447

0.1014145151561518

superiorlu•Mar 28, 2025

AITreasureBox

AI TreasureBox English | 中文 Collect practical AI repos, tools, websites, papers and tutorials on AI. Translated from ChatGPT, picture from Midjourney. Catalog Repos Tools Websites Report&Paper Tutorials Repos updated repos and stars every 2 hours and re-ranking automatically. | No. | Repos | Description | | ----:|:-----------------------------------------|:------------------------------------------------------------------------------------------------------| | 1|🔥codecrafters-io/build-your-own-x !2025-03-28364681428|Master programming by recreating your favorite technologies from scratch.| | 2|sindresorhus/awesome !2025-03-28353614145|😎 Awesome lists about all kinds of interesting topics| | 3|public-apis/public-apis !2025-03-28334299125|A collective list of free APIs| | 4|kamranahmedse/developer-roadmap !2025-03-2831269540|Interactive roadmaps, guides and other educational content to help developers grow in their careers.| | 5|vinta/awesome-python !2025-03-28238581114|A curated list of awesome Python frameworks, libraries, software and resources| | 6|practical-tutorials/project-based-learning !2025-03-28222661124|Curated list of project-based tutorials| | 7|tensorflow/tensorflow !2025-03-281888714|An Open Source Machine Learning Framework for Everyone| | 8|Significant-Gravitas/AutoGPT !2025-03-2817391338|An experimental open-source attempt to make GPT-4 fully autonomous.| | 9|jackfrued/Python-100-Days !2025-03-2816305141|Python - 100天从新手到大师| | 10|AUTOMATIC1111/stable-diffusion-webui !2025-03-2815011553|Stable Diffusion web UI| | 11|huggingface/transformers !2025-03-2814207850|🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.| | 12|ollama/ollama !2025-03-28135166151|Get up and running with Llama 2, Mistral, Gemma, and other large language models.| | 13|f/awesome-chatgpt-prompts !2025-03-2812212738 |This repo includes ChatGPT prompt curation to use ChatGPT better.| | 14|justjavac/free-programming-books-zhCN !2025-03-2811316119|📚 免费的计算机编程类中文书籍，欢迎投稿| | 15|krahets/hello-algo !2025-03-2811107930|《Hello 算法》：动画图解、一键运行的数据结构与算法教程。支持 Python, Java, C++, C, C#, JS, Go, Swift, Rust, Ruby, Kotlin, TS, Dart 代码。简体版和繁体版同步更新，English version ongoing| | 16|yt-dlp/yt-dlp !2025-03-28105801114|A feature-rich command-line audio/video downloader| | 17|langchain-ai/langchain !2025-03-2810449479|⚡ Building applications with LLMs through composability ⚡| | 18|goldbergyoni/nodebestpractices !2025-03-281021629|✅ The Node.js best practices list (July 2024)| | 19|puppeteer/puppeteer !2025-03-289018212|JavaScript API for Chrome and Firefox| | 20|pytorch/pytorch !2025-03-288833938|Tensors and Dynamic neural networks in Python with strong GPU acceleration| | 21|neovim/neovim !2025-03-288781482|Vim-fork focused on extensibility and usability| | 22|🔥🔥langgenius/dify !2025-03-2887342639 |One API for plugins and datasets, one interface for prompt engineering and visual operation, all for creating powerful AI applications.| | 23|mtdvio/every-programmer-should-know !2025-03-28867069|A collection of (mostly) technical things every software developer should know about| | 24|open-webui/open-webui !2025-03-2886025159|User-friendly WebUI for LLMs (Formerly Ollama WebUI)| | 25|ChatGPTNextWeb/NextChat !2025-03-288231521|✨ Light and Fast AI Assistant. Support: Web | | 26|supabase/supabase !2025-03-287990956|The open source Firebase alternative.| | 27|openai/whisper !2025-03-287905542|Robust Speech Recognition via Large-Scale Weak Supervision| | 28|home-assistant/core !2025-03-287773219|🏡 Open source home automation that puts local control and privacy first.| | 29|tensorflow/models !2025-03-28774694|Models and examples built with TensorFlow| | 30| ggerganov/llama.cpp !2025-03-287731836 | Port of Facebook's LLaMA model in C/C++ | | 31|3b1b/manim !2025-03-287641918|Animation engine for explanatory math videos| | 32|microsoft/generative-ai-for-beginners !2025-03-287623860|12 Lessons, Get Started Building with Generative AI 🔗 https://microsoft.github.io/generative-ai-for-beginners/| | 33|nomic-ai/gpt4all !2025-03-28729285 |gpt4all: an ecosystem of open-source chatbots trained on a massive collection of clean assistant data including code, stories and dialogue| | 34|comfyanonymous/ComfyUI !2025-03-2872635111|The most powerful and modular diffusion model GUI, api and backend with a graph/nodes interface.| | 35|bregman-arie/devops-exercises !2025-03-2872225209|Linux, Jenkins, AWS, SRE, Prometheus, Docker, Python, Ansible, Git, Kubernetes, Terraform, OpenStack, SQL, NoSQL, Azure, GCP, DNS, Elastic, Network, Virtualization. DevOps Interview Questions| | 36|elastic/elasticsearch !2025-03-28721419|Free and Open, Distributed, RESTful Search Engine| | 37|🔥n8n-io/n8n !2025-03-2872093495|Free and source-available fair-code licensed workflow automation tool. Easily automate tasks across different services.| | 38|fighting41love/funNLP !2025-03-287200422|The Most Powerful NLP-Weapon Arsenal| | 39|hoppscotch/hoppscotch !2025-03-287060134|Open source API development ecosystem - https://hoppscotch.io (open-source alternative to Postman, Insomnia)| | 40|abi/screenshot-to-code !2025-03-286932817|Drop in a screenshot and convert it to clean HTML/Tailwind/JS code| | 41|binary-husky/gptacademic !2025-03-28680374|Academic Optimization of GPT| | 42|d2l-ai/d2l-zh !2025-03-286774142|Targeting Chinese readers, functional and open for discussion. The Chinese and English versions are used for teaching in over 400 universities across more than 60 countries| | 43|josephmisiti/awesome-machine-learning !2025-03-286739215|A curated list of awesome Machine Learning frameworks, libraries and software.| | 44|grafana/grafana !2025-03-286725414|The open and composable observability and data visualization platform. Visualize metrics, logs, and traces from multiple sources like Prometheus, Loki, Elasticsearch, InfluxDB, Postgres and many more.| | 45|python/cpython !2025-03-286602218|The Python programming language| | 46|apache/superset !2025-03-286519020|Apache Superset is a Data Visualization and Data Exploration Platform| | 47|xtekky/gpt4free !2025-03-28639391 |decentralizing the Ai Industry, free gpt-4/3.5 scripts through several reverse engineered API's ( poe.com, phind.com, chat.openai.com etc...)| | 48|sherlock-project/sherlock !2025-03-286332536|Hunt down social media accounts by username across social networks| | 49|twitter/the-algorithm !2025-03-28630586 |Source code for Twitter's Recommendation Algorithm| | 50|keras-team/keras !2025-03-28627835|Deep Learning for humans| | 51|openai/openai-cookbook !2025-03-28625136 |Examples and guides for using the OpenAI API| | 52|immich-app/immich !2025-03-286238670|High performance self-hosted photo and video management solution.| | 53|AppFlowy-IO/AppFlowy !2025-03-286173528|Bring projects, wikis, and teams together with AI. AppFlowy is an AI collaborative workspace where you achieve more without losing control of your data. The best open source alternative to Notion.| | 54|scikit-learn/scikit-learn !2025-03-286158212|scikit-learn: machine learning in Python| | 55|binhnguyennus/awesome-scalability !2025-03-286117021|The Patterns of Scalable, Reliable, and Performant Large-Scale Systems| | 56|labmlai/annotateddeeplearningpaperimplementations !2025-03-285951726|🧑‍🏫 59 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠| | 57|OpenInterpreter/open-interpreter !2025-03-285894710|A natural language interface for computers| | 58|lobehub/lobe-chat !2025-03-285832054|🤖 Lobe Chat - an open-source, extensible (Function Calling), high-performance chatbot framework. It supports one-click free deployment of your private ChatGPT/LLM web application.| | 59|meta-llama/llama !2025-03-28579536|Inference code for Llama models| | 60|nuxt/nuxt !2025-03-28566437|The Intuitive Vue Framework.| | 61|imartinez/privateGPT !2025-03-28555192|Interact with your documents using the power of GPT, 100% privately, no data leaks| | 62|Stirling-Tools/Stirling-PDF !2025-03-285500846|#1 Locally hosted web application that allows you to perform various operations on PDF files| | 63|PlexPt/awesome-chatgpt-prompts-zh !2025-03-285459720|ChatGPT Chinese Training Guide. Guidelines for various scenarios. Learn how to make it listen to you| | 64|dair-ai/Prompt-Engineering-Guide !2025-03-285451025 |🐙 Guides, papers, lecture, notebooks and resources for prompt engineering| | 65|ageitgey/facerecognition !2025-03-28544382|The world's simplest facial recognition api for Python and the command line| | 66|CorentinJ/Real-Time-Voice-Cloning !2025-03-285384814|Clone a voice in 5 seconds to generate arbitrary speech in real-time| | 67|geekan/MetaGPT !2025-03-285375376|The Multi-Agent Meta Programming Framework: Given one line Requirement, return PRD, Design, Tasks, Repo | | 68|gpt-engineer-org/gpt-engineer !2025-03-285367419|Specify what you want it to build, the AI asks for clarification, and then builds it.| | 69|lencx/ChatGPT !2025-03-2853653-3|🔮 ChatGPT Desktop Application (Mac, Windows and Linux)| | 70|deepfakes/faceswap !2025-03-28535672|Deepfakes Software For All| | 71|langflow-ai/langflow !2025-03-285319584|Langflow is a low-code app builder for RAG and multi-agent AI applications. It’s Python-based and agnostic to any model, API, or database.| | 72|commaai/openpilot !2025-03-28529759|openpilot is an operating system for robotics. Currently, it upgrades the driver assistance system on 275+ supported cars.| | 73|clash-verge-rev/clash-verge-rev !2025-03-2852848124|Continuation of Clash Verge - A Clash Meta GUI based on Tauri (Windows, MacOS, Linux)| | 74|All-Hands-AI/OpenHands !2025-03-285150675|🙌 OpenHands: Code Less, Make More| | 75|xai-org/grok-1 !2025-03-28502504|Grok open release| | 76|meilisearch/meilisearch !2025-03-284999122|A lightning-fast search API that fits effortlessly into your apps, websites, and workflow| | 77|🔥browser-use/browser-use !2025-03-2849910294|Make websites accessible for AI agents| | 78|jgthms/bulma !2025-03-28496783|Modern CSS framework based on Flexbox| | 79|facebookresearch/segment-anything !2025-03-284947116|The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.| |!green-up-arrow.svg 80|hacksider/Deep-Live-Cam !2025-03-2848612146|real time face swap and one-click video deepfake with only a single image (uncensored)| |!red-down-arrow 81|mlabonne/llm-course !2025-03-284860934|Course with a roadmap and notebooks to get into Large Language Models (LLMs).| | 82|PaddlePaddle/PaddleOCR !2025-03-284785530|Awesome multilingual OCR toolkits based on PaddlePaddle (practical ultra lightweight OCR system, support 80+ languages recognition, provide data annotation and synthesis tools, support training and deployment among server, mobile, embedded and IoT devices)| | 83|alist-org/alist !2025-03-284732618|🗂️A file list/WebDAV program that supports multiple storages, powered by Gin and Solidjs. / 一个支持多存储的文件列表/WebDAV程序，使用 Gin 和 Solidjs。| | 84|infiniflow/ragflow !2025-03-2847027129|RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding.| | 85|Avik-Jain/100-Days-Of-ML-Code !2025-03-284679312|100 Days of ML Coding| | 86|v2ray/v2ray-core !2025-03-28458706|A platform for building proxies to bypass network restrictions.| | 87|hiyouga/LLaMA-Factory !2025-03-284555881|Easy-to-use LLM fine-tuning framework (LLaMA, BLOOM, Mistral, Baichuan, Qwen, ChatGLM)| | 88|Asabeneh/30-Days-Of-Python !2025-03-284544930|30 days of Python programming challenge is a step-by-step guide to learn the Python programming language in 30 days. This challenge may take more than100 days, follow your own pace. These videos may help too: https://www.youtube.com/channel/UC7PNRuno1rzYPb1xLa4yktw| | 89|type-challenges/type-challenges !2025-03-284488511|Collection of TypeScript type challenges with online judge| | 90|lllyasviel/Fooocus !2025-03-284402716|Focus on prompting and generating| | 91|RVC-Boss/GPT-SoVITS !2025-03-284327738|1 min voice data can also be used to train a good TTS model! (few shot voice cloning)| | 92|rasbt/LLMs-from-scratch !2025-03-284320667|Implementing a ChatGPT-like LLM from scratch, step by step| | 93|oobabooga/text-generation-webui !2025-03-284302012 |A gradio web UI for running Large Language Models like LLaMA, llama.cpp, GPT-J, OPT, and GALACTICA.| | 94|vllm-project/vllm !2025-03-2842982102|A high-throughput and memory-efficient inference and serving engine for LLMs| | 95|dani-garcia/vaultwarden !2025-03-284297121|Unofficial Bitwarden compatible server written in Rust, formerly known as bitwarden_rs| | 96|microsoft/autogen !2025-03-284233049|Enable Next-Gen Large Language Model Applications. Join our Discord: https://discord.gg/pAbnFJrkgZ| | 97|jeecgboot/JeecgBoot !2025-03-284205920|🔥「企业级低代码平台」前后端分离架构SpringBoot 2.x/3.x，SpringCloud，Ant Design&Vue3，Mybatis，Shiro，JWT。强大的代码生成器让前后端代码一键生成，无需写任何代码! 引领新的开发模式OnlineCoding->代码生成->手工MERGE，帮助Java项目解决70%重复工作，让开发更关注业务，既能快速提高效率，帮助公司节省成本，同时又不失灵活性。| | 98|Mintplex-Labs/anything-llm !2025-03-284186955|A full-stack application that turns any documents into an intelligent chatbot with a sleek UI and easier way to manage your workspaces.| | 99|THUDM/ChatGLM-6B !2025-03-28410192 |ChatGLM-6B: An Open Bilingual Dialogue Language Model| | 100|hpcaitech/ColossalAI !2025-03-28406902|Making large AI models cheaper, faster and more accessible| | 101|Stability-AI/stablediffusion !2025-03-28406337|High-Resolution Image Synthesis with Latent Diffusion Models| | 102|mingrammer/diagrams !2025-03-28405063|🎨 Diagram as Code for prototyping cloud system architectures| | 103|Kong/kong !2025-03-28404616|🦍 The Cloud-Native API Gateway and AI Gateway.| | 104|getsentry/sentry !2025-03-284040913|Developer-first error tracking and performance monitoring| | 105| karpathy/nanoGPT !2025-03-284034613 |The simplest, fastest repository for training/finetuning medium-sized GPTs| | 106|fastlane/fastlane !2025-03-2840014-1|🚀 The easiest way to automate building and releasing your iOS and Android apps| | 107|psf/black !2025-03-28399765|The uncompromising Python code formatter| | 108|OpenBB-finance/OpenBBTerminal !2025-03-283972074 |Investment Research for Everyone, Anywhere.| | 109|2dust/v2rayNG !2025-03-283943415|A V2Ray client for Android, support Xray core and v2fly core| | 110|apache/airflow !2025-03-283937314|Apache Airflow - A platform to programmatically author, schedule, and monitor workflows| | 111|KRTirtho/spotube !2025-03-283902746|🎧 Open source Spotify client that doesn't require Premium nor uses Electron! Available for both desktop & mobile!| | 112|coqui-ai/TTS !2025-03-283889719 |🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production| | 113|ggerganov/whisper.cpp !2025-03-283882116|Port of OpenAI's Whisper model in C/C++| | 114|ultralytics/ultralytics !2025-03-283866951|NEW - YOLOv8 🚀 in PyTorch > ONNX > OpenVINO > CoreML > TFLite| | 115|typst/typst !2025-03-283863914|A new markup-based typesetting system that is powerful and easy to learn.| | 116|streamlit/streamlit !2025-03-283845828|Streamlit — A faster way to build and share data apps.| | 117|LC044/WeChatMsg !2025-03-283836931|提取微信聊天记录，将其导出成HTML、Word、Excel文档永久保存，对聊天记录进行分析生成年度聊天报告，用聊天数据训练专属于个人的AI聊天助手| | 118|lm-sys/FastChat !2025-03-283822112 |An open platform for training, serving, and evaluating large languages. Release repo for Vicuna and FastChat-T5.| | 119|NaiboWang/EasySpider !2025-03-283819013|A visual no-code/code-free web crawler/spider易采集：一个可视化浏览器自动化测试/数据采集/爬虫软件，可以无代码图形化的设计和执行爬虫任务。别名：ServiceWrapper面向Web应用的智能化服务封装系统。| | 120|microsoft/DeepSpeed !2025-03-283765816 |A deep learning optimization library that makes distributed training and inference easy, efficient, and effective| | 121|QuivrHQ/quivr !2025-03-28376067|Your GenAI Second Brain 🧠 A personal productivity assistant (RAG) ⚡️🤖 Chat with your docs (PDF, CSV, ...) & apps using Langchain, GPT 3.5 / 4 turbo, Private, Anthropic, VertexAI, Ollama, LLMs, that you can share with users ! Local & Private alternative to OpenAI GPTs & ChatGPT powered by retrieval-augmented generation.| | 122|freqtrade/freqtrade !2025-03-283757817 |Free, open source crypto trading bot| | 123|suno-ai/bark !2025-03-28373178 |🔊 Text-Prompted Generative Audio Model| | 124|🔥cline/cline !2025-03-2837307282|Autonomous coding agent right in your IDE, capable of creating/editing files, executing commands, and more with your permission every step of the way.| | 125|LAION-AI/Open-Assistant !2025-03-28372712 |OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so.| | 126|penpot/penpot !2025-03-283716217|Penpot: The open-source design tool for design and code collaboration| | 127|gradio-app/gradio !2025-03-283713320|Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!| | 128|FlowiseAI/Flowise !2025-03-283667135 |Drag & drop UI to build your customized LLM flow using LangchainJS| | 129|SimplifyJobs/Summer2025-Internships !2025-03-28366506|Collection of Summer 2025 tech internships!| | 130|TencentARC/GFPGAN !2025-03-28365027 |GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.| | 131|ray-project/ray !2025-03-283626819|Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a toolkit of libraries (Ray AIR) for accelerating ML workloads.| | 132|babysor/MockingBird !2025-03-28360498|🚀AI拟声: 5秒内克隆您的声音并生成任意语音内容 Clone a voice in 5 seconds to generate arbitrary speech in real-time| | 133|unslothai/unsloth !2025-03-283603691|5X faster 50% less memory LLM finetuning| | 134|zhayujie/chatgpt-on-wechat !2025-03-283600124 |Wechat robot based on ChatGPT, which uses OpenAI api and itchat library| | 135|upscayl/upscayl !2025-03-283599824|🆙 Upscayl - Free and Open Source AI Image Upscaler for Linux, MacOS and Windows built with Linux-First philosophy.| | 136|freeCodeCamp/devdocs !2025-03-28359738|API Documentation Browser| | 137|XingangPan/DragGAN !2025-03-28359043 |Code for DragGAN (SIGGRAPH 2023)| | 138|2noise/ChatTTS !2025-03-283543922|ChatTTS is a generative speech model for daily dialogue.| | 139|google-research/google-research !2025-03-28352207 |Google Research| | 140|karanpratapsingh/system-design !2025-03-28351003|Learn how to design systems at scale and prepare for system design interviews| | 141|lapce/lapce !2025-03-28350855|Lightning-fast and Powerful Code Editor written in Rust| | 142| microsoft/TaskMatrix !2025-03-2834500-3 | Talking, Drawing and Editing with Visual Foundation Models| | 143|chatchat-space/Langchain-Chatchat !2025-03-283442020|Langchain-Chatchat (formerly langchain-ChatGLM), local knowledge based LLM (like ChatGLM) QA app with langchain| | 144|unclecode/crawl4ai !2025-03-283434163|🔥🕷️ Crawl4AI: Open-source LLM Friendly Web Crawler & Scrapper| | 145|Bin-Huang/chatbox !2025-03-283374733 |A desktop app for GPT-4 / GPT-3.5 (OpenAI API) that supports Windows, Mac & Linux| | 146|milvus-io/milvus !2025-03-283366525 |A cloud-native vector database, storage for next generation AI applications| | 147|mendableai/firecrawl !2025-03-2833297128|🔥 Turn entire websites into LLM-ready markdown| | 148|pola-rs/polars !2025-03-283269320|Fast multi-threaded, hybrid-out-of-core query engine focussing on DataFrame front-ends| | 149|Pythagora-io/gpt-pilot !2025-03-28325321|PoC for a scalable dev tool that writes entire apps from scratch while the developer oversees the implementation| | 150|hashicorp/vault !2025-03-28320797|A tool for secrets management, encryption as a service, and privileged access management| | 151|shardeum/shardeum !2025-03-28319580|Shardeum is an EVM based autoscaling blockchain| | 152|Chanzhaoyu/chatgpt-web !2025-03-28319242 |A demonstration website built with Express and Vue3 called ChatGPT| | 153|lllyasviel/ControlNet !2025-03-283186413 |Let us control diffusion models!| | 154|google/jax !2025-03-28317727|Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more| | 155|facebookresearch/detectron2 !2025-03-28315987|Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.| | 156|myshell-ai/OpenVoice !2025-03-28315233|Instant voice cloning by MyShell| | 157|TheAlgorithms/C-Plus-Plus !2025-03-283151411|Collection of various algorithms in mathematics, machine learning, computer science and physics implemented in C++ for educational purposes.| | 158|hiroi-sora/Umi-OCR !2025-03-283138129|OCR图片转文字识别软件，完全离线。截屏/批量导入图片，支持多国语言、合并段落、竖排文字。可排除水印区域，提取干净的文本。基于 PaddleOCR 。| | 159|mudler/LocalAI !2025-03-283127815|🤖 The free, Open Source OpenAI alternative. Self-hosted, community-driven and local-first. Drop-in replacement for OpenAI running on consumer-grade hardware. No GPU required. Runs gguf, transformers, diffusers and many more models architectures. It allows to generate Text, Audio, Video, Images. Also with voice cloning capabilities.| | 160|facebookresearch/fairseq !2025-03-28312124 |Facebook AI Research Sequence-to-Sequence Toolkit written in Python.| | 161|alibaba/nacos !2025-03-28310559|an easy-to-use dynamic service discovery, configuration and service management platform for building cloud native applications.| | 162|yunjey/pytorch-tutorial !2025-03-28310326|PyTorch Tutorial for Deep Learning Researchers| | 163|v2fly/v2ray-core !2025-03-28307448|A platform for building proxies to bypass network restrictions.| | 164|mckaywrigley/chatbot-ui !2025-03-283067714|The open-source AI chat interface for everyone.| | 165|TabbyML/tabby !2025-03-28305949 |Self-hosted AI coding assistant| | 166|deepseek-ai/awesome-deepseek-integration !2025-03-283053193|| | 167|danielmiessler/fabric !2025-03-283028914|fabric is an open-source framework for augmenting humans using AI.| | 168|xinntao/Real-ESRGAN !2025-03-283026623 |Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.| | 169|paul-gauthier/aider !2025-03-283014642|aider is GPT powered coding in your terminal| | 170|tatsu-lab/stanfordalpaca !2025-03-28299022 |Code and documentation to train Stanford's Alpaca models, and generate the data.| | 171|DataTalksClub/data-engineering-zoomcamp !2025-03-282971817|Free Data Engineering course!| | 172|HeyPuter/puter !2025-03-282967014|🌐 The Internet OS! Free, Open-Source, and Self-Hostable.| | 173|mli/paper-reading !2025-03-282962314|Classic Deep Learning and In-Depth Reading of New Papers Paragraph by Paragraph| | 174|linexjlin/GPTs !2025-03-28295568|leaked prompts of GPTs| | 175|s0md3v/roop !2025-03-28295286 |one-click deepfake (face swap)| | 176|JushBJJ/Mr.-Ranedeer-AI-Tutor !2025-03-2829465-1 |A GPT-4 AI Tutor Prompt for customizable personalized learning experiences.| | 177|opendatalab/MinerU !2025-03-282927074|A one-stop, open-source, high-quality data extraction tool, supports PDF/webpage/e-book extraction.一站式开源高质量数据提取工具，支持PDF/网页/多格式电子书提取。| | 178|mouredev/Hello-Python !2025-03-282920720|Curso para aprender el lenguaje de programación Python desde cero y para principiantes. 75 clases, 37 horas en vídeo, código, proyectos y grupo de chat. Fundamentos, frontend, backend, testing, IA...| | 179|Lightning-AI/pytorch-lightning !2025-03-28292039|Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes.| | 180|crewAIInc/crewAI !2025-03-282919344|Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.| | 181|facebook/folly !2025-03-282916612|An open-source C++ library developed and used at Facebook.| | 182|google-ai-edge/mediapipe !2025-03-28291519|Cross-platform, customizable ML solutions for live and streaming media.| | 183| getcursor/cursor !2025-03-282892025 | An editor made for programming with AI| | 184|chatanywhere/GPTAPIfree !2025-03-282856424|Free ChatGPT API Key, Free ChatGPT API, supports GPT-4 API (free), ChatGPT offers a free domestic forwarding API that allows direct connections without the need for a proxy. It can be used in conjunction with software/plugins like ChatBox, significantly reducing interface usage costs. Enjoy unlimited and unrestricted chatting within China| | 185|meta-llama/llama3 !2025-03-28285552|The official Meta Llama 3 GitHub site| | 186|tinygrad/tinygrad !2025-03-282845811|You like pytorch? You like micrograd? You love tinygrad! ❤️| | 187|google-research/tuningplaybook !2025-03-282841514|A playbook for systematically maximizing the performance of deep learning models.| | 188|huggingface/diffusers !2025-03-282830222|🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch and FLAX.| | 189|tokio-rs/tokio !2025-03-28282408|A runtime for writing reliable asynchronous applications with Rust. Provides I/O, networking, scheduling, timers, ...| | 190|RVC-Project/Retrieval-based-Voice-Conversion-WebUI !2025-03-282823817|Voice data !2025-03-282822612|Jan is an open source alternative to ChatGPT that runs 100% offline on your computer| | 192|openai/CLIP !2025-03-282814720|CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image| | 193|🔥khoj-ai/khoj !2025-03-2828112313|Your AI second brain. A copilot to get answers to your questions, whether they be from your own notes or from the internet. Use powerful, online (e.g gpt4) or private, local (e.g mistral) LLMs. Self-host locally or use our web app. Access from Obsidian, Emacs, Desktop app, Web or Whatsapp.| | 194| acheong08/ChatGPT !2025-03-2828054-2 | Reverse engineered ChatGPT API | | 195|iperov/DeepFaceLive !2025-03-28279345 |Real-time face swap for PC streaming or video calls| | 196|eugeneyan/applied-ml !2025-03-28278471|📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.| | 197|XTLS/Xray-core !2025-03-282778213|Xray, Penetrates Everything. Also the best v2ray-core, with XTLS support. Fully compatible configuration.| | 198|feder-cr/JobsApplierAIAgent !2025-03-282776410|AutoJobsApplierAI_Agent aims to easy job hunt process by automating the job application process. Utilizing artificial intelligence, it enables users to apply for multiple jobs in an automated and personalized way.| | 199|mindsdb/mindsdb !2025-03-282750631|The platform for customizing AI from enterprise data| | 200|DataExpert-io/data-engineer-handbook !2025-03-282721611|This is a repo with links to everything you'd ever want to learn about data engineering| | 201|exo-explore/exo !2025-03-282721633|Run your own AI cluster at home with everyday devices 📱💻 🖥️⌚| | 202|taichi-dev/taichi !2025-03-2826926-1|Productive, portable, and performant GPU programming in Python.| | 203|mem0ai/mem0 !2025-03-282689134|The memory layer for Personalized AI| | 204|svc-develop-team/so-vits-svc !2025-03-28268096 |SoftVC VITS Singing Voice Conversion| | 205|OpenBMB/ChatDev !2025-03-28265624|Create Customized Software using Natural Language Idea (through Multi-Agent Collaboration)| | 206|roboflow/supervision !2025-03-282632010|We write your reusable computer vision tools. 💜| | 207|drawdb-io/drawdb !2025-03-282626913|Free, simple, and intuitive online database design tool and SQL generator.| | 208|karpathy/llm.c !2025-03-28261633|LLM training in simple, raw C/CUDA| | 209|airbnb/lottie-ios !2025-03-28261431|An iOS library to natively render After Effects vector animations| | 210|openai/openai-python !2025-03-282607713|The OpenAI Python library provides convenient access to the OpenAI API from applications written in the Python language.| | 211|academic/awesome-datascience !2025-03-28259876|📝 An awesome Data Science repository to learn and apply for real world problems.| | 212|harry0703/MoneyPrinterTurbo !2025-03-282576618|Generate short videos with one click using a large model| | 213|gabime/spdlog !2025-03-282571511|Fast C++ logging library.| | 214|ocrmypdf/OCRmyPDF !2025-03-2825674217|OCRmyPDF adds an OCR text layer to scanned PDF files, allowing them to be searched| | 215|Vision-CAIR/MiniGPT-4 !2025-03-28256170 |Enhancing Vision-language Understanding with Advanced Large Language Models| | 216|Stability-AI/generative-models !2025-03-28255936|Generative Models by Stability AI| | 217|DS4SD/docling !2025-03-282555662|Get your docs ready for gen AI| | 218|PostHog/posthog !2025-03-282533227|🦔 PostHog provides open-source product analytics, session recording, feature flagging and A/B testing that you can self-host.| | 219|nrwl/nx !2025-03-282509612|Smart Monorepos · Fast CI| | 220|continuedev/continue !2025-03-282500737|⏩ the open-source copilot chat for software development—bring the power of ChatGPT to VS Code| | 221|opentofu/opentofu !2025-03-28247968|OpenTofu lets you declaratively manage your cloud infrastructure.| | 222|invoke-ai/InvokeAI !2025-03-28247293|InvokeAI is a leading creative engine for Stable Diffusion models, empowering professionals, artists, and enthusiasts to generate and create visual media using the latest AI-driven technologies. The solution offers an industry leading WebUI, supports terminal use through a CLI, and serves as the foundation for multiple commercial products.| | 223|deepinsight/insightface !2025-03-282471615 |State-of-the-art 2D and 3D Face Analysis Project| | 224|apache/flink !2025-03-28246865|Apache Flink| | 225|ComposioHQ/composio !2025-03-28246436|Composio equips agents with well-crafted tools empowering them to tackle complex tasks| | 226|Genesis-Embodied-AI/Genesis !2025-03-282458314|A generative world for general-purpose robotics & embodied AI learning.| | 227|stretchr/testify !2025-03-28243184|A toolkit with common assertions and mocks that plays nicely with the standard library| | 228| yetone/openai-translator !2025-03-28242921 | Browser extension and cross-platform desktop application for translation based on ChatGPT API | | 229|frappe/erpnext !2025-03-282425211|Free and Open Source Enterprise Resource Planning (ERP)| | 230|songquanpeng/one-api !2025-03-282410034|OpenAI 接口管理 & 分发系统，支持 Azure、Anthropic Claude、Google PaLM 2 & Gemini、智谱 ChatGLM、百度文心一言、讯飞星火认知、阿里通义千问、360 智脑以及腾讯混元，可用于二次分发管理 key，仅单可执行文件，已打包好 Docker 镜像，一键部署，开箱即用. OpenAI key management & redistribution system, using a single API for all LLMs, and features an English UI.| | 231| microsoft/JARVIS !2025-03-28240604 | a system to connect LLMs with ML community | | 232|google/flatbuffers !2025-03-28239965|FlatBuffers: Memory Efficient Serialization Library| | 233|microsoft/graphrag !2025-03-282398928|A modular graph-based Retrieval-Augmented Generation (RAG) system| | 234|rancher/rancher !2025-03-28239675|Complete container management platform| | 235|bazelbuild/bazel !2025-03-282384618|a fast, scalable, multi-language and extensible build system| | 236|modularml/mojo !2025-03-28238236 |The Mojo Programming Language| | 237|danny-avila/LibreChat !2025-03-282378753|Enhanced ChatGPT Clone: Features OpenAI, GPT-4 Vision, Bing, Anthropic, OpenRouter, Google Gemini, AI model switching, message search, langchain, DALL-E-3, ChatGPT Plugins, OpenAI Functions, Secure Multi-User System, Presets, completely open-source for self-hosting. More features in development| |!green-up-arrow.svg 238|🔥🔥🔥Shubhamsaboo/awesome-llm-apps !2025-03-28237391211|Collection of awesome LLM apps with RAG using OpenAI, Anthropic, Gemini and opensource models.| |!red-down-arrow 239|microsoft/semantic-kernel !2025-03-282373611|Integrate cutting-edge LLM technology quickly and easily into your apps| |!red-down-arrow 240|TheAlgorithms/Rust !2025-03-28236995|All Algorithms implemented in Rust| | 241|stanford-oval/storm !2025-03-28236326|An LLM-powered knowledge curation system that researches a topic and generates a full-length report with citations.| | 242|openai/gpt-2 !2025-03-28232483|Code for the paper "Language Models are Unsupervised Multitask Learners"| | 243|labring/FastGPT !2025-03-282319445|A platform that uses the OpenAI API to quickly build an AI knowledge base, supporting many-to-many relationships.| | 244|pathwaycom/llm-app !2025-03-2822928-10|Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. 🐳Docker-friendly.⚡Always in sync with Sharepoint, Google Drive, S3, Kafka, PostgreSQL, real-time data APIs, and more.| | 245|warpdotdev/Warp !2025-03-282286825|Warp is a modern, Rust-based terminal with AI built in so you and your team can build great software, faster.| | 246|🔥agno-agi/agno !2025-03-2822833298|Agno is a lightweight library for building Multimodal Agents. It exposes LLMs as a unified API and gives them superpowers like memory, knowledge, tools and reasoning.| | 247|qdrant/qdrant !2025-03-282275214 |Qdrant - Vector Database for the next generation of AI applications. Also available in the cloud https://cloud.qdrant.io/| | 248|ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code !2025-03-282271815|500 AI Machine learning Deep learning Computer vision NLP Projects with code| | 249|stanfordnlp/dspy !2025-03-282268321|Stanford DSPy: The framework for programming—not prompting—foundation models| | 250|PaddlePaddle/Paddle !2025-03-28226246|PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署）| | 251|zulip/zulip !2025-03-28225464|Zulip server and web application. Open-source team chat that helps teams stay productive and focused.| | 252|Hannibal046/Awesome-LLM !2025-03-282240721|Awesome-LLM: a curated list of Large Language Model| | 253|facefusion/facefusion !2025-03-282218812|Next generation face swapper and enhancer| | 254|Mozilla-Ocho/llamafile !2025-03-28220624|Distribute and run LLMs with a single file.| | 255|yuliskov/SmartTube !2025-03-282201614|SmartTube - an advanced player for set-top boxes and tvs running Android OS| | 256|haotian-liu/LLaVA !2025-03-282201316 |Large Language-and-Vision Assistant built towards multimodal GPT-4 level capabilities.| | 257|ashishps1/awesome-system-design-resources !2025-03-282189367|This repository contains System Design resources which are useful while preparing for interviews and learning Distributed Systems| | 258|Cinnamon/kotaemon !2025-03-28218248|An open-source RAG-based tool for chatting with your documents.| | 259|CodePhiliaX/Chat2DB !2025-03-282179757|🔥🔥🔥AI-driven database tool and SQL client, The hottest GUI client, supporting MySQL, Oracle, PostgreSQL, DB2, SQL Server, DB2, SQLite, H2, ClickHouse, and more.| | 260|blakeblackshear/frigate !2025-03-282177113|NVR with realtime local object detection for IP cameras| | 261|facebookresearch/audiocraft !2025-03-28217111|Audiocraft is a library for audio processing and generation with deep learning. It features the state-of-the-art EnCodec audio compressor / tokenizer, along with MusicGen, a simple and controllable music generation LM with textual and melodic conditioning.| | 262|karpathy/minGPT !2025-03-28216567|A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training| | 263|grpc/grpc-go !2025-03-282159510|The Go language implementation of gRPC. HTTP/2 based RPC| | 264|HumanSignal/label-studio !2025-03-282137618|Label Studio is a multi-type data labeling and annotation tool with standardized output format| | 265|yoheinakajima/babyagi !2025-03-28212764 |uses OpenAI and Pinecone APIs to create, prioritize, and execute tasks, This is a pared-down version of the original Task-Driven Autonomous Agent| | 266|deepseek-ai/DeepSeek-Coder !2025-03-282118210|DeepSeek Coder: Let the Code Write Itself| | 267|BuilderIO/gpt-crawler !2025-03-282118010|Crawl a site to generate knowledge files to create your own custom GPT from a URL| | 268| openai/chatgpt-retrieval-plugin !2025-03-2821152-1 | Plugins are chat extensions designed specifically for language models like ChatGPT, enabling them to access up-to-date information, run computations, or interact with third-party services in response to a user's request.| | 269|microsoft/OmniParser !2025-03-282113123|A simple screen parsing tool towards pure vision based GUI agent| | 270|black-forest-labs/flux !2025-03-282107219|Official inference repo for FLUX.1 models| | 271|ItzCrazyKns/Perplexica !2025-03-282099154|Perplexica is an AI-powered search engine. It is an Open source alternative to Perplexity AI| | 272|microsoft/unilm !2025-03-28209876|Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities| | 273|Sanster/lama-cleaner !2025-03-282077614|Image inpainting tool powered by SOTA AI Model. Remove any unwanted object, defect, people from your pictures or erase and replace(powered by stable diffusion) any thing on your pictures.| | 274|assafelovic/gpt-researcher !2025-03-282057222|GPT based autonomous agent that does online comprehensive research on any given topic| | 275|PromtEngineer/localGPT !2025-03-28204230 |Chat with your documents on your local device using GPT models. No data leaves your device and 100% private.| | 276|elastic/kibana !2025-03-28203482|Your window into the Elastic Stack| | 277|fishaudio/fish-speech !2025-03-282033222|Brand new TTS solution| | 278|mlc-ai/mlc-llm !2025-03-282028110 |Enable everyone to develop, optimize and deploy AI models natively on everyone's devices.| | 279|deepset-ai/haystack !2025-03-282005320|🔍 Haystack is an open source NLP framework to interact with your data using Transformer models and LLMs (GPT-4, ChatGPT and alike). Haystack offers production-ready tools to quickly build complex question answering, semantic search, text generation applications, and more.| | 280|tree-sitter/tree-sitter !2025-03-28200487|An incremental parsing system for programming tools| | 281|Anjok07/ultimatevocalremovergui !2025-03-281999811|GUI for a Vocal Remover that uses Deep Neural Networks.| | 282|guidance-ai/guidance !2025-03-28199622|A guidance language for controlling large language models.| | 283|ml-explore/mlx !2025-03-28199619|MLX: An array framework for Apple silicon| | 284|mlflow/mlflow !2025-03-281995314|Open source platform for the machine learning lifecycle| | 285|ml-tooling/best-of-ml-python !2025-03-28198631|🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.| | 286|BerriAI/litellm !2025-03-281981862|Call all LLM APIs using the OpenAI format. Use Bedrock, Azure, OpenAI, Cohere, Anthropic, Ollama, Sagemaker, HuggingFace, Replicate (100+ LLMs)| | 287|LazyVim/LazyVim !2025-03-281981320|Neovim config for the lazy| | 288|wez/wezterm !2025-03-281976018|A GPU-accelerated cross-platform terminal emulator and multiplexer written by @wez and implemented in Rust| | 289|valkey-io/valkey !2025-03-281970416|A flexible distributed key-value datastore that supports both caching and beyond caching workloads.| | 290|LiLittleCat/awesome-free-chatgpt !2025-03-28196185|🆓免费的 ChatGPT 镜像网站列表，持续更新。List of free ChatGPT mirror sites, continuously updated.| | 291|Byaidu/PDFMathTranslate !2025-03-281947645|PDF scientific paper translation with preserved formats - 基于 AI 完整保留排版的 PDF 文档全文双语翻译，支持 Google/DeepL/Ollama/OpenAI 等服务，提供 CLI/GUI/Docker| | 292|openai/swarm !2025-03-281947111|Educational framework exploring ergonomic, lightweight multi-agent orchestration. Managed by OpenAI Solution team.| | 293|HqWu-HITCS/Awesome-Chinese-LLM !2025-03-281921423|Organizing smaller, cost-effective, privately deployable open-source Chinese language models, including related datasets and tutorials| | 294|stitionai/devika !2025-03-28190903|Devika is an Agentic AI Software Engineer that can understand high-level human instructions, break them down into steps, research relevant information, and write code to achieve the given objective. Devika aims to be a competitive open-source alternative to Devin by Cognition AI.| | 295|OpenBMB/MiniCPM-o !2025-03-28190887|MiniCPM-o 2.6: A GPT-4o Level MLLM for Vision, Speech and Multimodal Live Streaming on Your Phone| | 296|samber/lo !2025-03-281904815|💥 A Lodash-style Go library based on Go 1.18+ Generics (map, filter, contains, find...)| | 297|chroma-core/chroma !2025-03-281895221 |the AI-native open-source embedding database| | 298|DarkFlippers/unleashed-firmware !2025-03-28189278|Flipper Zero Unleashed Firmware| | 299|brave/brave-browser !2025-03-281892710|Brave browser for Android, iOS, Linux, macOS, Windows.| | 300| tloen/alpaca-lora !2025-03-28188641 | Instruct-tune LLaMA on consumer hardware| | 301|VinciGit00/Scrapegraph-ai !2025-03-281884618|Python scraper based on AI| | 302|gitroomhq/postiz-app !2025-03-281879110|📨 Schedule social posts, measure them, exchange with other members and get a lot of help from AI 🚀| | 303|PrefectHQ/prefect !2025-03-281878715|Prefect is a workflow orchestration tool empowering developers to build, observe, and react to data pipelines| | 304|ymcui/Chinese-LLaMA-Alpaca !2025-03-28187723 |Chinese LLaMA & Alpaca LLMs| | 305|kenjihiranabe/The-Art-of-Linear-Algebra !2025-03-28187335|Graphic notes on Gilbert Strang's "Linear Algebra for Everyone"| | 306|joonspk-research/generativeagents !2025-03-28187288|Generative Agents: Interactive Simulacra of Human Behavior| | 307|renovatebot/renovate !2025-03-28186820|Universal dependency update tool that fits into your workflows.| | 308|gventuri/pandas-ai !2025-03-28186109 |Pandas AI is a Python library that integrates generative artificial intelligence capabilities into Pandas, making dataframes conversational| | 309|thingsboard/thingsboard !2025-03-28185184|Open-source IoT Platform - Device management, data collection, processing and visualization.| | 310|ente-io/ente !2025-03-28184722|Fully open source, End to End Encrypted alternative to Google Photos and Apple Photos| | 311|serengil/deepface !2025-03-281840113|A Lightweight Face Recognition and Facial Attribute Analysis (Age, Gender, Emotion and Race) Library for Python| | 312|Raphire/Win11Debloat !2025-03-281840132|A simple, easy to use PowerShell script to remove pre-installed apps from windows, disable telemetry, remove Bing from windows search as well as perform various other changes to declutter and improve your windows experience. This script works for both windows 10 and windows 11.| | 313|Avaiga/taipy !2025-03-28179235|Turns Data and AI algorithms into production-ready web applications in no time.| | 314|microsoft/qlib !2025-03-281784231|Qlib is an AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing productions. Qlib supports diverse machine learning modeling paradigms. including supervised learning, market dynamics modeling, and RL.| | 315|CopilotKit/CopilotKit !2025-03-281778571|Build in-app AI chatbots 🤖, and AI-powered Textareas ✨, into react web apps.| | 316|QwenLM/Qwen-7B !2025-03-281766017|The official repo of Qwen-7B (通义千问-7B) chat & pretrained large language model proposed by Alibaba Cloud.| | 317|w-okada/voice-changer !2025-03-28176078 |リアルタイムボイスチェンジャー Realtime Voice Changer| | 318|rlabbe/Kalman-and-Bayesian-Filters-in-Python !2025-03-281756011|Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.| | 319|Mikubill/sd-webui-controlnet !2025-03-28174794 |WebUI extension for ControlNet| | 320|jingyaogong/minimind !2025-03-2817380116|「大模型」3小时完全从0训练26M的小参数GPT，个人显卡即可推理训练！| | 321|apify/crawlee !2025-03-28172696|Crawlee—A web scraping and browser automation library for Node.js to build reliable crawlers. In JavaScript and TypeScript. Extract data for AI, LLMs, RAG, or GPTs. Download HTML, PDF, JPG, PNG, and other files from websites. Works with Puppeteer, Playwright, Cheerio, JSDOM, and raw HTTP. Both headful and headless mode. With proxy rotation.| | 322|apple/ml-stable-diffusion !2025-03-28172395|Stable Diffusion with Core ML on Apple Silicon| | 323| transitive-bullshit/chatgpt-api !2025-03-28172095 | Node.js client for the official ChatGPT API. | | 324|teableio/teable !2025-03-281719222|✨ The Next Gen Airtable Alternative: No-Code Postgres| | 325| xx025/carrot !2025-03-28170900 | Free ChatGPT Site List | | 326|microsoft/LightGBM !2025-03-28170723|A fast, distributed, high-performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.| | 327|VikParuchuri/surya !2025-03-28169827|Accurate line-level text detection and recognition (OCR) in any language| | 328|deepseek-ai/Janus !2025-03-281692825|Janus-Series: Unified Multimodal Understanding and Generation Models| | 329|ardalis/CleanArchitecture !2025-03-28168823|Clean Architecture Solution Template: A starting point for Clean Architecture with ASP.NET Core| | 330|neondatabase/neon !2025-03-28166466|Neon: Serverless Postgres. We separated storage and compute to offer autoscaling, code-like database branching, and scale to zero.| | 331|kestra-io/kestra !2025-03-281661313|⚡ Workflow Automation Platform. Orchestrate & Schedule code in any language, run anywhere, 500+ plugins. Alternative to Zapier, Rundeck, Camunda, Airflow...| | 332|Dao-AILab/flash-attention !2025-03-281659720|Fast and memory-efficient exact attention| | 333|RPCS3/rpcs3 !2025-03-281655712|PS3 emulator/debugger| | 334|meta-llama/llama-recipes !2025-03-28165486|Scripts for fine-tuning Llama2 with composable FSDP & PEFT methods to cover single/multi-node GPUs. Supports default & custom datasets for applications such as summarization & question answering. Supporting a number of candid inference solutions such as HF TGI, VLLM for local or cloud deployment.Demo apps to showcase Llama2 for WhatsApp & Messenger| | 335|emilwallner/Screenshot-to-code !2025-03-28165180|A neural network that transforms a design mock-up into a static website.| | 336|datawhalechina/llm-cookbook !2025-03-281650922|面向开发者的 LLM 入门教程，吴恩达大模型系列课程中文版| | 337|e2b-dev/awesome-ai-agents !2025-03-281643923|A list of AI autonomous agents| | 338|QwenLM/Qwen2.5 !2025-03-281641114|Qwen2.5 is the large language model series developed by Qwen team, Alibaba Cloud.| | 339|dair-ai/ML-YouTube-Courses !2025-03-28164114|📺 Discover the latest machine learning / AI courses on YouTube.| | 340|pybind/pybind11 !2025-03-28163620|Seamless operability between C++11 and Python| | 341|graphdeco-inria/gaussian-splatting !2025-03-281627116|Original reference implementation of "3D Gaussian Splatting for Real-Time Radiance Field Rendering"| | 342|meta-llama/codellama !2025-03-28162531|Inference code for CodeLlama models| | 343|TransformerOptimus/SuperAGI !2025-03-28161292 | SuperAGI - A dev-first open source autonomous AI agent framework. Enabling developers to build, manage & run useful autonomous agents quickly and reliably.| | 344|microsoft/onnxruntime !2025-03-28161169|ONNX Runtime: cross-platform, high-performance ML inferencing and training accelerator| | 345|IDEA-Research/Grounded-Segment-Anything !2025-03-281601411 |Marrying Grounding DINO with Segment Anything & Stable Diffusion & BLIP - Automatically Detect, Segment and Generate Anything with Image and Text Inputs| | 346|ddbourgin/numpy-ml !2025-03-28160054|Machine learning, in numpy| | 347|eosphoros-ai/DB-GPT !2025-03-281585225|Revolutionizing Database Interactions with Private LLM Technology| | 348|Stability-AI/StableLM !2025-03-28158310 |Stability AI Language Models| | 349|openai/evals !2025-03-28157935 |Evals is a framework for evaluating LLMs and LLM systems, and an open-source registry of benchmarks.| | 350|THUDM/ChatGLM2-6B !2025-03-28157500|ChatGLM2-6B: An Open Bilingual Chat LLM | | 351|sunner/ChatALL !2025-03-28156761 |Concurrently chat with ChatGPT, Bing Chat, Bard, Alpaca, Vincuna, Claude, ChatGLM, MOSS, iFlytek Spark, ERNIE and more, discover the best answers| | 352|abseil/abseil-cpp !2025-03-28156656|Abseil Common Libraries (C++)| | 353|NVIDIA/open-gpu-kernel-modules !2025-03-28156531|NVIDIA Linux open GPU kernel module source| | 354|letta-ai/letta !2025-03-281563718|Letta (formerly MemGPT) is a framework for creating LLM services with memory.| | 355|typescript-eslint/typescript-eslint !2025-03-28156211|✨ Monorepo for all the tooling which enables ESLint to support TypeScript| | 356|umijs/umi !2025-03-28156211|A framework in react community ✨| | 357|AI4Finance-Foundation/FinGPT !2025-03-281561215|Data-Centric FinGPT. Open-source for open finance! Revolutionize 🔥 We'll soon release the trained model.| | 358|amplication/amplication !2025-03-28156022|🔥🔥🔥 The Only Production-Ready AI-Powered Backend Code Generation| | 359|KindXiaoming/pykan !2025-03-28155477|Kolmogorov Arnold Networks| | 360|arc53/DocsGPT !2025-03-28154900|GPT-powered chat for documentation, chat with your documents| | 361|influxdata/telegraf !2025-03-28154502|Agent for collecting, processing, aggregating, and writing metrics, logs, and other arbitrary data.| | 362|microsoft/Bringing-Old-Photos-Back-to-Life !2025-03-28154084|Bringing Old Photo Back to Life (CVPR 2020 oral)| | 363|GaiZhenbiao/ChuanhuChatGPT !2025-03-2815394-2|GUI for ChatGPT API and many LLMs. Supports agents, file-based QA, GPT finetuning and query with web search. All with a neat UI.| | 364|Zeyi-Lin/HivisionIDPhotos !2025-03-281529710|⚡️HivisionIDPhotos: a lightweight and efficient AI ID photos tools. 一个轻量级的AI证件照制作算法。| | 365| mayooear/gpt4-pdf-chatbot-langchain !2025-03-281529518 | GPT4 & LangChain Chatbot for large PDF docs | | 366|1Panel-dev/MaxKB !2025-03-2815277148|? Based on LLM large language model knowledge base Q&A system. Ready to use out of the box, supports quick integration into third-party business systems. Officially produced by 1Panel| | 367|ai16z/eliza !2025-03-281526811|Conversational Agent for Twitter and Discord| | 368|apache/arrow !2025-03-28151684|Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing| | 369|princeton-nlp/SWE-agent !2025-03-281516119|SWE-agent: Agent Computer Interfaces Enable Software Engineering Language Models| | 370|mlc-ai/web-llm !2025-03-281509311 |Bringing large-language models and chat to web browsers. Everything runs inside the browser with no server support.| | 371|guillaumekln/faster-whisper !2025-03-281507117 |Faster Whisper transcription with CTranslate2| | 372|overleaf/overleaf !2025-03-28150316|A web-based collaborative LaTeX editor| | 373|triton-lang/triton !2025-03-28150169|Development repository for the Triton language and compiler| | 374|soxoj/maigret !2025-03-281500410|🕵️‍♂️ Collect a dossier on a person by username from thousands of sites| | 375|alibaba/lowcode-engine !2025-03-28149841|An enterprise-class low-code technology stack with scale-out design / 一套面向扩展设计的企业级低代码技术体系| | 376|espressif/esp-idf !2025-03-28148545|Espressif IoT Development Framework. Official development framework for Espressif SoCs.| | 377|pgvector/pgvector !2025-03-281484913|Open-source vector similarity search for Postgres| | 378|datawhalechina/leedl-tutorial !2025-03-28148246|《李宏毅深度学习教程》（李宏毅老师推荐👍），PDF下载地址：https://github.com/datawhalechina/leedl-tutorial/releases| | 379|xcanwin/KeepChatGPT !2025-03-28147972 |Using ChatGPT is more efficient and smoother, perfectly solving ChatGPT network errors. No longer do you need to frequently refresh the webpage, saving over 10 unnecessary steps| | 380|m-bain/whisperX !2025-03-281471313|WhisperX: Automatic Speech Recognition with Word-level Timestamps (& Diarization)| | 381|HumanAIGC/AnimateAnyone !2025-03-2814706-1|Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation| |!green-up-arrow.svg 382|naklecha/llama3-from-scratch !2025-03-281469024|llama3 implementation one matrix multiplication at a time| |!red-down-arrow 383| fauxpilot/fauxpilot !2025-03-28146871 | An open-source GitHub Copilot server | | 384|LlamaFamily/Llama-Chinese !2025-03-28145111|Llama Chinese Community, the best Chinese Llama large model, fully open source and commercially available| | 385|BradyFU/Awesome-Multimodal-Large-Language-Models !2025-03-281450121|Latest Papers and Datasets on Multimodal Large Language Models| | 386|vanna-ai/vanna !2025-03-281449819|🤖 Chat with your SQL database 📊. Accurate Text-to-SQL Generation via LLMs using RAG 🔄.| | 387|bleedline/aimoneyhunter !2025-03-28144845|AI Side Hustle Money Mega Collection: Teaching You How to Utilize AI for Various Side Projects to Earn Extra Income.| | 388|stefan-jansen/machine-learning-for-trading !2025-03-28144629|Code for Machine Learning for Algorithmic Trading, 2nd edition.| | 389|state-spaces/mamba !2025-03-28144139|Mamba: Linear-Time Sequence Modeling with Selective State Spaces| | 390|vercel/ai-chatbot !2025-03-281434614|A full-featured, hackable Next.js AI chatbot built by Vercel| | 391|steven-tey/novel !2025-03-281428410|Notion-style WYSIWYG editor with AI-powered autocompletions| | 392|unifyai/ivy !2025-03-281409348|Unified AI| | 393|chidiwilliams/buzz !2025-03-281402411 |Buzz transcribes and translates audio offline on your personal computer. Powered by OpenAI's Whisper.| | 394|lukas-blecher/LaTeX-OCR !2025-03-28139769|pix2tex: Using a ViT to convert images of equations into LaTeX code.| | 395|openai/tiktoken !2025-03-28139599|tiktoken is a fast BPE tokeniser for use with OpenAI's models.| | 396|nocobase/nocobase !2025-03-281391522|NocoBase is a scalability-first, open-source no-code/low-code platform for building business applications and enterprise solutions.| | 397|neonbjb/tortoise-tts !2025-03-28139010 |A multi-voice TTS system trained with an emphasis on quality| | 398|yamadashy/repomix !2025-03-281382036|📦 Repomix (formerly Repopack) is a powerful tool that packs your entire repository into a single, AI-friendly file. Perfect for when you need to feed your codebase to Large Language Models (LLMs) or other AI tools like Claude, ChatGPT, and Gemini.| | 399|adobe/react-spectrum !2025-03-28136766|A collection of libraries and tools that help you build adaptive, accessible, and robust user experiences.| | 400|THUDM/ChatGLM3 !2025-03-28136684|ChatGLM3 series: Open Bilingual Chat LLMs | | 401|NVIDIA/NeMo !2025-03-28134837|A scalable generative AI framework built for researchers and developers working on Large Language Models, Multimodal, and Speech AI (Automatic Speech Recognition and Text-to-Speech)| | 402|BlinkDL/RWKV-LM !2025-03-28134346 |RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it combines the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding.| | 403| fuergaosi233/wechat-chatgpt !2025-03-28133330 | Use ChatGPT On Wechat via wechaty | | 404|udecode/plate !2025-03-28133325|A rich-text editor powered by AI| | 405|xenova/transformers.js !2025-03-281331219|State-of-the-art Machine Learning for the web. Run 🤗 Transformers directly in your browser, with no need for a server!| | 406|stas00/ml-engineering !2025-03-281325615|Machine Learning Engineering Guides and Tools| | 407| wong2/chatgpt-google-extension !2025-03-2813241-1 | A browser extension that enhances search engines with ChatGPT, this repos will not be updated from 2023-02-20| | 408|mrdbourke/pytorch-deep-learning !2025-03-281317520|Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.| | 409|Koenkk/zigbee2mqtt !2025-03-28131544|Zigbee 🐝 to MQTT bridge 🌉, get rid of your proprietary Zigbee bridges 🔨| | 410|vercel-labs/ai !2025-03-281298528|Build AI-powered applications with React, Svelte, and Vue| | 411|netease-youdao/QAnything !2025-03-28129318|Question and Answer based on Anything.| | 412|huggingface/trl !2025-03-281289622|Train transformer language models with reinforcement learning.| | 413|microsoft/BitNet !2025-03-28128503|Official inference framework for 1-bit LLMs| | 414|mediar-ai/screenpipe !2025-03-281283915|24/7 local AI screen & mic recording. Build AI apps that have the full context. Works with Ollama. Alternative to Rewind.ai. Open. Secure. You own your data. Rust.| | 415|Skyvern-AI/skyvern !2025-03-281277612|Automate browser-based workflows with LLMs and Computer Vision| | 416|pytube/pytube !2025-03-28126591|A lightweight, dependency-free Python library (and command-line utility) for downloading YouTube Videos.| | 417|official-stockfish/Stockfish !2025-03-28126574|UCI chess engine| | 418|sgl-project/sglang !2025-03-281260143|SGLang is a structured generation language designed for large language models (LLMs). It makes your interaction with LLMs faster and more controllable.| | 419|plasma-umass/scalene !2025-03-28125535|Scalene: a high-performance, high-precision CPU, GPU, and memory profiler for Python with AI-powered optimization proposals| | 420|danswer-ai/danswer !2025-03-28125503|Ask Questions in natural language and get Answers backed by private sources. Connects to tools like Slack, GitHub, Confluence, etc.| | 421|OpenTalker/SadTalker !2025-03-28125226|[CVPR 2023] SadTalker：Learning Realistic 3D Motion Coefficients for Stylized Audio-Driven Single Image Talking Face Animation| | 422|facebookresearch/AnimatedDrawings !2025-03-28123693 |Code to accompany "A Method for Animating Children's Drawings of the Human Figure"| | 423|activepieces/activepieces !2025-03-28123609|Your friendliest open source all-in-one automation tool ✨ Workflow automation tool 100+ integration / Enterprise automation tool / Zapier Alternative| | 424|ggerganov/ggml !2025-03-28121992 |Tensor library for machine learning| | 425|bytebase/bytebase !2025-03-28121694|World's most advanced database DevOps and CI/CD for Developer, DBA and Platform Engineering teams. The GitLab/GitHub for database DevOps.| | 426| willwulfken/MidJourney-Styles-and-Keywords-Reference !2025-03-28120971 | A reference containing Styles and Keywords that you can use with MidJourney AI| | 427|Huanshere/VideoLingo !2025-03-281207013|Netflix-level subtitle cutting, translation, alignment, and even dubbing - one-click fully automated AI video subtitle team | | 428|OpenLMLab/MOSS !2025-03-28120330 |An open-source tool-augmented conversational language model from Fudan University| | 429|llmware-ai/llmware !2025-03-281200727|Providing enterprise-grade LLM-based development framework, tools, and fine-tuned models.| | 430|PKU-YuanGroup/Open-Sora-Plan !2025-03-28119362|This project aim to reproduce Sora (Open AI T2V model), but we only have limited resource. We deeply wish the all open source community can contribute to this project.| | 431|ShishirPatil/gorilla !2025-03-28119332 |Gorilla: An API store for LLMs| | 432|NVIDIA/Megatron-LM !2025-03-281192716|Ongoing research training transformer models at scale| | 433|illacloud/illa-builder !2025-03-28119192|Create AI-Driven Apps like Assembling Blocks| | 434|marimo-team/marimo !2025-03-281191521|A reactive notebook for Python — run reproducible experiments, execute as a script, deploy as an app, and version with git.| | 435|smol-ai/developer !2025-03-28119111 | With 100k context windows on the way, it's now feasible for every dev to have their own smol developer| | 436|Lightning-AI/litgpt !2025-03-28118878|Pretrain, finetune, deploy 20+ LLMs on your own data. Uses state-of-the-art techniques: flash attention, FSDP, 4-bit, LoRA, and more.| | 437|openai/shap-e !2025-03-28118474 |Generate 3D objects conditioned on text or images| | 438|eugeneyan/open-llms !2025-03-28118451 |A list of open LLMs available for commercial use.| | 439|andrewyng/aisuite !2025-03-28118124|Simple, unified interface to multiple Generative AI providers| | 440|hajimehoshi/ebiten !2025-03-28117816|Ebitengine - A dead simple 2D game engine for Go| | 441|kgrzybek/modular-monolith-with-ddd !2025-03-28117493|Full Modular Monolith application with Domain-Driven Design approach.| | 442|h2oai/h2ogpt !2025-03-2811736-1 |Come join the movement to make the world's best open source GPT led by H2O.ai - 100% private chat and document search, no data leaks, Apache 2.0| | 443|owainlewis/awesome-artificial-intelligence !2025-03-28117332|A curated list of Artificial Intelligence (AI) courses, books, video lectures and papers.| | 444|DataTalksClub/mlops-zoomcamp !2025-03-28116643|Free MLOps course from DataTalks.Club| | 445|Rudrabha/Wav2Lip !2025-03-281163410|This repository contains the codes of "A Lip Sync Expert Is All You Need for Speech to Lip Generation In the Wild", published at ACM Multimedia 2020.| | 446|aishwaryanr/awesome-generative-ai-guide !2025-03-281152810|A one stop repository for generative AI research updates, interview resources, notebooks and much more!| | 447|karpathy/micrograd !2025-03-28115146|A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API| | 448|InstantID/InstantID !2025-03-28115111|InstantID : Zero-shot Identity-Preserving Generation in Seconds 🔥| | 449|facebookresearch/seamlesscommunication !2025-03-28114434|Foundational Models for State-of-the-Art Speech and Text Translation| | 450|anthropics/anthropic-cookbook !2025-03-281140112|A collection of notebooks/recipes showcasing some fun and effective ways of using Claude.| | 451|mastra-ai/mastra !2025-03-281139240|the TypeScript AI agent framework| | 452|NVIDIA/TensorRT !2025-03-28113864|NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.| | 453|plandex-ai/plandex !2025-03-28113645|An AI coding engine for complex tasks| | 454|RUCAIBox/LLMSurvey !2025-03-28112735 |A collection of papers and resources related to Large Language Models.| | 455|kubeshark/kubeshark !2025-03-28112711|The API traffic analyzer for Kubernetes providing real-time K8s protocol-level visibility, capturing and monitoring all traffic and payloads going in, out and across containers, pods, nodes and clusters. Inspired by Wireshark, purposely built for Kubernetes| | 456|electric-sql/pglite !2025-03-28112617|Lightweight Postgres packaged as WASM into a TypeScript library for the browser, Node.js, Bun and Deno from https://electric-sql.com| | 457|lightaime/camel !2025-03-281124441 |🐫 CAMEL: Communicative Agents for “Mind” Exploration of Large Scale Language Model Society| | 458|huggingface/lerobot !2025-03-281120184|🤗 LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch| | 459|normal-computing/outlines !2025-03-28111657|Generative Model Programming| | 460|libretro/RetroArch !2025-03-28110701|Cross-platform, sophisticated frontend for the libretro API. Licensed GPLv3.| | 461|THUDM/CogVideo !2025-03-28110599|Text-to-video generation: CogVideoX (2024) and CogVideo (ICLR 2023)| | 462|bentoml/OpenLLM !2025-03-28110495|An open platform for operating large language models (LLMs) in production. Fine-tune, serve, deploy, and monitor any LLMs with ease.| | 463|vosen/ZLUDA !2025-03-28110429|CUDA on AMD GPUs| | 464|dair-ai/ML-Papers-of-the-Week !2025-03-28110304 |🔥Highlighting the top ML papers every week.| | 465|WordPress/gutenberg !2025-03-28110212|The Block Editor project for WordPress and beyond. Plugin is available from the official repository.| | 466|microsoft/data-formulator !2025-03-281099827|🪄 Create rich visualizations with AI| | 467|LibreTranslate/LibreTranslate !2025-03-28109887|Free and Open Source Machine Translation API. Self-hosted, offline capable and easy to setup.| | 468|block/goose !2025-03-281097737|an open-source, extensible AI agent that goes beyond code suggestions - install, execute, edit, and test with any LLM| | 469|getumbrel/llama-gpt !2025-03-28109553|A self-hosted, offline, ChatGPT-like chatbot. Powered by Llama 2. 100% private, with no data leaving your device.| | 470|HigherOrderCO/HVM !2025-03-28109182|A massively parallel, optimal functional runtime in Rust| | 471|databrickslabs/dolly !2025-03-2810812-3 | A large language model trained on the Databricks Machine Learning Platform| | 472|srush/GPU-Puzzles !2025-03-28108014|Solve puzzles. Learn CUDA.| | 473|Z3Prover/z3 !2025-03-28107952|The Z3 Theorem Prover| | 474|UFund-Me/Qbot !2025-03-281079313 |Qbot is an AI-oriented quantitative investment platform, which aims to realize the potential, empower AI technologies in quantitative investment| | 475|langchain-ai/langgraph !2025-03-281077336|| | 476|lz4/lz4 !2025-03-28107647|Extremely Fast Compression algorithm| | 477|magic-research/magic-animate !2025-03-28107160|MagicAnimate: Temporally Consistent Human Image Animation using Diffusion Model| | 478|PaperMC/Paper !2025-03-281071410|The most widely used, high performance Minecraft server that aims to fix gameplay and mechanics inconsistencies| | 479|getomni-ai/zerox !2025-03-281071015|Zero shot pdf OCR with gpt-4o-mini| |!green-up-arrow.svg 480|🔥NirDiamant/GenAIAgents !2025-03-2810693318|This repository provides tutorials and implementations for various Generative AI Agent techniques, from basic to advanced. It serves as a comprehensive guide for building intelligent, interactive AI systems.| |!red-down-arrow 481|Unstructured-IO/unstructured !2025-03-28106889|Open source libraries and APIs to build custom preprocessing pipelines for labeling, training, or production machine learning pipelines.| | 482|apache/thrift !2025-03-28106610|Apache Thrift| | 483| TheR1D/shellgpt !2025-03-28106097 | A command-line productivity tool powered by ChatGPT, will help you accomplish your tasks faster and more efficiently | | 484|TheRamU/Fay !2025-03-281060312 |Fay is a complete open source project that includes Fay controller and numeral models, which can be used in different applications such as virtual hosts, live promotion, numeral human interaction and so on| | 485|zyronon/douyin !2025-03-28105566|Vue3 + Pinia + Vite5 仿抖音，Vue 在移动端的最佳实践 . Imitate TikTok ，Vue Best practices on Mobile| | 486|THU-MIG/yolov10 !2025-03-28105485|YOLOv10: Real-Time End-to-End Object Detection| | 487|idootop/mi-gpt !2025-03-281052522|? Transform XiaoAi speaker into a personal voice assistant with ChatGPT and DouBao integration.| | 488|SakanaAI/AI-Scientist !2025-03-281051310|The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery 🧑‍🔬| | 489|szimek/sharedrop !2025-03-28105101|Easy P2P file transfer powered by WebRTC - inspired by Apple AirDrop| | 490|salesforce/LAVIS !2025-03-28103942 |LAVIS - A One-stop Library for Language-Vision Intelligence| | 491|aws/amazon-sagemaker-examples !2025-03-28103654|Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.| | 492|artidoro/qlora !2025-03-28103402 |QLoRA: Efficient Finetuning of Quantized LLMs| | 493|lllyasviel/stable-diffusion-webui-forge !2025-03-281029314| a platform on top of Stable Diffusion WebUI (based on Gradio) to make development easier, optimize resource management, and speed up inference| | 494|NielsRogge/Transformers-Tutorials !2025-03-28102487|This repository contains demos I made with the Transformers library by HuggingFace.| | 495|kedro-org/kedro !2025-03-28102371|Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.| | 496| chathub-dev/chathub !2025-03-28102301 | All-in-one chatbot client | | 497|microsoft/promptflow !2025-03-28101612|Build high-quality LLM apps - from prototyping, testing to production deployment and monitoring.| | 498|mistralai/mistral-src !2025-03-28101372|Reference implementation of Mistral AI 7B v0.1 model.| | 499|burn-rs/burn !2025-03-28101183|Burn - A Flexible and Comprehensive Deep Learning Framework in Rust| | 500|AIGC-Audio/AudioGPT !2025-03-28101150 |AudioGPT: Understanding and Generating Speech, Music, Sound, and Talking Head| | 501|facebookresearch/dinov2 !2025-03-281011210 |PyTorch code and models for the DINOv2 self-supervised learning method.| | 502|RockChinQ/LangBot !2025-03-281008455|😎丰富生态、🧩支持扩展、🦄多模态 - 大模型原生即时通信机器人平台 🤖 | | 503|78/xiaozhi-esp32 !2025-03-281008180|Build your own AI friend| | 504|cumulo-autumn/StreamDiffusion !2025-03-28100761|StreamDiffusion: A Pipeline-Level Solution for Real-Time Interactive Generation| | 505|DataTalksClub/machine-learning-zoomcamp !2025-03-28100664|The code from the Machine Learning Bookcamp book and a free course based on the book| | 506|nerfstudio-project/nerfstudio !2025-03-28100343|A collaboration friendly studio for NeRFs| | 507|cupy/cupy !2025-03-28100344|NumPy & SciPy for GPU| | 508|NVIDIA/TensorRT-LLM !2025-03-281000823|TensorRT-LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and build TensorRT engines that contain state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT-LLM also contains components to create Python and C++ runtimes that execute those TensorRT engines.| | 509|wasp-lang/open-saas !2025-03-2899665|A free, open-source SaaS app starter for React & Node.js with superpowers. Production-ready. Community-driven.| | 510|huggingface/text-generation-inference !2025-03-2899383|Large Language Model Text Generation Inference| | 511|jxnl/instructor !2025-03-2899224|structured outputs for llms| | 512|GoogleCloudPlatform/generative-ai !2025-03-2899086|Sample code and notebooks for Generative AI on Google Cloud| | 513|manticoresoftware/manticoresearch !2025-03-2898799|Easy to use open source fast database for search | | 514|langfuse/langfuse !2025-03-28985134|🪢 Open source LLM engineering platform. Observability, metrics, evals, prompt management, testing, prompt playground, datasets, LLM evaluations -- 🍊YC W23 🤖 integrate via Typescript, Python / Decorators, OpenAI, Langchain, LlamaIndex, Litellm, Instructor, Mistral, Perplexity, Claude, Gemini, Vertex| | 515|keephq/keep !2025-03-2897949|The open-source alert management and AIOps platform| | 516|sashabaranov/go-openai !2025-03-2897843|OpenAI ChatGPT, GPT-3, GPT-4, DALL·E, Whisper API wrapper for Go| | 517|autowarefoundation/autoware !2025-03-2897766|Autoware - the world's leading open-source software project for autonomous driving| | 518|anthropics/courses !2025-03-2897269|Anthropic's educational courses| | 519|popcorn-official/popcorn-desktop !2025-03-2896853|Popcorn Time is a multi-platform, free software BitTorrent client that includes an integrated media player ( Windows / Mac / Linux ) A Butter-Project Fork| | 520|getmaxun/maxun !2025-03-28968515|🔥 Open-source no-code web data extraction platform. Turn websites to APIs and spreadsheets with no-code robots in minutes! [In Beta]| | 521|wandb/wandb !2025-03-2896763|🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.| | 522|karpathy/minbpe !2025-03-2895353|Minimal, clean, code for the Byte Pair Encoding (BPE) algorithm commonly used in LLM tokenization.| | 523|bigscience-workshop/petals !2025-03-2895142|🌸 Run large language models at home, BitTorrent-style. Fine-tuning and inference up to 10x faster than offloading| | 524|OthersideAI/self-operating-computer !2025-03-2894931|A framework to enable multimodal models to operate a computer.| | 525|mshumer/gpt-prompt-engineer !2025-03-2894911|| | 526| BloopAI/bloop !2025-03-2894710 | A fast code search engine written in Rust| | 527|BlinkDL/ChatRWKV !2025-03-289467-1 |ChatRWKV is like ChatGPT but powered by RWKV (100% RNN) language model, and open source.| | 528|timlrx/tailwind-nextjs-starter-blog !2025-03-2894677|This is a Next.js, Tailwind CSS blogging starter template. Comes out of the box configured with the latest technologies to make technical writing a breeze. Easily configurable and customizable. Perfect as a replacement to existing Jekyll and Hugo individual blogs.| | 529|google/benchmark !2025-03-2893634|A microbenchmark support library| | 530|facebookresearch/nougat !2025-03-2893603|Implementation of Nougat Neural Optical Understanding for Academic Documents| | 531|modelscope/facechain !2025-03-2893536|FaceChain is a deep-learning toolchain for generating your Digital-Twin.| | 532|DrewThomasson/ebook2audiobook !2025-03-2893388|Convert ebooks to audiobooks with chapters and metadata using dynamic AI models and voice cloning. Supports 1,107+ languages!| | 533|RayTracing/raytracing.github.io !2025-03-2893035|Main Web Site (Online Books)| | 534|QwenLM/Qwen2.5-VL !2025-03-28930249|Qwen2.5-VL is the multimodal large language model series developed by Qwen team, Alibaba Cloud.| | 535|WongKinYiu/yolov9 !2025-03-2892201|Implementation of paper - YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information| | 536|alibaba-damo-academy/FunASR !2025-03-28920222|A Fundamental End-to-End Speech Recognition Toolkit and Open Source SOTA Pretrained Models.| | 537|Visualize-ML/Book4Power-of-Matrix !2025-03-2891931|Book4 'Power of Matrix' | | 538|dice2o/BingGPT !2025-03-289185-1 |Desktop application of new Bing's AI-powered chat (Windows, macOS and Linux)| | 539|browserbase/stagehand !2025-03-28917621|An AI web browsing framework focused on simplicity and extensibility.| | 540|FlagOpen/FlagEmbedding !2025-03-28914111|Dense Retrieval and Retrieval-augmented LLMs| | 541|Const-me/Whisper !2025-03-2890979|High-performance GPGPU inference of OpenAI's Whisper automatic speech recognition (ASR) model| | 542|lucidrains/denoising-diffusion-pytorch !2025-03-2890942|Implementation of Denoising Diffusion Probabilistic Model in Pytorch| | 543|Chainlit/chainlit !2025-03-28904422|Build Conversational AI in minutes ⚡️| | 544|togethercomputer/OpenChatKit !2025-03-2890160 |OpenChatKit provides a powerful, open-source base to create both specialized and general purpose chatbots for various applications| | 545|Stability-AI/StableStudio !2025-03-2889631 |Community interface for generative AI| | 546|voicepaw/so-vits-svc-fork !2025-03-2889482 |so-vits-svc fork with realtime support, improved interface and more features.| | 547|pymc-devs/pymc !2025-03-2889413|Bayesian Modeling and Probabilistic Programming in Python| | 548|espnet/espnet !2025-03-2889302|End-to-End Speech Processing Toolkit| | 549|kedacore/keda !2025-03-2888991|KEDA is a Kubernetes-based Event Driven Autoscaling component. It provides event driven scale for any container running in Kubernetes| | 550|open-mmlab/Amphion !2025-03-28886911|Amphion (/æmˈfaɪən/) is a toolkit for Audio, Music, and Speech Generation. Its purpose is to support reproducible research and help junior researchers and engineers get started in the field of audio, music, and speech generation research and development.| | 551|gorse-io/gorse !2025-03-2888451|Gorse open source recommender system engine| | 552|adams549659584/go-proxy-bingai !2025-03-288768-1 |A Microsoft New Bing demo site built with Vue3 and Go, providing a consistent UI experience, supporting ChatGPT prompts, and accessible within China| | 553|open-mmlab/mmsegmentation !2025-03-2887513|OpenMMLab Semantic Segmentation Toolbox and Benchmark.| | 554|bytedance/monolith !2025-03-2887223|ByteDance's Recommendation System| | 555|LouisShark/chatgptsystemprompt !2025-03-2887216|store all agent's system prompt| | 556|brexhq/prompt-engineering !2025-03-2887080 |Tips and tricks for working with Large Language Models like OpenAI's GPT-4.| | 557|erincatto/box2d !2025-03-2886841|Box2D is a 2D physics engine for games| | 558|🔥microsoft/ai-agents-for-beginners !2025-03-288669323|10 Lessons to Get Started Building AI Agents| | 559|nashsu/FreeAskInternet !2025-03-2886102|FreeAskInternet is a completely free, private and locally running search aggregator & answer generate using LLM, without GPU needed. The user can ask a question and the system will make a multi engine search and combine the search result to the ChatGPT3.5 LLM and generate the answer based on search results.| | 560|goldmansachs/gs-quant !2025-03-2885981|Python toolkit for quantitative finance| | 561|srbhr/Resume-Matcher !2025-03-2885800|Open Source Free ATS Tool to compare Resumes with Job Descriptions and create a score to rank them.| | 562|facebookresearch/ImageBind !2025-03-2885681 |ImageBind One Embedding Space to Bind Them All| | 563|ashawkey/stable-dreamfusion !2025-03-2885481 |A pytorch implementation of text-to-3D dreamfusion, powered by stable diffusion.| | 564|meetecho/janus-gateway !2025-03-2885232|Janus WebRTC Server| | 565|google/magika !2025-03-2885003|Detect file content types with deep learning| | 566|huggingface/chat-ui !2025-03-2884871 |Open source codebase powering the HuggingChat app| | 567|EleutherAI/lm-evaluation-harness !2025-03-28843012|A framework for few-shot evaluation of autoregressive language models.| | 568|jina-ai/reader !2025-03-2884089|Convert any URL to an LLM-friendly input with a simple prefix https://r.jina.ai/| | 569|microsoft/TypeChat !2025-03-288406-1|TypeChat is a library that makes it easy to build natural language interfaces using types.| | 570|thuml/Time-Series-Library !2025-03-28839715|A Library for Advanced Deep Time Series Models.| | 571|OptimalScale/LMFlow !2025-03-2883882|An Extensible Toolkit for Finetuning and Inference of Large Foundation Models. Large Model for All.| | 572|baptisteArno/typebot.io !2025-03-2883845|💬 Typebot is a powerful chatbot builder that you can self-host.| | 573|jzhang38/TinyLlama !2025-03-2883504|The TinyLlama project is an open endeavor to pretrain a 1.1B Llama model on 3 trillion tokens.| | 574|fishaudio/Bert-VITS2 !2025-03-2883472|vits2 backbone with multilingual-bert| | 575|OpenBMB/XAgent !2025-03-2882683|An Autonomous LLM Agent for Complex Task Solving| | 576|Acly/krita-ai-diffusion !2025-03-2882387|Streamlined interface for generating images with AI in Krita. Inpaint and outpaint with optional text prompt, no tweaking required.| | 577|jasonppy/VoiceCraft !2025-03-2882151|Zero-Shot Speech Editing and Text-to-Speech in the Wild| | 578|SJTU-IPADS/PowerInfer !2025-03-2881693|High-speed Large Language Model Serving on PCs with Consumer-grade GPUs| | 579|modelscope/DiffSynth-Studio !2025-03-28814713|Enjoy the magic of Diffusion models!| | 580|o3de/o3de !2025-03-2881443|Open 3D Engine (O3DE) is an Apache 2.0-licensed multi-platform 3D engine that enables developers and content creators to build AAA games, cinema-quality 3D worlds, and high-fidelity simulations without any fees or commercial obligations.| | 581|zmh-program/chatnio !2025-03-2881325|🚀 Next Generation AI One-Stop Internationalization Solution. 🚀 下一代 AI 一站式 B/C 端解决方案，支持 OpenAI，Midjourney，Claude，讯飞星火，Stable Diffusion，DALL·E，ChatGLM，通义千问，腾讯混元，360 智脑，百川 AI，火山方舟，新必应，Gemini，Moonshot 等模型，支持对话分享，自定义预设，云端同步，模型市场，支持弹性计费和订阅计划模式，支持图片解析，支持联网搜索，支持模型缓存，丰富美观的后台管理与仪表盘数据统计。| | 582|leptonai/searchwithlepton !2025-03-2880632|Building a quick conversation-based search demo with Lepton AI.| | 583|sebastianstarke/AI4Animation !2025-03-2880620|Bringing Characters to Life with Computer Brains in Unity| | 584|wangrongding/wechat-bot !2025-03-2880528|🤖一个基于 WeChaty 结合 DeepSeek / ChatGPT / Kimi / 讯飞等Ai服务实现的微信机器人 ，可以用来帮助你自动回复微信消息，或者管理微信群/好友，检测僵尸粉等...| | 585|openvinotoolkit/openvino !2025-03-2880528|OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference| | 586|steven2358/awesome-generative-ai !2025-03-28802610|A curated list of modern Generative Artificial Intelligence projects and services| | 587|adam-maj/tiny-gpu !2025-03-2880234|A minimal GPU design in Verilog to learn how GPUs work from the ground up| | 588| anse-app/chatgpt-demo !2025-03-2880180 | A demo repo based on OpenAI API (gpt-3.5-turbo) | | 589| acheong08/EdgeGPT !2025-03-288015-1 |Reverse engineered API of Microsoft's Bing Chat | | 590|ai-collection/ai-collection !2025-03-2879994 |The Generative AI Landscape - A Collection of Awesome Generative AI Applications| | 591|GreyDGL/PentestGPT !2025-03-2879953 |A GPT-empowered penetration testing tool| | 592|delta-io/delta !2025-03-2879112|An open-source storage framework that enables building a Lakehouse architecture with compute engines including Spark, PrestoDB, Flink, Trino, and Hive and APIs| | 593|dataelement/bisheng !2025-03-2879085|Bisheng is an open LLM devops platform for next generation AI applications.| | 594|e2b-dev/e2b !2025-03-2878447 |Vercel for AI agents. We help developers to build, deploy, and monitor AI agents. Focusing on specialized AI agents that build software for you - your personal software developers.| | 595|01-ai/Yi !2025-03-2878311|A series of large language models trained from scratch by developers @01-ai| | 596|Plachtaa/VALL-E-X !2025-03-287830-1|An open source implementation of Microsoft's VALL-E X zero-shot TTS model. The demo is available at https://plachtaa.github.io| | 597|abhishekkrthakur/approachingalmost !2025-03-2878204|Approaching (Almost) Any Machine Learning Problem| | 598|pydantic/pydantic-ai !2025-03-28781041|Agent Framework / shim to use Pydantic with LLMs| | 599|rany2/edge-tts !2025-03-2877901|Use Microsoft Edge's online text-to-speech service from Python WITHOUT needing Microsoft Edge or Windows or an API key| | 600|CASIA-IVA-Lab/FastSAM !2025-03-2877881|Fast Segment Anything| | 601|netease-youdao/EmotiVoice !2025-03-2877817|EmotiVoice 😊: a Multi-Voice and Prompt-Controlled TTS Engine| | 602|lllyasviel/IC-Light !2025-03-2877804|More relighting!| | 603|kroma-network/tachyon !2025-03-287774-1|Modular ZK(Zero Knowledge) backend accelerated by GPU| | 604|deep-floyd/IF !2025-03-2877731 |A novel state-of-the-art open-source text-to-image model with a high degree of photorealism and language understanding| | 605|oumi-ai/oumi !2025-03-2877705|Everything you need to build state-of-the-art foundation models, end-to-end.| | 606|reorproject/reor !2025-03-2877681|AI note-taking app that runs models locally.| | 607|lightpanda-io/browser !2025-03-28775813|Lightpanda: the headless browser designed for AI and automation| | 608|xiangsx/gpt4free-ts !2025-03-287755-1|Providing a free OpenAI GPT-4 API ! This is a replication project for the typescript version of xtekky/gpt4free| | 609|IDEA-Research/GroundingDINO !2025-03-28773311|Official implementation of the paper "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection"| | 610|bunkerity/bunkerweb !2025-03-2877326|🛡️ Make your web services secure by default !| | 611|vikhyat/moondream !2025-03-2877057|tiny vision language model| | 612|firmai/financial-machine-learning !2025-03-287703-1|A curated list of practical financial machine learning tools and applications.| | 613|n8n-io/self-hosted-ai-starter-kit !2025-03-28765121|The Self-hosted AI Starter Kit is an open-source template that quickly sets up a local AI environment. Curated by n8n, it provides essential tools for creating secure, self-hosted AI workflows.| | 614|intel-analytics/ipex-llm !2025-03-2876507|Accelerate local LLM inference and finetuning (LLaMA, Mistral, ChatGLM, Qwen, Baichuan, Mixtral, Gemma, etc.) on Intel CPU and GPU (e.g., local PC with iGPU, discrete GPU such as Arc, Flex and Max). A PyTorch LLM library that seamlessly integrates with llama.cpp, HuggingFace, LangChain, LlamaIndex, DeepSpeed, vLLM, FastChat, ModelScope, etc.| | 615|jrouwe/JoltPhysics !2025-03-28764510|A multi core friendly rigid body physics and collision detection library. Written in C++. Suitable for games and VR applications. Used by Horizon Forbidden West.| | 616|THUDM/CodeGeeX2 !2025-03-2876270|CodeGeeX2: A More Powerful Multilingual Code Generation Model| | 617|meta-llama/llama-stack !2025-03-2875866|Composable building blocks to build Llama Apps| | 618|sweepai/sweep !2025-03-287530-1|Sweep is an AI junior developer| | 619|lllyasviel/Omost !2025-03-2875301|Your image is almost there!| | 620|ahmedbahaaeldin/From-0-to-Research-Scientist-resources-guide !2025-03-2875050|Detailed and tailored guide for undergraduate students or anybody want to dig deep into the field of AI with solid foundation.| | 621|dair-ai/ML-Papers-Explained !2025-03-2875050|Explanation to key concepts in ML| | 622|zaidmukaddam/scira !2025-03-28750110|Scira (Formerly MiniPerplx) is a minimalistic AI-powered search engine that helps you find information on the internet. Powered by Vercel AI SDK! Search with models like Grok 2.0.| | 623|Portkey-AI/gateway !2025-03-28749416|A Blazing Fast AI Gateway. Route to 100+ LLMs with 1 fast & friendly API.| | 624|web-infra-dev/midscene !2025-03-28748729|An AI-powered automation SDK can control the page, perform assertions, and extract data in JSON format using natural language.| | 625|zilliztech/GPTCache !2025-03-2874801 |GPTCache is a library for creating semantic cache to store responses from LLM queries.| | 626|niedev/RTranslator !2025-03-2874742|RTranslator is the world's first open source real-time translation app.| |!green-up-arrow.svg 627|roboflow/notebooks !2025-03-2874666|Examples and tutorials on using SOTA computer vision models and techniques. Learn everything from old-school ResNet, through YOLO and object-detection transformers like DETR, to the latest models like Grounding DINO and SAM.| |!red-down-arrow 628|openlm-research/openllama !2025-03-2874652|OpenLLaMA, a permissively licensed open source reproduction of Meta AI’s LLaMA 7B trained on the RedPajama dataset| | 629|LiheYoung/Depth-Anything !2025-03-2874155|Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data| | 630|enso-org/enso !2025-03-2874040|Hybrid visual and textual functional programming.| | 631|bigcode-project/starcoder !2025-03-287401-1 |Home of StarCoder: fine-tuning & inference!| | 632|git-ecosystem/git-credential-manager !2025-03-2873975|Secure, cross-platform Git credential storage with authentication to GitHub, Azure Repos, and other popular Git hosting services.| | 633|OpenGVLab/InternVL !2025-03-2873634|[CVPR 2024 Oral] InternVL Family: A Pioneering Open-Source Alternative to GPT-4V. 接近GPT-4V表现的可商用开源模型| | 634|WooooDyy/LLM-Agent-Paper-List !2025-03-2873551|The paper list of the 86-page paper "The Rise and Potential of Large Language Model Based Agents: A Survey" by Zhiheng Xi et al.| | 635|lencx/Noi !2025-03-2873157|🦄 AI + Tools + Plugins + Community| | 636|udlbook/udlbook !2025-03-2873075|Understanding Deep Learning - Simon J.D. Prince| | 637|OpenBMB/MiniCPM !2025-03-2872841|MiniCPM-2B: An end-side LLM outperforms Llama2-13B.| | 638|jaywalnut310/vits !2025-03-2872815 |VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech| | 639|xorbitsai/inference !2025-03-28727528|Replace OpenAI GPT with another LLM in your app by changing a single line of code. Xinference gives you the freedom to use any LLM you need. With Xinference, you're empowered to run inference with any open-source language models, speech recognition models, and multimodal models, whether in the cloud, on-premises, or even on your laptop.| | 640|PWhiddy/PokemonRedExperiments !2025-03-2872492|Playing Pokemon Red with Reinforcement Learning| | 641|Canner/WrenAI !2025-03-28723213|🤖 Open-source AI Agent that empowers data-driven teams to chat with their data to generate Text-to-SQL, charts, spreadsheets, reports, and BI. 📈📊📋🧑‍💻| | 642|miurla/morphic !2025-03-2872258|An AI-powered answer engine with a generative UI| | 643|ml-explore/mlx-examples !2025-03-2872168|Examples in the MLX framework| | 644|PKU-YuanGroup/ChatLaw !2025-03-2872010|Chinese Legal Large Model| | 645|NVIDIA/cutlass !2025-03-2871883|CUDA Templates for Linear Algebra Subroutines| | 646|FoundationVision/VAR !2025-03-28717444|[GPT beats diffusion🔥] [scaling laws in visual generation📈] Official impl. of "Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction"| | 647|ymcui/Chinese-LLaMA-Alpaca-2 !2025-03-2871561|Chinese LLaMA-2 & Alpaca-2 LLMs| | 648|nadermx/backgroundremover !2025-03-2871514 |Background Remover lets you Remove Background from images and video using AI with a simple command line interface that is free and open source.| | 649|onuratakan/gpt-computer-assistant !2025-03-28714514|gpt-4o for windows, macos and ubuntu| | 650|graviraja/MLOps-Basics !2025-03-2871326|| | 651|Future-House/paper-qa !2025-03-287118-1|High accuracy RAG for answering questions from scientific documents with citations| | 652|open-mmlab/mmagic !2025-03-2871102 |OpenMMLab Multimodal Advanced, Generative, and Intelligent Creation Toolbox| | 653|bhaskatripathi/pdfGPT !2025-03-2870941 |PDF GPT allows you to chat with the contents of your PDF file by using GPT capabilities. The only open source solution to turn your pdf files in a chatbot!| | 654|ollama/ollama-python !2025-03-28709117|Ollama Python library| | 655|facebookresearch/DiT !2025-03-2870376|Official PyTorch Implementation of "Scalable Diffusion Models with Transformers"| | 656|geekyutao/Inpaint-Anything !2025-03-2870262 |Inpaint anything using Segment Anything and inpainting models.| | 657|AbdullahAlfaraj/Auto-Photoshop-StableDiffusion-Plugin !2025-03-2870160 |A user-friendly plug-in that makes it easy to generate stable diffusion images inside Photoshop using Automatic1111-sd-webui as a backend.| | 658|apple/corenet !2025-03-2869990|CoreNet: A library for training deep neural networks| | 659|openstatusHQ/openstatus !2025-03-2869926|🏓 The open-source synthetic monitoring platform 🏓| | 660|weaviate/Verba !2025-03-2869772|Retrieval Augmented Generation (RAG) chatbot powered by Weaviate| | 661|meshery/meshery !2025-03-2869630|Meshery, the cloud native manager| | 662|OpenTalker/video-retalking !2025-03-2869530|[SIGGRAPH Asia 2022] VideoReTalking: Audio-based Lip Synchronization for Talking Head Video Editing In the Wild| | 663|digitalinnovationone/dio-lab-open-source !2025-03-28689013|Repositório do lab "Contribuindo em um Projeto Open Source no GitHub" da Digital Innovation One.| | 664|jianchang512/ChatTTS-ui !2025-03-2868842|一个简单的本地网页界面，直接使用ChatTTS将文字合成为语音，同时支持对外提供API接口。| | 665|patchy631/ai-engineering-hub !2025-03-28686434|In-depth tutorials on LLMs, RAGs and real-world AI agent applications.| | 666|gunnarmorling/1brc !2025-03-2868512|1️⃣🐝🏎️ The One Billion Row Challenge -- A fun exploration of how quickly 1B rows from a text file can be aggregated with Java| | 667|Azure-Samples/azure-search-openai-demo !2025-03-2868482 |A sample app for the Retrieval-Augmented Generation pattern running in Azure, using Azure Cognitive Search for retrieval and Azure OpenAI large language models to power ChatGPT-style and Q&A experiences.| | 668|mit-han-lab/streaming-llm !2025-03-2868382|Efficient Streaming Language Models with Attention Sinks| | 669|InternLM/InternLM !2025-03-2868352|InternLM has open-sourced a 7 billion parameter base model, a chat model tailored for practical scenarios and the training system.| | 670|dependency-check/DependencyCheck !2025-03-2868191|OWASP dependency-check is a software composition analysis utility that detects publicly disclosed vulnerabilities in application dependencies.| | 671|Soulter/AstrBot !2025-03-28678643|✨易上手的多平台 LLM 聊天机器人及开发框架✨。支持 QQ、QQ频道、Telegram、微信平台(Gewechat, 企业微信)、内置 Web Chat，OpenAI GPT、DeepSeek、Ollama、Llama、GLM、Gemini、OneAPI、LLMTuner，支持 LLM Agent 插件开发，可视化面板。一键部署。支持 Dify 工作流、代码执行器、Whisper 语音转文字。| | 672|react-native-webview/react-native-webview !2025-03-2867792|React Native Cross-Platform WebView| | 673|modelscope/agentscope !2025-03-28676916|Start building LLM-empowered multi-agent applications in an easier way.| | 674|mylxsw/aidea !2025-03-2867381|AIdea is a versatile app that supports GPT and domestic large language models，also supports "Stable Diffusion" text-to-image generation, image-to-image generation, SDXL 1.0, super-resolution, and image colorization| | 675|langchain-ai/ollama-deep-researcher !2025-03-28668635|Fully local web research and report writing assistant| | 676|threestudio-project/threestudio !2025-03-2866653|A unified framework for 3D content generation.| | 677|gaomingqi/Track-Anything !2025-03-2866631 |A flexible and interactive tool for video object tracking and segmentation, based on Segment Anything, XMem, and E2FGVI.| | 678|spdustin/ChatGPT-AutoExpert !2025-03-2866570|🚀🧠💬 Supercharged Custom Instructions for ChatGPT (non-coding) and ChatGPT Advanced Data Analysis (coding).| | 679|HariSekhon/DevOps-Bash-tools !2025-03-2866463|1000+ DevOps Bash Scripts - AWS, GCP, Kubernetes, Docker, CI/CD, APIs, SQL, PostgreSQL, MySQL, Hive, Impala, Kafka, Hadoop, Jenkins, GitHub, GitLab, BitBucket, Azure DevOps, TeamCity, Spotify, MP3, LDAP, Code/Build Linting, pkg mgmt for Linux, Mac, Python, Perl, Ruby, NodeJS, Golang, Advanced dotfiles: .bashrc, .vimrc, .gitconfig, .screenrc, tmux..| | 680|modelscope/swift !2025-03-28661530|ms-swift: Use PEFT or Full-parameter to finetune 200+ LLMs or 15+ MLLMs| | 681|langchain-ai/opengpts !2025-03-2866080|This is an open source effort to create a similar experience to OpenAI's GPTs and Assistants API| | 682| yihong0618/xiaogpt !2025-03-2865131 | Play ChatGPT with xiaomi ai speaker | | 683| civitai/civitai !2025-03-2865111 | Build a platform where people can share their stable diffusion models | | 684|KoljaB/RealtimeSTT !2025-03-28649513|A robust, efficient, low-latency speech-to-text library with advanced voice activity detection, wake word activation and instant transcription.| | 685|qunash/chatgpt-advanced !2025-03-2864910 | A browser extension that augments your ChatGPT prompts with web results.| | 686|Licoy/ChatGPT-Midjourney !2025-03-2864850|🎨 Own your own ChatGPT+Midjourney web service with one click| | 687|friuns2/BlackFriday-GPTs-Prompts !2025-03-2864744|List of free GPTs that doesn't require plus subscription| | 688|PixarAnimationStudios/OpenUSD !2025-03-2864700|Universal Scene Description| | 689|linyiLYi/street-fighter-ai !2025-03-2864630 |This is an AI agent for Street Fighter II Champion Edition.| | 690|run-llama/rags !2025-03-2864380|Build ChatGPT over your data, all with natural language| | 691|frdel/agent-zero !2025-03-2864154|Agent Zero AI framework| | 692|microsoft/DeepSpeedExamples !2025-03-2863911 |Example models using DeepSpeed| | 693|k8sgpt-ai/k8sgpt !2025-03-2863882|Giving Kubernetes Superpowers to everyone| | 694|open-metadata/OpenMetadata !2025-03-2863514|OpenMetadata is a unified platform for discovery, observability, and governance powered by a central metadata repository, in-depth lineage, and seamless team collaboration.| | 695|google/gemma.cpp !2025-03-2863163|lightweight, standalone C++ inference engine for Google's Gemma models.| | 696|RayVentura/ShortGPT !2025-03-286314-1|🚀🎬 ShortGPT - An experimental AI framework for automated short/video content creation. Enables creators to rapidly produce, manage, and deliver content using AI and automation.| | 697|openai/consistencymodels !2025-03-2862940 |Official repo for consistency models.| | 698|yangjianxin1/Firefly !2025-03-2862924|Firefly: Chinese conversational large language model (full-scale fine-tuning + QLoRA), supporting fine-tuning of Llma2, Llama, Baichuan, InternLM, Ziya, Bloom, and other large models| | 699|enricoros/big-AGI !2025-03-2862665|Generative AI suite powered by state-of-the-art models and providing advanced AI/AGI functions. It features AI personas, AGI functions, multi-model chats, text-to-image, voice, response streaming, code highlighting and execution, PDF import, presets for developers, much more. Deploy on-prem or in the cloud.| | 700|aptos-labs/aptos-core !2025-03-2862633|Aptos is a layer 1 blockchain built to support the widespread use of blockchain through better technology and user experience.| | 701|wenda-LLM/wenda !2025-03-286262-1 |Wenda: An LLM invocation platform. Its objective is to achieve efficient content generation tailored to specific environments while considering the limited computing resources of individuals and small businesses, as well as knowledge security and privacy concerns| | 702|Project-MONAI/MONAI !2025-03-2862603|AI Toolkit for Healthcare Imaging| | 703|HVision-NKU/StoryDiffusion !2025-03-2862470|Create Magic Story!| | 704|deepseek-ai/DeepSeek-LLM !2025-03-2862463|DeepSeek LLM: Let there be answers| | 705|Tohrusky/Final2x !2025-03-2862393|2^x Image Super-Resolution| | 706|OpenSPG/KAG !2025-03-28619611|KAG is a logical form-guided reasoning and retrieval framework based on OpenSPG engine and LLMs. It is used to build logical reasoning and factual Q&A solutions for professional domain knowledge bases. It can effectively overcome the shortcomings of the traditional RAG vector similarity calculation model.| | 707|Moonvy/OpenPromptStudio !2025-03-2861861 |AIGC Hint Word Visualization Editor| | 708|levihsu/OOTDiffusion !2025-03-2861761|Official implementation of OOTDiffusion| | 709|tmc/langchaingo !2025-03-2861729|LangChain for Go, the easiest way to write LLM-based programs in Go| | 710|vladmandic/automatic !2025-03-2861374|SD.Next: Advanced Implementation of Stable Diffusion and other Diffusion-based generative image models| | 711|clovaai/donut !2025-03-2861231 |Official Implementation of OCR-free Document Understanding Transformer (Donut) and Synthetic Document Generator (SynthDoG), ECCV 2022| | 712|Shaunwei/RealChar !2025-03-286121-1|🎙️🤖Create, Customize and Talk to your AI Character/Companion in Realtime(All in One Codebase!). Have a natural seamless conversation with AI everywhere(mobile, web and terminal) using LLM OpenAI GPT3.5/4, Anthropic Claude2, Chroma Vector DB, Whisper Speech2Text, ElevenLabs Text2Speech🎙️🤖| | 713|microsoft/TinyTroupe !2025-03-2861142|LLM-powered multiagent persona simulation for imagination enhancement and business insights.| | 714| rustformers/llm !2025-03-2861010 | Run inference for Large Language Models on CPU, with Rust| | 715|firebase/firebase-ios-sdk !2025-03-2860950|Firebase SDK for Apple App Development| | 716|vespa-engine/vespa !2025-03-2860824|The open big data serving engine. https://vespa.ai| | 717|n4ze3m/page-assist !2025-03-28607610|Use your locally running AI models to assist you in your web browsing| | 718|Dooy/chatgpt-web-midjourney-proxy !2025-03-2860646|chatgpt web, midjourney, gpts,tts, whisper 一套ui全搞定| | 719|ethereum-optimism/optimism !2025-03-2860213|Optimism is Ethereum, scaled.| | 720|sczhou/ProPainter !2025-03-2859971|[ICCV 2023] ProPainter: Improving Propagation and Transformer for Video Inpainting| | 721|MineDojo/Voyager !2025-03-2859951 |An Open-Ended Embodied Agent with Large Language Models| | 722|lavague-ai/LaVague !2025-03-2859800|Automate automation with Large Action Model framework| | 723|SevaSk/ecoute !2025-03-2859770 |Ecoute is a live transcription tool that provides real-time transcripts for both the user's microphone input (You) and the user's speakers output (Speaker) in a textbox. It also generates a suggested response using OpenAI's GPT-3.5 for the user to say based on the live transcription of the conversation.| | 724|google/mesop !2025-03-2859661|| | 725|pengxiao-song/LaWGPT !2025-03-2859542 |Repo for LaWGPT, Chinese-Llama tuned with Chinese Legal knowledge| | 726|fr0gger/Awesome-GPT-Agents !2025-03-2859434|A curated list of GPT agents for cybersecurity| | 727|google-deepmind/graphcast !2025-03-2859412|| | 728|comet-ml/opik !2025-03-28594126|Open-source end-to-end LLM Development Platform| | 729|SciPhi-AI/R2R !2025-03-28594033|A framework for rapid development and deployment of production-ready RAG systems| | 730|SkalskiP/courses !2025-03-2859272 |This repository is a curated collection of links to various courses and resources about Artificial Intelligence (AI)| | 731|QuivrHQ/MegaParse !2025-03-2859122|File Parser optimised for LLM Ingestion with no loss 🧠 Parse PDFs, Docx, PPTx in a format that is ideal for LLMs.| | 732|pytorch-labs/gpt-fast !2025-03-2858971|Simple and efficient pytorch-native transformer text generation in !2025-03-2858886|Curated list of chatgpt prompts from the top-rated GPTs in the GPTs Store. Prompt Engineering, prompt attack & prompt protect. Advanced Prompt Engineering papers.| | 734|nilsherzig/LLocalSearch !2025-03-2858852|LLocalSearch is a completely locally running search aggregator using LLM Agents. The user can ask a question and the system will use a chain of LLMs to find the answer. The user can see the progress of the agents and the final answer. No OpenAI or Google API keys are needed.| | 735|kuafuai/DevOpsGPT !2025-03-285874-2|Multi agent system for AI-driven software development. Convert natural language requirements into working software. Supports any development language and extends the existing base code.| | 736|myshell-ai/MeloTTS !2025-03-2858486|High-quality multi-lingual text-to-speech library by MyShell.ai. Support English, Spanish, French, Chinese, Japanese and Korean.| | 737|OpenGVLab/LLaMA-Adapter !2025-03-2858421 |Fine-tuning LLaMA to follow Instructions within 1 Hour and 1.2M Parameters| | 738|volcengine/verl !2025-03-28582563|veRL: Volcano Engine Reinforcement Learning for LLM| | 739|a16z-infra/companion-app !2025-03-2858171|AI companions with memory: a lightweight stack to create and host your own AI companions| | 740|HumanAIGC/OutfitAnyone !2025-03-285816-1|Outfit Anyone: Ultra-high quality virtual try-on for Any Clothing and Any Person| | 741|josStorer/RWKV-Runner !2025-03-2857472|A RWKV management and startup tool, full automation, only 8MB. And provides an interface compatible with the OpenAI API. RWKV is a large language model that is fully open source and available for commercial use.| | 742|648540858/wvp-GB28181-pro !2025-03-2857414|WEB VIDEO PLATFORM是一个基于GB28181-2016标准实现的网络视频平台，支持NAT穿透，支持海康、大华、宇视等品牌的IPC、NVR、DVR接入。支持国标级联，支持rtsp/rtmp等视频流转发到国标平台，支持rtsp/rtmp等推流转发到国标平台。| | 743|ToonCrafter/ToonCrafter !2025-03-2857345|a research paper for generative cartoon interpolation| | 744|PawanOsman/ChatGPT !2025-03-2857191|OpenAI API Free Reverse Proxy| | 745|apache/hudi !2025-03-2857091|Upserts, Deletes And Incremental Processing on Big Data.| | 746| nsarrazin/serge !2025-03-2857081 | A web interface for chatting with Alpaca through llama.cpp. Fully dockerized, with an easy to use API| | 747|homanp/superagent !2025-03-2857021|🥷 Superagent - Build, deploy, and manage LLM-powered agents| | 748|ramonvc/freegpt-webui !2025-03-2856910|GPT 3.5/4 with a Chat Web UI. No API key is required.| | 749|baichuan-inc/baichuan-7B !2025-03-2856901|A large-scale 7B pretraining language model developed by BaiChuan-Inc.| | 750|Azure/azure-sdk-for-net !2025-03-2856792|This repository is for active development of the Azure SDK for .NET. For consumers of the SDK we recommend visiting our public developer docs at https://learn.microsoft.com/dotnet/azure/ or our versioned developer docs at https://azure.github.io/azure-sdk-for-net.| | 751|mnotgod96/AppAgent !2025-03-2856643|AppAgent: Multimodal Agents as Smartphone Users, an LLM-based multimodal agent framework designed to operate smartphone apps.| | 752|microsoft/TaskWeaver !2025-03-2856243|A code-first agent framework for seamlessly planning and executing data analytics tasks.| | 753| yetone/bob-plugin-openai-translator !2025-03-285600-1 | A Bob Plugin base ChatGPT API | | 754|PrefectHQ/marvin !2025-03-2855840 |A batteries-included library for building AI-powered software| | 755|microsoft/promptbase !2025-03-2855832|All things prompt engineering| | 756|fullstackhero/dotnet-starter-kit !2025-03-2855560|Production Grade Cloud-Ready .NET 8 Starter Kit (Web API + Blazor Client) with Multitenancy Support, and Clean/Modular Architecture that saves roughly 200+ Development Hours! All Batteries Included.| | 757|deepseek-ai/DeepSeek-Coder-V2 !2025-03-2855435|DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence| | 758|aiwaves-cn/agents !2025-03-2855391|An Open-source Framework for Autonomous Language Agents| | 759|microsoft/Mastering-GitHub-Copilot-for-Paired-Programming !2025-03-2855158|A 6 Lesson course teaching everything you need to know about harnessing GitHub Copilot and an AI Paired Programing resource.| | 760|allenai/OLMo !2025-03-2854506|Modeling, training, eval, and inference code for OLMo| | 761|apify/crawlee-python !2025-03-2854493|Crawlee—A web scraping and browser automation library for Python to build reliable crawlers. Extract data for AI, LLMs, RAG, or GPTs. Download HTML, PDF, JPG, PNG, and other files from websites. Works with BeautifulSoup, Playwright, and raw HTTP. Both headful and headless mode. With proxy rotation.| | 762|k2-fsa/sherpa-onnx !2025-03-28541520|Speech-to-text, text-to-speech, and speaker recongition using next-gen Kaldi with onnxruntime without Internet connection. Support embedded systems, Android, iOS, Raspberry Pi, RISC-V, x86_64 servers, websocket server/client, C/C++, Python, Kotlin, C#, Go, NodeJS, Java, Swift| | 763|TEN-framework/TEN-Agent !2025-03-28541411|TEN Agent is a realtime conversational AI agent powered by TEN. It seamlessly integrates the OpenAI Realtime API, RTC capabilities, and advanced features like weather updates, web search, computer vision, and Retrieval-Augmented Generation (RAG).| | 764|google/gemmapytorch !2025-03-2854010|The official PyTorch implementation of Google's Gemma models| | 765|snakers4/silero-vad !2025-03-2853858|Silero VAD: pre-trained enterprise-grade Voice Activity Detector| | 766|livekit/agents !2025-03-2853836|Build real-time multimodal AI applications 🤖🎙️📹| | 767|pipecat-ai/pipecat !2025-03-28537811|Open Source framework for voice and multimodal conversational AI| | 768|EricLBuehler/mistral.rs !2025-03-28536324|Blazingly fast LLM inference.| | 769|asg017/sqlite-vec !2025-03-28535810|Work-in-progress vector search SQLite extension that runs anywhere.| | 770|albertan017/LLM4Decompile !2025-03-2853563|Reverse Engineering: Decompiling Binary Code with Large Language Models| | 771|Permify/permify !2025-03-2853235|An open-source authorization as a service inspired by Google Zanzibar, designed to build and manage fine-grained and scalable authorization systems for any application.| | 772|imoneoi/openchat !2025-03-2853171|OpenChat: Advancing Open-source Language Models with Imperfect Data| | 773|mosaicml/composer !2025-03-2853140|Train neural networks up to 7x faster| | 774|dsdanielpark/Bard-API !2025-03-285277-1 |The python package that returns a response of Google Bard through API.| | 775|lxfater/inpaint-web !2025-03-2852552|A free and open-source inpainting & image-upscaling tool powered by webgpu and wasm on the browser。| | 776|leanprover/lean4 !2025-03-2852441|Lean 4 programming language and theorem prover| | 777|AILab-CVC/YOLO-World !2025-03-2852415|Real-Time Open-Vocabulary Object Detection| | 778|openchatai/OpenChat !2025-03-2852260 |Run and create custom ChatGPT-like bots with OpenChat, embed and share these bots anywhere, the open-source chatbot console.| | 779|mufeedvh/code2prompt !2025-03-28519414|A CLI tool to convert your codebase into a single LLM prompt with source tree, prompt templating, and token counting.| | 780|biobootloader/wolverine !2025-03-2851700 |Automatically repair python scripts through GPT-4 to give them regenerative abilities.| | 781|huggingface/parler-tts !2025-03-2851671|Inference and training library for high-quality TTS models.| | 782|Akegarasu/lora-scripts !2025-03-2851308 |LoRA training scripts use kohya-ss's trainer, for diffusion model.| | 783|openchatai/OpenCopilot !2025-03-285128-3|🤖 🔥 Let your users chat with your product features and execute things by text - open source Shopify sidekick| | 784|e2b-dev/fragments !2025-03-2851228|Open-source Next.js template for building apps that are fully generated by AI. By E2B.| | 785|microsoft/SynapseML !2025-03-2851132|Simple and Distributed Machine Learning| | 786|aigc-apps/sd-webui-EasyPhoto !2025-03-285108-1|📷 EasyPhoto | | 787|ChaoningZhang/MobileSAM !2025-03-2850944|This is the official code for Faster Segment Anything (MobileSAM) project that makes SAM lightweight| | 788|huggingface/alignment-handbook !2025-03-2850932|Robust recipes for to align language models with human and AI preferences| | 789|alpkeskin/mosint !2025-03-2850920|An automated e-mail OSINT tool| | 790|TaskingAI/TaskingAI !2025-03-2850891|The open source platform for AI-native application development.| | 791|lipku/metahuman-stream !2025-03-28507615|Real time interactive streaming digital human| | 792|OpenInterpreter/01 !2025-03-2850530|The open-source language model computer| | 793|open-compass/opencompass !2025-03-28505111|OpenCompass is an LLM evaluation platform, supporting a wide range of models (InternLM2,GPT-4,LLaMa2, Qwen,GLM, Claude, etc) over 100+ datasets.| | 794|xxlong0/Wonder3D !2025-03-2850491|A cross-domain diffusion model for 3D reconstruction from a single image| | 795|pytorch/torchtune !2025-03-2850342|A Native-PyTorch Library for LLM Fine-tuning| | 796|SuperDuperDB/superduperdb !2025-03-2850192|🔮 SuperDuperDB: Bring AI to your database: Integrate, train and manage any AI models and APIs directly with your database and your data.| | 797|WhiskeySockets/Baileys !2025-03-2850057|Lightweight full-featured typescript/javascript WhatsApp Web API| | 798| mpociot/chatgpt-vscode !2025-03-2849890 | A VSCode extension that allows you to use ChatGPT | | 799|OpenGVLab/DragGAN !2025-03-2849880|Unofficial Implementation of DragGAN - "Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold" （DragGAN 全功能实现，在线Demo，本地部署试用，代码、模型已全部开源，支持Windows, macOS, Linux）| | 800|microsoft/LLMLingua !2025-03-2849824|To speed up LLMs' inference and enhance LLM's perceive of key information, compress the prompt and KV-Cache, which achieves up to 20x compression with minimal performance loss.| | 801|Zipstack/unstract !2025-03-2849745|No-code LLM Platform to launch APIs and ETL Pipelines to structure unstructured documents| | 802|OpenBMB/ToolBench !2025-03-2849621|An open platform for training, serving, and evaluating large language model for tool learning.| | 803|Fanghua-Yu/SUPIR !2025-03-2849593|SUPIR aims at developing Practical Algorithms for Photo-Realistic Image Restoration In the Wild| | 804|GaiaNet-AI/gaianet-node !2025-03-2849360|Install and run your own AI agent service| | 805|qodo-ai/qodo-cover !2025-03-284922-1|Qodo-Cover: An AI-Powered Tool for Automated Test Generation and Code Coverage Enhancement! 💻🤖🧪🐞| | 806|Zejun-Yang/AniPortrait !2025-03-2849042|AniPortrait: Audio-Driven Synthesis of Photorealistic Portrait Animation| | 807|lvwzhen/law-cn-ai !2025-03-2848901 |⚖️ AI Legal Assistant| | 808|developersdigest/llm-answer-engine !2025-03-2848740|Build a Perplexity-Inspired Answer Engine Using Next.js, Groq, Mixtral, Langchain, OpenAI, Brave & Serper| | 809|Plachtaa/VITS-fast-fine-tuning !2025-03-2848640|This repo is a pipeline of VITS finetuning for fast speaker adaptation TTS, and many-to-many voice conversion| | 810|espeak-ng/espeak-ng !2025-03-2848601|eSpeak NG is an open source speech synthesizer that supports more than hundred languages and accents.| | 811|ant-research/CoDeF !2025-03-2848581|[CVPR'24 Highlight] Official PyTorch implementation of CoDeF: Content Deformation Fields for Temporally Consistent Video Processing| | 812|deepseek-ai/DeepSeek-V2 !2025-03-2848512|| | 813|XRPLF/rippled !2025-03-2848210|Decentralized cryptocurrency blockchain daemon implementing the XRP Ledger protocol in C++| | 814|AutoMQ/automq !2025-03-28478721|AutoMQ is a cloud-first alternative to Kafka by decoupling durability to S3 and EBS. 10x cost-effective. Autoscale in seconds. Single-digit ms latency.| | 815|AILab-CVC/VideoCrafter !2025-03-2847800|VideoCrafter1: Open Diffusion Models for High-Quality Video Generation| | 816|nautechsystems/nautilustrader !2025-03-2847702|A high-performance algorithmic trading platform and event-driven backtester| | 817|kyegomez/swarms !2025-03-2847563|The Enterprise-Grade Production-Ready Multi-Agent Orchestration Framework Join our Community: https://discord.com/servers/agora-999382051935506503| | 818|Deci-AI/super-gradients !2025-03-2847310 |Easily train or fine-tune SOTA computer vision models with one open source training library. The home of Yolo-NAS.| | 819|QwenLM/Qwen2.5-Coder !2025-03-2847236|Qwen2.5-Coder is the code version of Qwen2.5, the large language model series developed by Qwen team, Alibaba Cloud.| | 820|SCIR-HI/Huatuo-Llama-Med-Chinese !2025-03-2847191 |Repo for HuaTuo (华驼), Llama-7B tuned with Chinese medical knowledge| | 821|togethercomputer/RedPajama-Data !2025-03-2846841 |code for preparing large datasets for training large language models| | 822|mishushakov/llm-scraper !2025-03-2846704|Turn any webpage into structured data using LLMs| | 823|1rgs/jsonformer !2025-03-2846663 |A Bulletproof Way to Generate Structured JSON from Language Models| | 824|anti-work/shortest !2025-03-2846565|QA via natural language AI tests| | 825|dnhkng/GlaDOS !2025-03-2846510|This is the Personality Core for GLaDOS, the first steps towards a real-life implementation of the AI from the Portal series by Valve.| | 826|Nukem9/dlssg-to-fsr3 !2025-03-2846380|Adds AMD FSR3 Frame Generation to games by replacing Nvidia DLSS-G Frame Generation (nvngx_dlssg).| | 827|BuilderIO/ai-shell !2025-03-2846373 |A CLI that converts natural language to shell commands.| | 828|facebookincubator/AITemplate !2025-03-2846220 |AITemplate is a Python framework which renders neural network into high performance CUDA/HIP C++ code. Specialized for FP16 TensorCore (NVIDIA GPU) and MatrixCore (AMD GPU) inference.| | 829|terraform-aws-modules/terraform-aws-eks !2025-03-2846030|Terraform module to create AWS Elastic Kubernetes (EKS) resources 🇺🇦| | 830|timescale/pgai !2025-03-2845915|A suite of tools to develop RAG, semantic search, and other AI applications more easily with PostgreSQL| | 831|awslabs/multi-agent-orchestrator !2025-03-2845788|Flexible and powerful framework for managing multiple AI agents and handling complex conversations| | 832|sanchit-gandhi/whisper-jax !2025-03-2845771 |Optimised JAX code for OpenAI's Whisper Model, largely built on the Hugging Face Transformers Whisper implementation| | 833|NVIDIA/NeMo-Guardrails !2025-03-2845755|NeMo Guardrails is an open-source toolkit for easily adding programmable guardrails to LLM-based conversational systems.| | 834|PathOfBuildingCommunity/PathOfBuilding !2025-03-2845480|Offline build planner for Path of Exile.| | 835|UX-Decoder/Segment-Everything-Everywhere-All-At-Once !2025-03-2845412 |Official implementation of the paper "Segment Everything Everywhere All at Once"| | 836|build-trust/ockam !2025-03-2845171|Orchestrate end-to-end encryption, cryptographic identities, mutual authentication, and authorization policies between distributed applications – at massive scale.| | 837|google-research/timesfm !2025-03-2845135|TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.| | 838|luosiallen/latent-consistency-model !2025-03-2844842|Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference| | 839|NVlabs/neuralangelo !2025-03-2844740|Official implementation of "Neuralangelo: High-Fidelity Neural Surface Reconstruction" (CVPR 2023)| | 840|kyegomez/tree-of-thoughts !2025-03-2844720 |Plug in and Play Implementation of Tree of Thoughts: Deliberate Problem Solving with Large Language Models that Elevates Model Reasoning by atleast 70%| | 841|sjvasquez/handwriting-synthesis !2025-03-2844720 |Handwriting Synthesis with RNNs ✏️| | 842| madawei2699/myGPTReader !2025-03-2844420 | A slack bot that can read any webpage, ebook or document and summarize it with chatGPT | | 843|OpenBMB/AgentVerse !2025-03-2844413|🤖 AgentVerse 🪐 provides a flexible framework that simplifies the process of building custom multi-agent environments for large language models (LLMs).| | 844|argmaxinc/WhisperKit !2025-03-2844395|Swift native speech recognition on-device for iOS and macOS applications.| | 845|landing-ai/vision-agent !2025-03-2844346|Vision agent| | 846|InternLM/xtuner !2025-03-2844273|An efficient, flexible and full-featured toolkit for fine-tuning large models (InternLM, Llama, Baichuan, Qwen, ChatGLM)| | 847|google-deepmind/alphageometry !2025-03-284421-1|Solving Olympiad Geometry without Human Demonstrations| | 848|ostris/ai-toolkit !2025-03-2844093|Various AI scripts. Mostly Stable Diffusion stuff.| | 849|LLM-Red-Team/kimi-free-api !2025-03-2844004|🚀 KIMI AI 长文本大模型白嫖服务，支持高速流式输出、联网搜索、长文档解读、图像解析、多轮对话，零配置部署，多路token支持，自动清理会话痕迹。| | 850|argilla-io/argilla !2025-03-2843991|Argilla is a collaboration platform for AI engineers and domain experts that require high-quality outputs, full data ownership, and overall efficiency.| | 851|spring-projects/spring-ai !2025-03-28438419|An Application Framework for AI Engineering| | 852|alibaba-damo-academy/FunClip !2025-03-2843555|Open-source, accurate and easy-to-use video clipping tool, LLM based AI clipping intergrated | | 853|yisol/IDM-VTON !2025-03-2843541|IDM-VTON : Improving Diffusion Models for Authentic Virtual Try-on in the Wild| | 854|fchollet/ARC-AGI !2025-03-2843368|The Abstraction and Reasoning Corpus| | 855|MahmoudAshraf97/whisper-diarization !2025-03-2843064|Automatic Speech Recognition with Speaker Diarization based on OpenAI Whisper| | 856|Speykious/cve-rs !2025-03-2843047|Blazingly 🔥 fast 🚀 memory vulnerabilities, written in 100% safe Rust. 🦀| | 857|Blealtan/efficient-kan !2025-03-2842770|An efficient pure-PyTorch implementation of Kolmogorov-Arnold Network (KAN).| | 858|smol-ai/GodMode !2025-03-284249-1|AI Chat Browser: Fast, Full webapp access to ChatGPT / Claude / Bard / Bing / Llama2! I use this 20 times a day.| | 859|openai/plugins-quickstart !2025-03-284235-4 |Get a ChatGPT plugin up and running in under 5 minutes!| | 860|Doriandarko/maestro !2025-03-2842260|A framework for Claude Opus to intelligently orchestrate subagents.| | 861|philz1337x/clarity-upscaler !2025-03-2842204|Clarity-Upscaler: Reimagined image upscaling for everyone| | 862|facebookresearch/co-tracker !2025-03-2842142|CoTracker is a model for tracking any point (pixel) on a video.| | 863|xlang-ai/OpenAgents !2025-03-2842031|OpenAgents: An Open Platform for Language Agents in the Wild| | 864|alibaba/higress !2025-03-28419514|🤖 AI Gateway | | 865|ray-project/llm-numbers !2025-03-2841920 |Numbers every LLM developer should know| | 866|fudan-generative-vision/champ !2025-03-2841820|Champ: Controllable and Consistent Human Image Animation with 3D Parametric Guidance| | 867|NVIDIA/garak !2025-03-2841795|the LLM vulnerability scanner| | 868|leetcode-mafia/cheetah !2025-03-2841740 |Whisper & GPT-based app for passing remote SWE interviews| | 869|ragapp/ragapp !2025-03-2841710|The easiest way to use Agentic RAG in any enterprise| | 870|collabora/WhisperSpeech !2025-03-2841692|An Open Source text-to-speech system built by inverting Whisper.| | 871|Facico/Chinese-Vicuna !2025-03-2841520 |Chinese-Vicuna: A Chinese Instruction-following LLaMA-based Model| | 872|openai/grok !2025-03-2841381|| | 873|CrazyBoyM/llama3-Chinese-chat !2025-03-2841361|Llama3 Chinese Repository with modified versions, and training and deployment resources| | 874|luban-agi/Awesome-AIGC-Tutorials !2025-03-2841301|Curated tutorials and resources for Large Language Models, AI Painting, and more.| | 875|damo-vilab/AnyDoor !2025-03-2841192|Official implementations for paper: Anydoor: zero-shot object-level image customization| | 876|raspberrypi/pico-sdk !2025-03-2841072|| | 877|mshumer/gpt-llm-trainer !2025-03-284097-1|| | 878|metavoiceio/metavoice-src !2025-03-284076-1|AI for human-level speech intelligence| | 879|intelowlproject/IntelOwl !2025-03-2840763|IntelOwl: manage your Threat Intelligence at scale| | 880|a16z-infra/ai-getting-started !2025-03-2840682|A Javascript AI getting started stack for weekend projects, including image/text models, vector stores, auth, and deployment configs| | 881|MarkFzp/mobile-aloha !2025-03-2840641|Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation| | 882| keijiro/AICommand !2025-03-2840380 | ChatGPT integration with Unity Editor | | 883|Tencent/HunyuanDiT !2025-03-2840214|Hunyuan-DiT : A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding| | 884|hengyoush/kyanos !2025-03-2840061|Visualize the time packets spend in the kernel, watch & analyze in command line.| | 885|agiresearch/AIOS !2025-03-2840045|AIOS: LLM Agent Operating System| | 886|truefoundry/cognita !2025-03-2839773|RAG (Retrieval Augmented Generation) Framework for building modular, open source applications for production by TrueFoundry| | 887|X-PLUG/MobileAgent !2025-03-2839557|Mobile-Agent: Autonomous Multi-Modal Mobile Device Agent with Visual Perception| | 888|jackMort/ChatGPT.nvim !2025-03-2839231|ChatGPT Neovim Plugin: Effortless Natural Language Generation with OpenAI's ChatGPT API| | 889|microsoft/RD-Agent !2025-03-28388422|Research and development (R&D) is crucial for the enhancement of industrial productivity, especially in the AI era, where the core aspects of R&D are mainly focused on data and models. We are committed to automate these high-value generic R&D processes through our open source R&D automation tool RD-Agent, which let AI drive data-driven AI.| | 890|Significant-Gravitas/Auto-GPT-Plugins !2025-03-283882-1 |Plugins for Auto-GPT| | 891|apple/ml-mgie !2025-03-2838770|| | 892|OpenDriveLab/UniAD !2025-03-2838727|[CVPR 2023 Best Paper] Planning-oriented Autonomous Driving| | 893|llSourcell/DoctorGPT !2025-03-2838640|DoctorGPT is an LLM that can pass the US Medical Licensing Exam. It works offline, it's cross-platform, & your health data stays private.| | 894|FlagAI-Open/FlagAI !2025-03-2838601|FlagAI (Fast LArge-scale General AI models) is a fast, easy-to-use and extensible toolkit for large-scale model.| | 895|krishnaik06/Roadmap-To-Learn-Generative-AI-In-2024 !2025-03-2838513|Roadmap To Learn Generative AI In 2024| | 896|SysCV/sam-hq !2025-03-2838491|Segment Anything in High Quality| | 897|google/security-research !2025-03-2838420|This project hosts security advisories and their accompanying proof-of-concepts related to research conducted at Google which impact non-Google owned code.| | 898|shroominic/codeinterpreter-api !2025-03-2838330|Open source implementation of the ChatGPT Code Interpreter 👾| | 899|Yonom/assistant-ui !2025-03-2838308|React Components for AI Chat 💬 🚀| | 900|nucleuscloud/neosync !2025-03-2838262|Open source data anonymization and synthetic data orchestration for developers. Create high fidelity synthetic data and sync it across your environments.| | 901|ravenscroftj/turbopilot !2025-03-2838230 |Turbopilot is an open source large-language-model based code completion engine that runs locally on CPU| | 902|NVlabs/Sana !2025-03-28380810|SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformer| | 903|huggingface/distil-whisper !2025-03-2838061|Distilled variant of Whisper for speech recognition. 6x faster, 50% smaller, within 1% word error rate.| | 904|Codium-ai/AlphaCodium !2025-03-2837971|code generation tool that surpasses most human competitors in CodeContests| | 905|fixie-ai/ultravox !2025-03-2837710|A fast multimodal LLM for real-time voice| | 906|unit-mesh/auto-dev !2025-03-28375715|🧙‍AutoDev: The AI-powered coding wizard with multilingual support 🌐, auto code generation 🏗️, and a helpful bug-slaying assistant 🐞! Customizable prompts 🎨 and a magic Auto Dev/Testing/Document/Agent feature 🧪 included! 🚀| | 907|Marker-Inc-Korea/AutoRAG !2025-03-2837432|AutoML tool for RAG| | 908|deepseek-ai/DeepSeek-VL !2025-03-283734-1|DeepSeek-VL: Towards Real-World Vision-Language Understanding| | 909|hiyouga/ChatGLM-Efficient-Tuning !2025-03-283692-1|Fine-tuning ChatGLM-6B with PEFT | | 910| Yue-Yang/ChatGPT-Siri !2025-03-2836921 | Shortcuts for Siri using ChatGPT API gpt-3.5-turbo model | | 911|0hq/WebGPT !2025-03-2836901 |Run GPT model on the browser with WebGPU. An implementation of GPT inference in less than ~2000 lines of vanilla Javascript.| | 912|cvg/LightGlue !2025-03-2836903|LightGlue: Local Feature Matching at Light Speed (ICCV 2023)| | 913|deanxv/coze-discord-proxy !2025-03-2836791|代理Discord-Bot对话Coze-Bot，实现API形式请求GPT4对话模型/微调模型| | 914|MervinPraison/PraisonAI !2025-03-2836764|PraisonAI application combines AutoGen and CrewAI or similar frameworks into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customisation, and efficient human-agent collaboration.| | 915|Ironclad/rivet !2025-03-2836345 |The open-source visual AI programming environment and TypeScript library| | 916|BasedHardware/OpenGlass !2025-03-2835851|Turn any glasses into AI-powered smart glasses| | 917|ricklamers/gpt-code-ui !2025-03-2835840 |An open source implementation of OpenAI's ChatGPT Code interpreter| | 918|whoiskatrin/chart-gpt !2025-03-2835830 |AI tool to build charts based on text input| | 919|github/CopilotForXcode !2025-03-2835788|Xcode extension for GitHub Copilot| | 920|hemansnation/God-Level-Data-Science-ML-Full-Stack !2025-03-2835570 |A collection of scientific methods, processes, algorithms, and systems to build stories & models. This roadmap contains 16 Chapters, whether you are a fresher in the field or an experienced professional who wants to transition into Data Science & AI| | 921|pytorch/torchchat !2025-03-2835461|Run PyTorch LLMs locally on servers, desktop and mobile| | 922| Kent0n-Li/ChatDoctor !2025-03-2835451 | A Medical Chat Model Fine-tuned on LLaMA Model using Medical Domain Knowledge | | 923|xtekky/chatgpt-clone !2025-03-283519-1 |ChatGPT interface with better UI| | 924|jupyterlab/jupyter-ai !2025-03-2835120|A generative AI extension for JupyterLab| | 925|pytorch/torchtitan !2025-03-2835064|A native PyTorch Library for large model training| | 926|minimaxir/simpleaichat !2025-03-2835031|Python package for easily interfacing with chat apps, with robust features and minimal code complexity.| | 927|srush/Tensor-Puzzles !2025-03-2834930|Solve puzzles. Improve your pytorch.| | 928|Helicone/helicone !2025-03-2834918|🧊 Open source LLM-Observability Platform for Developers. One-line integration for monitoring, metrics, evals, agent tracing, prompt management, playground, etc. Supports OpenAI SDK, Vercel AI SDK, Anthropic SDK, LiteLLM, LLamaIndex, LangChain, and more. 🍓 YC W23| | 929|run-llama/llama-hub !2025-03-2834740|A library of data loaders for LLMs made by the community -- to be used with LlamaIndex and/or LangChain| | 930|NExT-GPT/NExT-GPT !2025-03-2834700|Code and models for NExT-GPT: Any-to-Any Multimodal Large Language Model| | 931|souzatharsis/podcastfy !2025-03-2834661|An Open Source Python alternative to NotebookLM's podcast feature: Transforming Multimodal Content into Captivating Multilingual Audio Conversations with GenAI| | 932|Dataherald/dataherald !2025-03-2834450|Interact with your SQL database, Natural Language to SQL using LLMs| | 933|iryna-kondr/scikit-llm !2025-03-2834350 |Seamlessly integrate powerful language models like ChatGPT into scikit-learn for enhanced text analysis tasks.| | 934|Netflix/maestro !2025-03-2834230|Maestro: Netflix’s Workflow Orchestrator| | 935|CanadaHonk/porffor !2025-03-2833560|A from-scratch experimental AOT JS engine, written in JS| | 936|hustvl/Vim !2025-03-2833323|Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model| | 937|pashpashpash/vault-ai !2025-03-2833250 |OP Vault ChatGPT: Give ChatGPT long-term memory using the OP Stack (OpenAI + Pinecone Vector Database). Upload your own custom knowledge base files (PDF, txt, etc) using a simple React frontend.| | 938|tencentmusic/supersonic !2025-03-28330611|SuperSonic is the next-generation BI platform that integrates Chat BI (powered by LLM) and Headless BI (powered by semantic layer) paradigms.| | 939|billmei/every-chatgpt-gui !2025-03-2832981|Every front-end GUI client for ChatGPT| | 940|microsoft/torchgeo !2025-03-2832772|TorchGeo: datasets, samplers, transforms, and pre-trained models for geospatial data| | 941|LLMBook-zh/LLMBook-zh.github.io !2025-03-28326110|《大语言模型》作者：赵鑫，李军毅，周昆，唐天一，文继荣| | 942|dvlab-research/MiniGemini !2025-03-2832601|Official implementation for Mini-Gemini| | 943|rashadphz/farfalle !2025-03-2832460|🔍 AI search engine - self-host with local or cloud LLMs| | 944|Luodian/Otter !2025-03-2832450|🦦 Otter, a multi-modal model based on OpenFlamingo (open-sourced version of DeepMind's Flamingo), trained on MIMIC-IT and showcasing improved instruction-following and in-context learning ability.| | 945|AprilNEA/ChatGPT-Admin-Web !2025-03-2832370 | ChatGPT WebUI with user management and admin dashboard system| | 946|MarkFzp/act-plus-plus !2025-03-2832365|Imitation Learning algorithms with Co-traing for Mobile ALOHA: ACT, Diffusion Policy, VINN| | 947|ethen8181/machine-learning !2025-03-2832310|🌎 machine learning tutorials (mainly in Python3)| | 948|opengeos/segment-geospatial !2025-03-2832312 |A Python package for segmenting geospatial data with the Segment Anything Model (SAM)| | 949|iusztinpaul/hands-on-llms !2025-03-283225-2|🦖 𝗟𝗲𝗮𝗿𝗻 about 𝗟𝗟𝗠𝘀, 𝗟𝗟𝗠𝗢𝗽𝘀, and 𝘃𝗲𝗰𝘁𝗼𝗿 𝗗𝗕𝘀 for free by designing, training, and deploying a real-time financial advisor LLM system ~ 𝘴𝘰𝘶𝘳𝘤𝘦 𝘤𝘰𝘥𝘦 + 𝘷𝘪𝘥𝘦𝘰 & 𝘳𝘦𝘢𝘥𝘪𝘯𝘨 𝘮𝘢𝘵𝘦𝘳𝘪𝘢𝘭𝘴| | 950|ToTheBeginning/PuLID !2025-03-2832221|Official code for PuLID: Pure and Lightning ID Customization via Contrastive Alignment| | 951|neo4j-labs/llm-graph-builder !2025-03-2832164|Neo4j graph construction from unstructured data using LLMs| | 952|OpenGVLab/InternGPT !2025-03-2832150 |InternGPT (iGPT) is an open source demo platform where you can easily showcase your AI models. Now it supports DragGAN, ChatGPT, ImageBind, multimodal chat like GPT-4, SAM, interactive image editing, etc. Try it at igpt.opengvlab.com (支持DragGAN、ChatGPT、ImageBind、SAM的在线Demo系统)| | 953|PKU-YuanGroup/Video-LLaVA !2025-03-2832060 |Video-LLaVA: Learning United Visual Representation by Alignment Before Projection| | 954|DataTalksClub/llm-zoomcamp !2025-03-2832030|LLM Zoomcamp - a free online course about building an AI bot that can answer questions about your knowledge base| | 955|gptscript-ai/gptscript !2025-03-2832010|Natural Language Programming| |!green-up-arrow.svg 956|isaac-sim/IsaacLab !2025-03-28320113|Unified framework for robot learning built on NVIDIA Isaac Sim| |!red-down-arrow 957|ai-boost/Awesome-GPTs !2025-03-2832003|Curated list of awesome GPTs 👍.| | 958|huggingface/safetensors !2025-03-2831901|Simple, safe way to store and distribute tensors| | 959|linyiLYi/bilibot !2025-03-2831771|A local chatbot fine-tuned by bilibili user comments.| | 960| project-baize/baize-chatbot !2025-03-283168-1 | Let ChatGPT teach your own chatbot in hours with a single GPU! | | 961|Azure-Samples/cognitive-services-speech-sdk !2025-03-2831280|Sample code for the Microsoft Cognitive Services Speech SDK| | 962|microsoft/Phi-3CookBook !2025-03-2831231|This is a Phi-3 book for getting started with Phi-3. Phi-3, a family of open AI models developed by Microsoft. Phi-3 models are the most capable and cost-effective small language models (SLMs) available, outperforming models of the same size and next size up across a variety of language, reasoning, coding, and math benchmarks.| | 963|neuralmagic/deepsparse !2025-03-2831180|Sparsity-aware deep learning inference runtime for CPUs| | 964|sugarforever/chat-ollama !2025-03-2831000|ChatOllama is an open source chatbot based on LLMs. It supports a wide range of language models, and knowledge base management.| | 965|amazon-science/chronos-forecasting !2025-03-2830974|Chronos: Pretrained (Language) Models for Probabilistic Time Series Forecasting| | 966|damo-vilab/i2vgen-xl !2025-03-2830902|Official repo for VGen: a holistic video generation ecosystem for video generation building on diffusion models| | 967|google-deepmind/gemma !2025-03-2830733|Open weights LLM from Google DeepMind.| | 968|iree-org/iree !2025-03-2830733|A retargetable MLIR-based machine learning compiler and runtime toolkit.| | 969|NVlabs/VILA !2025-03-2830724|VILA - a multi-image visual language model with training, inference and evaluation recipe, deployable from cloud to edge (Jetson Orin and laptops)| | 970|microsoft/torchscale !2025-03-2830661|Foundation Architecture for (M)LLMs| | 971|openai/openai-realtime-console !2025-03-2830656|React app for inspecting, building and debugging with the Realtime API| | 972|daveshap/OpenAIAgentSwarm !2025-03-2830610|HAAS = Hierarchical Autonomous Agent Swarm - "Resistance is futile!"| | 973|microsoft/PromptWizard !2025-03-2830555|Task-Aware Agent-driven Prompt Optimization Framework| | 974|CVI-SZU/Linly !2025-03-2830490 |Chinese-LLaMA basic model; ChatFlow Chinese conversation model; NLP pre-training/command fine-tuning dataset| | 975|cohere-ai/cohere-toolkit !2025-03-2830130|Toolkit is a collection of prebuilt components enabling users to quickly build and deploy RAG applications.| | 976|adamcohenhillel/ADeus !2025-03-2830131|An open source AI wearable device that captures what you say and hear in the real world and then transcribes and stores it on your own server. You can then chat with Adeus using the app, and it will have all the right context about what you want to talk about - a truly personalized, personal AI.| | 977|Lightning-AI/LitServe !2025-03-2830132|Lightning-fast serving engine for AI models. Flexible. Easy. Enterprise-scale.| | 978|potpie-ai/potpie !2025-03-2829973|Prompt-To-Agent : Create custom engineering agents for your codebase| | 979|ant-design/x !2025-03-28299529|Craft AI-driven interfaces effortlessly 🤖| | 980|meta-llama/PurpleLlama !2025-03-2829832|Set of tools to assess and improve LLM security.| | 981|williamyang1991/RerenderAVideo !2025-03-2829800|[SIGGRAPH Asia 2023] Rerender A Video: Zero-Shot Text-Guided Video-to-Video Translation| | 982|baichuan-inc/Baichuan-13B !2025-03-2829790|A 13B large language model developed by Baichuan Intelligent Technology| | 983|Stability-AI/stable-audio-tools !2025-03-2829761|Generative models for conditional audio generation| | 984|li-plus/chatglm.cpp !2025-03-2829720|C++ implementation of ChatGLM-6B & ChatGLM2-6B & ChatGLM3 & more LLMs| | 985|NVIDIA/GenerativeAIExamples !2025-03-2829546|Generative AI reference workflows optimized for accelerated infrastructure and microservice architecture.| | 986|Josh-XT/AGiXT !2025-03-2829521 |AGiXT is a dynamic AI Automation Platform that seamlessly orchestrates instruction management and complex task execution across diverse AI providers. Combining adaptive memory, smart features, and a versatile plugin system, AGiXT delivers efficient and comprehensive AI solutions.| | 987|MrForExample/ComfyUI-3D-Pack !2025-03-2829515|An extensive node suite that enables ComfyUI to process 3D inputs (Mesh & UV Texture, etc) using cutting edge algorithms (3DGS, NeRF, etc.)| | 988|olimorris/codecompanion.nvim !2025-03-28295111|✨ AI-powered coding, seamlessly in Neovim. Supports Anthropic, Copilot, Gemini, Ollama, OpenAI and xAI LLMs| | 989|salesforce/CodeT5 !2025-03-282940-1 |Home of CodeT5: Open Code LLMs for Code Understanding and Generation| | 990|facebookresearch/ijepa !2025-03-2829391|Official codebase for I-JEPA, the Image-based Joint-Embedding Predictive Architecture. First outlined in the CVPR paper, "Self-supervised learning from images with a joint-embedding predictive architecture."| | 991|eureka-research/Eureka !2025-03-2829351|Official Repository for "Eureka: Human-Level Reward Design via Coding Large Language Models"| | 992|NVIDIA/trt-llm-rag-windows !2025-03-282934-1|A developer reference project for creating Retrieval Augmented Generation (RAG) chatbots on Windows using TensorRT-LLM| | 993|gmpetrov/databerry !2025-03-282930-1|The no-code platform for building custom LLM Agents| | 994|AI4Finance-Foundation/FinRobot !2025-03-28291946|FinRobot: An Open-Source AI Agent Platform for Financial Applications using LLMs 🚀 🚀 🚀| | 995|nus-apr/auto-code-rover !2025-03-2829013|A project structure aware autonomous software engineer aiming for autonomous program improvement| | 996|deepseek-ai/DreamCraft3D !2025-03-2828921|[ICLR 2024] Official implementation of DreamCraft3D: Hierarchical 3D Generation with Bootstrapped Diffusion Prior| | 997|mlabonne/llm-datasets !2025-03-2828848|High-quality datasets, tools, and concepts for LLM fine-tuning.| | 998|facebookresearch/jepa !2025-03-2828712|PyTorch code and models for V-JEPA self-supervised learning from video.| | 999|facebookresearch/habitat-sim !2025-03-2828604|A flexible, high-performance 3D simulator for Embodied AI research.| | 1000|xenova/whisper-web !2025-03-2828581|ML-powered speech recognition directly in your browser| | 1001|cvlab-columbia/zero123 !2025-03-2828530|Zero-1-to-3: Zero-shot One Image to 3D Object: https://zero123.cs.columbia.edu/| | 1002|yuruotong1/autoMate !2025-03-28285121|Like Manus, Computer Use Agent(CUA) and Omniparser, we are computer-using agents.AI-driven local automation assistant that uses natural language to make computers work by themselves| | 1003|muellerberndt/mini-agi !2025-03-282845-1 |A minimal generic autonomous agent based on GPT3.5/4. Can analyze stock prices, perform network security tests, create art, and order pizza.| | 1004|allenai/open-instruct !2025-03-2828432|| | 1005|CodingChallengesFYI/SharedSolutions !2025-03-2828360|Publicly shared solutions to Coding Challenges| | 1006|hegelai/prompttools !2025-03-2828220|Open-source tools for prompt testing and experimentation, with support for both LLMs (e.g. OpenAI, LLaMA) and vector databases (e.g. Chroma, Weaviate).| | 1007|mazzzystar/Queryable !2025-03-2828222|Run CLIP on iPhone to Search Photos.| | 1008|Doubiiu/DynamiCrafter !2025-03-2828173|DynamiCrafter: Animating Open-domain Images with Video Diffusion Priors| | 1009|SamurAIGPT/privateGPT !2025-03-282805-1 |An app to interact privately with your documents using the power of GPT, 100% privately, no data leaks| | 1010|facebookresearch/Pearl !2025-03-2827951|A Production-ready Reinforcement Learning AI Agent Library brought by the Applied Reinforcement Learning team at Meta.| | 1011|intuitem/ciso-assistant-community !2025-03-2827954|CISO Assistant is a one-stop-shop for GRC, covering Risk, AppSec and Audit Management and supporting +70 frameworks worldwide with auto-mapping: NIST CSF, ISO 27001, SOC2, CIS, PCI DSS, NIS2, CMMC, PSPF, GDPR, HIPAA, Essential Eight, NYDFS-500, DORA, NIST AI RMF, 800-53, 800-171, CyFun, CJIS, AirCyber, NCSC, ECC, SCF and so much more| | 1012|facebookresearch/audio2photoreal !2025-03-2827840|Code and dataset for photorealistic Codec Avatars driven from audio| | 1013|Azure/azure-rest-api-specs !2025-03-2827770|The source for REST API specifications for Microsoft Azure.| | 1014|SCUTlihaoyu/open-chat-video-editor !2025-03-2827690 |Open source short video automatic generation tool| | 1015|Alpha-VLLM/LLaMA2-Accessory !2025-03-2827642|An Open-source Toolkit for LLM Development| | 1016|johnma2006/mamba-minimal !2025-03-2827601|Simple, minimal implementation of the Mamba SSM in one file of PyTorch.| | 1017|nerfstudio-project/gsplat !2025-03-2827576|CUDA accelerated rasterization of gaussian splatting| | 1018|Physical-Intelligence/openpi !2025-03-28274617|| | 1019|leptonai/leptonai !2025-03-2827246|A Pythonic framework to simplify AI service building| |!green-up-arrow.svg 1020|joanrod/star-vector !2025-03-28271149|StarVector is a foundation model for SVG generation that transforms vectorization into a code generation task. Using a vision-language modeling architecture, StarVector processes both visual and textual inputs to produce high-quality SVG code with remarkable precision.| |!red-down-arrow 1021|jqnatividad/qsv !2025-03-2827092|CSVs sliced, diced & analyzed.| | 1022|FranxYao/chain-of-thought-hub !2025-03-2826991|Benchmarking large language models' complex reasoning ability with chain-of-thought prompting| | 1023|princeton-nlp/SWE-bench !2025-03-2826965|[ICLR 2024] SWE-Bench: Can Language Models Resolve Real-world Github Issues?| | 1024|elastic/otel-profiling-agent !2025-03-2826930|The production-scale datacenter profiler| | 1025|src-d/hercules !2025-03-2826900|Gaining advanced insights from Git repository history.| | 1026|lanqian528/chat2api !2025-03-2826695|A service that can convert ChatGPT on the web to OpenAI API format.| | 1027|ishan0102/vimGPT !2025-03-2826681|Browse the web with GPT-4V and Vimium| | 1028|TMElyralab/MuseV !2025-03-2826650|MuseV: Infinite-length and High Fidelity Virtual Human Video Generation with Visual Conditioned Parallel Denoising| | 1029|georgia-tech-db/eva !2025-03-2826600 |AI-Relational Database System | | 1030|kubernetes-sigs/controller-runtime !2025-03-2826590|Repo for the controller-runtime subproject of kubebuilder (sig-apimachinery)| | 1031|gptlink/gptlink !2025-03-2826550 |Build your own free commercial ChatGPT environment in 10 minutes. The setup is simple and includes features such as user management, orders, tasks, and payments| | 1032|pytorch/executorch !2025-03-2826534|On-device AI across mobile, embedded and edge for PyTorch| | 1033|NVIDIA/nv-ingest !2025-03-2826290|NVIDIA Ingest is an early access set of microservices for parsing hundreds of thousands of complex, messy unstructured PDFs and other enterprise documents into metadata and text to embed into retrieval systems.| | 1034|SuperTux/supertux !2025-03-2826081|SuperTux source code| | 1035|abi/secret-llama !2025-03-2826050|Fully private LLM chatbot that runs entirely with a browser with no server needed. Supports Mistral and LLama 3.| | 1036|liou666/polyglot !2025-03-2825841 |Desktop AI Language Practice Application| | 1037|janhq/nitro !2025-03-2825821|A fast, lightweight, embeddable inference engine to supercharge your apps with local AI. OpenAI-compatible API| | 1038|deepseek-ai/DeepSeek-Math !2025-03-2825825|DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models| | 1039|anthropics/prompt-eng-interactive-tutorial !2025-03-2825781|Anthropic's Interactive Prompt Engineering Tutorial| | 1040|microsoft/promptbench !2025-03-2825741|A unified evaluation framework for large language models| | 1041|baaivision/Painter !2025-03-2825580 |Painter & SegGPT Series: Vision Foundation Models from BAAI| | 1042|OpenPipe/OpenPipe !2025-03-2825581|Turn expensive prompts into cheap fine-tuned models| | 1043|TracecatHQ/tracecat !2025-03-2825531|😼 The AI-native, open source alternative to Tines / Splunk SOAR.| | 1044|JoshuaC215/agent-service-toolkit !2025-03-2825528|Full toolkit for running an AI agent service built with LangGraph, FastAPI and Streamlit| | 1045|databricks/dbrx !2025-03-2825460|Code examples and resources for DBRX, a large language model developed by Databricks| | 1046|lamini-ai/lamini !2025-03-2825271 |Official repo for Lamini's data generator for generating instructions to train instruction-following LLMs| | 1047|mshumer/gpt-author !2025-03-282510-1|| | 1048|TMElyralab/MusePose !2025-03-2824971|MusePose: a Pose-Driven Image-to-Video Framework for Virtual Human Generation| | 1049|Kludex/fastapi-tips !2025-03-2824974|FastAPI Tips by The FastAPI Expert!| | 1050|openai/simple-evals !2025-03-2824813|| | 1051|iterative/datachain !2025-03-2824732|AI-data warehouse to enrich, transform and analyze data from cloud storages| | 1052|girafe-ai/ml-course !2025-03-2824703|Open Machine Learning course| | 1053|kevmo314/magic-copy !2025-03-2824620 |Magic Copy is a Chrome extension that uses Meta's Segment Anything Model to extract a foreground object from an image and copy it to the clipboard.| | 1054|Eladlev/AutoPrompt !2025-03-2824432|A framework for prompt tuning using Intent-based Prompt Calibration| | 1055|OpenBMB/CPM-Bee !2025-03-282434-1 |A bilingual large-scale model with trillions of parameters| | 1056|IDEA-Research/T-Rex !2025-03-2824310|T-Rex2: Towards Generic Object Detection via Text-Visual Prompt Synergy| | 1057|microsoft/genaiscript !2025-03-2824202|Automatable GenAI Scripting| | 1058|paulpierre/RasaGPT !2025-03-2824090 |💬 RasaGPT is the first headless LLM chatbot platform built on top of Rasa and Langchain. Built w/ Rasa, FastAPI, Langchain, LlamaIndex, SQLModel, pgvector, ngrok, telegram| | 1059|ashishpatel26/LLM-Finetuning !2025-03-2823911|LLM Finetuning with peft| | 1060|SoraWebui/SoraWebui !2025-03-2823570|SoraWebui is an open-source Sora web client, enabling users to easily create videos from text with OpenAI's Sora model.| | 1061|6drf21e/ChatTTScolab !2025-03-2823491|🚀 一键部署（含离线整合包）！基于 ChatTTS ，支持音色抽卡、长音频生成和分角色朗读。简单易用，无需复杂安装。| | 1062|Azure/PyRIT !2025-03-2823343|The Python Risk Identification Tool for generative AI (PyRIT) is an open access automation framework to empower security professionals and machine learning engineers to proactively find risks in their generative AI systems.| | 1063|tencent-ailab/V-Express !2025-03-2823201|V-Express aims to generate a talking head video under the control of a reference image, an audio, and a sequence of V-Kps images.| | 1064|THUDM/CogVLM2 !2025-03-2823170|GPT4V-level open-source multi-modal model based on Llama3-8B| | 1065|dvmazur/mixtral-offloading !2025-03-2823001|Run Mixtral-8x7B models in Colab or consumer desktops| | 1066|semanser/codel !2025-03-2822950|✨ Fully autonomous AI Agent that can perform complicated tasks and projects using terminal, browser, and editor.| | 1067|mshumer/gpt-investor !2025-03-2822590|| | 1068|aixcoder-plugin/aiXcoder-7B !2025-03-2822550|official repository of aiXcoder-7B Code Large Language Model| | 1069|Azure-Samples/graphrag-accelerator !2025-03-2822503|One-click deploy of a Knowledge Graph powered RAG (GraphRAG) in Azure| | 1070|emcf/engshell !2025-03-2821830 |An English-language shell for any OS, powered by LLMs| | 1071|hncboy/chatgpt-web-java !2025-03-2821771|ChatGPT project developed in Java, based on Spring Boot 3 and JDK 17, supports both AccessToken and ApiKey modes| | 1072|openai/consistencydecoder !2025-03-2821692|Consistency Distilled Diff VAE| | 1073|Alpha-VLLM/Lumina-T2X !2025-03-2821681|Lumina-T2X is a unified framework for Text to Any Modality Generation| | 1074|bghira/SimpleTuner !2025-03-2821612|A general fine-tuning kit geared toward Stable Diffusion 2.1, Stable Diffusion 3, DeepFloyd, and SDXL.| | 1075|JiauZhang/DragGAN !2025-03-2821530 |Implementation of DragGAN: Interactive Point-based Manipulation on the Generative Image Manifold| | 1076|cgpotts/cs224u !2025-03-2821390|Code for Stanford CS224u| | 1077|PKU-YuanGroup/MoE-LLaVA !2025-03-2821300|Mixture-of-Experts for Large Vision-Language Models| | 1078|darrenburns/elia !2025-03-2820831|A snappy, keyboard-centric terminal user interface for interacting with large language models. Chat with ChatGPT, Claude, Llama 3, Phi 3, Mistral, Gemma and more.| | 1079|ageerle/ruoyi-ai !2025-03-28207898|RuoYi AI 是一个全栈式 AI 开发平台，旨在帮助开发者快速构建和部署个性化的 AI 应用。| | 1080|NVIDIA/gpu-operator !2025-03-2820510|NVIDIA GPU Operator creates/configures/manages GPUs atop Kubernetes| | 1081|BAAI-Agents/Cradle !2025-03-2820481|The Cradle framework is a first attempt at General Computer Control (GCC). Cradle supports agents to ace any computer task by enabling strong reasoning abilities, self-improvment, and skill curation, in a standardized general environment with minimal requirements.| | 1082|microsoft/aici !2025-03-2820080|AICI: Prompts as (Wasm) Programs| | 1083|PRIS-CV/DemoFusion !2025-03-2820040|Let us democratise high-resolution generation! (arXiv 2023)| | 1084|apple/axlearn !2025-03-2820012|An Extensible Deep Learning Library| | 1085|naver/mast3r !2025-03-2819685|Grounding Image Matching in 3D with MASt3R| | 1086|liltom-eth/llama2-webui !2025-03-281958-1|Run Llama 2 locally with gradio UI on GPU or CPU from anywhere (Linux/Windows/Mac). Supporting Llama-2-7B/13B/70B with 8-bit, 4-bit. Supporting GPU inference (6 GB VRAM) and CPU inference.| | 1087|GaParmar/img2img-turbo !2025-03-2819582|One-step image-to-image with Stable Diffusion turbo: sketch2image, day2night, and more| | 1088|Niek/chatgpt-web !2025-03-2819560|ChatGPT web interface using the OpenAI API| | 1089|huggingface/cookbook !2025-03-2819421|Open-source AI cookbook| | 1090|pytorch/ao !2025-03-2819241|PyTorch native quantization and sparsity for training and inference| | 1091|emcie-co/parlant !2025-03-2819053|The behavior guidance framework for customer-facing LLM agents| | 1092|ymcui/Chinese-LLaMA-Alpaca-3 !2025-03-2818980|中文羊驼大模型三期项目 (Chinese Llama-3 LLMs) developed from Meta Llama 3| | 1093|Nutlope/notesGPT !2025-03-2818811|Record voice notes & transcribe, summarize, and get tasks| | 1094|InstantStyle/InstantStyle !2025-03-2818791|InstantStyle: Free Lunch towards Style-Preserving in Text-to-Image Generation 🔥| | 1095|idaholab/moose !2025-03-2818771|Multiphysics Object Oriented Simulation Environment| | 1096|The-OpenROAD-Project/OpenROAD !2025-03-2818351|OpenROAD's unified application implementing an RTL-to-GDS Flow. Documentation at https://openroad.readthedocs.io/en/latest/| | 1097|alibaba/spring-ai-alibaba !2025-03-281831121|Agentic AI Framework for Java Developers| | 1098|ytongbai/LVM !2025-03-2817990|Sequential Modeling Enables Scalable Learning for Large Vision Models| | 1099|microsoft/sample-app-aoai-chatGPT !2025-03-2817981|[PREVIEW] Sample code for a simple web chat experience targeting chatGPT through AOAI.| | 1100|AI-Citizen/SolidGPT !2025-03-2817830|Chat everything with your code repository, ask repository level code questions, and discuss your requirements. AI Scan and learning your code repository, provide you code repository level answer🧱 🧱| | 1101|YangLing0818/RPG-DiffusionMaster !2025-03-2817784|Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs (PRG)| | 1102|kyegomez/BitNet !2025-03-2817710|Implementation of "BitNet: Scaling 1-bit Transformers for Large Language Models" in pytorch| | 1103|eloialonso/diamond !2025-03-2817671|DIAMOND (DIffusion As a Model Of eNvironment Dreams) is a reinforcement learning agent trained in a diffusion world model.| | 1104|flowdriveai/flowpilot !2025-03-2817250|flow-pilot is an openpilot based driver assistance system that runs on linux, windows and android powered machines.| | 1105|xlang-ai/OSWorld !2025-03-2817200|OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments| | 1106|linyiLYi/snake-ai !2025-03-2817031|An AI agent that beats the classic game "Snake".| | 1107|baaivision/Emu !2025-03-2816991|Emu Series: Generative Multimodal Models from BAAI| | 1108|kevmo314/scuda !2025-03-2816870|SCUDA is a GPU over IP bridge allowing GPUs on remote machines to be attached to CPU-only machines.| | 1109|SharifiZarchi/IntroductiontoMachineLearning !2025-03-2816701|دوره‌ی مقدمه‌ای بر یادگیری ماشین، برای دانشجویان| | 1110|google/maxtext !2025-03-2816670|A simple, performant and scalable Jax LLM!| | 1111|ml-explore/mlx-swift-examples !2025-03-2816471|Examples using MLX Swift| | 1112|unitreerobotics/unitreerlgym !2025-03-2816256|| | 1113|collabora/WhisperFusion !2025-03-2815901|WhisperFusion builds upon the capabilities of WhisperLive and WhisperSpeech to provide a seamless conversations with an AI.| | 1114|lichao-sun/Mora !2025-03-2815520|Mora: More like Sora for Generalist Video Generation| | 1115|GoogleCloudPlatform/localllm !2025-03-2815370|Run LLMs locally on Cloud Workstations| | 1116|TencentARC/BrushNet !2025-03-2815330|The official implementation of paper "BrushNet: A Plug-and-Play Image Inpainting Model with Decomposed Dual-Branch Diffusion"| | 1117|ai-christianson/RA.Aid !2025-03-2815288|Develop software autonomously.| | 1118|stephansturges/WALDO !2025-03-2815170|Whereabouts Ascertainment for Low-lying Detectable Objects. The SOTA in FOSS AI for drones!| | 1119|skills/copilot-codespaces-vscode !2025-03-2815112|Develop with AI-powered code suggestions using GitHub Copilot and VS Code| | 1120|andrewnguonly/Lumos !2025-03-2814920|A RAG LLM co-pilot for browsing the web, powered by local LLMs| | 1121|TeamNewPipe/NewPipeExtractor !2025-03-2814811|NewPipe's core library for extracting data from streaming sites| | 1122|mhamilton723/FeatUp !2025-03-2814770|Official code for "FeatUp: A Model-Agnostic Frameworkfor Features at Any Resolution" ICLR 2024| | 1123|AnswerDotAI/fsdpqlora !2025-03-2814671|Training LLMs with QLoRA + FSDP| | 1124|jgravelle/AutoGroq !2025-03-2814330|| | 1125|OpenGenerativeAI/llm-colosseum !2025-03-2814130|Benchmark LLMs by fighting in Street Fighter 3! The new way to evaluate the quality of an LLM| | 1126|microsoft/vscode-ai-toolkit !2025-03-2814000|| | 1127|McGill-NLP/webllama !2025-03-2813930|Llama-3 agents that can browse the web by following instructions and talking to you| | 1128|lucidrains/self-rewarding-lm-pytorch !2025-03-2813760|Implementation of the training framework proposed in Self-Rewarding Language Model, from MetaAI| | 1129|ishaan1013/sandbox !2025-03-2813650|A cloud-based code editing environment with an AI copilot and real-time collaboration.| | 1130|goatcorp/Dalamud !2025-03-2813275|FFXIV plugin framework and API| | 1131|Lightning-AI/lightning-thunder !2025-03-2813151|Make PyTorch models Lightning fast! Thunder is a source to source compiler for PyTorch. It enables using different hardware executors at once.| | 1132|PKU-YuanGroup/MagicTime !2025-03-2813052|MagicTime: Time-lapse Video Generation Models as Metamorphic Simulators| | 1133|SakanaAI/evolutionary-model-merge !2025-03-2813000|Official repository of Evolutionary Optimization of Model Merging Recipes| | 1134|a-real-ai/pywinassistant !2025-03-2812950|The first open source Large Action Model generalist Artificial Narrow Intelligence that controls completely human user interfaces by only using natural language. PyWinAssistant utilizes Visualization-of-Thought Elicits Spatial Reasoning in Large Language Models.| | 1135|TraceMachina/nativelink !2025-03-2812630|NativeLink is an open source high-performance build cache and remote execution server, compatible with Bazel, Buck2, Reclient, and other RBE-compatible build systems. It offers drastically faster builds, reduced test flakiness, and significant infrastructure cost savings.| | 1136|MLSysOps/MLE-agent !2025-03-2812500|🤖 MLE-Agent: Your intelligent companion for seamless AI engineering and research. 🔍 Integrate with arxiv and paper with code to provide better code/research plans 🧰 OpenAI, Ollama, etc supported. 🎆 Code RAG| | 1137|wpilibsuite/allwpilib !2025-03-2811610|Official Repository of WPILibJ and WPILibC| | 1138|elfvingralf/macOSpilot-ai-assistant !2025-03-2811470|Voice + Vision powered AI assistant that answers questions about any application, in context and in audio.| | 1139|langchain-ai/langchain-extract !2025-03-2811210|🦜⛏️ Did you say you like data?| | 1140|FoundationVision/GLEE !2025-03-2811120|【CVPR2024】GLEE: General Object Foundation Model for Images and Videos at Scale| | 1141|Profluent-AI/OpenCRISPR !2025-03-2810990|AI-generated gene editing systems| | 1142|zju3dv/EasyVolcap !2025-03-2810821|[SIGGRAPH Asia 2023 (Technical Communications)] EasyVolcap: Accelerating Neural Volumetric Video Research| | 1143|PaddlePaddle/PaddleHelix !2025-03-2810560|Bio-Computing Platform Featuring Large-Scale Representation Learning and Multi-Task Deep Learning “螺旋桨”生物计算工具集| | 1144|myshell-ai/JetMoE !2025-03-289800|Reaching LLaMA2 Performance with 0.1M Dollars| | 1145|likejazz/llama3.np !2025-03-289770|llama3.np is pure NumPy implementation for Llama 3 model.| | 1146|mustafaaljadery/gemma-2B-10M !2025-03-289500|Gemma 2B with 10M context length using Infini-attention.| | 1147|HITsz-TMG/FilmAgent !2025-03-289382|Resources of our paper "FilmAgent: A Multi-Agent Framework for End-to-End Film Automation in Virtual 3D Spaces". New versions in the making!| | 1148|aws-samples/amazon-bedrock-samples !2025-03-289362|This repository contains examples for customers to get started using the Amazon Bedrock Service. This contains examples for all available foundational models| | 1149|Akkudoktor-EOS/EOS !2025-03-2893154|This repository features an Energy Optimization System (EOS) that optimizes energy distribution, usage for batteries, heat pumps& household devices. It includes predictive models for electricity prices (planned), load forecasting& dynamic optimization to maximize energy efficiency & minimize costs. Founder Dr. Andreas Schmitz (YouTube @akkudoktor)| Tip: | symbol| rule | | :----| :---- | |🔥 | 256 1k| |!green-up-arrow.svg !red-down-arrow | ranking up / down| |⭐ | on trending page today| [Back to Top] Tools | No. | Tool | Description | | ----:|:----------------------------------------------- |:------------------------------------------------------------------------------------------- | | 1 | ChatGPT | A sibling model to InstructGPT, which is trained to follow instructions in a prompt and provide a detailed response | | 2 | DALL·E 2 | Create original, realistic images and art from a text description | | 3 | Murf AI | AI enabled, real people's voices| | 4 | Midjourney | An independent research lab that produces an artificial intelligence program under the same name that creates images from textual descriptions, used in Discord | 5 | Make-A-Video | Make-A-Video is a state-of-the-art AI system that generates videos from text | | 6 | Creative Reality™ Studio by D-ID| Use generative AI to create future-facing videos| | 7 | chat.D-ID| The First App Enabling Face-to-Face Conversations with ChatGPT| | 8 | Notion AI| Access the limitless power of AI, right inside Notion. Work faster. Write better. Think bigger. | | 9 | Runway| Text to Video with Gen-2 | | 10 | Resemble AI| Resemble’s AI voice generator lets you create human–like voice overs in seconds | | 11 | Cursor| Write, edit, and chat about your code with a powerful AI | | 12 | Hugging Face| Build, train and deploy state of the art models powered by the reference open source in machine learning | | 13 | Claude | A next-generation AI assistant for your tasks, no matter the scale | | 14 | Poe| Poe lets you ask questions, get instant answers, and have back-and-forth conversations with AI. Gives access to GPT-4, gpt-3.5-turbo, Claude from Anthropic, and a variety of other bots| [Back to Top] Websites | No. | WebSite |Description | | ----:|:------------------------------------------ |:---------------------------------------------------------------------------------------- | | 1 | OpenAI | An artificial intelligence research lab | | 2 | Bard | Base Google's LaMDA chatbots and pull from internet | | 3 | ERNIE Bot | Baidu’s new generation knowledge-enhanced large language model is a new member of the Wenxin large model family | | 4 | DALL·E 2 | An AI system that can create realistic images and art from a description in natural language | | 5 | Whisper | A general-purpose speech recognition model | | 6| CivitAI| A platform that makes it easy for people to share and discover resources for creating AI art| | 7|D-ID| D-ID’s Generative AI enables users to transform any picture or video into extraordinary experiences| | 8| Nvidia eDiff-I| Text-to-Image Diffusion Models with Ensemble of Expert Denoisers | | 9| Stability AI| The world's leading open source generative AI company which opened source Stable Diffusion | | 10| Meta AI| Whether it be research, product or infrastructure development, we’re driven to innovate responsibly with AI to benefit the world | | 11| ANTHROPIC| AI research and products that put safety at the frontier | [Back to Top] Reports&Papers | No. | Report&Paper | Description | |:---- |:-------------------------------------------------------------------------------------------------------------- |:---------------------------------------------------- | | 1 | GPT-4 Technical Report | GPT-4 Technical Report | | 2 | mli/paper-reading | Deep learning classics and new papers are read carefully paragraph by paragraph. | | 3 | labmlai/annotateddeeplearningpaperimplementations| A collection of simple PyTorch implementations of neural networks and related algorithms, which are documented with explanations | | 4 | Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models | Talking, Drawing and Editing with Visual Foundation Models | | 5 | OpenAI Research | The latest research report and papers from OpenAI | | 6 | Make-A-Video: Text-to-Video Generation without Text-Video Data|Meta's Text-to-Video Generation| | 7 | eDiff-I: Text-to-Image Diffusion Models with Ensemble of Expert Denoisers| Nvidia eDiff-I - New generation of generative AI content creation tool | | 8 | Training an Assistant-style Chatbot with Large Scale Data Distillation from GPT-3.5-Turbo | 2023 GPT4All Technical Report | | 9 | Segment Anything| Meta Segment Anything | | 10 | LLaMA: Open and Efficient Foundation Language Models| LLaMA: a collection of foundation language models ranging from 7B to 65B parameters| | 11 | papers-we-love/papers-we-love |Papers from the computer science community to read and discuss| | 12 | CVPR 2023 papers |The most exciting and influential CVPR 2023 papers| [Back to Top] Tutorials | No. | Tutorial | Description| |:---- |:---------------------------------------------------------------- | --- | | 1 | Coursera - Machine Learning | The Machine Learning Specialization Course taught by Dr. Andrew Ng| | 2 | microsoft/ML-For-Beginners | 12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all| | 3 | ChatGPT Prompt Engineering for Developers | This short course taught by Isa Fulford (OpenAI) and Andrew Ng (DeepLearning.AI) will teach how to use a large language model (LLM) to quickly build new and powerful applications | | 4 | Dive into Deep Learning |Targeting Chinese readers, functional and open for discussion. The Chinese and English versions are used for teaching in over 400 universities across more than 60 countries | | 5 | AI Expert Roadmap | Roadmap to becoming an Artificial Intelligence Expert in 2022 | | 6 | Computer Science courses |List of Computer Science courses with video lectures| | 7 | Machine Learning with Python | Machine Learning with Python Certification on freeCodeCamp| | 8 | Building Systems with the ChatGPT API | This short course taught by Isa Fulford (OpenAI) and Andrew Ng (DeepLearning.AI), you will learn how to automate complex workflows using chain calls to a large language model| | 9 | LangChain for LLM Application Development | This short course taught by Harrison Chase (Co-Founder and CEO at LangChain) and Andrew Ng. you will gain essential skills in expanding the use cases and capabilities of language models in application development using the LangChain framework| | 10 | How Diffusion Models Work | This short course taught by Sharon Zhou (CEO, Co-founder, Lamini). you will gain a deep familiarity with the diffusion process and the models which carry it out. More than simply pulling in a pre-built model or using an API, this course will teach you to build a diffusion model from scratch| | 11 | Free Programming Books For AI |📚 Freely available programming books for AI | | 12 | microsoft/AI-For-Beginners |12 Weeks, 24 Lessons, AI for All!| | 13 | hemansnation/God-Level-Data-Science-ML-Full-Stack |A collection of scientific methods, processes, algorithms, and systems to build stories & models. This roadmap contains 16 Chapters, whether you are a fresher in the field or an experienced professional who wants to transition into Data Science & AI| | 14 | datawhalechina/prompt-engineering-for-developers |Chinese version of Andrew Ng's Big Model Series Courses, including "Prompt Engineering", "Building System", and "LangChain"| | 15 | ossu/computer-science |🎓 Path to a free self-taught education in Computer Science!| | 16 | microsoft/Data-Science-For-Beginners | 10 Weeks, 20 Lessons, Data Science for All! | |17 |jwasham/coding-interview-university !2023-09-29268215336 |A complete computer science study plan to become a software engineer.| [Back to Top] Thanks If this project has been helpful to you in any way, please give it a ⭐️ by clicking on the star.

#github#aitreasurebox

github

0.507

0.11415188209660238

aiortc•Mar 28, 2025

aiortc

.. image:: docs/_static/aiortc.svg :width: 120px :alt: aiortc .. image:: https://img.shields.io/pypi/l/aiortc.svg :target: https://pypi.python.org/pypi/aiortc :alt: License .. image:: https://img.shields.io/pypi/v/aiortc.svg :target: https://pypi.python.org/pypi/aiortc :alt: Version .. image:: https://img.shields.io/pypi/pyversions/aiortc.svg :target: https://pypi.python.org/pypi/aiortc :alt: Python versions .. image:: https://github.com/aiortc/aiortc/workflows/tests/badge.svg :target: https://github.com/aiortc/aiortc/actions :alt: Tests .. image:: https://img.shields.io/codecov/c/github/aiortc/aiortc.svg :target: https://codecov.io/gh/aiortc/aiortc :alt: Coverage .. image:: https://readthedocs.org/projects/aiortc/badge/?version=latest :target: https://aiortc.readthedocs.io/ :alt: Documentation What is `aiortc? aiortc is a library for Web Real-Time Communication (WebRTC)_ and Object Real-Time Communication (ORTC)_ in Python. It is built on top of asyncio, Python's standard asynchronous I/O framework. The API closely follows its Javascript counterpart while using pythonic constructs: promises are replaced by coroutines events are emitted using pyee.EventEmitter To learn more about aiortc please read the documentation_. .. _Web Real-Time Communication (WebRTC): https://webrtc.org/ .. _Object Real-Time Communication (ORTC): https://ortc.org/ .. _read the documentation: https://aiortc.readthedocs.io/en/latest/ Why should I use aiortc? The main WebRTC and ORTC implementations are either built into web browsers, or come in the form of native code. While they are extensively battle tested, their internals are complex and they do not provide Python bindings. Furthermore they are tightly coupled to a media stack, making it hard to plug in audio or video processing algorithms. In contrast, the aiortc implementation is fairly simple and readable. As such it is a good starting point for programmers wishing to understand how WebRTC works or tinker with its internals. It is also easy to create innovative products by leveraging the extensive modules available in the Python ecosystem. For instance you can build a full server handling both signaling and data channels or apply computer vision algorithms to video frames using OpenCV. Furthermore, a lot of effort has gone into writing an extensive test suite for the aiortc code to ensure best-in-class code quality. Implementation status aiortc allows you to exchange audio, video and data channels and interoperability is regularly tested against both Chrome and Firefox. Here are some of its features: SDP generation / parsing Interactive Connectivity Establishment, with half-trickle and mDNS support DTLS key and certificate generation DTLS handshake, encryption / decryption (for SCTP) SRTP keying, encryption and decryption for RTP and RTCP Pure Python SCTP implementation Data Channels Sending and receiving audio (Opus / PCMU / PCMA) Sending and receiving video (VP8 / H.264) Bundling audio / video / data channels RTCP reports, including NACK / PLI to recover from packet loss Installing The easiest way to install aiortc is to run: .. code:: bash pip install aiortc Building from source If there are no wheels for your system or if you wish to build aiortc from source you will need a couple of libraries installed on your system: Opus for audio encoding / decoding LibVPX for video encoding / decoding Linux ..... On Debian/Ubuntu run: .. code:: bash apt install libopus-dev libvpx-dev OS X .... On OS X run: .. code:: bash brew install opus libvpx License aiortc is released under the BSD license`_. .. _BSD license: https://aiortc.readthedocs.io/en/latest/license.html

#github#aiortc

github

0.619

0.8326638433689385

alirezadir•Mar 28, 2025

Production-Level-Deep-Learning

:bulb: A Guide to Production Level Deep Learning :clapper: :scroll: :ferry: 🇨🇳 Translation in Chinese.md) :label: NEW: Machine Learning Interviews :label: Note: This repo is under continous development, and all feedback and contribution are very welcome :blush: Deploying deep learning models in production can be challenging, as it is far beyond training models with good performance. Several distinct components need to be designed and developed in order to deploy a production level deep learning system (seen below): This repo aims to be an engineering guideline for building production-level deep learning systems which will be deployed in real world applications. The material presented here is borrowed from Full Stack Deep Learning Bootcamp (by Pieter Abbeel at UC Berkeley, Josh Tobin at OpenAI, and Sergey Karayev at Turnitin), TFX workshop by Robert Crowe, and Pipeline.ai's Advanced KubeFlow Meetup by Chris Fregly. Machine Learning Projects Fun :flushed: fact: 85% of AI projects fail. 1 Potential reasons include: Technically infeasible or poorly scoped Never make the leap to production Unclear success criteria (metrics) Poor team management ML Projects lifecycle Importance of understanding state of the art in your domain: Helps to understand what is possible Helps to know what to try next Mental Model for ML project The two important factors to consider when defining and prioritizing ML projects: High Impact: Complex parts of your pipeline Where "cheap prediction" is valuable Where automating complicated manual process is valuable Low Cost: Cost is driven by: Data availability Performance requirements: costs tend to scale super-linearly in the accuracy requirement Problem difficulty: Some of the hard problems include: unsupervised learning, reinforcement learning, and certain categories of supervised learning Full stack pipeline The following figure represents a high level overview of different components in a production level deep learning system: In the following, we will go through each module and recommend toolsets and frameworks as well as best practices from practitioners that fit each component. Data Management 1.1 Data Sources Supervised deep learning requires a lot of labeled data Labeling own data is costly! Here are some resources for data: Open source data (good to start with, but not an advantage) Data augmentation (a MUST for computer vision, an option for NLP) Synthetic data (almost always worth starting with, esp. in NLP) 1.2 Data Labeling Requires: separate software stack (labeling platforms), temporary labor, and QC Sources of labor for labeling: Crowdsourcing (Mechanical Turk): cheap and scalable, less reliable, needs QC Hiring own annotators: less QC needed, expensive, slow to scale Data labeling service companies: FigureEight Labeling platforms: Diffgram: Training Data Software (Computer Vision) Prodigy: An annotation tool powered by active learning (by developers of Spacy), text and image HIVE: AI as a Service platform for computer vision Supervisely: entire computer vision platform Labelbox: computer vision Scale AI data platform (computer vision & NLP) 1.3. Data Storage Data storage options: Object store: Store binary data (images, sound files, compressed texts) Amazon S3 Ceph Object Store Database: Store metadata (file paths, labels, user activity, etc). Postgres is the right choice for most of applications, with the best-in-class SQL and great support for unstructured JSON. Data Lake: to aggregate features which are not obtainable from database (e.g. logs) Amazon Redshift Feature Store: store, access, and share machine learning features (Feature extraction could be computationally expensive and nearly impossible to scale, hence re-using features by different models and teams is a key to high performance ML teams). FEAST (Google cloud, Open Source) Michelangelo Palette (Uber) Suggestion: At training time, copy data into a local or networked filesystem (NFS). 1 1.4. Data Versioning It's a "MUST" for deployed ML models: Deployed ML models are part code, part data. 1 No data versioning means no model versioning. Data versioning platforms: DVC: Open source version control system for ML projects Pachyderm: version control for data Dolt: a SQL database with Git-like version control for data and schema 1.5. Data Processing Training data for production models may come from different sources, including Stored data in db and object stores, log processing, and outputs of other classifiers*. There are dependencies between tasks, each needs to be kicked off after its dependencies are finished. For example, training on new log data, requires a preprocessing step before training. Makefiles are not scalable. "Workflow manager"s become pretty essential in this regard. Workflow orchestration: Luigi by Spotify Airflow by Airbnb: Dynamic, extensible, elegant, and scalable (the most widely used) DAG workflow Robust conditional execution: retry in case of failure Pusher supports docker images with tensorflow serving Whole workflow in a single .py file Development, Training, and Evaluation 2.1. Software engineering Winner language: Python Editors: Vim Emacs VS Code (Recommended by the author): Built-in git staging and diff, Lint code, open projects remotely through ssh Notebooks: Great as starting point of the projects, hard to scale (fun fact: Netflix’s Notebook-Driven Architecture is an exception, which is entirely based on nteract suites). nteract: a next-gen React-based UI for Jupyter notebooks Papermill: is an nteract library built for parameterizing, executing, and analyzing* Jupyter Notebooks. Commuter: another nteract project which provides a read-only display of notebooks (e.g. from S3 buckets). Streamlit: interactive data science tool with applets Compute recommendations 1: For individuals or startups*: Development: a 4x Turing-architecture PC Training/Evaluation: Use the same 4x GPU PC. When running many experiments, either buy shared servers or use cloud instances. For large companies:* Development: Buy a 4x Turing-architecture PC per ML scientist or let them use V100 instances Training/Evaluation: Use cloud instances with proper provisioning and handling of failures Cloud Providers: GCP: option to connect GPUs to any instance + has TPUs AWS: 2.2. Resource Management Allocating free resources to programs Resource management options: Old school cluster job scheduler ( e.g. Slurm workload manager ) Docker + Kubernetes Kubeflow Polyaxon (paid features) 2.3. DL Frameworks Unless having a good reason not to, use Tensorflow/Keras or PyTorch. 1 The following figure shows a comparison between different frameworks on how they stand for "developement" and "production"*. 2.4. Experiment management Development, training, and evaluation strategy: Always start simple Train a small model on a small batch. Only if it works, scale to larger data and models, and hyperparameter tuning! Experiment management tools: Tensorboard provides the visualization and tooling needed for ML experimentation Losswise (Monitoring for ML) Comet: lets you track code, experiments, and results on ML projects Weights & Biases: Record and visualize every detail of your research with easy collaboration MLFlow Tracking: for logging parameters, code versions, metrics, and output files as well as visualization of the results. Automatic experiment tracking with one line of code in python Side by side comparison of experiments Hyper parameter tuning Supports Kubernetes based jobs 2.5. Hyperparameter Tuning Approaches: Grid search Random search Bayesian Optimization HyperBand and Asynchronous Successive Halving Algorithm (ASHA) Population-based Training Platforms: RayTune: Ray Tune is a Python library for hyperparameter tuning at any scale (with a focus on deep learning and deep reinforcement learning). Supports any machine learning framework, including PyTorch, XGBoost, MXNet, and Keras. Katib: Kubernete's Native System for Hyperparameter Tuning and Neural Architecture Search, inspired by Google vizier and supports multiple ML/DL frameworks (e.g. TensorFlow, MXNet, and PyTorch). Hyperas: a simple wrapper around hyperopt for Keras, with a simple template notation to define hyper-parameter ranges to tune. SIGOPT: a scalable, enterprise-grade optimization platform Sweeps from [Weights & Biases] (https://www.wandb.com/): Parameters are not explicitly specified by a developer. Instead they are approximated and learned by a machine learning model. Keras Tuner: A hyperparameter tuner for Keras, specifically for tf.keras with TensorFlow 2.0. 2.6. Distributed Training Data parallelism: Use it when iteration time is too long (both tensorflow and PyTorch support) Ray Distributed Training Model parallelism: when model does not fit on a single GPU Other solutions: Horovod Troubleshooting [TBD] Testing and Deployment 4.1. Testing and CI/CD Machine Learning production software requires a more diverse set of test suites than traditional software: Unit and Integration Testing: Types of tests: Training system tests: testing training pipeline Validation tests: testing prediction system on validation set Functionality tests: testing prediction system on few important examples Continuous Integration: Running tests after each new code change pushed to the repo SaaS for continuous integration: Argo: Open source Kubernetes native workflow engine for orchestrating parallel jobs (incudes workflows, events, CI and CD). CircleCI: Language-Inclusive Support, Custom Environments, Flexible Resource Allocation, used by instacart, Lyft, and StackShare. Travis CI Buildkite: Fast and stable builds, Open source agent runs on almost any machine and architecture, Freedom to use your own tools and services Jenkins: Old school build system 4.2. Web Deployment Consists of a Prediction System and a Serving System Prediction System: Process input data, make predictions Serving System (Web server): Serve prediction with scale in mind Use REST API to serve prediction HTTP requests Calls the prediction system to respond Serving options: Deploy to VMs, scale by adding instances Deploy as containers, scale via orchestration Containers Docker Container Orchestration: Kubernetes (the most popular now) MESOS Marathon Deploy code as a "serverless function" Deploy via a model serving solution Model serving: Specialized web deployment for ML models Batches request for GPU inference Frameworks: Tensorflow serving MXNet Model server Clipper (Berkeley) SaaS solutions Seldon: serve and scale models built in any framework on Kubernetes Algorithmia Decision making: CPU or GPU? CPU inference: CPU inference is preferable if it meets the requirements. Scale by adding more servers, or going serverless. GPU inference: TF serving or Clipper Adaptive batching is useful (Bonus) Deploying Jupyter Notebooks: Kubeflow Fairing is a hybrid deployment package that let's you deploy your Jupyter notebook* codes! 4.5 Service Mesh and Traffic Routing Transition from monolithic applications towards a distributed microservice architecture could be challenging. A Service mesh (consisting of a network of microservices) reduces the complexity of such deployments, and eases the strain on development teams. Istio: a service mesh to ease creation of a network of deployed services with load balancing, service-to-service authentication, monitoring, with few or no code changes in service code. 4.4. Monitoring: Purpose of monitoring: Alerts for downtime, errors, and distribution shifts Catching service and data regressions Cloud providers solutions are decent Kiali:an observability console for Istio with service mesh configuration capabilities. It answers these questions: How are the microservices connected? How are they performing? Are we done? 4.5. Deploying on Embedded and Mobile Devices Main challenge: memory footprint and compute constraints Solutions: Quantization Reduced model size MobileNets Knowledge Distillation DistillBERT (for NLP) Embedded and Mobile Frameworks: Tensorflow Lite PyTorch Mobile Core ML ML Kit FRITZ OpenVINO Model Conversion: Open Neural Network Exchange (ONNX): open-source format for deep learning models 4.6. All-in-one solutions Tensorflow Extended (TFX) Michelangelo (Uber) Google Cloud AI Platform Amazon SageMaker Neptune FLOYD Paperspace Determined AI Domino data lab Tensorflow Extended (TFX) [TBD] Airflow and KubeFlow ML Pipelines [TBD] Other useful links: Lessons learned from building practical deep learning systems Machine Learning: The High Interest Credit Card of Technical Debt Contributing References: [1]: Full Stack Deep Learning Bootcamp, Nov 2019. [2]: Advanced KubeFlow Workshop by Pipeline.ai, 2019. [3]: TFX: Real World Machine Learning in Production

#github#production-level-deep-learning

github

0.491

0.09890315835809398

hackobi•Mar 28, 2025

AI-Scalpel-Trading-Bot

AI-Scalpel-Trading-Bot Disclaimer This software is for educational purposes only. Do not risk money which you are afraid to lose. USE THE SOFTWARE AT YOUR OWN RISK. THE AUTHORS AND ALL AFFILIATES ASSUME NO RESPONSIBILITY FOR YOUR TRADING RESULTS. Always start by running a trading bot in Dry-run and do not engage money before you understand how it works and what profit/loss you should expect. This is an implementation of freqtrade where different machine learning implementations will be tested. Freqtrade is a free and open source crypto trading bot written in Python. It is designed to support all major exchanges and be controlled via Telegram. It contains backtesting, plotting and money management tools as well as strategy optimization by machine learning. !freqtrade Exchange marketplaces supported [X] Bittrex [X] Binance (*Note for binance users) [ ] 113 others to tests. (Some of them might not work) Documentation Documentation. Features [x] Based on Python 3.6+: For botting on any operating system - Windows, macOS and Linux. [x] Persistence: Persistence is achieved through sqlite. [x] Dry-run: Run the bot without playing money. [x] Backtesting: Run a simulation of your buy/sell strategy. [x] Strategy Optimization by machine learning: Use machine learning to optimize your buy/sell strategy parameters with real exchange data. [x] Edge position sizing Calculate your win rate, risk reward ratio, the best stoploss and adjust your position size before taking a position for each specific market. Learn more. [x] Whitelist crypto-currencies: Select which crypto-currency you want to trade or use dynamic whitelists. [x] Blacklist crypto-currencies: Select which crypto-currency you want to avoid. [x] Manageable via Telegram: Manage the bot with Telegram. [x] Display profit/loss in fiat: Display your profit/loss in 33 fiat. [x] Daily summary of profit/loss: Provide a daily summary of your profit/loss. [x] Performance status report: Provide a performance status of your current trades. Quick start Freqtrade provides a Linux/macOS script to install all dependencies and help you to configure the bot. Other installations. Basic Usage Bot commands Telegram RPC commands Telegram is not mandatory. However, this is a great way to control your bot. More details on our documentation /start: Starts the trader /stop: Stops the trader /status [table]: Lists all open trades /count: Displays number of open trades /profit: Lists cumulative profit from all finished trades /forcesell |all: Instantly sells the given trade (Ignoring minimum_roi). /performance: Show performance of each finished trade grouped by pair /balance: Show account balance per currency /daily : Shows profit or loss per day, over the last n days /help: Show help message /version: Show version Development branches The project is currently setup in two main branches: develop - This branch has often new features, but might also cause breaking changes. master - This branch contains the latest stable release. The bot 'should' be stable on this branch, and is generally well tested. feat/* - These are feature branches, which are being worked on heavily. Please don't use these unless you want to test a specific feature. A note on Binance For Binance, please add "BNB/" to your blacklist to avoid issues. Accounts having BNB accounts use this to pay for fees - if your first trade happens to be on BNB, further trades will consume this position and make the initial BNB order unsellable as the expected amount is not there anymore. Support Help / Slack For any questions not covered by the documentation or for further information about the bot, I encourage you to join freqtrade's slack channel. Click here to join Slack channel. Bugs / Issues If you discover a bug in the bot, please search their issue tracker first. If it hasn't been reported, please create a new issue and ensure you follow the template guide so that our team can assist you as quickly as possible. Feature Requests Have you a great idea to improve the bot you want to share? Please, first search if this feature was not already discussed. If it hasn't been requested, please create a new request and ensure you follow the template guide so that it does not get lost in the bug reports. Pull Requests Feel like the bot is missing a feature? Keep em pull requests coming! Please read the Contributing document to understand the requirements before sending pull-requests. Coding is not a neccessity to contribute - maybe start with improving our documentation? Issues labeled good first issue can be good first contributions, and will help get you familiar with the codebase. Note before starting any major new feature work, please open an issue describing what you are planning to do or talk to the team on Slack. This will ensure that interested parties can give valuable feedback on the feature, and let others know that you are working on it. Important: Always create your PR against the develop branch, not master. Requirements Uptodate clock The clock must be accurate, syncronized to a NTP server very frequently to avoid problems with communication to the exchanges. Min hardware required To run this bot we recommend you a cloud instance with a minimum of: Minimal (advised) system requirements: 2GB RAM, 1GB disk space, 2vCPU Software requirements Python 3.6.x pip git TA-Lib virtualenv (Recommended) Docker (Recommended)

#github#ai-scalpel-trading-bot

github

0.575

0.33114909407186394

aimacode•Mar 28, 2025

aima-python

aima-python Python code for the book Artificial Intelligence: A Modern Approach. You can use this in conjunction with a course on AI, or for study on your own. We're looking for solid contributors to help. Updates for 4th Edition The 4th edition of the book as out now in 2020, and thus we are updating the code. All code here will reflect the 4th edition. Changes include: Move from Python 3.5 to 3.7. More emphasis on Jupyter (Ipython) notebooks. More projects using external packages (tensorflow, etc.). Structure of the Project When complete, this project will have Python implementations for all the pseudocode algorithms in the book, as well as tests and examples of use. For each major topic, such as search, we provide the following files: search.ipynb and search.py: Implementations of all the pseudocode algorithms, and necessary support functions/classes/data. The .py file is generated automatically from the .ipynb file; the idea is that it is easier to read the documentation in the .ipynb file. search_XX.ipynb: Notebooks that show how to use the code, broken out into various topics (the XX). tests/test_search.py: A lightweight test suite, using assert statements, designed for use with py.test, but also usable on their own. Python 3.7 and up The code for the 3rd edition was in Python 3.5; the current 4th edition code is in Python 3.7. It should also run in later versions, but does not run in Python 2. You can install Python or use a browser-based Python interpreter such as repl.it. You can run the code in an IDE, or from the command line with python -i filename.py where the -i option puts you in an interactive loop where you can run Python functions. All notebooks are available in a binder environment. Alternatively, visit jupyter.org for instructions on setting up your own Jupyter notebook environment. Features from Python 3.6 and 3.7 that we will be using for this version of the code: f-strings: all string formatting should be done with f'var = {var}', not with 'var = {}'.format(var) nor 'var = %s' % var. typing module: declare functions with type hints: def successors(state) -> List[State]:; that is, give type declarations, but omit them when it is obvious. I don't need to say state: State, but in another context it would make sense to say s: State. Underscores in numerics: write a million as 1000000 not as 1000000. dataclasses module: replace namedtuple with dataclass. [//]: (There is a sibling [aima-docker]https://github.com/rajatjain1997/aima-docker project that shows you how to use docker containers to run more complex problems in more complex software environments.) Installation Guide To download the repository: git clone https://github.com/aimacode/aima-python.git Then you need to install the basic dependencies to run the project on your system: You also need to fetch the datasets from the aima-data repository: Wait for the datasets to download, it may take a while. Once they are downloaded, you need to install pytest, so that you can run the test suite: pip install pytest Then to run the tests: py.test And you are good to go! Index of Algorithms Here is a table of algorithms, the figure, name of the algorithm in the book and in the repository, and the file where they are implemented in the repository. This chart was made for the third edition of the book and is being updated for the upcoming fourth edition. Empty implementations are a good place for contributors to look for an issue. The aima-pseudocode project describes all the algorithms from the book. An asterisk next to the file name denotes the algorithm is not fully implemented. Another great place for contributors to start is by adding tests and writing on the notebooks. You can see which algorithms have tests and notebook sections below. If the algorithm you want to work on is covered, don't worry! You can still add more tests and provide some examples of use in the notebook! | Figure | Name (in 3rd edition) | Name (in repository) | File | Tests | Notebook |:-------|:----------------------------------|:------------------------------|:--------------------------------|:-----|:---------| | 2 | Random-Vacuum-Agent | RandomVacuumAgent | [agents.py][agents] | Done | Included | | 2 | Model-Based-Vacuum-Agent | ModelBasedVacuumAgent | [agents.py][agents] | Done | Included | | 2.1 | Environment | Environment | [agents.py][agents] | Done | Included | | 2.1 | Agent | Agent | [agents.py][agents] | Done | Included | | 2.3 | Table-Driven-Vacuum-Agent | TableDrivenVacuumAgent | [agents.py][agents] | Done | Included | | 2.7 | Table-Driven-Agent | TableDrivenAgent | [agents.py][agents] | Done | Included | | 2.8 | Reflex-Vacuum-Agent | ReflexVacuumAgent | [agents.py][agents] | Done | Included | | 2.10 | Simple-Reflex-Agent | SimpleReflexAgent | [agents.py][agents] | Done | Included | | 2.12 | Model-Based-Reflex-Agent | ReflexAgentWithState | [agents.py][agents] | Done | Included | | 3 | Problem | Problem | [search.py][search] | Done | Included | | 3 | Node | Node | [search.py][search] | Done | Included | | 3 | Queue | Queue | [utils.py][utils] | Done | No Need | | 3.1 | Simple-Problem-Solving-Agent | SimpleProblemSolvingAgent | [search.py][search] | Done | Included | | 3.2 | Romania | romania | [search.py][search] | Done | Included | | 3.7 | Tree-Search | depth/breadthfirsttree_search | [search.py][search] | Done | Included | | 3.7 | Graph-Search | depth/breadthfirstgraph_search | [search.py][search] | Done | Included | | 3.11 | Breadth-First-Search | breadthfirstgraph_search | [search.py][search] | Done | Included | | 3.14 | Uniform-Cost-Search | uniformcostsearch | [search.py][search] | Done | Included | | 3.17 | Depth-Limited-Search | depthlimitedsearch | [search.py][search] | Done | Included | | 3.18 | Iterative-Deepening-Search | iterativedeepeningsearch | [search.py][search] | Done | Included | | 3.22 | Best-First-Search | bestfirstgraph_search | [search.py][search] | Done | Included | | 3.24 | A\*-Search | astar_search | [search.py][search] | Done | Included | | 3.26 | Recursive-Best-First-Search | recursivebestfirst_search | [search.py][search] | Done | Included | | 4.2 | Hill-Climbing | hill_climbing | [search.py][search] | Done | Included | | 4.5 | Simulated-Annealing | simulated_annealing | [search.py][search] | Done | Included | | 4.8 | Genetic-Algorithm | genetic_algorithm | [search.py][search] | Done | Included | | 4.11 | And-Or-Graph-Search | andorgraph_search | [search.py][search] | Done | Included | | 4.21 | Online-DFS-Agent | onlinedfsagent | [search.py][search] | Done | Included | | 4.24 | LRTA\*-Agent | LRTAStarAgent | [search.py][search] | Done | Included | | 5.3 | Minimax-Decision | minimax_decision | [games.py][games] | Done | Included | | 5.7 | Alpha-Beta-Search | alphabeta_search | [games.py][games] | Done | Included | | 6 | CSP | CSP | [csp.py][csp] | Done | Included | | 6.3 | AC-3 | AC3 | [csp.py][csp] | Done | Included | | 6.5 | Backtracking-Search | backtracking_search | [csp.py][csp] | Done | Included | | 6.8 | Min-Conflicts | min_conflicts | [csp.py][csp] | Done | Included | | 6.11 | Tree-CSP-Solver | treecspsolver | [csp.py][csp] | Done | Included | | 7 | KB | KB | [logic.py][logic] | Done | Included | | 7.1 | KB-Agent | KB_AgentProgram | [logic.py][logic] | Done | Included | | 7.7 | Propositional Logic Sentence | Expr | [utils.py][utils] | Done | Included | | 7.10 | TT-Entails | tt_entails | [logic.py][logic] | Done | Included | | 7.12 | PL-Resolution | pl_resolution | [logic.py][logic] | Done | Included | | 7.14 | Convert to CNF | to_cnf | [logic.py][logic] | Done | Included | | 7.15 | PL-FC-Entails? | plfcentails | [logic.py][logic] | Done | Included | | 7.17 | DPLL-Satisfiable? | dpll_satisfiable | [logic.py][logic] | Done | Included | | 7.18 | WalkSAT | WalkSAT | [logic.py][logic] | Done | Included | | 7.20 | Hybrid-Wumpus-Agent | HybridWumpusAgent | | | | | 7.22 | SATPlan | SAT_plan | [logic.py][logic] | Done | Included | | 9 | Subst | subst | [logic.py][logic] | Done | Included | | 9.1 | Unify | unify | [logic.py][logic] | Done | Included | | 9.3 | FOL-FC-Ask | folfcask | [logic.py][logic] | Done | Included | | 9.6 | FOL-BC-Ask | folbcask | [logic.py][logic] | Done | Included | | 10.1 | Air-Cargo-problem | air_cargo | [planning.py][planning] | Done | Included | | 10.2 | Spare-Tire-Problem | spare_tire | [planning.py][planning] | Done | Included | | 10.3 | Three-Block-Tower | threeblocktower | [planning.py][planning] | Done | Included | | 10.7 | Cake-Problem | havecakeandeatcake_too | [planning.py][planning] | Done | Included | | 10.9 | Graphplan | GraphPlan | [planning.py][planning] | Done | Included | | 10.13 | Partial-Order-Planner | PartialOrderPlanner | [planning.py][planning] | Done | Included | | 11.1 | Job-Shop-Problem-With-Resources | jobshopproblem | [planning.py][planning] | Done | Included | | 11.5 | Hierarchical-Search | hierarchical_search | [planning.py][planning] | Done | Included | | 11.8 | Angelic-Search | angelic_search | [planning.py][planning] | Done | Included | | 11.10 | Doubles-tennis | doubletennisproblem | [planning.py][planning] | Done | Included | | 13 | Discrete Probability Distribution | ProbDist | [probability.py][probability] | Done | Included | | 13.1 | DT-Agent | DTAgent | [probability.py][probability] | Done | Included | | 14.9 | Enumeration-Ask | enumeration_ask | [probability.py][probability] | Done | Included | | 14.11 | Elimination-Ask | elimination_ask | [probability.py][probability] | Done | Included | | 14.13 | Prior-Sample | prior_sample | [probability.py][probability] | Done | Included | | 14.14 | Rejection-Sampling | rejection_sampling | [probability.py][probability] | Done | Included | | 14.15 | Likelihood-Weighting | likelihood_weighting | [probability.py][probability] | Done | Included | | 14.16 | Gibbs-Ask | gibbs_ask | [probability.py][probability] | Done | Included | | 15.4 | Forward-Backward | forward_backward | [probability.py][probability] | Done | Included | | 15.6 | Fixed-Lag-Smoothing | fixedlagsmoothing | [probability.py][probability] | Done | Included | | 15.17 | Particle-Filtering | particle_filtering | [probability.py][probability] | Done | Included | | 16.9 | Information-Gathering-Agent | InformationGatheringAgent | [probability.py][probability] | Done | Included | | 17.4 | Value-Iteration | value_iteration | [mdp.py][mdp] | Done | Included | | 17.7 | Policy-Iteration | policy_iteration | [mdp.py][mdp] | Done | Included | | 17.9 | POMDP-Value-Iteration | pomdpvalueiteration | [mdp.py][mdp] | Done | Included | | 18.5 | Decision-Tree-Learning | DecisionTreeLearner | [learning.py][learning] | Done | Included | | 18.8 | Cross-Validation | cross_validation | [learning.py][learning]\* | | | | 18.11 | Decision-List-Learning | DecisionListLearner | [learning.py][learning]\* | | | | 18.24 | Back-Prop-Learning | BackPropagationLearner | [learning.py][learning] | Done | Included | | 18.34 | AdaBoost | AdaBoost | [learning.py][learning] | Done | Included | | 19.2 | Current-Best-Learning | currentbestlearning | knowledge.py | Done | Included | | 19.3 | Version-Space-Learning | versionspacelearning | knowledge.py | Done | Included | | 19.8 | Minimal-Consistent-Det | minimalconsistentdet | knowledge.py | Done | Included | | 19.12 | FOIL | FOIL_container | knowledge.py | Done | Included | | 21.2 | Passive-ADP-Agent | PassiveADPAgent | [rl.py][rl] | Done | Included | | 21.4 | Passive-TD-Agent | PassiveTDAgent | [rl.py][rl] | Done | Included | | 21.8 | Q-Learning-Agent | QLearningAgent | [rl.py][rl] | Done | Included | | 22.1 | HITS | HITS | [nlp.py][nlp] | Done | Included | | 23 | Chart-Parse | Chart | [nlp.py][nlp] | Done | Included | | 23.5 | CYK-Parse | CYK_parse | [nlp.py][nlp] | Done | Included | | 25.9 | Monte-Carlo-Localization | montecarlolocalization | [probability.py][probability] | Done | Included | Index of data structures Here is a table of the implemented data structures, the figure, name of the implementation in the repository, and the file where they are implemented. | Figure | Name (in repository) | File | |:-------|:--------------------------------|:--------------------------| | 3.2 | romania_map | [search.py][search] | | 4.9 | vacumm_world | [search.py][search] | | 4.23 | onedimstate_space | [search.py][search] | | 6.1 | australia_map | [search.py][search] | | 7.13 | wumpusworldinference | [logic.py][logic] | | 7.16 | hornclausesKB | [logic.py][logic] | | 17.1 | sequentialdecisionenvironment | [mdp.py][mdp] | | 18.2 | waitingdecisiontree | [learning.py][learning] | Acknowledgements Many thanks for contributions over the years. I got bug reports, corrected code, and other support from Darius Bacon, Phil Ruggera, Peng Shao, Amit Patil, Ted Nienstedt, Jim Martin, Ben Catanzariti, and others. Now that the project is on GitHub, you can see the contributors who are doing a great job of actively improving the project. Many thanks to all contributors, especially @darius, @SnShine, @reachtarunhere, @antmarakis, @Chipe1, @ad71 and @MariannaSpyrakou. [agents]:../master/agents.py [csp]:../master/csp.py [games]:../master/games.py [grid]:../master/grid.py [knowledge]:../master/knowledge.py [learning]:../master/learning.py [logic]:../master/logic.py [mdp]:../master/mdp.py [nlp]:../master/nlp.py [planning]:../master/planning.py [probability]:../master/probability.py [rl]:../master/rl.py [search]:../master/search.py [utils]:../master/utils.py [text]:../master/text.py

#github#aima-python

github

0.548

0.27921589729164453

microsoft•Mar 28, 2025

RD-Agent

🖥️ Live Demo | 🎥 Demo Video ▶️YouTube | 📖 Documentation | 📃 Papers Data Science Agent Preview Check out our demo video showcasing the current progress of our Data Science Agent under development: https://github.com/user-attachments/assets/3eccbecb-34a4-4c81-bce4-d3f8862f7305 📰 News | 🗞️ News | 📝 Description | | -- | ------ | | Support LiteLLM Backend | We now fully support LiteLLM as a backend for integration with multiple LLM providers. | | More General Data Science Agent | 🚀Coming soon! | | Kaggle Scenario release | We release Kaggle Agent, try the new features! | | Official WeChat group release | We created a WeChat group, welcome to join! (🗪QR Code) | | Official Discord release | We launch our first chatting channel in Discord (🗪) | | First release | RDAgent is released on GitHub | 🌟 Introduction RDAgent aims to automate the most critical and valuable aspects of the industrial R&D process, and we begin with focusing on the data-driven scenarios to streamline the development of models and data. Methodologically, we have identified a framework with two key components: 'R' for proposing new ideas and 'D' for implementing them. We believe that the automatic evolution of R&D will lead to solutions of significant industrial value. R&D is a very general scenario. The advent of RDAgent can be your 💰 Automatic Quant Factory (🎥Demo Video|▶️YouTube) 🤖 Data Mining Agent: Iteratively proposing data & models (🎥Demo Video 1|▶️YouTube) (🎥Demo Video 2|▶️YouTube) and implementing them by gaining knowledge from data. 🦾 Research Copilot: Auto read research papers (🎥Demo Video|▶️YouTube) / financial reports (🎥Demo Video|▶️YouTube) and implement model structures or building datasets. 🤖 Kaggle Agent: Auto Model Tuning and Feature Engineering([🎥Demo Video Coming Soon...]()) and implementing them to achieve more in competitions. ... You can click the links above to view the demo. We're continuously adding more methods and scenarios to the project to enhance your R&D processes and boost productivity. Additionally, you can take a closer look at the examples in our 🖥️ Live Demo. ⚡ Quick start You can try above demos by running the following command: 🐳 Docker installation. Users must ensure Docker is installed before attempting most scenarios. Please refer to the official 🐳Docker page for installation instructions. Ensure the current user can run Docker commands without using sudo. You can verify this by executing docker run hello-world. 🐍 Create a Conda Environment Create a new conda environment with Python (3.10 and 3.11 are well-tested in our CI): Activate the environment: 🛠️ Install the RDAgent You can directly install the RDAgent package from PyPI: 💊 Health check rdagent provides a health check that currently checks two things. whether the docker installation was successful. whether the default port used by the rdagent ui is occupied. ⚙️ Configuration The demos requires following ability: ChatCompletion json_mode embedding query For example: If you are using the OpenAI API, you have to configure your GPT model in the .env file like this. However, not every API services support these features by default. For example: AZURE OpenAI, you have to configure your GPT model in the .env file like this. We now support LiteLLM as a backend for integration with multiple LLM providers. If you use LiteLLM Backend to use models, you can configure as follows: For more configuration information, please refer to the documentation. 🚀 Run the Application The 🖥️ Live Demo is implemented by the following commands(each item represents one demo, you can select the one you prefer): Run the Automated Quantitative Trading & Iterative Factors Evolution: Qlib self-loop factor proposal and implementation application Run the Automated Quantitative Trading & Iterative Model Evolution: Qlib self-loop model proposal and implementation application Run the Automated Medical Prediction Model Evolution: Medical self-loop model proposal and implementation application (1) Apply for an account at PhysioNet. (2) Request access to FIDDLE preprocessed data: FIDDLE Dataset. (3) Place your username and password in .env. Run the Automated Quantitative Trading & Factors Extraction from Financial Reports: Run the Qlib factor extraction and implementation application based on financial reports Run the Automated Model Research & Development Copilot: model extraction and implementation application Run the Automated Kaggle Model Tuning & Feature Engineering: self-loop model proposal and feature engineering implementation application Using sf-crime (San Francisco Crime Classification) as an example. Register and login on the Kaggle website. Configuring the Kaggle API. (1) Click on the avatar (usually in the top right corner of the page) -> Settings -> Create New Token, A file called kaggle.json will be downloaded. (2) Move kaggle.json to ~/.config/kaggle/ (3) Modify the permissions of the kaggle.json file. Reference command: chmod 600 ~/.config/kaggle/kaggle.json Join the competition: Click Join the competition -> I Understand and Accept at the bottom of the competition details page. Description of the above example: Kaggle competition data, contains two parts: competition description file (json file) and competition dataset (zip file). We prepare the competition description file for you, the competition dataset will be downloaded automatically when you run the program, as in the example. If you want to download the competition description file automatically, you need to install chromedriver, The instructions for installing chromedriver can be found in the documentation. The Competition List Available can be found here. 🖥️ Monitor the Application Results You can run the following command for our demo program to see the run logs. Note: Although port 19899 is not commonly used, but before you run this demo, you need to check if port 19899 is occupied. If it is, please change it to another port that is not occupied. You can check if a port is occupied by running the following command. 🏭 Scenarios We have applied RD-Agent to multiple valuable data-driven industrial scenarios. 🎯 Goal: Agent for Data-driven R&D In this project, we are aiming to build an Agent to automate Data-Driven R\&D that can 📄 Read real-world material (reports, papers, etc.) and extract key formulas, descriptions of interested features and models, which are the key components of data-driven R&D . 🛠️ Implement the extracted formulas (e.g., features, factors, and models) in runnable codes. Due to the limited ability of LLM in implementing at once, build an evolving process for the agent to improve performance by learning from feedback and knowledge. 💡 Propose new ideas based on current knowledge and observations. 📈 Scenarios/Demos In the two key areas of data-driven scenarios, model implementation and data building, our system aims to serve two main roles: 🦾Copilot and 🤖Agent. The 🦾Copilot follows human instructions to automate repetitive tasks. The 🤖Agent, being more autonomous, actively proposes ideas for better results in the future. The supported scenarios are listed below: | Scenario/Target | Model Implementation | Data Building | | -- | -- | -- | | 💹 Finance | 🤖 Iteratively Proposing Ideas & Evolving▶️YouTube | 🤖 Iteratively Proposing Ideas & Evolving ▶️YouTube 🦾 Auto reports reading & implementation▶️YouTube | | 🩺 Medical | 🤖 Iteratively Proposing Ideas & Evolving▶️YouTube | - | | 🏭 General | 🦾 Auto paper reading & implementation▶️YouTube 🤖 Auto Kaggle Model Tuning | 🤖Auto Kaggle feature Engineering | RoadMap: Currently, we are working hard to add new features to the Kaggle scenario. Different scenarios vary in entrance and configuration. Please check the detailed setup tutorial in the scenarios documents. Here is a gallery of successful explorations (5 traces showed in 🖥️ Live Demo). You can download and view the execution trace using this command from the documentation. Please refer to 📖readthedocs_scen for more details of the scenarios. ⚙️ Framework Automating the R&D process in data science is a highly valuable yet underexplored area in industry. We propose a framework to push the boundaries of this important research field. The research questions within this framework can be divided into three main categories: | Research Area | Paper/Work List | |--------------------|-----------------| | Benchmark the R&D abilities | Benchmark | | Idea proposal: Explore new ideas or refine existing ones | Research | | Ability to realize ideas: Implement and execute ideas | Development | We believe that the key to delivering high-quality solutions lies in the ability to evolve R&D capabilities. Agents should learn like human experts, continuously improving their R&D skills. More documents can be found in the 📖 readthedocs. 📃 Paper/Work list 📊 Benchmark Towards Data-Centric Automatic R&D !image 🔍 Research In a data mining expert's daily research and development process, they propose a hypothesis (e.g., a model structure like RNN can capture patterns in time-series data), design experiments (e.g., finance data contains time-series and we can verify the hypothesis in this scenario), implement the experiment as code (e.g., Pytorch model structure), and then execute the code to get feedback (e.g., metrics, loss curve, etc.). The experts learn from the feedback and improve in the next iteration. Based on the principles above, we have established a basic method framework that continuously proposes hypotheses, verifies them, and gets feedback from the real-world practice. This is the first scientific research automation framework that supports linking with real-world verification. For more detail, please refer to our 🖥️ Live Demo page. 🛠️ Development Collaborative Evolving Strategy for Automatic Data-Centric Development !image 🤝 Contributing We welcome contributions and suggestions to improve RD-Agent. Please refer to the Contributing Guide for more details on how to contribute. Before submitting a pull request, ensure that your code passes the automatic CI checks. 📝 Guidelines This project welcomes contributions and suggestions. Contributing to this project is straightforward and rewarding. Whether it's solving an issue, addressing a bug, enhancing documentation, or even correcting a typo, every contribution is valuable and helps improve RDAgent. To get started, you can explore the issues list, or search for TODO: comments in the codebase by running the command grep -r "TODO:". Before we released RD-Agent as an open-source project on GitHub, it was an internal project within our group. Unfortunately, the internal commit history was not preserved when we removed some confidential code. As a result, some contributions from our group members, including Haotian Chen, Wenjun Feng, Haoxue Wang, Zeqi Ye, Xinjie Shen, and Jinhui Li, were not included in the public commits. ⚖️ Legal disclaimer The RD-agent is provided “as is”, without warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose and noninfringement. The RD-agent is aimed to facilitate research and development process in the financial industry and not ready-to-use for any financial investment or advice. Users shall independently assess and test the risks of the RD-agent in a specific use scenario, ensure the responsible use of AI technology, including but not limited to developing and integrating risk mitigation measures, and comply with all applicable laws and regulations in all applicable jurisdictions. The RD-agent does not provide financial opinions or reflect the opinions of Microsoft, nor is it designed to replace the role of qualified financial professionals in formulating, assessing, and approving finance products. The inputs and outputs of the RD-agent belong to the users and users shall assume all liability under any theory of liability, whether in contract, torts, regulatory, negligence, products liability, or otherwise, associated with use of the RD-agent and any inputs and outputs thereof.

#github#rd-agent

github

0.515

0.023472450495103967

instill-ai•Mar 28, 2025

instill-core

🔮 Instill Core A complete unstructured data solution: ETL processing, AI-readiness, open-source LLM hosting, and RAG capabilities in one powerful platform. Quick start Follow the installation steps below or documentation for more details to build versatile AI applications locally. What is Instill Core? Instill Core is an end-to-end AI platform for data, pipeline and model orchestration. 🔮 Instill Core simplifies infrastructure hassle and encompasses these core features: 💧 Pipeline: Quickly build versatile AI-first APIs or automated workflows. ⚗️ Model: Deploy and monitor AI models without GPU infrastructure hassles. 💾 Artifact: Transform unstructured data (e.g., documents, images, audio, video) into AI-ready formats. ⚙️ Component: Connect essential building blocks to construct powerful pipelines. What can you build? 📖 Parsing PDF Files to Markdown: Cookbook 🧱 Generating Structured Outputs from LLMs: Cookbook & Tutorial 🕸️ Web scraping & Google Search with Structured Insights 🌱 Instance segmentation on microscopic plant stomata images: Cookbook See Examples for more! Installation Prerequisites | Operating System | Requirements and Instructions | | ---------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | macOS or Linux | Instill Core works natively | | Windows | • Use Windows Subsystem for Linux (WSL2)• Install latest yq from GitHub Repository• Install latest Docker Desktop and enable WSL2 integration (tutorial)• (Optional) Install cuda-toolkit on WSL2 (NVIDIA tutorial) | | All Systems | • Docker Engine v25 or later• Docker Compose v2 or later• Install latest stable Docker and Docker Compose | Steps Use stable release version Execute the following commands to pull pre-built images with all the dependencies to launch: [!NOTE] We have restructured our project repositories. If you need to access 🔮 Instill Core projects up to version v0.13.0-beta, please refer to the instill-ai/deprecated-core repository. Use the latest version for local development Execute the following commands to build images with all the dependencies to launch: [!IMPORTANT] Code in the main branch tracks under-development progress towards the next release and may not work as expected. If you are looking for a stable alpha version, please use latest release. 🚀 That's it! Once all the services are up with health status, the UI is ready to go at . Please find the default login credentials in the documentation. To shut down all running services: Deployment Visit the Deployment Overview for more details. Client Access 📺 Console ⌨️ CLI 📦 SDK: Python SDK TypeScript SDK Stay tuned, as more SDKs are on the way! Documentation Please visit our official documentation for more. Additional resources: API Reference Cookbooks Tutorials Examples Contributing We welcome contributions from our community! Checkout the methods below: Cookbooks: Help us create helpful pipelines and guides for the community. Visit our Cookbook repository to get started. Issues: Contribute to improvements by raising tickets using templates here or discuss in existing ones you think you can help with. Community Standards We are committed to maintaining a respectful and welcoming atmosphere for all contributors. Before contributing, please read: Contributing Guidelines Code of Conduct Support Get help by joining our Discord community where you can post any questions on our #ask-for-help channel. Contributors ✨ Thank you to all these wonderful people (emoji key): Vibhor Bhatt Miguel Ortiz Sajda Kabir Henry Chen Hari Bhandari Shiva Gaire Zubeen ShihChun-H Ikko Eltociear Ashimine Farookh Zaheer Siddiqui Brian Gallagher hairyputtar David Marx Deniz Parlak Po-Yu Chen Po Chun Chiu Sarthak HR Wu phelan Chang, Hui-Tang Xiaofei Du Ping-Lin Chang Tony Wang Pratik date Juan Vallés Naman Anand totuslink Praharsh Jain Utsav Paul CaCaBlocker Rafael Melo Jeremy Shih Romit Mohane ChunHao Amelia C 楊竣凱 andre.liang Zoodane George Strong Anni Mubeen Kodvavi RCKT Wojciech Bandzerewicz Gary Leo felixcorleone Zoe Daniel Manul Thanura Akash Jana Anish0203 Prathamesh Tugaonkar Shubham This project follows the all-contributors specification. Contributions of any kind welcome! License See the LICENSE file for licensing information.

#github#instill-core

github

0.539

0.20972324263626374

beehive-lab•Mar 28, 2025

TornadoVM

TornadoVM !TornadoVM version TornadoVM is a plug-in to OpenJDK and GraalVM that allows programmers to automatically run Java programs on heterogeneous hardware. TornadoVM targets OpenCL, PTX and SPIR-V compatible devices which include multi-core CPUs, dedicated GPUs (Intel, NVIDIA, AMD), integrated GPUs (Intel HD Graphics and ARM Mali), and FPGAs (Intel and Xilinx). TornadoVM has three backends that generate OpenCL C, NVIDIA CUDA PTX assembly, and SPIR-V binary. Developers can choose which backends to install and run. Website: tornadovm.org Documentation: https://tornadovm.readthedocs.io/en/latest/ For a quick introduction please read the following FAQ. Latest Release: TornadoVM 1.0.10 - 31/01/2025 : See CHANGELOG. Installation In Linux and macOS, TornadoVM can be installed automatically with the installation script. For example: NOTE Select the desired backend: opencl: Enables the OpenCL backend (requires OpenCL drivers) ptx: Enables the PTX backend (requires NVIDIA CUDA drivers) spirv: Enables the SPIRV backend (requires Intel Level Zero drivers) Example of installation: Alternatively, TornadoVM can be installed either manually from source or by using Docker. If you are planning to use Docker with TornadoVM on GPUs, you can also follow these guidelines. You can also run TornadoVM on Amazon AWS CPUs, GPUs, and FPGAs following the instructions here. Usage Instructions TornadoVM is currently being used to accelerate machine learning and deep learning applications, computer vision, physics simulations, financial applications, computational photography, and signal processing. Featured use-cases: kfusion-tornadovm: Java application for accelerating a computer-vision application using the Tornado-APIs to run on discrete and integrated GPUs. Java Ray-Tracer: Java application accelerated with TornadoVM for real-time ray-tracing. We also have a set of examples that includes NBody, DFT, KMeans computation and matrix computations. Additional Information General Documentation Benchmarks How TornadoVM executes reductions Execution Flags FPGA execution Profiler Usage Programming Model TornadoVM exposes to the programmer task-level, data-level and pipeline-level parallelism via a light Application Programming Interface (API). In addition, TornadoVM uses single-source property, in which the code to be accelerated and the host code live in the same Java program. Compute-kernels in TornadoVM can be programmed using two different approaches (APIs): a) Loop Parallel API Compute kernels are written in a sequential form (tasks programmed for a single thread execution). To express parallelism, TornadoVM exposes two annotations that can be used in loops and parameters: a) @Parallel for annotating parallel loops; and b) @Reduce for annotating parameters used in reductions. The following code snippet shows a full example to accelerate Matrix-Multiplication using TornadoVM and the loop-parallel API: To run TornadoVM, you need to either install the TornadoVM extension for GraalVM/OpenJDK, or run with our Docker images. Additional Resources Here you can find videos, presentations, tech-articles and artefacts describing TornadoVM, and how to use it. Academic Publications If you are using TornadoVM >= 0.2 (which includes the Dynamic Reconfiguration, the initial FPGA support and CPU/GPU reductions), please use the following citation: If you are using Tornado 0.1 (Initial release), please use the following citation in your work. Selected publications can be found here. Acknowledgments This work is partially funded by Intel corporation. In addition, it has been supported by the following EU & UKRI grants (most recent first): EU Horizon Europe & UKRI AERO 101092850. EU Horizon Europe & UKRI INCODE 101093069. EU Horizon Europe & UKRI ENCRYPT 101070670. EU Horizon Europe & UKRI TANGO 101070052. EU Horizon 2020 ELEGANT 957286. EU Horizon 2020 E2Data 780245. EU Horizon 2020 ACTiCLOUD 732366. Furthermore, TornadoVM has been supported by the following EPSRC grants: PAMELA EP/K008730/1. AnyScale Apps EP/L000725/1. Contributions and Collaborations We welcome collaborations! Please see how to contribute to the project in the CONTRIBUTING page. Write your questions and proposals: Additionally, you can open new proposals on the GitHub discussions page. Alternatively, you can share a Google document with us. Collaborations: For Academic & Industry collaborations, please contact here. TornadoVM Team Visit our website to meet the team. Licenses Per Module To use TornadoVM, you can link the TornadoVM API to your application which is under Apache 2. Each Java TornadoVM module is licensed as follows: | Module | License | |--------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Tornado-API | | | Tornado-Runtime | | | Tornado-Assembly | | | Tornado-Drivers | | | Tornado-Drivers-OpenCL-Headers | | | Tornado-scripts | | | Tornado-Annotation | | | Tornado-Unittests | | | Tornado-Benchmarks | | | Tornado-Examples | | | Tornado-Matrices | | | | |

#github#tornadovm

github

0.532

0.28780746199907875

glouppe•Mar 28, 2025

info8006-introduction-to-ai

INFO8006 Introduction to Artificial Intelligence Lectures for INFO8006 Introduction to Artificial Intelligence, ULiège, Fall 2024. Instructor: Gilles Louppe Teaching assistants: Gérôme Andry, Arnaud Delaunoy When: Fall 2024, Thursday 8:30 AM to 12:30 AM Classroom: B31/Laurent (4/89) Contact: info8006@montefiore.ulg.ac.be Discord: https://discord.gg/Y8UP2SBu2h Agenda | Date | Topic | | ---- | ----- | | September 19 | [Course syllabus][syllabus] [[PDF][syllabus-pdf]] Lecture 0: [Introduction to artificial intelligence][l0] [[PDF][l0-pdf]] Lecture 1: [Intelligent agents][l1] [[PDF][l1-pdf]] | | September 26 | Lecture 2: [Solving problems by searching][l2] [[PDF][l2-pdf]] Tutorial: Project 0 | | October 3 | Lecture 3: [Games and adversarial search][l3] [[PDF][l3-pdf]] Exercises 1: Solving problems by searching [[PDF][e1]] [[Solutions][e1s]] | | October 10 | Lecture 4: [Quantifying uncertainty][l4] [[PDF][l4-pdf]] Exercises 2: Games and adversarial search [[PDF][e2]] [[Solutions][e2s]]| | October 17 | Lecture 5: [Probabilistic reasoning][l5] [[PDF][l5-pdf]] Exercises 3: Quantifying uncertainty [[PDF][e3]] [[Solutions][e3s]]| | October 24 | Lecture 6: [Reasoning over time][l6] [[PDF][l6-pdf]]No exercises| | October 31 | No class | | November 3 | Deadline for Project 1 | | November 7 | Lecture 7: [Machine learning and neural networks][l7] [[PDF][l7-pdf]] Exercises 4: Probabilistic reasoning [[PDF][e4]] [[Solutions][e4s]]| | November 14 | Lecture 7: [Machine learning and neural networks][l7] (continued) [[PDF][l7-pdf]] Exercises 5: Reasoning over time [[PDF][e5]] [[Solutions][e5s]]| | November 21 |Lecture 8: [Making decisions][l8] [[PDF][l8-pdf]] Exercises 6: Reasoning over time (continued) [notebook] | | November 28 | Lecture 9: [Reinforcement Learning][l9] [[PDF][l9-pdf]] Exercises 7: Machine learning [[PDF][e6]] [[Solutions][e6s]]| | December 5 | No lecture Exercises 8: Making decisions & RL [[PDF][e7]] [[Solutions][e7s]]| | December 8 | Deadline for Project 2 | | December 12 | No class | | December 19 | No class | [syllabus]: https://glouppe.github.io/info8006-introduction-to-ai/?p=course-syllabus.md [syllabus-pdf]: https://glouppe.github.io/info8006-introduction-to-ai/pdf/course-syllabus.pdf [l0]: https://glouppe.github.io/info8006-introduction-to-ai/?p=lecture0.md [l0-pdf]: https://glouppe.github.io/info8006-introduction-to-ai/pdf/lec0.pdf [l1]: https://glouppe.github.io/info8006-introduction-to-ai/?p=lecture1.md [l1-pdf]: https://glouppe.github.io/info8006-introduction-to-ai/pdf/lec1.pdf [l2]: https://glouppe.github.io/info8006-introduction-to-ai/?p=lecture2.md [l2-pdf]: https://glouppe.github.io/info8006-introduction-to-ai/pdf/lec2.pdf [l3]: https://glouppe.github.io/info8006-introduction-to-ai/?p=lecture3.md [l3-pdf]: https://glouppe.github.io/info8006-introduction-to-ai/pdf/lec3.pdf [l4]: https://glouppe.github.io/info8006-introduction-to-ai/?p=lecture4.md [l4-pdf]: https://glouppe.github.io/info8006-introduction-to-ai/pdf/lec4.pdf [l5]: https://glouppe.github.io/info8006-introduction-to-ai/?p=lecture5.md [l5-pdf]: https://glouppe.github.io/info8006-introduction-to-ai/pdf/lec5.pdf [l6]: https://glouppe.github.io/info8006-introduction-to-ai/?p=lecture6.md [l6-pdf]: https://glouppe.github.io/info8006-introduction-to-ai/pdf/lec6.pdf [l7]: https://glouppe.github.io/info8006-introduction-to-ai/?p=lecture7.md [l7-pdf]: https://glouppe.github.io/info8006-introduction-to-ai/pdf/lec7.pdf [l8]: https://glouppe.github.io/info8006-introduction-to-ai/?p=lecture8.md [l8-pdf]: https://glouppe.github.io/info8006-introduction-to-ai/pdf/lec8.pdf [l9]: https://glouppe.github.io/info8006-introduction-to-ai/?p=lecture9.md [l9-pdf]: https://glouppe.github.io/info8006-introduction-to-ai/pdf/lec9.pdf [e1]: https://glouppe.github.io/info8006-introduction-to-ai/pdf/exercises-1.pdf [e1s]: https://glouppe.github.io/info8006-introduction-to-ai/pdf/exercises-1-solutions.pdf [e2]: https://glouppe.github.io/info8006-introduction-to-ai/pdf/exercises-2.pdf [e2s]: https://glouppe.github.io/info8006-introduction-to-ai/pdf/exercises-2-solutions.pdf [e3]: https://glouppe.github.io/info8006-introduction-to-ai/pdf/exercises-3.pdf [e3s]: https://glouppe.github.io/info8006-introduction-to-ai/pdf/exercises-3-solutions.pdf [e4]: https://glouppe.github.io/info8006-introduction-to-ai/pdf/exercises-4.pdf [e4s]: https://glouppe.github.io/info8006-introduction-to-ai/pdf/exercises-4-solutions.pdf [e5]: https://glouppe.github.io/info8006-introduction-to-ai/pdf/exercises-5.pdf [e5s]: https://glouppe.github.io/info8006-introduction-to-ai/pdf/exercises-5-solutions.pdf [e6]: https://glouppe.github.io/info8006-introduction-to-ai/pdf/exercises-6.pdf [e6s]: https://glouppe.github.io/info8006-introduction-to-ai/pdf/exercises-6-solutions.pdf [e7]: https://glouppe.github.io/info8006-introduction-to-ai/pdf/exercises-7.pdf [e7s]: https://glouppe.github.io/info8006-introduction-to-ai/pdf/exercises-7-solutions.pdf [e8]: https://glouppe.github.io/info8006-introduction-to-ai/pdf/exercises-8.pdf [e8s]: https://glouppe.github.io/info8006-introduction-to-ai/pdf/exercises-8-solutions.pdf [e9]: https://glouppe.github.io/info8006-introduction-to-ai/pdf/exercises-9.pdf [e9s]: https://glouppe.github.io/info8006-introduction-to-ai/pdf/exercises-9-solutions.pdf Pacman programming projects General instructions Python tutorial [video (Linux), video (Windows)] Part 0: Search algorithms (tutorial session in class) Part 1: Adversarial search (due by November 3) Part 2: Bayes filter (due by December 8) Previous exams January 2019 (solutions) August 2019 January 2020 August 2020 (solutions) January 2021 (solutions) August 2021 January 2022 (solutions) August 2022 January 2023 (solutions) August 2023 January 2024 August 2024 Materials covered by the exam are listed here. Archives Previous editions 2023-2024 2022-2023 2021-2022 2020-2021 2019-2020 2018-2019 2017-2018 Archived lectures Due to progress in the field, some of the lectures have become less relevant. However, they are still available for those who are interested. | Topic | | --- | | Lecture: Constraint satisfaction problems [PDF] | | Lecture: Inference in Bayesian networks [PDF] | | Lecture: Communication [PDF] | | Lecture: Artificial general intelligence and beyond [PDF] |

#github#info8006-introduction-to-ai

github

0.58

1

georgezouq•Mar 28, 2025

awesome-ai-in-finance

Awesome AI in Finance There are millions of trades made in the global financial market every day. Data grows very quickly and people are hard to understand. With the power of the latest artificial intelligence research, people analyze & trade automatically and intelligently. This list contains the research, tools and code that people use to beat the market. [中文资源] Contents LLMs Papers Courses & Books Strategies & Research Time Series Data Portfolio Management High Frequency Trading Event Drive Crypto Currencies Strategies Technical Analysis Lottery & Gamble Arbitrage Data Sources Research Tools Trading System TA Lib Exchange API Articles Others LLMs 🌟🌟 MarS - A Financial Market Simulation Engine Powered by Generative Foundation Model. 🌟🌟 Financial Statement Analysis with Large Language Models - GPT-4 can outperform professional financial analysts in predicting future earnings changes, generating useful narrative insights, and resulting in superior trading strategies with higher Sharpe ratios and alphas, thereby suggesting a potential central role for LLMs in financial decision-making. PIXIU - An open-source resource providing a financial large language model, a dataset with 136K instruction samples, and a comprehensive evaluation benchmark. FinGPT - Provides a playground for all people interested in LLMs and NLP in Finance. MACD + RSI + ADX Strategy (ChatGPT-powered) by TradeSmart - Asked ChatGPT on which indicators are the most popular for trading. We used all of the recommendations given. A ChatGPT trading algorithm delivered 500% returns in stock market. My breakdown on what this means for hedge funds and retail investors Use chatgpt to adjust strategy parameters Hands-on LLMs: Train and Deploy a Real-time Financial Advisor - Train and deploy a real-time financial advisor chatbot with Falcon 7B and CometLLM. ChatGPT Strategy by OctoBot - Use ChatGPT to determine which cryptocurrency to trade based on technical indicators. Papers The Theory of Speculation L. Bachelier, 1900 - The influences which determine the movements of the Stock Exchange are. Brownian Motion in the Stock Market Osborne, 1959 - The common-stock prices can be regarded as an ensemble of decisions in statistical equilibrium. An Investigation into the Use of Reinforcement Learning Techniques within the Algorithmic Trading Domain, 2015 A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem Reinforcement Learning for Trading, 1994 Dragon-Kings, Black Swans and the Prediction of Crises Didier Sornette - The power laws in the distributions of event sizes under a broad range of conditions in a large variety of systems. Financial Trading as a Game: A Deep Reinforcement Learning Approach - Deep reinforcement learning provides a framework toward end-to-end training of such trading agent. Machine Learning for Trading - With an appropriate choice of the reward function, reinforcement learning techniques can successfully handle the risk-averse case. Ten Financial Applications of Machine Learning, 2018 - Slides review few important financial ML applications. FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance, 2020 - Introduce a DRL library FinRL that facilitates beginners to expose themselves to quantitative finance and to develop their own stock trading strategies. Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy, 2020 - Propose an ensemble strategy that employs deep reinforcement schemes to learn a stock trading strategy by maximizing investment return. Courses & Books & Blogs 🌟 QuantResearch - Quantitative analysis, strategies and backtests https://letianzj.github.io/ NYU: Overview of Advanced Methods of Reinforcement Learning in Finance Udacity: Artificial Intelligence for Trading AI in Finance - Learn Fintech Online. Advanced-Deep-Trading - Experiments based on "Advances in financial machine learning" book. Advances in Financial Machine Learning - Using advanced ML solutions to overcome real-world investment problems. Build Financial Software with Generative AI - Book about how to build financial software hands-on using generative AI tools like ChatGPT and Copilot. Mastering Python for Finance - Sources codes for: Mastering Python for Finance, Second Edition. MLSys-NYU-2022 - Slides, scripts and materials for the Machine Learning in Finance course at NYU Tandon, 2022. Train and Deploy a Serverless API to predict crypto prices - In this tutorial you won't build an ML system that will make you rich. But you will master the MLOps frameworks and tools you need to build ML systems that, together with tons of experimentation, can take you there. Strategies & Research Time Series Data Price and Volume process with Technology Analysis Indices 🌟🌟 stockpredictionai - A complete process for predicting stock price movements. 🌟 Personae - Implements and environment of Deep Reinforcement Learning & Supervised Learning for Quantitative Trading. 🌟 Ensemble-Strategy - Deep Reinforcement Learning for Automated Stock Trading. FinRL - A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance. AutomatedStockTrading-DeepQ-Learning - Build a Deep Q-learning reinforcement agent model as automated trading robot. tfdeeprltrader - Trading environment(OpenAI Gym) + PPO(TensorForce). trading-gym - Trading agent to train with episode of short term trading itself. trading-rl - Deep Reinforcement Learning for Financial Trading using Price Trailing. deeprltrader - Trading environment(OpenAI Gym) + DDQN (Keras-RL). Quantitative-Trading - Papers and code implementing Quantitative-Trading. gym-trading - Environment for reinforcement-learning algorithmic trading models. zenbrain - A framework for machine-learning bots. DeepLearningNotes - Machine learning in quant analysis. stockmarketreinforcementlearning - Stock market trading OpenAI Gym environment with Deep Reinforcement Learning using Keras. Chaos Genius - ML powered analytics engine for outlier/anomaly detection and root cause analysis.. mlforecast - Scalable machine learning based time series forecasting. Portfolio Management Deep-Reinforcement-Stock-Trading - A light-weight deep reinforcement learning framework for portfolio management. qtrader - Reinforcement Learning for portfolio management. PGPortfolio - A Deep Reinforcement Learning framework for the financial portfolio management problem. DeepDow - Portfolio optimization with deep learning. skfolio - Python library for portfolio optimization built on top of scikit-learn. High Frequency Trading High-Frequency-Trading-Model-with-IB - A high-frequency trading model using Interactive Brokers API with pairs and mean-reversion. 🌟 SGX-Full-OrderBook-Tick-Data-Trading-Strategy - Solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data. HFTBitcoin - Analysis of High Frequency Trading on Bitcoin exchanges. Event Drive 🌟🌟 stockpredictionai - Complete process for predicting stock price movements. 🌟 trump2cash - A stock trading bot powered by Trump tweets. Crypto Currencies Strategies LSTM-Crypto-Price-Prediction - Predicting price trends in crypto markets using an LSTM-RNN for trading. tforcebtctrader - TensorForce Bitcoin trading bot. Tensorflow-NeuroEvolution-Trading-Bot - A population model that trade cyrpto and breed and mutate iteratively. gekkoga - Genetic algorithm for solving optimization of trading strategies using Gekko. GekkoANNStrategies - ANN trading strategies for the Gekko trading bot. gekko-neuralnet - Neural network strategy for Gekko. bitcoinprediction - Code for "Bitcoin Prediction" by Siraj Raval on YouTube. Technical Analysis quant-trading - Python quantitative trading strategies. Gekko-Bot-Resources - Gekko bot resources. gekkotools - Gekko strategies, tools etc. gekko RSIWR - Gekko RSIWR strategies. gekko HL - Calculate down peak and trade on. EthTradingAlgorithm - Ethereum trading algorithm using Python 3.5 and the library ZipLine. gekkotradingstuff - Awesome crypto currency trading platform. forex.analytics - Node.js native library performing technical analysis over an OHLC dataset with use of genetic algorithmv. BitcoinMACDStrategy - Bitcoin MACD crossover trading strategy backtest. crypto-signal - Automated crypto trading & technical analysis (TA) bot for Bittrex, Binance, GDAX, and more. Gekko-Strategies - Strategies to Gekko trading bot with backtests results and some useful tools. gekko-gannswing - Gann's Swing trade strategy for Gekko trade bot. Lottery & Gamble LotteryPredict - Use LSTM to predict lottery. Arbitrage ArbitrageBot - Arbitrage bot that currently works on bittrex & poloniex. r2 - Automatic arbitrage trading system powered by Node.js + TypeScript. cryptocurrency-arbitrage - A crypto currency arbitrage opportunity calculator. Over 800 currencies and 50 markets. bitcoin-arbitrage - Bitcoin arbitrage opportunity detector. blackbird - Long / short market-neutral strategy. Data Sources Traditional Markets 🌟 Quandl - Get millions of financial and economic dataset from hundreds of publishers via a single free API. yahoo-finance - Python module to get stock data from Yahoo! Finance. Tushare - Crawling historical data of Chinese stocks. Financial Data - Stock Market and Financial Data API. Crypto Currencies CryptoInscriber - A live crypto currency historical trade data blotter. Download live historical trade data from any crypto exchange. Gekko-Datasets - Gekko trading bot dataset dumps. Download and use history files in SQLite format. Research Tools Synthical - AI-powered collaborative environment for Research. 🌟🌟 TensorTrade - Trade efficiently with reinforcement learning. ML-Quant - Quant resources from ArXiv (sanity), SSRN, RePec, Journals, Podcasts, Videos, and Blogs. JAQS - An open source quant strategies research platform. pyfolio - Portfolio and risk analytics in Python. alphalens - Performance analysis of predictive (alpha) stock factors. empyrical - Common financial risk and performance metrics. Used by Zipline and pyfolio. zvt - Zero vector trader. Trading System For Back Test & Live trading Traditional Market System 🌟🌟🌟 OpenBB - AI-powered opensource research and analytics workspace. 🌟🌟 zipline - A python algorithmic trading library. 🌟 TradingView - Get real-time information and market insights. rqalpha - A extendable, replaceable Python algorithmic backtest & trading framework. backtrader - Python backtesting library for trading strategies. kungfu - Kungfu Master trading system. lean - Algorithmic trading engine built for easy strategy research, backtesting and live trading. Combine & Rebuild pylivetrader - Python live trade execution library with zipline interface. CoinMarketCapBacktesting - As backtest frameworks for coin trading strategy. Crypto Currencies zenbot - Command-line crypto currency trading bot using Node.js and MongoDB. bot18 - High-frequency crypto currency trading bot developed by Zenbot. magic8bot - Crypto currency trading bot using Node.js and MongoDB. catalyst - An algorithmic trading library for Crypto-Assets in python. QuantResearchDev - Quant Research dev & Traders open source project. MACD - Zenbot MACD Auto-Trader. abu - A quant trading system base on python. Plugins CoinMarketCapBacktesting - Tests bt and Quantopian Zipline as backtesting frameworks for coin trading strategy. Gekko-BacktestTool - Batch backtest, import and strategy params optimalization for Gekko Trading Bot. TA Lib pandastalib - A Python Pandas implementation of technical analysis indicators. finta - Common financial technical indicators implemented in Python-Pandas (70+ indicators). tulipnode - Official Node.js wrapper for Tulip Indicators. Provides over 100 technical analysis overlay and indicator functions. techan.js - A visual, technical analysis and charting (Candlestick, OHLC, indicators) library built on D3. Exchange API Do it in real world! IbPy - Python API for the Interactive Brokers on-line trading system. HuobiFeeder - Connect HUOBIPRO exchange, get market/historical data for ABAT trading platform backtest analysis and live trading. ctpwrapper - Shanghai future exchange CTP api. PENDAX - Javascript SDK for Trading/Data API and Websockets for cryptocurrency exchanges like FTX, FTXUS, OKX, Bybit, & More Framework tf-quant-finance - High-performance TensorFlow library for quantitative finance. Visualizing playground - Play with neural networks. netron - Visualizer for deep learning and machine learning models. KLineChart - Highly customizable professional lightweight financial charts GYM Environment 🌟 TradingGym - Trading and Backtesting environment for training reinforcement learning agent. TradzQAI - Trading environment for RL agents, backtesting and training. btgym - Scalable, event-driven, deep-learning-friendly backtesting library. Articles The-Economist - The Economist. nyu-mlif-notes - NYU machine learning in finance notes. Using LSTMs to Turn Feelings Into Trades Others zipline-tensorboard - TensorBoard as a Zipline dashboard. gekko-quasar-ui - An UI port for gekko trading bot using Quasar framework. Floom AI gateway and marketplace for developers, enables streamlined integration and least volatile approach of AI features into products Other Resource 🌟🌟🌟 Stock-Prediction-Models - Stock-Prediction-Models, Gathers machine learning and deep learning models for Stock forecasting, included trading bots and simulations. 🌟🌟 Financial Machine Learning - A curated list of practical financial machine learning (FinML) tools and applications. This collection is primarily in Python. 🌟 Awesome-Quant-Machine-Learning-Trading - Quant / Algorithm trading resources with an emphasis on Machine Learning. awesome-quant - A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance). FinancePy - A Python Finance Library that focuses on the pricing and risk-management of Financial Derivatives, including fixed-income, equity, FX and credit derivatives. Explore Finance Service Libraries & Projects - Explore a curated list of Fintech popular & new libraries, top authors, trending project kits, discussions, tutorials & learning resources on kandi.

#github#awesome-ai-in-finance

github

0.437

0.07783740211883924

sinanuozdemir•Mar 28, 2025

oreilly-ai-agents

!oreilly-logo AI Agents A-Z This repository contains code for the O'Reilly Live Online Training for AI Agents A-Z This course provides a comprehensive guide to understanding, implementing, and managing AI agents both at the prototype stage and in production. Attendees will start with foundational concepts and progressively delve into more advanced topics, including various frameworks like CrewAI, LangChain, and AutoGen as well as building agents from scratch using powerful prompt engineering techniques. The course emphasizes practical application, guiding participants through hands-on exercises to implement and deploy AI agents, evaluate their performance, and iterate on their designs. We will go over key aspects like cost projections, open versus closed source options, and best practices are thoroughly covered to equip attendees with the knowledge to make informed decisions in their AI projects. Setup Instructions Using Python 3.11 Virtual Environment At the time of writing, we need a Python virtual environment with Python 3.11. Option 1: Python 3.11 is Already Installed Step 1: Verify Python 3.11 Installation Step 2: Create a Virtual Environment This creates a .venv folder in your current directory. Step 3: Activate the Virtual Environment macOS/Linux: Windows: You should see (.venv) in your terminal prompt. Step 4: Verify the Python Version Step 5: Install Packages Step 6: Deactivate the Virtual Environment Option 2: Install Python 3.11 If you don’t have Python 3.11, follow the steps below for your OS. macOS (Using Homebrew) Ubuntu/Debian Windows (Using Windows Installer) Go to Python Downloads. Download the installer for Python 3.11. Run the installer and ensure "Add Python 3.11 to PATH" is checked. Verify Installation Notebooks In the activated environment, run Using 3rd party agent frameworks Intro to CrewAI - An introductory notebook for CrewAI See the streamlit directory for an example of deploying crew on a streamlit app Intro to Autogen - An introductory notebook for Microsoft's Autogen Intro to OpenAI Swarm - An introductory notebook for OpenAI's Swarm Intro to LangGraph - An introductory notebook for LangGraph Agents playing Chess - An implementation of two ReAct Agents playing Chess with each other Evaluating Agents Evaluating Agent Output with Rubrics - Exploring a rubric prompt to evaluate generative output. This notebook also notes positional biases when choosing between agent responses. Advanced - Evaluating Alignment - A longer notebook doing a much more in depth analysis on how an LLM can judge agent's responses Evaluating Tool Selection - Calculating the accuracy of tool selection between different LLMs and quantifying the positional bias present in auto-regressive LLMs. See the additions here for V3 + DeepSeek Distilled Models and here for DeepSeek R1 Building our own agents First Steps with our own Agent - Working towards building our own agent framework See Squad Goals for a very simple example of my own agent framework Intro to Squad Goals - using my own framework to do some basic tasks Multimodal Agents - Incorporating Dalle-3 to allow our squad to generate images Modern Agent Paradigms Plan & Execute Agents - Plan & Execute Agents use a planner to create multi-step plans with an LLM and an executor to complete each step by invoking tools. Reflection Agents - Reflection Agents combine a generator to perform tasks and a reflector to provide feedback and guide improvements. Instructor Sinan Ozdemir is the Founder and CTO of LoopGenius where he uses State of the art AI to help people run digital ads on Meta, Google, and more. Sinan is a former lecturer of Data Science at Johns Hopkins University and the author of multiple textbooks on data science and machine learning. Additionally, he is the founder of the recently acquired Kylie.ai, an enterprise-grade conversational AI platform with RPA capabilities. He holds a master’s degree in Pure Mathematics from Johns Hopkins University and is based in San Francisco, CA.

#github#oreilly-ai-agents

github

0.514

0.16971233963995486

codertimo•Mar 28, 2025

BERT-pytorch

BERT-pytorch !GitHub issues Pytorch implementation of Google AI's 2018 BERT, with simple annotation BERT 2018 BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding Paper URL : https://arxiv.org/abs/1810.04805 Introduction Google AI's BERT paper shows the amazing result on various NLP task (new 17 NLP tasks SOTA), including outperform the human F1 score on SQuAD v1.1 QA task. This paper proved that Transformer(self-attention) based encoder can be powerfully used as alternative of previous language model with proper language model training method. And more importantly, they showed us that this pre-trained language model can be transfer into any NLP task without making task specific model architecture. This amazing result would be record in NLP history, and I expect many further papers about BERT will be published very soon. This repo is implementation of BERT. Code is very simple and easy to understand fastly. Some of these codes are based on The Annotated Transformer Currently this project is working on progress. And the code is not verified yet. Installation Quickstart NOTICE : Your corpus should be prepared with two sentences in one line with tab(\t) separator Prepare your corpus or tokenized corpus (tokenization is not in package) Building vocab based on your corpus Train your own BERT model Language Model Pre-training In the paper, authors shows the new language model training methods, which are "masked language model" and "predict next sentence". Masked Language Model Original Paper : 3.3.1 Task #1: Masked LM Rules: Randomly 15% of input token will be changed into something, based on under sub-rules Randomly 80% of tokens, gonna be a [MASK] token Randomly 10% of tokens, gonna be a [RANDOM] token(another word) Randomly 10% of tokens, will be remain as same. But need to be predicted. Predict Next Sentence Original Paper : 3.3.2 Task #2: Next Sentence Prediction "Is this sentence can be continuously connected?" understanding the relationship, between two text sentences, which is not directly captured by language modeling Rules: Randomly 50% of next sentence, gonna be continuous sentence. Randomly 50% of next sentence, gonna be unrelated sentence. Author Junseong Kim, Scatter Lab (codertimo@gmail.com / junseong.kim@scatterlab.co.kr) License This project following Apache 2.0 License as written in LICENSE file Copyright 2018 Junseong Kim, Scatter Lab, respective BERT contributors Copyright (c) 2018 Alexander Rush : The Annotated Trasnformer

#github#bert-pytorch

github

0.64

1

krishnakumarsekar•Mar 27, 2025

awesome-quantum-machine-learning

Awesome Quantum Machine Learning A curated list of awesome quantum machine learning algorithms,study materials,libraries and software (by language). Table of Contents INTRODUCTION Why Quantum Machine Learning? BASICS What is Quantum Mechanics? What is Quantum Computing? What is Topological Quantum Computing? Quantum Computing vs Classical Computing QUANTUM COMPUTING Atom Structure Photon wave Electron Fluctuation or spin States SuperPosition SuperPosition specific for machine learning(Quantum Walks) Classical Bit Quantum Bit or Qubit or Qbit Basic Gates in Quantum Computing Quantum Diode Quantum Transistor Quantum Processor Quantum Registery QRAM Quantum Entanglement QUANTUM COMPUTING MACHINE LEARNING BRIDGE Complex Numbers Tensors Tensors Network Oracle Hadamard transform Hilbert Space eigenvalues and eigenvectors Schr¨odinger Operators Quantum lambda calculus Quantum Amplitute Phase Qubits Encode and Decode convert classical bit to qubit Quantum Dirac and Kets Quantum Complexity Arbitrary State Generation QUANTUM ALGORITHMS Quantum Fourier Transform Variational-Quantum-Eigensolver Grovers Algorithm Shor's algorithm Hamiltonian Oracle Model Bernstein-Vazirani Algorithm Simon’s Algorithm Deutsch-Jozsa Algorithm Gradient Descent Phase Estimation Haar Tansform Quantum Ridgelet Transform Quantum NP Problem QUANTUM MACHINE LEARNING ALGORITHMS Quantum K-Nearest Neighbour Quantum K-Means Quantum Fuzzy C-Means Quantum Support Vector Machine Quantum Genetic Algorithm Quantum Hidden Morkov Models Quantum state classification with Bayesian methods Quantum Ant Colony Optimization Quantum Cellular Automata Quantum Classification using Principle Component Analysis Quantum Inspired Evolutionary Algorithm Quantum Approximate Optimization Algorithm Quantum Elephant Herding Optimization Quantum-behaved Particle Swarm Optimization Quantum Annealing Expectation-Maximization QAUNTUM NEURAL NETWORK Quantum perceptrons Qurons Quantum Auto Encoder Quantum Annealing Photonic Implementation of Quantum Neural Network Quantum Feed Forward Neural Network Quantum Boltzman Neural Network Quantum Neural Net Weight Storage Quantum Upside Down Neural Net Quantum Hamiltonian Neural Net QANN QPN SAL Quantum Hamiltonian Learning Compressed Quantum Hamiltonian Learning QAUNTUM STATISTICAL DATA ANALYSIS Quantum Probability Theory Kolmogorovian Theory Quantum Measurement Problem Intuitionistic Logic Heyting Algebra Quantum Filtering Paradoxes Quantum Stochastic Process Double Negation Quantum Stochastic Calculus Hamiltonian Calculus Quantum Ito's Formula Quantum Stochastic Differential Equations(QSDE) Quantum Stochastic Integration Itō Integral Quasiprobability Distributions Quantum Wiener Processes Quantum Statistical Ensemble Quantum Density Operator or Density Matrix Gibbs Canonical Ensemble Quantum Mean Quantum Variance Envariance Polynomial Optimization Quadratic Unconstrained Binary Optimization Quantum Gradient Descent Quantum Based Newton's Method for Constrained Optimization Quantum Based Newton's Method for UnConstrained Optimization Quantum Ensemble Quantum Topology Quantum Topological Data Analysis Quantum Bayesian Hypothesis Quantum Statistical Decision Theory Quantum Minimax Theorem Quantum Hunt-Stein Theorem Quantum Locally Asymptotic Normality Quantum Ising Model Quantum Metropolis Sampling Quantum Monte Carlo Approximation Quantum Bootstrapping Quantum Bootstrap Aggregation Quantum Decision Tree Classifier Quantum Outlier Detection Cholesky-Decomposition for Quantum Chemistry Quantum Statistical Inference Asymptotic Quantum Statistical Inference Quantum Gaussian Mixture Modal Quantum t-design Quantum Central Limit Theorem Quantum Hypothesis Testing Quantum Chi-squared and Goodness of Fit Testing Quantum Estimation Theory Quantum Way of Linear Regression Asymptotic Properties of Quantum Outlier Detection in Quantum Concepts QAUNTUM ARTIFICIAL INTELLIGENCE Heuristic Quantum Mechanics Consistent Quantum Reasoning Quantum Reinforcement Learning QAUNTUM COMPUTER VISION QUANTUM PROGRAMMING LANGUAGES , TOOLs and SOFTWARES ALL QUANTUM ALGORITHMS SOURCE CODES , GITHUBS QUANTUM HOT TOPICS Quantum Cognition Quantum Camera Quantum Mathematics Quantum Information Processing Quantum Image Processing Quantum Cryptography Quantum Elastic Search Quantum DNA Computing Adiabetic Quantum Computing Topological Big Data Anlytics using Quantum Hamiltonian Time Based Quantum Computing Deep Quantum Learning Quantum Tunneling Quantum Entanglment Quantum Eigen Spectrum Quantum Dots Quantum elctro dynamics Quantum teleportation Quantum Supremacy Quantum Zeno Effect Quantum Cohomology Quantum Chromodynamics Quantum Darwinism Quantum Coherence Quantum Decoherence Topological Quantum Computing Topological Quantum Field Theory Quantum Knots Topological Entanglment Boson Sampling Quantum Convolutional Code Stabilizer Code Quantum Chaos Quantum Game Theory Quantum Channel Tensor Space Theory Quantum Leap Quantum Mechanics for Time Travel Quantum Secured Block Chain Quantum Internet Quantum Optical Network Quantum Interference Quantum Optical Network Quantum Operating System Electron Fractionalization Flip-Flop Quantum Computer Quantum Information with Gaussian States Quantum Anomaly Detection Distributed Secure Quantum Machine Learning Decentralized Quantum Machine Learning Artificial Agents for Quantum Designs Light Based Quantum Chips for AI Training QUANTUM STATE PREPARATION ALGORITHM FOR MACHINE LEARNING Pure Quantum State Product State Matrix Product State Greenberger–Horne–Zeilinger State W state AKLT model Majumdar–Ghosh Model Multistate Landau–Zener Models Projected entangled-pair States Infinite Projected entangled-pair States Corner Transfer Matrix Method Tensor-entanglement Renormalization Tree Tensor Network for Supervised Learning QUANTUM MACHINE LEARNING VS DEEP LEARNING QUANTUM MEETUPS QUANTUM GOOGLE GROUPS QUANTUM BASED COMPANIES QUANTUM LINKEDLIN QUANTUM BASED DEGREES CONSOLIDATED QUANTUM ML BOOKS CONSOLIDATED QUANTUM ML VIDEOS CONSOLIDATED QUANTUM ML Reserach Papers CONSOLIDATED QUANTUM ML Reserach Scientist RECENT QUANTUM UPDATES FORUM ,PAGES AND NEWSLETTER INTRODUCTION Why Quantum Machine Learning? Machine Learning(ML) is just a term in recent days but the work effort start from 18th century. What is Machine Learning ? , In Simple word the answer is making the computer or application to learn themselves . So its totally related with computing fields like computer science and IT ? ,The answer is not true . ML is a common platform which is mingled in all the aspects of the life from agriculture to mechanics . Computing is a key component to use ML easily and effectively . To be more clear ,Who is the mother of ML ?, As no option Mathematics is the mother of ML . The world tremendous invention complex numbers given birth to this field . Applying mathematics to the real life problem always gives a solution . From Neural Network to the complex DNA is running under some specific mathematical formulas and theorems. As computing technology growing faster and faster mathematics entered into this field and makes the solution via computing to the real world . In the computing technology timeline once a certain achievements reached peoples interested to use advanced mathematical ideas such as complex numbers ,eigen etc and its the kick start for the ML field such as Artificial Neural Network ,DNA Computing etc. Now the main question, why this field is getting boomed now a days ? , From the business perspective , 8-10 Years before during the kick start time for ML ,the big barrier is to merge mathematics into computing field . people knows well in computing has no idea on mathematics and research mathematician has no idea on what is computing . The education as well as the Job Opportunities is like that in that time . Even if a person tried to study both then the business value for making a product be not good. Then the top product companies like Google ,IBM ,Microsoft decided to form a team with mathematician ,a physician and a computer science person to come up with various ideas in this field . Success of this team made some wonderful products and they started by providing cloud services using this product . Now we are in this stage. So what's next ? , As mathematics reached the level of time travel concepts but the computing is still running under classical mechanics . the companies understood, the computing field must have a change from classical to quantum, and they started working on the big Quantum computing field, and the market named this field as Quantum Information Science .The kick start is from Google and IBM with the Quantum Computing processor (D-Wave) for making Quantum Neural Network .The field of Quantum Computer Science and Quantum Information Science will do a big change in AI in the next 10 years. Waiting to see that........... .(google, ibm). References D-Wave - Owner of a quantum processor Google - Quantum AI Lab IBM - Quantum Computer Lab Quora - Question Regarding future of quantum AI NASA - NASA Quantum Works Youtube - Google Video of a Quantum Processor external-link - MIT Review microsoft new product - Newly Launched Microsoft Quantum Language and Development Kit microsoft - Microsoft Quantum Related Works Google2 - Google Quantum Machine Learning Blog BBC - About Google Quantum Supremacy,IBM Quantum Computer and Microsoft Q Google Quantum Supremacy - Latest 2019 Google Quantum Supremacy Achievement IBM Quantum Supremacy - IBM Talk on Quantum Supremacy as a Primer VICE on the fight - IBM Message on Google Quantum Supremacy IBM Zurich Quantum Safe Cryptography - An interesting startup to replace all our Certificate Authority Via Cloud and IBM Q BASICS What is Quantum Mechanics? In a single line study of an electron moved out of the atom then its classical mechanic ,vibrates inside the atom its quantum mechanics WIKIPEDIA - Basic History and outline LIVESCIENCE. - A survey YOUTUBE - Simple Animation Video Explanining Great. What is Quantum Computing? A way of parallel execution of multiple processess in a same time using qubit ,It reduces the computation time and size of the processor probably in neuro size WIKIPEDIA - Basic History and outline WEBOPEDIA. - A survey YOUTUBE - Simple Animation Video Explanining Great. Quantum Computing vs Classical Computing LINK - Basic outline Quantum Computing Atom Structure one line : Electron Orbiting around the nucleous in an eliptical format YOUTUBE - A nice animation video about the basic atom structure Photon Wave one line : Light nornmally called as wave transmitted as photons as similar as atoms in solid particles YOUTUBE - A nice animation video about the basic photon 1 YOUTUBE - A nice animation video about the basic photon 2 Electron Fluctuation or spin one line : When a laser light collide with solid particles the electrons of the atom will get spin between the orbitary layers of the atom ) YOUTUBE - A nice animation video about the basic Electron Spin 1 YOUTUBE - A nice animation video about the basic Electron Spin 2 YOUTUBE - A nice animation video about the basic Electron Spin 3 States one line : Put a point on the spinning electron ,if the point is in the top then state 1 and its in bottom state 0 YOUTUBE - A nice animation video about the Quantum States SuperPosition two line : During the spin of the electron the point may be in the middle of upper and lower position, So an effective decision needs to take on the point location either 0 or 1 . Better option to analyse it along with other electrons using probability and is called superposition YOUTUBE - A nice animation video about the Quantum Superposition SuperPosition specific for machine learning(Quantum Walks) one line : As due to computational complexity ,quantum computing only consider superposition between limited electrons ,In case to merge more than one set quantum walk be the idea YOUTUBE - A nice video about the Quantum Walks Classical Bits one line : If electron moved from one one atom to other ,from ground state to excited state a bit value 1 is used else bit value 0 used Qubit one line : The superposition value of states of a set of electrons is Qubit YOUTUBE - A nice video about the Quantum Bits 1 YOUTUBE - A nice video about the Bits and Qubits 2 Basic Gates in Quantum Computing one line : As like NOT, OR and AND , Basic Gates like NOT, Hadamard gate , SWAP, Phase shift etc can be made with quantum gates YOUTUBE - A nice video about the Quantum Gates Quantum Diode one line : Quantum Diodes using a different idea from normal diode, A bunch of laser photons trigger the electron to spin and the quantum magnetic flux will capture the information YOUTUBE - A nice video about the Quantum Diode Quantum Transistors one line : A transistor default have Source ,drain and gate ,Here source is photon wave ,drain is flux and gate is classical to quantum bits QUORA -Discussion about the Quantum Transistor YOUTUBE - Well Explained Quantum Processor one line : A nano integration circuit performing the quantum gates operation sorrounded by cooling units to reduce the tremendous amount of heat YOUTUBE - Well Explained Quantum Registery QRAM one line : Comapring the normal ram ,its ultrafast and very small in size ,the address location can be access using qubits superposition value ,for a very large memory set coherent superposition(address of address) be used PDF - very Well Explained QUANTUM COMPUTING MACHINE LEARNING BRIDGE Complex Numbers one line : Normally Waves Interference is in n dimensional structure , to find a polynomial equation n order curves ,better option is complex number YOUTUBE - Wonderful Series very super Explained Tensors one line : Vectors have a direction in 2D vector space ,If on a n dimensional vector space ,vectors direction can be specify with the tensor ,The best solution to find the superposition of a n vector electrons spin space is representing vectors as tensors and doing tensor calculus YOUTUBE - Wonderful super Explained tensors basics YOUTUBE - Quantum tensors basics Tensors Network one line : As like connecting multiple vectors ,multple tensors form a network ,solving such a network reduce the complexity of processing qubits YOUTUBE - Tensors Network Some ideas specifically for quantum algorithms QUANTUM MACHINE LEARNING ALGORITHMS Quantum K-Nearest Neighbour info : Here the centroid(euclidean distance) can be detected using the swap gates test between two states of the qubit , As KNN is regerssive loss can be tally using the average PDF1 from Microsoft - Theory Explanation PDF2 - A Good Material to understand the basics Matlab - Yet to come soon Python - Yet to come soon Quantum K-Means info : Two Approaches possible ,1. FFT and iFFT to make an oracle and calculate the means of superposition 2. Adiobtic Hamiltonian generation and solve the hamiltonian to determine the cluster PDF1 - Applying Quantum Kmeans on Images in a nice way PDF2 - Theory PDF3 - Explaining well the K-means clustering using hamiltonian Matlab - Yet to come soon Python - Yet to come soon Quantum Fuzzy C-Means info : As similar to kmeans fcm also using the oracle dialect ,but instead of means,here oracle optimization followed by a rotation gate is giving a good result PDF1 - Theory Matlab - Yet to come soon Python - Yet to come soon Quantum Support Vector Machine info : A little different from above as here kernel preparation is via classical and the whole training be in oracles and oracle will do the classification, As SVM is linear ,An optimal Error(Optimum of the Least Squares Dual Formulation) Based regression is needed to improve the performance PDF1 - Nice Explanation but little hard to understand :) PDF2 - Nice Application of QSVM Matlab - Yet to come soon Python - Yet to come soon Quantum Genetic Algorithm info : One of the best algorithm suited for Quantum Field ,Here the chromosomes act as qubit vectors ,the crossover part carrying by an evaluation and the mutation part carrying by the rotation of gates ![Flow Chart]() PDF1 - Very Beautiful Article , well explained and superp PDF2 - A big theory :) PDF3 - Super Comparison Matlab - Simulation Python1 - Simulation Python2 - Yet to come Quantum Hidden Morkov Models info : As HMM is already state based ,Here the quantum states acts as normal for the markov chain and the shift between states is using quantum operation based on probability distribution ![Flow Chart]() PDF1 - Nice idea and explanation PDF2 - Nice but a different concept little Matlab - Yet to come Python1 - Yet to come Python2 - Yet to come Quantum state classification with Bayesian methods info : Quantum Bayesian Network having the same states concept using quantum states,But here the states classification to make the training data as reusable is based on the density of the states(Interference) ![Bayesian Network Sample1]() ![Bayesian Network Sample2]() ![Bayesian Network Sample3]() PDF1 - Good Theory PDF2 - Good Explanation Matlab - Yet to come Python1 - Yet to come Python2 - Yet to come Quantum Ant Colony Optimization info : A good algorithm to process multi dimensional equations, ACO is best suited for Sales man issue , QACO is best suited for Sales man in three or more dimension, Here the quantum rotation circuit is doing the peromene update and qubits based colony communicating all around the colony in complex space ![Ant Colony Optimization 1]() PDF1 - Good Concept PDF2 - Good Application Matlab - Yet to come Python1 - Yet to come Python2 - Yet to come Quantum Cellular Automata info : One of the very complex algorithm with various types specifically used for polynomial equations and to design the optimistic gates for a problem, Here the lattice is formed using the quatum states and time calculation is based on the change of the state between two qubits ,Best suited for nano electronics ![Quantum Cellular Automata]() Wikipedia - Basic PDF1 - Just to get the keywords PDF2 - Nice Explanation and an easily understandable application Matlab - Yet to come Python1 - Yet to come Python2 - Yet to come QAUNTUM NEURAL NETWORK one line : Its really one of the hardest topic , To understand easily ,Normal Neural Network is doing parallel procss ,QNN is doing parallel of parallel processess ,In theory combination of various activation functions is possible in QNN ,In Normal NN more than one activation function reduce the performance and increase the complexity Quantum perceptrons info : Perceptron(layer) is the basic unit in Neural Network ,The quantum version of perceptron must satisfy both linear and non linear problems , Quantum Concepts is combination of linear(calculus of superposition) and nonlinear(State approximation using probability) ,To make a perceptron in quantum world ,Transformation(activation function) of non linearity to certain limit is needed ,which is carrying by phase estimation algorithm ![Quantum Perceptron 3]() PDF1 - Good Theory PDF2 - Good Explanation Matlab - Yet to come Python1 - Yet to come Python2 - Yet to come QAUNTUM STATISTICAL DATA ANALYSIS one line : An under research concept ,It can be seen in multiple ways, one best way if you want to apply n derivative for a problem in current classical theory its difficult to compute as its serialization problem instead if you do parallelization of differentiation you must estimate via probability the value in all flows ,Quantum Probability Helps to achieve this ,as the loss calculation is very less . the other way comparatively booming is Quantum Bayesianism, its a solution to solve most of the uncertainity problem in statistics to combine time and space in highly advanced physical research QUANTUM PROGRAMMING LANGUAGES , TOOLs and SOFTWARES All info : All Programming languages ,softwares and tools in alphabetical order Software - Nice content of all Python library - A python library Matlab based python library - Matlab Python Library Quantum Tensor Network Github - Tensor Network Bayesforge - A Beautiful Amazon Web Service Enabled Framework for Quantum Alogorithms and Data Analytics Rigetti - A best tools repository to use quantum computer in real time Rigetti Forest - An API to connect Quantum Computer quil/pyQuil - A quantum instruction language to use forest framework Grove - Grove is a repository to showcase quantum Fourier transform, phase estimation, the quantum approximate optimization algorithm, and others developed using Forest QISKit - A IBM Kit to access quantum computer and mainly for quantum circuits IBM Bluemix Simulator - A Bluemix Simulator for Quantum Circuits Microsoft Quantum Development Kit - Microsoft Visual Studio Enbaled Kit for Quantum Circuit Creation Microsoft "Q#" - Microsoft Q Sharp a new Programming Language for Quantum Circuit Creation qiskit api python - An API to connect IBM Quantum Computer ,With the generated token its easy to connect ,but very limited utils ,Lot of new utils will come soon Cyclops Tensor Framework - A framework to do tensor network simulations Python ToolKit for chemistry and physics Quantum Algorithm simulations - A New Started Project for simulating molecule and solids Bayesian Based Quatum Projects Repository - A nice repository and the kickstarter of bayesforge Google Fermion Products - A newly launched product specifivally for chemistry simulation Tree Tensor Networks - Interesting Tensor Network in Incubator Deep Tensor Neural Network - Some useful information about Tensor Neural Network in Incubator Generative Tensorial Networks - A startup to apply machine learning via tensor network for drug discovery Google Bristlecone - A new Quantum Processor from Google , Aimed for Future Hardwares with full fledged AI support XANADU - A Light based Quantum Hardware(chips supports) and Software Company Started in Preparation Stage. Soon will be in market fathom computing - A new concept to train the ai in a processor using light and quantum based concepts. soon products will be launch Alibaba Quantum Computing Cloud Service - Cloud Service to access 11 Bit Quantum Computing Processor Atomistic Machine Learning Project - Seems something Interesting with Deep Tensor Network for Quantum Chemistry Applications circQ and Google Works - Google Top Efforts on Tools IBM Safe Cryptography on Cloud - IBM Started and Developing a Quantm Safe Cryptography to replace all our Certificate Authority via Cloud Google Tensor Network Open Source - Google Started the Most Scientist Preferred Way To Use a Quantum Computer Circuit. Tensor Flow Which Makes Easy to Design the Network and Will Leave the Work Effect Of Gates, Processor Preparation and also going to tell the beauty of Maths Google Tensor Network Github - Github Project of Google Tensor Network Quantum Tensorflow - Yet to come soon Quantum Spark - Yet to come soon Quatum Map Reduce - Yet to come soon Quantum Database - Yet to come soon Quantum Server - Yet to come soon Quantum Data Analytics - Yet to come soon QUANTUM HOT TOPICS Deep Quantum Learning why and what is deep learning? In one line , If you know deep learning you can get a good job :) ,Even a different platform undergraduated and graduated person done a master specialization in deep learning can work in this big sector :), Practically speaking machine learning (vector mathematics) , deep learning (vector space(Graphics) mathematics) and big data are the terms created by big companies to make a trend in the market ,but in science and research there is no word such that , Now a days if you ask a junior person working in this big companies ,what is deep learning ,you will get some reply as "doing linear regression with stochastic gradient for a unsupervised data using Convolutional Neural Network :)" ,They knows the words clearly and knows how to do programming using that on a bunch of "relative data" , If you ask them about the FCM , SVM and HMM etc algorithms ,they will simply say these are olden days algorithms , deep learning replaced all :), But actually they dont know from the birth to the till level and the effectiveness of algorithms and mathematics ,How many mathematical theorems in vector, spaces , tensors etc solved to find this "hiding the complexity technology", They did not played with real non relative data like medical images, astro images , geology images etc , finding a relation and features is really complex and looping over n number of images to do pattern matching is a giant work , Now a days the items mentioned as deep learning (= multiple hidden artifical neural network) is not suitable for that why quantum deep learning or deep quantum learning? In the mid of Artificial Neural Network Research people realised at the maximum extreme only certain mathematical operations possible to do with ANN and the aim of this ANN is to achieve parallel execution of many mathematical operations , In artificial Intelligence ,the world intelligence stands for mathematics ,how effective if a probem can be solvable is based on the mathematics logic applying on the problem , more the logic will give more performance(more intelligent), This goal open the gate for quantum artificial neural network, On applying the ideas behind the deep learning to quantum mechanics environment, its possible to apply complex mathematical equations to n number of non relational data to find more features and can improve the performance Quantum Machine Learning vs Deep Learning Its fun to discuss about this , In recent days most of the employees from Product Based Companies Like google,microsoft etc using the word deep learning ,What actually Deep Learning ? and is it a new inventions ? how to learn this ? Is it replacing machine learning ? these question come to the mind of junior research scholars and mid level employees The one answer to all questions is deep learning = parallel "for" loops ,No more than that ,Its an effective way of executing multiple tasks repeatly and to reduce the computation cost, But it introduce a big cap between mathematics and computerscience , How ? All classical algorithms based on serial processing ,Its depends on the feedback of the first loop ,On applying a serial classical algorithm in multiple clusters wont give a good result ,but some light weight parallel classical algorithms(Deep learning) doing the job in multiple clusters and its not suitable for complex problems, What is the solution for then? As in the title Quantum Machine Learning ,The advantage behind is deep learning is doing the batch processing simply on the data ,but quantum machine learning designed to do batch processing as per the algorithm The product companies realised this one and they started migrating to quantum machine learning and executing the classical algorithms on quantum concept gives better result than deep learning algorithms on classical computer and the target to merge both to give very wonderful result References Quora - Good Discussion Quora - The Bridge Discussion Pdf - Nice Discussion Google - Google Research Discussion Microsoft - Microsoft plan to merge both IBM - IBM plan to merge both IBM Project - IBM Project idea MIT and Google - Solutions for all questions QUANTUM MEETUPS Meetup 1 - Quantum Physics Meetup 2 - Quantum Computing London Meetup 3 - Quantum Computing New York Meetup 4 - Quantum Computing Canada Meetup 5 - Quantum Artificial Intelligence Texas Meetup 6 - Genarl Quantum Mechanics , Mathematics New York Meetup 7 - Quantum Computing Mountain View California Meetup 8 - Statistical Analysis New York Meetup 9 - Quantum Mechanics London UK Meetup 10 - Quantum Physics Sydney Australia Meetup 11 - Quantum Physics Berkeley CA Meetup 12 - Quantum Computing London UK Meetup 13 - Quantum Mechanics Carmichael CA Meetup 14 - Maths and Science Group Portland Meetup 15 - Quantum Physics Santa Monica, CA Meetup 16 - Quantum Mechanics London Meetup 17 - Quantum Computing London Meetup 18 - Quantum Meta Physics ,Kansas City , Missouri ,US Meetup 19 - Quantum Mechanics and Physics ,Boston ,Massachusetts ,US Meetup 20 - Quantum Physics and Mechanics ,San Francisco ,California Meetup 21 - Quantum Mechanics ,Langhorne, Pennsylvania Meetup 22 - Quantum Mechanics ,Portland QUANTUM BASED DEGREES Plenty of courses around the world and many Universities Launching it day by day ,Instead of covering only Quantum ML , Covering all Quantum Related topics gives more idea in the order below Available Courses Quantum Mechanics for Science and Engineers Online Standford university - Nice Preparatory Course edx - Quantum Mechanics for Everyone NPTEL 1 - Nice Series of Courses to understand basics and backbone of quantum mechanics NPTEL 2 NPTEL 3 NPTEL 4 NPTEL 5 Class Based Course UK Bristol Australia Australian National University Europe Maxs Planks University Quantum Physics Online MIT - Super Explanation and well basics NPTEL - Nice Series of Courses to understand basics and backbone of quantum Physics Class Based Course Europe University of Copenhagen Quantum Chemistry Online NPTEL 1 - Nice Series of Courses to understand basics and backbone of quantum Chemistry NPTEL 2 - Class Based Course Europe UGent Belgium Quantum Computing Online MIT - Super Explanation and well basics edx - Nice Explanation NPTEL - Nice Series of Courses to understand basics and backbone of quantum Computing Class Based Course Canada uwaterloo Singapore National University Singapore USA Berkley China Baidu Quantum Technology Class Based Course Canada uwaterloo Singapore National University Singapore Europe Munich Russia Skoltech Quantum Information Science External Links quantwiki Online MIT - Super Explanation and well basics edx - Nice Explanation NPTEL - Nice Series of Courses to understand basics and backbone of quantum information and computing Class Based Course USA MIT Standford University Joint Center for Quantum Information and Computer Science - University of Maryland Canada Perimeter Institute Singapore National University Singapore Europe ULB Belgium IQOQI Quantum Electronics Online MIT - Wonderful Course NPTEL - Nice Series of Courses to understand basics and backbone of quantum Electronics Class Based Course USA Texas Europe Zurich ICFO Asia Tata Institute Quantum Field Theory Online Standford university - Nice Preparatory Course edx - Some QFT Concepts available Class Based Course UK Imperial Europe Vrije Quantum Computer Science Class Based Course USA Oxford Joint Center for Quantum Information and Computer Science - University of Maryland Quantum Artificial Intelligence and Machine Learning External Links Quora 1 Quora 1 Artificial Agents Research for Quantum Designs Quantum Mathematics Class Based Course USA University of Notre CONSOLIDATED Quantum Research Papers scirate - Plenty of Quantum Research Papers Available Peter Wittek - Famous Researcher for the Quantum Machine Leanrning , Published a book in this topic [Murphy Yuezhen Niu] (https://scholar.google.com/citations?user=0wJPxfkAAAAJ&hl=en) - A good researcher published some nice articles Recent Quantum Updates forum ,pages and newsletter Quantum-Tech - A Beautiful Newsletter Page Publishing Amazing Links facebook Quantum Machine Learning - Running By me . Not that much good :). You can get some ideas Linkedlin Quantum Machine Learning - A nice page running by experts. Can get plenty of ideas FOSDEM 2019 Quantum Talks - A one day talk in fosdem 2019 with more than 10 research topics,tools and ideas FOSDEM 2020 Quantum Talks - Live talk in fosdem 2020 with plenty new research topics,tools and ideas License Dedicated Opensources ![Dedicated Opensources]() Source code of plenty of Algortihms in Image Processing , Data Mining ,etc in Matlab, Python ,Java and VC++ Scripts Good Explanations of Plenty of algorithms with flow chart etc Comparison Matrix of plenty of algorithms Is Quantum Machine Learning Will Reveal the Secret Maths behind Astrology? Awesome Machine Learning and Deep Learning Mathematics is online Published Basic Presentation of the series Quantum Machine Learning Contribution If you think this page might helpful. Please help for World Education Charity or kids who wants to learn

#github#awesome-quantum-machine-learning

github

0.629

0.011755969008053826

polterguy•Mar 27, 2025

magic

An AI-based Low-Code and No-Code Software Development Automation Framework IMPORTANT - Magic is no longer open source. You can read the arguments here. We will keep this repository as is, but it should be considered "legacy" and will no longer receive any updates, fixes, or changes. All work is currently committed to a closed source fork of this repository, which inevitably over time will rapidly make this repository insecure and obsolete for obvious reasons. Magic Cloud is a software development automation platform created and maintained by AINIRO.IO based upon AI, Low-Code, and No-Code. It's based upon Hyperlambda, allowing you to dynamically create and orchestrate workflows, almost within a "drag'n'drop development environment". !Editing code in HyperIDE In addition to its workflows, Magic also comes with a CRUD generator, allowing you to point it at your database, click a button, and wrap all your tables into CRUD endpoints. Combined with its workflow capabilities, this can sometimes save you 90% of your time when delivering backend APIs. Magic is built on top of .Net 8 and Angular. !CRUD generator Magic comes with Docker containers and is easy to install, but AINIRO.IO also hosts Magic for a fee. Modules Magic was created to make it very easy to create small and medium sized backend APIs, and contains components for all problems related to backend development. For more information about Magic, please refer to its documentation below. Magic Cloud Documentation License This project, and all of its satellite project, is licensed under the terms of the GPL license version 3, as published by the Free Software Foundation unless an explicit and signed exception has been provided by Thomas Hansen its copyright owner. See LICENSE file for details. For licensing inquiries you can contact Thomas Hansen thomas@ainiro.io Copyright and maintenance The projects is copyright of Thomas Hansen, Ltd 2021 - 2023, and professionally maintained by AINIRO.IO.

#github#magic

github

0.518

0.04117299426077279

aiortc•Mar 27, 2025

aioquic

aioquic ======= .. image:: https://img.shields.io/pypi/l/aioquic.svg :target: https://pypi.python.org/pypi/aioquic :alt: License .. image:: https://img.shields.io/pypi/v/aioquic.svg :target: https://pypi.python.org/pypi/aioquic :alt: Version .. image:: https://img.shields.io/pypi/pyversions/aioquic.svg :target: https://pypi.python.org/pypi/aioquic :alt: Python versions .. image:: https://github.com/aiortc/aioquic/workflows/tests/badge.svg :target: https://github.com/aiortc/aioquic/actions :alt: Tests .. image:: https://img.shields.io/codecov/c/github/aiortc/aioquic.svg :target: https://codecov.io/gh/aiortc/aioquic :alt: Coverage .. image:: https://readthedocs.org/projects/aioquic/badge/?version=latest :target: https://aioquic.readthedocs.io/ :alt: Documentation What is `aioquic? aioquic is a library for the QUIC network protocol in Python. It features a minimal TLS 1.3 implementation, a QUIC stack and an HTTP/3 stack. aioquic is used by Python opensource projects such as dnspython_, hypercorn, mitmproxy and the Web Platform Tests_ cross-browser test suite. It has also been used extensively in research papers about QUIC. To learn more about aioquic please read the documentation_. Why should I use aioquic? aioquic has been designed to be embedded into Python client and server libraries wishing to support QUIC and / or HTTP/3. The goal is to provide a common codebase for Python libraries in the hope of avoiding duplicated effort. Both the QUIC and the HTTP/3 APIs follow the "bring your own I/O" pattern, leaving actual I/O operations to the API user. This approach has a number of advantages including making the code testable and allowing integration with different concurrency models. A lot of effort has gone into writing an extensive test suite for the aioquic code to ensure best-in-class code quality, and it is regularly tested for interoperability against other QUIC implementations. Features minimal TLS 1.3 implementation conforming with RFC 8446_ QUIC stack conforming with RFC 9000 (QUIC v1) and RFC 9369 (QUIC v2) IPv4 and IPv6 support connection migration and NAT rebinding logging TLS traffic secrets logging QUIC events in QLOG format version negotiation conforming with RFC 9368_ HTTP/3 stack conforming with RFC 9114_ server push support WebSocket bootstrapping conforming with RFC 9220_ datagram support conforming with RFC 9297_ Installing The easiest way to install aioquic is to run: .. code:: bash pip install aioquic Building from source If there are no wheels for your system or if you wish to build aioquic from source you will need the OpenSSL development headers. Linux ..... On Debian/Ubuntu run: .. code-block:: console sudo apt install libssl-dev python3-dev On Alpine Linux run: .. code-block:: console sudo apk add openssl-dev python3-dev bsd-compat-headers libffi-dev OS X .... On OS X run: .. code-block:: console brew install openssl You will need to set some environment variables to link against OpenSSL: .. code-block:: console export CFLAGS=-I$(brew --prefix openssl)/include export LDFLAGS=-L$(brew --prefix openssl)/lib Windows ....... On Windows the easiest way to install OpenSSL is to use Chocolatey_. .. code-block:: console choco install openssl You will need to set some environment variables to link against OpenSSL: .. code-block:: console $Env:INCLUDE = "C:\Progra~1\OpenSSL\include" $Env:LIB = "C:\Progra~1\OpenSSL\lib" Running the examples aioquic comes with a number of examples illustrating various QUIC usecases. You can browse these examples here: https://github.com/aiortc/aioquic/tree/main/examples License aioquic is released under the BSD license`_. .. _read the documentation: https://aioquic.readthedocs.io/en/latest/ .. _dnspython: https://github.com/rthalley/dnspython .. _hypercorn: https://github.com/pgjones/hypercorn .. _mitmproxy: https://github.com/mitmproxy/mitmproxy .. _Web Platform Tests: https://github.com/web-platform-tests/wpt .. _tested for interoperability: https://interop.seemann.io/ .. _QUIC implementations: https://github.com/quicwg/base-drafts/wiki/Implementations .. _cryptography: https://cryptography.io/ .. _Chocolatey: https://chocolatey.org/ .. _BSD license: https://aioquic.readthedocs.io/en/latest/license.html .. _RFC 8446: https://datatracker.ietf.org/doc/html/rfc8446 .. _RFC 9000: https://datatracker.ietf.org/doc/html/rfc9000 .. _RFC 9114: https://datatracker.ietf.org/doc/html/rfc9114 .. _RFC 9220: https://datatracker.ietf.org/doc/html/rfc9220 .. _RFC 9297: https://datatracker.ietf.org/doc/html/rfc9297 .. _RFC 9368: https://datatracker.ietf.org/doc/html/rfc9368 .. _RFC 9369: https://datatracker.ietf.org/doc/html/rfc9369

#github#aioquic

github

0.545

0.10175553624190911

obsei•Mar 27, 2025

obsei

Note: Obsei is still in alpha stage hence carefully use it in Production. Also, as it is constantly undergoing development hence master branch may contain many breaking changes. Please use released version. Obsei (pronounced "Ob see" | /əb-'sē/) is an open-source, low-code, AI powered automation tool. Obsei consists of - Observer: Collect unstructured data from various sources like tweets from Twitter, Subreddit comments on Reddit, page post's comments from Facebook, App Stores reviews, Google reviews, Amazon reviews, News, Website, etc. Analyzer: Analyze unstructured data collected with various AI tasks like classification, sentiment analysis, translation, PII, etc. Informer: Send analyzed data to various destinations like ticketing platforms, data storage, dataframe, etc so that the user can take further actions and perform analysis on the data. All the Observers can store their state in databases (Sqlite, Postgres, MySQL, etc.), making Obsei suitable for scheduled jobs or serverless applications. !Obsei diagram Future direction - Text, Image, Audio, Documents and Video oriented workflows Collect data from every possible private and public channels Add every possible workflow to an AI downstream application to automate manual cognitive workflows Use cases Obsei use cases are following, but not limited to - Social listening: Listening about social media posts, comments, customer feedback, etc. Alerting/Notification: To get auto-alerts for events such as customer complaints, qualified sales leads, etc. Automatic customer issue creation based on customer complaints on Social Media, Email, etc. Automatic assignment of proper tags to tickets based content of customer complaint for example login issue, sign up issue, delivery issue, etc. Extraction of deeper insight from feedbacks on various platforms Market research Creation of dataset for various AI tasks Many more based on creativity 💡 Installation Prerequisite Install the following (if not present already) - Install Python 3.7+ Install PIP Install Obsei You can install Obsei either via PIP or Conda based on your preference. To install latest released version - Install from master branch (if you want to try the latest features) - Note: all option will install all the dependencies which might not be needed for your workflow, alternatively following options are available to install minimal dependencies as per need - pip install obsei[source]: To install dependencies related to all observers pip install obsei[sink]: To install dependencies related to all informers pip install obsei[analyzer]: To install dependencies related to all analyzers, it will install pytorch as well pip install obsei[twitter-api]: To install dependencies related to Twitter observer pip install obsei[google-play-scraper]: To install dependencies related to Play Store review scrapper observer pip install obsei[google-play-api]: To install dependencies related to Google official play store review API based observer pip install obsei[app-store-scraper]: To install dependencies related to Apple App Store review scrapper observer pip install obsei[reddit-scraper]: To install dependencies related to Reddit post and comment scrapper observer pip install obsei[reddit-api]: To install dependencies related to Reddit official api based observer pip install obsei[pandas]: To install dependencies related to TSV/CSV/Pandas based observer and informer pip install obsei[google-news-scraper]: To install dependencies related to Google news scrapper observer pip install obsei[facebook-api]: To install dependencies related to Facebook official page post and comments api based observer pip install obsei[atlassian-api]: To install dependencies related to Jira official api based informer pip install obsei[elasticsearch]: To install dependencies related to elasticsearch informer pip install obsei[slack-api]:To install dependencies related to Slack official api based informer You can also mix multiple dependencies together in single installation command. For example to install dependencies Twitter observer, all analyzer, and Slack informer use following command - How to use Expand the following steps and create a workflow - Step 1: Configure Source/Observer Twitter Youtube Scrapper Facebook Email Google Maps Reviews Scrapper AppStore Reviews Scrapper Play Store Reviews Scrapper Reddit Reddit Scrapper Note: Reddit heavily rate limit scrappers, hence use it to fetch small data during long period Google News Web Crawler Pandas DataFrame Step 2: Configure Analyzer Note: To run transformers in an offline mode, check transformers offline mode. Some analyzer support GPU and to utilize pass device parameter. List of possible values of device parameter (default value auto): auto: GPU (cuda:0) will be used if available otherwise CPU will be used cpu: CPU will be used cuda:{id} - GPU will be used with provided CUDA device id Text Classification Text classification: Classify text into user provided categories. Sentiment Analyzer Sentiment Analyzer: Detect the sentiment of the text. Text classification can also perform sentiment analysis but if you don't want to use heavy-duty NLP model then use less resource hungry dictionary based Vader Sentiment detector. NER Analyzer NER (Named-Entity Recognition) Analyzer: Extract information and classify named entities mentioned in text into pre-defined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc Translator PII Anonymizer Dummy Analyzer Dummy Analyzer: Does nothing. Its simply used for transforming the input (TextPayload) to output (TextPayload) and adding the user supplied dummy data. Step 3: Configure Sink/Informer Slack Zendesk Jira ElasticSearch Http Pandas DataFrame Logger This is useful for testing and dry running the pipeline. Step 4: Join and create workflow source will fetch data from the selected source, then feed it to the analyzer for processing, whose output we feed into a sink to get notified at that sink. Step 5: Execute workflow Copy the code snippets from Steps 1 to 4 into a python file, for example example.py and execute the following command - Demo We have a minimal streamlit based UI that you can use to test Obsei. !Screenshot Watch UI demo video Check demo at (Note: Sometimes the Streamlit demo might not work due to rate limiting, use the docker image (locally) in such cases.) To test locally, just run To run Obsei workflow easily using GitHub Actions (no sign ups and cloud hosting required), refer to this repo. Companies/Projects using Obsei Here are some companies/projects (alphabetical order) using Obsei. To add your company/project to the list, please raise a PR or contact us via email. Oraika: Contextually understand customer feedback 1Page: Giving a better context in meetings and calls Spacepulse: The operating system for spaces Superblog: A blazing fast alternative to WordPress and Medium Zolve: Creating a financial world beyond borders Utilize: No-code app builder for businesses with a deskless workforce Articles Sr. No. Title Author 1 AI based Comparative Customer Feedback Analysis Using Obsei Reena Bapna 2 LinkedIn App - User Feedback Analysis Himanshu Sharma Tutorials Sr. No. Workflow Colab Binder 1 Observe app reviews from Google play store, Analyze them by performing text classification and then Inform them on console via logger PlayStore Reviews → Classification → Logger 2 Observe app reviews from Google play store, PreProcess text via various text cleaning functions, Analyze them by performing text classification, Inform them to Pandas DataFrame and store resultant CSV to Google Drive PlayStore Reviews → PreProcessing → Classification → Pandas DataFrame → CSV in Google Drive 3 Observe app reviews from Apple app store, PreProcess text via various text cleaning function, Analyze them by performing text classification, Inform them to Pandas DataFrame and store resultant CSV to Google Drive AppStore Reviews → PreProcessing → Classification → Pandas DataFrame → CSV in Google Drive 4 Observe news article from Google news, PreProcess text via various text cleaning function, Analyze them via performing text classification while splitting text in small chunks and later computing final inference using given formula Google News → Text Cleaner → Text Splitter → Classification → Inference Aggregator 💡Tips: Handle large text classification via Obsei Documentation For detailed installation instructions, usages and examples, refer to our documentation. Support and Release Matrix Linux Mac Windows Remark Tests ✅ ✅ ✅ Low Coverage as difficult to test 3rd party libs PIP ✅ ✅ ✅ Fully Supported Conda ❌ ❌ ❌ Not Supported Discussion forum Discussion about Obsei can be done at community forum Changelogs Refer releases for changelogs Security Issue For any security issue please contact us via email Stargazers over time Maintainers This project is being maintained by Oraika Technologies. Lalit Pagaria and Girish Patel are maintainers of this project. License Copyright holder: Oraika Technologies Overall Apache 2.0 and you can read License file. Multiple other secondary permissive or weak copyleft licenses (LGPL, MIT, BSD etc.) for third-party components refer Attribution. To make project more commercial friendly, we void third party components which have strong copyleft licenses (GPL, AGPL etc.) into the project. Attribution This could not have been possible without these open source softwares. Contribution First off, thank you for even considering contributing to this package, every contribution big or small is greatly appreciated. Please refer our Contribution Guideline and Code of Conduct. Thanks so much to all our contributors

#github#obsei

github

0.548

0.003720964303080932

sinaptik-ai•Mar 25, 2025

panda-etl

🐼 PandaETL !Version PandaETL is an open-source, no-code ETL (Extract, Transform, Load) tool designed to extract and parse data from various document types including PDFs, emails, websites, audio files, and more. With an intuitive interface and powerful backend, PandaETL simplifies the process of data extraction and transformation, making it accessible to users without programming skills. ✨ Features 📝 No-Code Interface: Easily set up and manage ETL processes without writing a single line of code. 📄 Multi-Document Support: Extract data from PDFs, emails, websites, audio files, and more. 🔧 Customizable Workflows: Create and customize extraction workflows to fit your specific needs (coming soon). 🔗 Extensive Integrations: Integrate with various data sources and destinations (coming soon). 💬 Chat with Documents: Chat with your documents to retrieve information and answer questions (coming soon). 🚀 Getting Started 📋 Prerequisites Node.js and npm (or yarn) Python 3.x Conda Poetry (Python package manager) 🖥️ Project Setup Clone the repository: Frontend Setup Navigate to the frontend directory: Install dependencies (including Husky): Create a .env file in the frontend directory with the following: or copy the .env.example file to .env Run the development server: Open http://localhost:3000 with your browser to see the result. Backend Setup Navigate to the backend directory: Create and activate a Conda environment: Install Poetry within the Conda environment: Install dependencies using Poetry (including pre-commit): Set up pre-commit hooks: Create an environment file from the example: Apply database migrations: Start the backend server: 📚 Usage 🆕 Creating a New Project Navigate to the "Projects" page. Click on "New Project". Fill in the project details and click "Create". ⚙️ Setting Up an Extraction Process Open a project and navigate to the "Processes" tab. Click on "New Process". Follow the steps to configure your extraction process. 💬 Chat with Your Documents (Coming Soon) Stay tuned for our upcoming feature that allows you to chat with your documents, making data retrieval even more interactive and intuitive. 🤝 Contributing We welcome contributions from the community. To contribute: Fork the repository. Create a new branch for your feature or bugfix. Commit your changes and push to your fork. Create a pull request with a detailed description of your changes. 📜 License This project is licensed under the MIT Expat License. See the LICENSE file for details. 🙏 Acknowledgements We would like to thank all the contributors and the open-source community for their support. 📞 Contact For any questions or feedback, please open an issue on GitHub. Development Setup This project uses pre-commit hooks in the backend and Husky in the frontend to ensure code quality and consistency. Frontend (Husky) Husky is set up in the frontend to run linting checks before each commit. To manually run the frontend linting:

#github#panda-etl

github

0.394

0.02196798710271764

carpentries-incubator•Mar 25, 2025

dennis.tim-gmail.com

Intro to AI for GLAM Our aim with this lesson is to empower GLAM (Galleries, Libraries, Archives, and Museums)) staff with the foundation to support, participate in and begin to undertake in their own right, machine learning based research and projects with heritage collections. After following this lesson, learners will be able to: Explain and differentiate key terms, phrases, and concepts associated with AI and Machine Learning in GLAM Describe ways in which AI is being innovatively used in the cultural heritage context today Identify what kinds of tasks machine learning models excel at in GLAM applications Identify weaknesses in machine learning models Reflect on ethical implications of applying machine learning to cultural heritage collections and discuss potential mitigation strategies Summarise the practical, technical steps involved in undertaking machine learning projects Identify additional resources on AI and Machine Learning in GLAM Contributing We welcome all contributions to improve the lesson! Maintainers will do their best to help you if you have any questions, concerns, or experience any difficulties along the way. We'd like to ask you to familiarize yourself with our Contribution Guide and have a look at the [more detailed guidelines][lesson-example] on proper formatting, ways to render the lesson locally, and even how to write new episodes. Please see the current list of issues for ideas for contributing to this repository. For making your contribution, we use the GitHub flow, which is nicely explained in the chapter Contributing to a Project in Pro Git by Scott Chacon. Look for the tag !good\first\issue. This indicates that the maintainers will welcome a pull request fixing this issue. Maintainer(s) Current maintainers of this lesson are Mark Bell Nora McGregor Daniel van Strien Mike Trizna Authors A list of contributors to the lesson can be found in Citation To cite this lesson, please consult with [lesson-example]: https://carpentries.github.io/lesson-example

#github#dennis.tim-gmail.com

github

0.515

0.15343274988484568

wbsth•Mar 24, 2025

cs50ai

CS50 AI Mine solutions for CS50's Introduction to Artificial Intelligence with Python course Warning : before visiting this repo files, please read about CS50's Academic Honesty rules. Includes: Quizzes answers Projects solutions Course info: Name: CS50's Introduction to Artificial Intelligence with Python University: Harvard University WWW: https://cs50.harvard.edu/ai/2020/

#github#cs50ai

youtube

0.4

0.89

Syntax•Mar 24, 2025

Vibe Coding is a problem...

Is Vibe Coding the future of software development or just a fun way to experiment? Scott and Wes talk about coding with AI, the risks of ignoring the details, and the cool (and sometimes cursed) projects they’ve Vibe Coded. 🔥 Be the ~14,700th person to join our super tasty newsletter bit.ly/syntax_snackpack Show Notes 00:00 Welcome to Syntax! 00:31 “Personal Software”. 01:30 What exactly is Vibe Coding. 02:46 The challenges of coding with AI. 04:49 Brought to you by Sentry.io. 05:39 Vibe coding quality concerns. 07:25 Vibe Coding as a learning tool. 10:11 Things we’ve Vibe Coded. All links available at https://syntax.fm/887 Hit us up on Socials! Syntax: https://x.com/syntaxfm Scott: https://x.com/stolinski Wes: https://x.com/wesbos CJ: https://x.com/CodingGarden Randy: https://www.youtube.com/@randyrektor http://www.syntax.fm Brought to you by Sentry.io #webdevelopment #webdeveloper #javascript #syntax #syntaxfm #webdev

#webdevelopment#webdeveloper#javascript

github

0.496

0.029786815978503328

maum-ai•Mar 24, 2025

voicefilter

VoiceFilter Note from Seung-won (2020.10.25) Hi everyone! It's Seung-won from MINDs Lab, Inc. It's been a long time since I've released this open-source, and I didn't expect this repository to grab such a great amount of attention for a long time. I would like to thank everyone for giving such attention, and also Mr. Quan Wang (the first author of the VoiceFilter paper) for referring this project in his paper. Actually, this project was done by me when it was only 3 months after I started studying deep learning & speech separation without a supervisor in the relevant field. Back then, I didn't know what is a power-law compression, and the correct way to validate/test the models. Now that I've spent more time on deep learning & speech since then (I also wrote a paper published at Interspeech 2020 😊), I can observe some obvious mistakes that I've made. Those issues were kindly raised by GitHub users; please refer to the Issues and Pull Requests for that. That being said, this repository can be quite unreliable, and I would like to remind everyone to use this code at their own risk (as specified in LICENSE). Unfortunately, I can't afford extra time on revising this project or reviewing the Issues / Pull Requests. Instead, I would like to offer some pointers to newer, more reliable resources: VoiceFilter-Lite: This is a newer version of VoiceFilter presented at Interspeech 2020, which is also written by Mr. Quan Wang (and his colleagues at Google). I highly recommend checking this paper, since it focused on a more realistic situation where VoiceFilter is needed. List of VoiceFilter implementation available on GitHub: In March 2019, this repository was the only available open-source implementation of VoiceFilter. However, much better implementations that deserve more attention became available across GitHub. Please check them, and choose the one that meets your demand. PyTorch Lightning: Back in 2019, I could not find a great deep-learning project template for myself, so I and my colleagues had used this project as a template for other new projects. For people who are searching for such project template, I would like to strongly recommend PyTorch Lightning. Even though I had done a lot of effort into developing my own template during 2019 (VoiceFilter -> RandWireNN -> MelNet -> MelGAN), I found PyTorch Lightning much better than my own template. Thanks for reading, and I wish everyone good health during the global pandemic situation. Best regards, Seung-won Park Unofficial PyTorch implementation of Google AI's: VoiceFilter: Targeted Voice Separation by Speaker-Conditioned Spectrogram Masking. Result Training took about 20 hours on AWS p3.2xlarge(NVIDIA V100). Audio Sample Listen to audio sample at webpage: http://swpark.me/voicefilter/ Metric | Median SDR | Paper | Ours | | ---------------------- | ----- | ---- | | before VoiceFilter | 2.5 | 1.9 | | after VoiceFilter | 12.6 | 10.2 | SDR converged at 10, which is slightly lower than paper's. Dependencies Python and packages This code was tested on Python 3.6 with PyTorch 1.0.1. Other packages can be installed by: Miscellaneous ffmpeg-normalize is used for resampling and normalizing wav files. See README.md of ffmpeg-normalize for installation. Prepare Dataset Download LibriSpeech dataset To replicate VoiceFilter paper, get LibriSpeech dataset at http://www.openslr.org/12/. train-clear-100.tar.gz(6.3G) contains speech of 252 speakers, and train-clear-360.tar.gz(23G) contains 922 speakers. You may use either, but the more speakers you have in dataset, the more better VoiceFilter will be. Resample & Normalize wav files First, unzip tar.gz file to desired folder: Next, copy utils/normalize-resample.sh to root directory of unzipped data folder. Then: Edit config.yaml Preprocess wav files In order to boost training speed, perform STFT for each files before training by: This will create 100,000(train) + 1000(test) data. (About 160G) Train VoiceFilter Get pretrained model for speaker recognition system VoiceFilter utilizes speaker recognition system (d-vector embeddings). Here, we provide pretrained model for obtaining d-vector embeddings. This model was trained with VoxCeleb2 dataset, where utterances are randomly fit to time length [70, 90] frames. Tests are done with window 80 / hop 40 and have shown equal error rate about 1%. Data used for test were selected from first 8 speakers of VoxCeleb1 test dataset, where 10 utterances per each speakers are randomly selected. Update: Evaluation on VoxCeleb1 selected pair showed 7.4% EER. The model can be downloaded at this GDrive link. Run After specifying traindir, testdir at config.yaml, run: This will create chkpt/name and logs/name at base directory(-b option, . in default) View tensorboardX Resuming from checkpoint Evaluate Possible improvments Try power-law compressed reconstruction error as loss function, instead of MSE. (See #14) Author Seungwon Park at MINDsLab (yyyyy@snu.ac.kr, swpark@mindslab.ai) License Apache License 2.0 This repository contains codes adapted/copied from the followings: utils/adabound.py from https://github.com/Luolc/AdaBound (Apache License 2.0) utils/audio.py from https://github.com/keithito/tacotron (MIT License) utils/hparams.py from https://github.com/HarryVolek/PyTorchSpeakerVerification (No License specified) utils/normalize-resample.sh from https://unix.stackexchange.com/a/216475

#github#voicefilter

github

0.524

0.1392403398579415

emilwallner•Mar 23, 2025

How-to-learn-Deep-Learning

Approach A practical, top-down approach, starting with high-level frameworks with a focus on Deep Learning. UPDATED VERSION: 👉 Check out my 60-page guide, No ML Degree, on how to land a machine learning job without a degree. Getting started [2 months] There are three main goals to get up to speed with deep learning: 1) Get familiar to the tools you will be working with, e.g. Python, the command line and Jupyter notebooks 2) Get used to the workflow, everything from finding the data to deploying a trained model 3) Building a deep learning mindset, an intuition for how deep learning models behave and how to improve them Spend a week on codecademy.com and learn the python syntax, command line and git. If you don't have any previous programming experience, it's good to spend a few months learning how to program. Otherwise, it's easy to become overwhelmed. Spend one to two weeks using Pandas and Scikit-learn on Kaggle problems using Jupyter Notebook on Colab, e.g. Titanic, House prices, and Iris. This gives you an overview of the machine learning mindset and workflow. Spend one month implementing models on cloud GPUs. Start with FastAI and PyTorch. The FastAI community is the go-to place for people wanting to apply deep learning and share the state of the art techniques. Once you have done this, you will know how to add value with ML. Portfolio [3 - 12 months] Think of your portfolio as evidence to a potential employer that you can provide value for them. When you are looking for your first job, there are four main roles you can apply for Machine Learning Engineering, Applied Machine Learning Researcher / Residencies, Machine Learning Research Scientist, and Software Engineering. A lot of the work related to machine learning is pure software engineering roles (category 4), e.g. scaling infrastructure, but that's out of scope for this article. It's easiest to get a foot in the door if you aim for Machine Learning Engineering roles. There are a magnitude more ML engineering roles compared to category 2 & 3 roles, they require little to no theory, and they are less competitive. Most employers prefer scaling and leveraging stable implementations, often ~1 year old, instead of allocating scarce resources to implement SOTA papers, which are often time-consuming and seldom work well in practice. Once you can cover your bills and have a few years of experience, you are in a better position to learn theory and advance to category 2 & 3 roles. This is especially true if you are self-taught, you often have an edge against an average university graduate. In general, graduates have weak practical skills and strong theory skills. Context You'll have a mix of 3 - 10 technical and non-technical people looking at your portfolio, regardless of their background, you want to spark the following reactions: the applicant has experience tackling our type of problems, the applicant's work is easy to understand and well organized, and the work was without a doubt 100% made by the applicant. Most ML learners end up with the same portfolio as everyone else. Portfolio items include things as MOOC participation, dog/cat classifiers, and implementations on toy datasets such as the titanic and iris datasets. They often indicate that you actively avoid real-world problem-solving, and prefer being in your comfort zone by copy-pasting from tutorials. These portfolio items often signal negative value instead of signaling that you are a high-quality candidate. A unique portfolio item implies that you have tackled a unique problem without a solution, and thus have to engage in the type of problem-solving an employee does daily. A good starting point is to look for portfolio ideas on active Kaggle competitions, and machine learning consulting projects, and demo versions of common production pipelines. Here's a Twitter thread on how to come up with portfolio ideas. Here are rough guidelines to self-assess the strength of your portfolio: Machine learning engineering: Even though ML engineering roles are the most strategic entry point, they are still highly competitive. In general, there are ~50 software engineering roles for every ML role. From the self-learners I know, 2/3 fail to get a foot in the door and end up taking software engineering roles instead. You are ready to look for a job when you have two high-quality projects that are well-documented, have unique datasets, and are relevant to a specific industry, say banking or insurance. Project Type | Base score | -------------| -----------| Common project | -1 p || Unique project | 10 p | Multiplier Type | Factor -----------------|----------------- Strong documentation | 5x 5000-word article | 5x Kaggle Medal | 10x Employer relevancy | 20x Hireable: 5,250 p Competative: 15,000 p Applied research / research assistant/ residencies: For most companies, the risk of pursuing cutting edge research is often too high, thus only the biggest companies tend to need this skillset. There are smaller research organizations that hire for these positions, but these positions tend to be poorly advertised and have a bias for people in their existing community. Many of these roles don't require a Ph.D., which makes them available to most people with a Bachelor's or Master's degrees, or self-learners with one year of focussed study. Given the status, scarcity, and requirements for these positions, they are the most competitive ML positions. Positions at well-known companies tend to get more than a thousand applicants per position. Daily, these roles require that you understand and can implement SOTA papers, thus that's what they will be looking for in your portfolio. Projects type | Base score --------------| ----------- Common project | -10 p Unique project | 1 p SOTA paper implementation | 20 p Multiplier type | Factor ----------------| --------------- Strong documentation | 5x 5000-word article | 5x SOTA performance | 5x Employer relevancy | 20x Hireable: 52,500 p Competitive: 150,000 p Research Scientist: Research scientist roles require a Ph.D. or equivalent experience. While the former category requires the ability to implement SOTA papers, this category requires you to come up with research ideas. The mainstream research community measure the quality of research ideas by their impact, here is a list of the venues and their impact. To have a competitive portfolio, you need two published papers in the top venues in an area that's relevant to your potential employer. Project type | Base score -------------| ---------------- Common project | -100 p An unpublished paper | 5 p ICML/ICLR/NeurIPS publication | 500p All other publications | 50 p Multiplier type | Factor ------------------| ------------------ First author paper | 10x Employer relevancy | 20x Hireable: 20,000 p Competitive roles and elite PhD positions: 200,000 p Examples: My first portfolio item (after 2 months of learning): Code | Write-up My second portfolio item (after 4 months of learning): Code | Write-up Dylan Djian's first portfolio item: Code | Write-up Dylan Djian's second portfolio item: Code | Write-up Reiichiro Nakano's first portfolio item: Code | Write-up Reiichiro Nakano's second portfolio item: Write-up Most recruiters will spend 10-20 seconds on each of your portfolio items. Unless they can understand the value in that time frame, the value of the project is close to zero. Thus, writing and documentation are key. Here's another thread on how to write about portfolio items. The last key point is relevancy. It's more fun to make a wide range of projects, but if you want to optimize for breaking into the industry, you want to do all projects in one niche, thus making your skillset super relevant for a specific pool of employers. Further Inspiration: FastAI student projects Stanford NLP student projects Stanford CNN student projects Theory 101 [4 months] Learning how to read papers is critical if you want to get into research, and a brilliant asset as an ML engineer. There are three key areas to feel comfortable reading papers: 1) Understanding the details of the most frequent algorithms, gradient descent, linear regression, and MLPs, etc 2) Learning how to translate the most frequent math notations into code 3) Learn the basics of algebra, calculus, statistics, and machine learning For the first week, spend it on 3Blue1Brown's Essence of linear algebra, the Essence of Calculus, and StatQuests' the Basics (of statistics) and Machine Learning. Use a spaced repetition app like Anki and memorize all the key concepts. Use images as much as possible, they are easier to memorize. Spend one month recoding the core concepts in python numpy, including least squares, gradient descent, linear regression, and a vanilla neural network. This will help you reduce a lot of cognitive load down the line. Learning that notations are compact logic and how to translate it into code will make you feel less anxious about the theory. I believe the best deep learning theory curriculum is the Deep Learning Book by Ian Goodfellow and Yoshua Bengio and Aaron Courville. I use it as a curriculum, and the use online courses and internet resources to learn the details about each concept. Spend three months on part 1 of the Deep learning book. Use lectures and videos to understand the concepts, Khan academy type exercises to master each concept, and Anki flashcards to remember them long-term. Key Books: Deep Learning Book by Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD by Jeremy Howard and Sylvain. Gugger. Deep Learning with Python by François Chollet. Neural Networks and Deep Learning by Michael Nielsen. Grokking Deep Learning by Andrew W. Trask. Forums FastAI Keras Slack Distill Slack Pytorch Twitter Other good learning strategies: Emil Wallner S. Zayd Enam Catherine Olsson Greg Brockman V2 Greg Brockman V1 Andrew Ng Amid Fish Spinning Up by OpenAI Confession as an AI researcher YC Threads: One and Two If you have suggestions/questions create an issue or ping me on Twitter. UPDATED VERSION: 👉 Check out my 60-page guide, No ML Degree, on how to land a machine learning job without a degree. Language versions: Korean | English

#github#how-to-learn-deep-learning

youtube

0.379

0.77

Greg Isenberg•Mar 21, 2025

He makes $750 a day 'Vibe Coding' Apps (using Replit, ChatGPT, Upwork)

Billy Howell shares his strategy for making money by building and selling custom web applications using AI tools like Replit. He demonstrates the process by finding projects on Upwork, creating a product requirements document with ChatGPT, and using Replit to automatically generate a functional web application. Billy explains that this approach is less risky than building SaaS products because it validates demand before significant development work. Timestamps: 00:00 - Intro 02:19 - Searching for App Ideas on Upwork 11:04 - Using ChatGPT for PRD Creation 12:22 - Why choose Replit for Development 15:15 - Building Prototype with Replit 19:53 - Areas of Concern when building with AI coders 23:30 - Earning Potential on Upwork 27:55 - The process for selling these Apps 32:03 - Comparing Different Business Models 35:40 - Huge opportunity: Unbundling SaaS 37:44 - Testing App 39:39 - How to standout on Upwork 40:35 - Integrating v0 UI to Replit Key Points • Billy Howell explains his method of "vibe coding" - using AI tools like Replit to quickly build and sell custom web applications • The process involves finding clients on Upwork who need solutions, creating a prototype, and selling it before building the complete app • Billy demonstrates how to use Repl.it with AI assistance to rapidly build a case management system for a nonprofit • The approach focuses on creating simple CRUD (Create, Read, Update, Delete) applications rather than complex systems 1) The "Sell First, Build Later" Framework Billy's #1 rule: Find someone to BUY your app BEFORE you build it. Most developers get this backward - they build something cool then struggle to find users. The secret? Don't market. SELL. How? Look for people ALREADY trying to pay for solutions 2) Upwork Gold Mining Strategy Billy's exact process: • Search Upwork for jobs mentioning expensive SaaS tools (Airtable, HubSpot, etc) • Look for simple CRUD apps (data entry, visualization) • Build a quick prototype in Repl.it • Send a Loom video demo to potential clients His first sale? $750 replacing an Airtable solution! 3) The Vibe Coding Tech Stack Billy's weapons of choice: • Replit for rapid prototyping (zero setup friction!) • ChatGPT to format requirements into PRDs • V0 for beautiful UI mockups • ShadCN components for clean interfaces The magic combo: Feed requirements to Replit + "build me this app" = working prototype in MINUTES. 4) What to Avoid When Vibe Coding Not all projects are created equal! Watch out for: • Payment processing (risky) • DocuSign integrations (complex) • Calendar functionality (AI struggles with time zones) • Anything changing data in other apps Start with simple CRUD apps that store and display information. 5) The Real Money-Making Model Billy's approach isn't just about one-off projects: • Initial build: $750-2,500 • Charge for hosting • Recurring revenue from feature requests • Get referrals to similar businesses One recent client is now reselling his solution to other companies in the same industry! 6) Why This Beats Building a SaaS Building a traditional SaaS = "nightmare money pit" according to Billy. With vibe coding consulting: • De-risk by getting paid upfront • Learn across multiple projects • No marketing costs • Discover validated problems • Build a portfolio of solutions Six figures on Upwork is VERY doable. 7) The 60-Second Sales Pitch Billy's exact closing technique: • Find job posting • Make mockup in V0 or Replit • Record 1-minute Loom: "I'm Billy, I make apps. I know you wanted Airtable, but I made this custom for you." • Personalize with company name • Send and repeat Simple. Effective. PROFITABLE. The future of coding isn't about knowing every framework—it's about SOLVING PROBLEMS quickly. Anyone can do this with the right tools and approach. Notable Quotes: "The number one thing is how to sell an app that you've built... And the secret is not to market. It's just to sell it." - Billy Howell "We start, we need to find someone to buy the app before we build it. That's where most people get this wrong, is they build something and then try to sell it or try to get users." - Billy Howell LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/ BoringAds — ads agency that will build you profitable ad campaigns http://boringads.com/ BoringMarketing — SEO agency and tools to get your organic customers http://boringmarketing.com/ Startup Empire — a membership for builders who want to build cash-flowing businesses https://www.startupempire.co FIND ME ON SOCIAL X/Twitter: https://twitter.com/gregisenberg Instagram: https://instagram.com/gregisenberg/ LinkedIn: https://www.linkedin.com/in/gisenberg/ FIND BILLY ON SOCIAL X/Twitter: https://x.com/billyjhowell Youtube: https://www.youtube.com/@billyjhowell

#1

github

0.541

0.0750848814969247

phodal•Mar 21, 2025

coca

Coca - toolbox for system refactoring and analysis !GitHub release (latest SemVer) !GitHub go.mod Go version Coca is a toolbox which is design for legacy system refactoring and analysis, includes call graph, concept analysis, api tree, design patterns suggest. Coca 是一个用于系统重构、系统迁移和系统分析的工具箱。它可以分析代码中的测试坏味道、模块化分析、行数统计、分析调用与依赖、Git 分析以及自动化重构等。 Related Tools: Coco is an effective DevOps analysis and auto-suggest tool. Kotlin version: Chapi Migration Guide (Chinese Version): 《系统重构与迁移指南》 Inspired by: newlee & Tequila Refactoring Modeling: !Refactoring Modeling Languages Support: Java (full features) Features List: Getting started Requirements: graphviz for dot file to image (such as svg, png) The easiest way to get coca is to use one of the pre-built release binaries which are available for OSX, Linux, Windows on the release page. You can also install yourself : Usage Analysis Arch Android Studio Gradle DSL Module (merge header) command: coca arch -x "com.android.tools.idea.gradle.dsl" -H true !Gradle Demo Android Studio Gradle DSL Module Elements Part: command: coca arch -x "com.android.tools.idea.gradle.dsl.parser.elements" !Gradle Demo Find Bad Smells Examples Result: Code Line Count Results: Results to json Cloc by directory results csv: Cloc Top File output to: cocareporter/sortcloc.json and also: Build Deps Tree Examples Results: !Call Demo Identify Spring API !API Demo With Count or multi package: coca api -r com.macro.mall.demo.controller.,com.zheng.cms.admin.,com.phodal.pholedge -c Git Analysis Results: Concept Analyser Results Examples: Count Refs Results: Reverse Call Graph Results: !RCall Demo Auto Refactor support: rename move remove unused import remove unused class Evaluate Arduino Results(Old Version): New Version: Evaluate.json examples Todo results: coca suggest +--------+------------------+--------------------------------+ | CLASS | PATTERN | REASON | +--------+------------------+--------------------------------+ | Insect | factory | too many constructor | | Bee | factory, builder | complex constructor, too | | | | many constructor, too many | | | | parameters | +--------+------------------+--------------------------------+ coca tbs bash +---------------------+---------------------------------------------------------------+------+ | TYPE | FILENAME | LINE | +---------------------+---------------------------------------------------------------+------+ | DuplicateAssertTest | app/test/cc/arduino/i18n/ExternalProcessOutputParserTest.java | 107 | | DuplicateAssertTest | app/test/cc/arduino/i18n/ExternalProcessOutputParserTest.java | 41 | | DuplicateAssertTest | app/test/cc/arduino/i18n/ExternalProcessOutputParserTest.java | 63 | | RedundantPrintTest | app/test/cc/arduino/i18n/I18NTest.java | 71 | | RedundantPrintTest | app/test/cc/arduino/i18n/I18NTest.java | 72 | | RedundantPrintTest | app/test/cc/arduino/i18n/I18NTest.java | 77 | | DuplicateAssertTest | app/test/cc/arduino/net/PACSupportMethodsTest.java | 19 | | DuplicateAssertTest | app/test/processing/app/macosx/SystemProfilerParserTest.java | 51 | | DuplicateAssertTest | app/test/processing/app/syntax/PdeKeywordsTest.java | 41 | | DuplicateAssertTest | app/test/processing/app/tools/ZipDeflaterTest.java | 57 | | DuplicateAssertTest | app/test/processing/app/tools/ZipDeflaterTest.java | 83 | | DuplicateAssertTest | app/test/processing/app/tools/ZipDeflaterTest.java | 109 | +---------------------+---------------------------------------------------------------+------+ coca deps -p fixtures/deps/mavensample +---------------------------+----------------------------------------+---------+ | GROUPID | ARTIFACTID | SCOPE | +---------------------------+----------------------------------------+---------+ | org.flywaydb | flyway-core | | | mysql | mysql-connector-java | runtime | | org.springframework.cloud | spring-cloud-starter-contract-verifier | test | +---------------------------+----------------------------------------+---------+ bash brew install go bash export GOROOT=/usr/local/opt/go/libexec export GOPATH=$HOME/.go export PATH=$PATH:$GOROOT/bin:$GOPATH/bin git clone https://github.com/modernizing/coca go get github.com/onsi/ginkgo go get github.com/onsi/gomega `` License Arch based on Tequila Git Analysis inspired by Code Maat Test bad smells inspired by Test Smell Examples @ 2019 A Phodal Huang's Idea. This code is distributed under the MPL license. See LICENSE` in this directory.

#github#coca

github

0.454

0.14391064025794564

springframeworkguru•Mar 18, 2025

spring-ai-intro

Introduction to Spring AI This repository contains source code examples used to support my on-line courses about the Spring Framework. All Spring Framework Guru Courses Spring Framework 6 Spring Framework 6 - Beginner to Guru Hibernate and Spring Data JPA: Beginner to Guru API First Engineering with Spring Boot Introduction to Kafka with Spring Boot Spring Security: Beginner to Guru Spring Framework 5 Spring Framework 5: Beginner to Guru - Get the most modern and comprehensive course available for the Spring Framework! Join over 17,200 over Guru's in an Slack community exclusive to this course! More than 5,700 students have given this 53 hour course a 5 star review! Spring Boot Microservices with Spring Cloud Beginner to Guru - Master Microservice Architectures Using Spring Boot 2 and Cloud Based Deployments with Spring Cloud and Docker Reactive Programming with Spring Framework 5 - Keep your skills razor sharp and take a deep dive into Reactive Programming! Testing Spring Boot: Beginner to Guru - Best Selling Course Become an expert in testing Java and Spring Applications with JUnit 5, Mockito and much more! SQL SQL Beginner to Guru: MySQL Edition - SQL is a fundamental must have skill, which employers are looking for. Learn to master SQL on MySQL, the worlds most popular database! DevOps Apache Maven: Beginner to Guru - Best Selling Course Take the mystery out of Apache Maven. Learn how to use Maven to build your Java and Spring Boot projects! OpenAPI: Beginner to Guru - Master OpenAPI (formerly Swagger) to Create Specifications for Your APIs OpenAPI: Specification With Redocly Docker for Java Developers - Best Selling Course on Udemy! Learn how you can supercharge your development by leveraging Docker. Collaborate with other students in a Slack community exclusive to the course! Spring Framework DevOps on AWS - Learn how to build and deploy Spring applications on Amazon AWS! Ready for Production with Spring Boot Actuator - Learn how to leverage Spring Boot Actuator to monitor your applications running in production. Web Development with Spring Framework Mastering Thymeleaf with Spring Boot - Once you learn Thymeleaf, you'll never want to go back to using JSPs for web development! Connect with Spring Framework Guru Spring Framework Guru Blog Subscribe to Spring Framework Guru on YouTube Like Spring Framework Guru on Facebook Follow Spring Framework Guru on Twitter Connect with John Thompson on LinkedIn

#github#spring-ai-intro

youtube

0

0.28

a16z•Mar 13, 2025

Vibe Coding is Here - How AI is Changing How We Build Online

Vibe Coding: The Future of Software Development? (with Yoko Li & Justine Moore | a16z) What if you could build an app just by describing it? That’s the idea behind vibe coding — a new AI-driven approach that’s reshaping software development for engineers and non-technical users alike. Instead of writing detailed code, users guide an AI coding agent with simple prompts like “make this look cleaner” or “I want a button that does X.” In this episode, we sit down with Yoko Li and Justine Moore from a16z to break down the rise of vibe coding, its impact on software development, and why AI-powered text-to-web tools are taking off. We explore: How vibe coding works and why it’s gaining traction The emerging companies leading the space (Cursor, Lovable, Bolt, VZero, and more) Why engineers and total beginners are both using these tools The challenges of AI-driven development (when “vibes” go wrong!) Where this trend is heading—and what it means for the future of coding From software for one to enterprise-level applications, vibe coding is opening up new possibilities for creating on the web. Tune in to learn how it’s changing the way we build. Learn more and check out everything a16z is doing, including articles, projects, and more podcasts here – https://a16z.com/ai-web-app-builders/ Follow everyone on X: Yoko Li - https://x.com/stuffyokodraws Justine Moore - https://x.com/venturetwins Steph Smith - https://x.com/stephsmithio

github

0.43

0.050863657300783044

LinkedInLearning•Feb 28, 2025

introduction-to-ai-orchestration-with-langchain-and-llamaindex-3820082

Introduction to AI Orchestration with LangChain and LlamaIndex This is the repository for the LinkedIn Learning course Introduction to AI Orchestration with LangChain and LlamaIndex. The full course is available from [LinkedIn Learning][lil-course-url]. ![lil-thumbnail-url] Are you ready to dive into the world of AI applications? This course was designed for you. AI orchestration frameworks let you step back from the details of artificial intelligence tools and APIs and instead focus on building more general, effective systems that solve real-world problems. Join instructor M.Joel Dubinko as he explores the business benefits of AI orchestration—faster development, smarter interfaces, lower costs, and more. This course provides an overview of AI fundamentals and key capabilities, like accessing external tools and databases, with a special focus on exploring local models running on your own hardware, alongside or instead of cloud services like those from OpenAI. Every step of the way, Joel offers hands-on demonstrations of two industry-leading frameworks: LangChain and LlamaIndex. By the end of this course, you’ll be prepared to start building chatbots, intelligent agents, and other useful tools, while monitoring for errors and troubleshooting as you go. Welcome to the course! AI is a fast-changing field, so be sure to check this repo for newer versions of the sample code. Installing Clone this repository into your local machine using the terminal (Mac), CMD (Windows), or a GUI tool like SourceTree. Ensure you have Python 3.10 or later (version 3.11 recommended) To prevent conflicts with other installed software on your computer, the author recommends setting up a virtual environment as follows: python3.11 -m venv .venv Activate the virtual environment with one of these commands: Install the necessary Python packages: (use the upgrade flag to ensure you have current versions) Specific projects in this course might have additional optional requirements. If so, it will be noted within the relevant video. Updates Recent versions of LM Studio have changed the UI from what's shown in the videos. These are generally welcome improvements. For example the maximum context length and other model parameters are viewable in the sidebar. Recent versions of LlamaIndex have changed their import and package structure in a way that breaks existing code. In many cases, you can fix imports as follows: Specific third party components require installing new packages. These will be noted in comments. Example: For code in Chap04, From March 1, 2024, LlamaHub has been deprecated and most projects migrated into LlamaIndex. (sort of--it's complicated) Specifically: Additionally, LlamaIndex ServiceContext has been deprecated and replaced with Settings. See Ch02/rag_llamaindex.py for updated sample code. LangChain too has changed their import structure, though as of this writing it produces warnings rather than errors. In many cases you will need to import from langchaincommunity or langchainopenai as follows: Instructor M. Joel Dubinko Software Generalist | Consultant | Instructor | Problem Solver Check out my other courses on [LinkedIn Learning][URL-instructor-home]. [lil-course-url]: https://www.linkedin.com/learning/introduction-to-ai-orchestration-with-langchain-and-llamaindex [lil-thumbnail-url]: https://media.licdn.com/dms/image/D560DAQEi6KQmA4fF1Q/learning-public-crop6751200/0/1707936616297?e=2147483647&v=beta&t=3vzvDRzpKq9Nd99ss8r2pqMZmyTOKYgKwk825XoSEHU [URL-instructor-home]: https://www.linkedin.com/learning/instructors/m-joel-dubinko?u=104

#github#introduction-to-ai-orchestration-with-langchain-and-llamaindex-3820082

github

0.385

0.0020930582944730723

aliammari1•Feb 21, 2025

Awesome-Ai-Tools

Awesome-Ai-Tools This repo contains AI tools that will help you achieve your goals. The tools are categorized into different sections based on their functionality. Contents Awesome-Ai-Tools Contents Productivity Time Management Task Management Email Management Creativity Art Music Writing Communication Writing Personality Analysis Translation Data Science Machine Learning Data Analysis Data Visualization Natural Language Processing Text Classification Named Entity Recognition Computer Vision Image Classification Object Detection Robotics Robot Simulation Robot Control Miscellaneous Language Models Generative Models Productivity If you're looking to boost your productivity, there are a number of AI tools that can help. Time Management RescueTime - RescueTime is an AI-powered time tracking tool that helps you understand how you're spending your time on your computer. It can help you identify areas where you're wasting time and make adjustments to your workflow to be more productive. Focus@Will - Focus@Will is an AI-powered music service that helps you stay focused and productive while you work. It uses neuroscience to create music that is scientifically optimized to help you concentrate. Clockify - Clockify is an AI-powered time tracking tool that helps you track your time across different projects and tasks. It can help you identify areas where you're spending too much time and make adjustments to your workflow to be more productive. Trello - Trello is an AI-powered task management tool that helps you stay organized and on top of your to-do list. It can help you prioritize tasks, set deadlines, and even collaborate with others on projects. Motion - Motion is an AI-powered calendar and task management tool that automatically schedules your tasks and meetings for optimal productivity. Reclaim.ai - Reclaim is an intelligent calendar assistant that helps you protect your time by automatically scheduling meetings and tasks. Task Management Todoist - Todoist is an AI-powered task management tool that helps you stay organized and on top of your to-do list. It can help you prioritize tasks, set deadlines, and even suggest tasks based on your previous activity. Asana - Asana is an AI-powered task management tool that helps you stay organized and on top of your to-do list. It can help you prioritize tasks, set deadlines, and even collaborate with others on projects. Notion - Notion is an AI-powered productivity tool that can help you manage tasks, take notes, and collaborate with others on projects. It can also be used to create wikis, databases, and other types of content. Taskade - Taskade is an AI-powered productivity tool that can manage tasks and notes for individuals and teams. ClickUp - ClickUp is an AI-enhanced project management tool that helps teams organize work with automated task distributions and smart notifications. Monday.com - Monday.com uses AI to streamline workflow management and automate routine tasks. Email Management Boomerang - Boomerang is an AI-powered email management tool that helps you manage your inbox more efficiently. It can help you schedule emails to be sent later, remind you to follow up on emails, and even suggest responses to emails. SaneBox - SaneBox is an AI-powered email management tool that helps you manage your inbox more efficiently. It can help you prioritize emails, unsubscribe from unwanted emails, and even snooze emails to be dealt with later. Mailstrom - Mailstrom is an AI-powered email management tool that helps you clean up your inbox. It can help you quickly identify and delete unwanted emails, and even unsubscribe from newsletters and other types of email subscriptions. Creativity If you're looking to get more creative, there are a number of AI tools that can help. Art Artbreeder - Artbreeder is an AI-powered tool that allows you to create unique digital art by combining different images and styles. Runway ML - Runway is an AI-powered tool that allows users to edit and generate videos using natural language descriptions. Prisma - Prisma is an AI-powered tool that allows you to transform your photos into works of art using neural networks. Music AIVA - AIVA is an AI-powered music composition tool that can help you create original music for your projects. Writing monica - Monica is a chrome extension powered by ChatGPT API. It is designed to be your personal AI assistant for effortless chatting and copywriting. CopyAI - CopyAI is an AI-powered writing assistant that can help you generate high-quality marketing copy, product descriptions, and more. Grammarly - Grammarly is an AI-powered writing assistant that helps you catch grammar and spelling errors in your writing. It can also suggest improvements to your writing style to help you communicate more effectively. Jasper - Jasper is an AI writing assistant that helps create marketing copy, blog posts, and social media content. Rytr - Rytr is an AI writing tool that helps generate content in different tones and styles. Communication If you're looking to improve your communication skills, there are a number of AI tools that can help. Writing Linguix - Linguix is an AI-powered writing assistant that can help you improve your writing skills. It can catch grammar and spelling errors, suggest improvements to your writing style, and even help you avoid plagiarism. Hemingway Editor - Hemingway Editor is an AI-powered writing tool that helps you simplify your writing and make it more readable. It can help you identify complex sentences, passive voice, and other issues that can make your writing difficult to understand. Personality Analysis Crystal - Crystal is an AI-powered tool that helps you understand the personality of the people you're communicating with. It can provide insights into their communication style and suggest ways to communicate more effectively with them. IBM Watson Personality Insights - IBM Watson Personality Insights is a tool that uses natural language processing and machine learning algorithms to analyze text and provide insights into the personality traits of the author. Translation DeepL - DeepL is an AI-powered translation tool that provides high-quality translations in multiple languages. It uses neural network algorithms to provide more accurate translations than traditional translation tools. Google Translate - Google Translate is a free online translation tool that uses machine learning algorithms to provide translations in over 100 languages. Data Science If you're working with data, there are a number of AI tools that can help you analyze and make sense of it. Machine Learning DataRobot - DataRobot is an AI-powered platform that helps you build and deploy machine learning models. It can help you automate the process of building models and make predictions based on your data. TensorFlow - TensorFlow is an open-source machine learning framework developed by Google. It can help you build and train machine learning models for a variety of applications. PyTorch - PyTorch is another open-source machine learning framework that is popular among researchers and developers. It is known for its ease of use and flexibility. H2O.ai - H2O.ai is an open-source machine learning platform that allows you to build and deploy machine learning models at scale. PyTorch3d - Pytorch 3d is an open-source library for deep learning with 3d data. Auto-sklearn - Auto-sklearn is an automated machine learning toolkit that helps find the best machine learning pipeline for your dataset. Ludwig - Ludwig is a declarative machine learning framework that makes it easy to build and train models without writing code. Data Analysis Pandas - Pandas is an open-source data analysis library for Python. It can help you manipulate and analyze data in a variety of formats, including CSV, Excel, and SQL databases. RapidMiner - RapidMiner is an AI-powered data science platform that allows you to build and deploy predictive models without writing any code. Apache Spark - Apache Spark is an open-source big data processing framework that can help you analyze large datasets in a distributed computing environment. Data Visualization Tableau - Tableau is a data visualization tool that uses AI to help you explore and understand your data. It can help you identify patterns and trends in your data that might not be immediately obvious. Plotly - Plotly is an open-source data visualization library for Python. It can help you create interactive charts and graphs that can be embedded in web pages and other applications. D3.js - D3.js is a JavaScript library for data visualization that allows you to create dynamic and interactive visualizations using web standards like HTML, CSS, and SVG. Natural Language Processing If you're interested in natural language processing, there are a number of AI tools that can help you get started. Text Classification TextBlob - TextBlob is an open-source library for processing textual data in Python. It can help you perform tasks like sentiment analysis, part-of-speech tagging, and text classification. NLTK - NLTK (Natural Language Toolkit) is another open-source library for natural language processing in Python. It can help you perform tasks like tokenization, stemming, and named entity recognition. Amazon Comprehend - Amazon Comprehend is a natural language processing service that uses machine learning to analyze text and provide insights into the content and sentiment of the text. Named Entity Recognition spaCy - spaCy is an open-source library for advanced natural language processing in Python. It can help you build applications that can understand and analyze human language. One of its key features is named entity recognition, which can identify and classify entities like people, organizations, and locations. Google Cloud Natural Language API - Google Cloud Natural Language API is a natural language processing service that can analyze text and provide insights into the sentiment, entities, and syntax of the text. Computer Vision If you're interested in computer vision, there are a number of AI tools that can help you get started. Image Classification Clarifai - Clarifai is an AI-powered image recognition tool that can help you classify images based on their content. It can recognize objects, scenes, and even specific concepts like emotions and colors. Google Cloud Vision API - Google Cloud Vision API is a computer vision service that can analyze images and provide insights into the content of the images, including objects, faces, and text. Object Detection YOLO - YOLO (You Only Look Once) is an open-source object detection system that can detect objects in real-time video streams. It is known for its speed and accuracy. Amazon Rekognition - Amazon Rekognition is a computer vision service that can analyze images and videos and provide insights into the content of the media, including objects, faces, and text. Robotics If you're interested in robotics, there are a number of AI tools that can help you get started. Robot Simulation Gazebo - Gazebo is an open-source robot simulation tool that allows you to simulate robots in a virtual environment. It can help you test and debug your robot control algorithms before deploying them on a physical robot. Webots - Webots is another open-source robot simulation tool that allows you to simulate robots in a virtual environment. It supports a wide range of robots and sensors, and can be used for both research and education. Robot Control ROS - ROS (Robot Operating System) is an open-source framework for building robotics software. It can help you build and control robots using a variety of programming languages. Miscellaneous If you're looking for AI tools that don't fit into any of the above categories, here are a few to check out: Language Models GPT-3 - GPT-3 is an AI-powered language model developed by OpenAI. It can generate human-like text, answer questions, and even write code. BERT - BERT is a language model developed by Google AI. It is trained on a massive dataset of text and code, and can be used for a variety of tasks, including natural language understanding, question answering, and text classification. LLama 2 - LLama 2 models are a collection of pretrained and fine-tuned large language models developed and released by Meta AI . These models are built upon the success of LLama 1 and provide significant improvements, including a larger scale and more extensive context. Claude - Claude is an AI assistant developed by Anthropic that excels at analysis, writing, and coding tasks. PaLM 2 - PaLM 2 is Google's next-generation language model with improved multilingual, reasoning, and coding capabilities. Generative Models StyleGAN - StyleGAN is an AI-powered generative model that can create high-quality images of faces, animals, and other objects. It is known for its ability to create realistic and diverse images. Generative Pre-trained Transformer 3 (GPT-3) - GPT-3 is an AI-powered language model developed by OpenAI. It can generate human-like text, answer questions, and even write code.

#github#awesome-ai-tools

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Tina Huang•Feb 16, 2025

AI Agents Fundamentals In 21 Minutes

Improve your AI skills with the FREE Prompting QuickStart Guide I made in collaboration with Hubspot: https://clickhubspot.com/1gg9 Want to get ahead in your career using AI? Join my FREE workshop: https://www.lonelyoctopus.com/workshop A few notebooks to try out from crewAI & Autogen that are easy to follow and get started. All credit goes to these companies and Deep Learning AI. Please make a copy: https://drive.google.com/file/d/1mtv-gdKV9HMsGvGIqZZ9tSdW-nXq6kEf/view?usp=sharing https://drive.google.com/file/d/1u9gGPqWSJ4PacLNWbUWPhSvxen3Bvx/view?usp=sharing https://drive.google.com/file/d/1T07WHydxBN-T-kcgi6qme-j1T94efBYf/view?usp=sharing https://drive.google.com/file/d/1vPWpYvcHPROMC3BOEK3alT1QFSIvgA8P/view?usp=sharing Resources I consulted in making this video: crewAI course: https://www.deeplearning.ai/short-courses/multi-ai-agent-systems-with-crewai/ Autogen course: https://www.deeplearning.ai/short-courses/ai-agentic-design-patterns-with-autogen/ LangGraph course: https://www.deeplearning.ai/short-courses/ai-agents-in-langgraph/ David Ondrej n8n tutorial: https://youtu.be/XVO3zsHdvio?si=AQcMnYn8kJOogqLr Andrew Ng Snowflake agentic design patterns: https://youtu.be/KrRD7r7y7NY?si=tFtd6wJKB6idtfKb Andrew Ng Sequoia agentic design patterns: https://youtu.be/sal78ACtGTc?si=2i8Wyy57n8m6TbBK YC business advice: https://youtu.be/ASABxNenD_U?si=k19a310Tj3USuKNe 🐙 Lonely Octopus: https://www.lonelyoctopus.com/ Check it out if you're interested in learning AI & data skill, then applying them to real freelance projects! 🤝 Business Inquiries: https://tally.so/r/mRDV99 🖱️Links mentioned in video ======================== 🔗Affiliates ======================== My SQL for data science interviews course (10 full interviews): https://365datascience.com/learn-sql-for-data-science-interviews/ 365 Data Science: https://365datascience.pxf.io/WD0za3 (link for 57% discount for their complete data science training) Check out StrataScratch for data science interview prep: https://stratascratch.com/?via=tina 🎥 My filming setup ======================== 📷 camera: https://amzn.to/3LHbi7N 🎤 mic: https://amzn.to/3LqoFJb 🔭 tripod: https://amzn.to/3DkjGHe 💡 lights: https://amzn.to/3LmOhqk ⏰Timestamps ======================== 00:00 intro 📲Socials ======================== instagram: https://www.instagram.com/hellotinah/ linkedin: https://www.linkedin.com/in/tinaw-h/ discord: https://discord.gg/5mMAtprshX 🎥Other videos you might be interested in ======================== How I consistently study with a full time job: https://www.youtube.com/watch?v=INymz5VwLmk How I would learn to code (if I could start over): https://www.youtube.com/watch?v=MHPGeQD8TvI&t=84s 🐈‍⬛🐈‍⬛About me ======================== Hi, my name is Tina and I'm an ex-Meta data scientist turned internet person! 📧Contact ======================== youtube: youtube comments are by far the best way to get a response from me! linkedin: https://www.linkedin.com/in/tinaw-h/ email for business inquiries only: hellotinah@gmail.com ======================== Some links are affiliate links and I may receive a small portion of sales price at no cost to you. I really appreciate your support in helping improve this channel! :)

github

0.476

0.11235605711653615

noahgift•Feb 10, 2025

pragmaticai

🎓 Pragmatic AI Labs | Join 1M+ ML Engineers 🔥 Hot Course Offers: 🤖 Master GenAI Engineering - Build Production AI Systems 🦀 Learn Professional Rust - Industry-Grade Development 📊 AWS AI & Analytics - Scale Your ML in Cloud ⚡ Production GenAI on AWS - Deploy at Enterprise Scale 🛠️ Rust DevOps Mastery - Automate Everything 🚀 Level Up Your Career: 💼 Production ML Program - Complete MLOps & Cloud Mastery 🎯 Start Learning Now - Fast-Track Your ML Career 🏢 Trusted by Fortune 500 Teams Learn end-to-end ML engineering from industry veterans at PAIML.COM Pragmatic AI: An Introduction To Cloud-based Machine Learning !pai Book Resources This books was written in partnership with Pragmatic AI Labs. !alt text You can continue learning about these topics by: Foundations of Data Engineering (Specialization: 4 Courses) Publisher: Coursera + Duke Release Date: 4/1/2022 !duke-data Take the Specialization Course1: Python and Pandas for Data Engineering Course2: Linux and Bash for Data Engineering Course3: Scripting with Python and SQL for Data Engineering Course4: Web Development and Command-Line Tools in Python for Data Engineering Cloud Computing (Specialization: 4 Courses) Publisher: Coursera + Duke Release Date: 4/1/2021 Building Cloud Computing Solutions at Scale Specialization Launch Your Career in Cloud Computing. Master strategies and tools to become proficient in developing data science and machine learning (MLOps) solutions in the Cloud What You Will Learn Build websites involving serverless technology and virtual machines, using the best practices of DevOps Apply Machine Learning Engineering to build a Flask web application that serves out Machine Learning predictions Create Microservices using technologies like Flask and Kubernetes that are continuously deployed to a Cloud platform: AWS, Azure or GCP Courses in Specialization Take the Specialization Cloud Computing Foundations Cloud Virtualization, Containers and APIs Cloud Data Engineering Cloud Machine Learning Engineering and MLOps Get the latest content and updates from Pragmatic AI Labs: Subscribe to the mailing list! Taking the course AWS Certified Cloud Practitioner 2020-Real World & Pragmatic. Buying a copy of Pragmatic AI: An Introduction to Cloud-Based Machine Learning Reading book online on Safari: Online Version of Pragmatic AI: An Introduction to Cloud-Based Machine Learning, First Edition Watching 8+ Hour Video Series on Safari: Essential Machine Learning and AI with Python and Jupyter Notebook Viewing more content at noahgift.com Viewing more content at Pragmatic AI Labs Exploring related colab notebooks from Safari Online Training Learning about emerging topics in Hardware AI & Managed/AutoML Viewing more content on the Pragmatic AI Labs YouTube Channel Reading content on Pragmatic AI Medium Attend an upcoming Safari Live Training About Pragmatic AI is the first truly practical guide to solving real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Writing for business professionals, decision-makers, and students who aren’t professional data scientists, Noah Gift demystifies all the tools and technologies you need to get results. He illuminates powerful off-the-shelf cloud-based solutions from Google, Amazon, and Microsoft, as well as accessible techniques using Python and R. Throughout, you’ll find simple, clear, and effective working solutions that show how to apply machine learning, AI and cloud computing together in virtually any organization, creating solutions that deliver results, and offer virtually unlimited scalability. Coverage includes: Getting and configuring all the tools you’ll need Quickly and efficiently deploying AI applications using spreadsheets, R, and Python Mastering the full application lifecycle: Download, Extract, Transform, Model, Serve Results Getting started with Cloud Machine Learning Services, Amazon’s AWS AI Services, and Microsoft’s Cognitive Services API Uncovering signals in Facebook, Twitter and Wikipedia Listening to channels via Slack bots running on AWS Lambda (serverless) Retrieving data via the Twitter API and extract follower relationships Solving project problems and find highly-productive developers for data science projects Forecasting current and future home sales prices with Zillow Using the increasingly popular Jupyter Notebook to create and share documents integrating live code, equations, visualizations, and text And much more Book Chapter Juypter Notebooks Note, it is recommended to also watch companion Video Material: Essential Machine Learning and AI with Python and Jupyter Notebook Chapter 1: Introduction to Pragmatic AI Chapter 2: AI & ML Toolchain Chapter 3: Spartan AI Lifecyle Chapter 4: Cloud AI Development with Google Cloud Platform Chapter 5: Cloud AI Development with Amazon Web Services Chapter 6: Social Power NBA Chapter 7: Creating an Intelligent Slack Bot on AWS Chapter 8: Finding Project Management Insights from A Github Organization Chapter 9: Dynamically Optimizing EC2 Instances on AWS Chapter 10: Real Estate Chapter 11: Production AI for User Generated Content (UGC) License This code is released under the MIT license Text The text content of notebooks is released under the CC-BY-NC-ND license Additional Related Topics from Noah Gift His most recent books are: Pragmatic A.I.:   An introduction to Cloud-Based Machine Learning (Pearson, 2018) Python for DevOps (O'Reilly, 2020).  Cloud Computing for Data Analysis, 2020 Testing in Python, 2020 His most recent video courses are: Essential Machine Learning and A.I. with Python and Jupyter Notebook LiveLessons (Pearson, 2018) AWS Certified Machine Learning-Specialty (ML-S) (Pearson, 2019) Python for Data Science Complete Video Course Video Training (Pearson, 2019) AWS Certified Big Data - Specialty Complete Video Course and Practice Test Video Training (Pearson, 2019) Building A.I. Applications on Google Cloud Platform (Pearson, 2019) Pragmatic AI and Machine Learning Core Principles (Pearson, 2019) Data Engineering with Python and AWS Lambda (Pearson, 2019) His most recent online courses are: Microservices with this Udacity DevOps Nanodegree (Udacity, 2019) Command Line Automation in Python (DataCamp, 2019) AWS Certified Cloud Practitioner 2020-Real World & Pragmatic.

#github#pragmaticai

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0.64

Cosmic Hippo•Feb 10, 2025

In the Zone - Coding Music for Focus & Clarity

Get in the zone and stay focused with this chill coding music designed for mental clarity and deep work. Whether you're programming, designing, or studying, these beats will help you block out distractions and lock into your flow state. Featuring a blend of chillstep and ambient synthwave, this playlist is perfect for long coding sessions, creative work, or late-night productivity. Put on your headphones, dive into your projects, and let the music guide your focus. You can get the artwork featured in this video as a digital download on Etsy here: https://www.etsy.com/listing/1858065246/in-the-zone Tracklist 0:00 Unraveling the Moment 3:37 Luna's Glow 6:24 Echoes of Purpose 9:56 The Art of Being Present 13:27 Breathing Through Time 16:13 Falling Into Rhythm 17:59 Into the Current of Creation 21:45 Mindscapes in Motion 24:01 Shadows of Stillness 28:03 Threading Through Time 31:09 Tuning the Infinite 34:15 Unseen Currents 37:55 Vibrations of Clarity 39:58 Where Thoughts Flow Free 43:59 Blurring Boundaries 47:38 Carved from Stillness 51:39 In the Flow of Thought 54:08 Luminous Quietude 56:39 Submerged in Clarity Let me know in the comments how this playlist helps your workflow! Disclaimer: This music has been created with the help of AI tools. Tags: #CodingMusic #FocusBeats #FlowState #DeepWork #ProgrammingMusic #Synthwave #Chillstep #StudyBeats #ProductivityMusic #WorkVibes #ConcentrationMusic #MentalClarity #CodingSession #CodeAndChill #LoFiBeats #DeveloperLife #MusicForFocus #ChillVibes #CreativeFlow #CodeFlow #chillstep

#CodingMusic#FocusBeats#FlowState

github

0.468

0.008973276778019746

Polyconseil•Feb 10, 2025

aioamqp

aioamqp ======= .. image:: https://badge.fury.io/py/aioamqp.svg :target: http://badge.fury.io/py/aioamqp .. image:: https://travis-ci.org/Polyconseil/aioamqp.svg?branch=master :target: https://travis-ci.org/Polyconseil/aioamqp .. image:: https://readthedocs.org/projects/aioamqp/badge/?version=latest :alt: Documentation Status :scale: 100% :target: https://aioamqp.readthedocs.org/en/latest/?badge=latest `aioamqp library is a pure-Python implementation of the AMQP 0.9.1 protocol_. Built on top on Python's asynchronous I/O support introduced in PEP 3156_, it provides an API based on coroutines, making it easy to write highly concurrent applications. Bug reports, patches and suggestions welcome! Just open an issue or send a pull request. tests To run the tests, you'll need to install the Python test dependencies:: pip install -r requirements_dev.txt Tests require an instance of RabbitMQ. You can start a new instance using docker:: docker run -d --log-driver=syslog -e RABBITMQ_NODENAME=my-rabbit --name rabbitmq -p 5672:5672 -p 15672:15672 rabbitmq:3-management Then you can run the tests with make test. tests using docker-compose ^^^^^^^^^^^^^^^^^^^^^^^^^^ Start RabbitMQ using docker-compose up -d rabbitmq. When RabbitMQ has started, start the tests using docker-compose up --build aioamqp-test` .. _AMQP 0.9.1 protocol: https://www.rabbitmq.com/amqp-0-9-1-quickref.html .. _PEP 3156: http://www.python.org/dev/peps/pep-3156/ .. _issue: https://github.com/Polyconseil/aioamqp/issues/new .. _pull request: https://github.com/Polyconseil/aioamqp/compare/

#github#aioamqp

github

0.416

0.012874224994657315

revodavid•Feb 9, 2025

PracticalAI

Practical AI for the Working Software Engineer by David M Smith (@revodavid), Cloud Advocate at Microsoft Last updated: December 4, 2018 Presented at: AI Live (AIF01), Orlando, December 7 2018 About these notebooks This library includes three notebooks to support the workshop: The AI behind Seeing AI. Use the web-interfaces to Cognitive Services to learn about the AI services behind the "Seeing AI" app Computer Vision API with R. Use an R script to interact with the Computer Vision API and generate captions for random Wikimedia images. Custom Vision with R. An R function to classify an image as a "Hot Dog" or "Not Hot Dog", using the Custom Vision service. MNIST with scikit-learn. Use sckikit-learn to build a digit recognizer for the MNIST data using a regression model. MNIST with tensorflow. Use Tensorflow (from Python) to build a digit recognizer for the MNIST data using a convolutional neural network. These notebooks are hosted on Azure Notebooks at https://notebooks.azure.com/davidsmi/projects/practicalai, where you can run them interactively. You can also download them to run them using Jupyter. Find the slides for the workshop here. Setup (for use in Azure Notebooks) Sign in to Azure Notebooks. You'll need a Microsoft Account: your O365, Xbox, or Hotmail account will work. If you're new to Notebooks, check out the Jupyter Notebook documentation and the Azure Notebook documentation. If you have an iPhone, install the free SeeingAI app. (optional) To generate keys and use Azure services, you'll need an Azure subscription. You can get a free Azure account here, with $200 in free credits for new subscribers. You'll need a credit card, but most of the things we'll use in this workshop will be free. Contact If you get stuck or just have other questions, you can contact me here: David Smith davidsmi@microsoft.com Twitter: @revodavid

#github#practicalai

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Riley Brown•Feb 6, 2025

Karpathy Vibe Coding Full Tutorial with Cursor (Zero Coding)

Today we talked about the concept and execution of vibe coding, a method where you speak your coding ideas into existence using cutting‐edge AI tools. We explored how to use Cursor Composer alongside Sonnet and WhisperFlow to generate, edit, and run code with minimal manual intervention. The tutorial guided viewers through setting up a project from a Next.js template, cloning a repository, and managing API keys through an .env file to maintain secure credentials. Additionally, the video detailed the process of building a ChatGPT clone using the latest OpenAI API, complete with real-time debugging and iterative improvements on design elements such as input fields, sidebars, and smooth text animations. The discussion also emphasized the importance of keeping the AI prompt context minimal for optimal performance, and it provided insights on how to save and upload projects to GitHub effortlessly. Finally, we touched on integrating real-time voice interaction using the 11Labs API to further enhance the coding experience and pay homage to AI pioneers like Karpathy Footnotes Perplexity Spaces (Just like Custom GPT's) Prompt: i am making app in nextjs: user is going to give input that they want to put in their site: you're job is to find a method to do that: describe what the api does, then output example code. then put a direct link to find the api key. Links: Whispr Flow - https://wisprflow.ai/ Cursor - https://www.cursor.com/ Cursor for Writing: https://app.yapthread.com/ Community of Vibe Coders: https://www.softwarecomposer.com/ Time Stamps: 00:00 Intro to Vibe Coding 03:02 Opening Cursor 04:07 Starting Your First Project 05:12 Building a ChatGPT Clone 06:38 Prompting, API's and Documentation Explanation 08:49 Using Perplexity 12:07 Vibe Code Prompt 1 13:58 Result of Vibe Coding Prompt 1 15:22 Seeing Prompt 2 15:43 Managing Cursor Composer Context Length 16:25 Prompt 3 - Designing 17:21 Debugging with Inspect on Web View 18:20 Fixing Formatting 19:04 More Vibing, Lol 20:51 Saving and Uploading Projects to GitHub 21:59 Enhancing the User Experience 22:33 Honoring Karpathy 26:26 Implementing Real Time Karpathy Voice 28:30 Getting Karpathys Voice (Don't Do this It's Illegal)

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Ishan Sharma•Feb 3, 2025

15 EASY Business Ideas to GET RICH with AI Agents in 2025

Check out the AI Income Ideas Guide by HubSpot Today: https://clickhubspot.com/6rig In 2025, so many companies want to automate their work and they are looking for tools that can help them with that. Imagine earning your first thousand dollars just by helping a business automate its repetitive tasks with AI agents. In this video, I’ve shared 15 business ideas that you can start using AI agents and help companies in different aspects and earn money doing that! You don't need years of technical skills to master this - you just need to understand how to solve real problems with AI. And right now, there's so much opportunity because most people are not even aware of this. Watch the video till the end and take notes 📸 Instagram: https://bit.ly/ishansharma7390ig Join MarkitUpX Discord Server: https://discord.gg/fwSpTje4rh CHAPTERS: 00:00 - Introduction 00:49 - Business Idea 1 01:53 - Business Idea 2 03:02 - Business Idea 3 04:40 - Business Idea 4 05:31 - Business Idea 5 06:05 - Business Idea 6 07:38 - Business Idea 7 09:00 - Business Idea 8 10:18 - Business Idea 9 10:52 - Business Idea 10 12:07 - Business Idea 11 13:45 - Business Idea 12 15:55 - Business Idea 13 16:52 - Business Idea 14 17:38 - Business Idea 15 18:42 - Conclusion 😁 About Me: https://bit.ly/aboutishansharma 📱 Twitter: https://bit.ly/ishansharma7390twt 📝 LinkedIn: https://bit.ly/ishansharma7390li 🌟 Please leave a LIKE ❤️ and SUBSCRIBE for more AMAZING content! 🌟 3 Books You Should Read 📈Psychology of Money: https://amzn.to/30wx4bW 👀Subtle Art of Not Giving a F: https://amzn.to/30zwWbP 💼Rework: https://amzn.to/3ALsAuz Tech I use every day 💻MacBook Air M1: https://amzn.to/2YWKPjG 📺LG 29' Ultrawide Monitor: https://amzn.to/3aG0p5p 🎥Sony ZV1: https://amzn.to/3ANqgDb 🎙Blue Yeti Mic: https://amzn.to/2YYbiNN ⽴Tripod Stand: https://amzn.to/3mVUiQc 🔅Ring Light: https://amzn.to/2YQlzLJ 🎧Marshall Major II Headphone: https://amzn.to/3lLhTDQ 🖱Logitech mouse: https://amzn.to/3p8edOC 💺Green Soul Chair: https://amzn.to/3mWIxZP ✨ Tags ✨ ishan sharma,ai agents,ai agents explained,ai agents 2025,ai assistant,get rich with ai agents,make money ai agents,make money online,earn money online,ai agents tutorial,ai agent,ai,ai tools,make money with ai,make money with ai tools,artificial intelligence,deepseek r1,ai agents use cases,ai agents projects,business ideas with low investment,zero investment business ideas,business ideas for students,business ideas for beginners,best business ideas,how to start a business,online business ideas,startup business ideas,ai business ideas,business ideas using ai ✨ Hashtags ✨ #ai #artificialintelligence #businessideas

#ai#artificialintelligence#businessideas

github

0.384

0.0032098142352129998

nooqta•Feb 2, 2025

kodyfire

Kody is a command-line tool for generating artifact files, powered by both classic and AI code generation techniques. It can be used by both technical and non-technical users to generate files across a wide range of technologies and programming languages. The code generation feature in Kody relies on OpenAI GPT, a language model that uses deep learning to generate human-like text, and ChatGPT to provide natural language processing capabilities. Table of Contents Installation Usage Getting Started Terminology Contributing License Installation Prerequisites Node.js (version 14 or later) To install kody, use npm with the following command: or You can check the documentation with Usage Options -v, --version: Output the current version -h, --help: Display help for command Commands prompt|ai [options] [prompt...]: AI powered prompt assistant to quickly generate an artifact batch [options]: Generate multiple digital artifact create [options] : Generate a new blank kody project generate|g [options] [kody] [concept]: Prompt assistant to quickly generate an artifact import|in [options] : Mass create artifacts from a source. init: Initialize a new kodyfire project install|i [kody]: Prompt user to choose to install list|ls [options] [kodyName]: List installed kodies within your current project. publish [template]: Publish the templates of the kody along with the assets.json and schema.ts files ride|↻: Prompt assistant to help build your kody.json file run [options]: Generate a digital artifact based on the selected technology run-script|rs: Run scripts search|s [keywords...]: Search kodyfire packages from npm registry watch|w [options]: Watch for file changes and run kody help [command]: Display help for command Getting Started Open the project you are willing to work on using vscode or your prefered editor. Generate artifacts using AI In case you want to exclusivly rely on AI to generate your artifacts. You don't need to install any additional kodies. Run the kody ai [prompt] command and follow the prompts. For example, to create a Laravel Controller named SampleController under API/V1 and add a comment on top saying Hello Kodyfire, run the following command You can use the experimental Speech-to-Text option to pass your prompt using your voice. The transcription relies on Whisper and requires SoX installed and available in your \$PATH. for the audio recording. For Linux For MacOS For Windows Download the binaries Generate your artifact using the classical method Search and install a kody Based on your project, search availables kodies and select the one that fits your need.. To search availables kodies by keyword runthe following command. if you don't specify a keyword all available kodies will be listed. Install your kody of choice. For example, if you want to install the react kody or Please note you can install as many kodies in the same project as you wish. Generate your artifact There are 2 methods you can generate your artifacts with: The generate command The run command Method 1: Generator mode kody generate The recommended way of using kody is using the generate command. The command will assist you creating your artifact based on the chosen concept. For example, a react component is considered a concept. In order to generate your artifacts, run the generate command. The syntax is kody g|generate [kody] [concept]. the assistant will prompt you to select the missing arguments. As an example, run the following command from your terminal: Method 2: Runner mode kody run The run command is similar to the generate command. The run requires a definition file which is simply a json file containing all the concept definitions you have created using the ride command. The generate command on the other hand creates one or more concept definition on the run and process them on one run. Every command has its use cases. Initialize kody In order to start using kody, you need to initialize your project. This will add the definition files required for kody runs. Important: Please run the command only once. The command will override existing definition files. We will disable overriding in a future version. Ride your kody In order to update your definition, use the kody ride command to assist you populate the required fields Launch a kody run Once you are satisified with your definition file, execute the run command to generate your artifacts. To run all kodies defined within your project, run the following command: Create your own kody In most cases you might need a custom kody to suit your needs Scaffold a new kody Create a basic kody using the scaffold command. Follow the prompts to setup your kody This will create a folder containing the basic structure for a kody. You can start using right away within your project. Setup your kody Install npm dependencies Build your kody Add your concepts and related templates //TODO This will build your kody and export the basic templates files. Add your kody as an NPM dependency to a test project In order to be able to use it within your test project run the following command Publish your kody Please remember that Kody is still in exploration phase and things will change frequently. Contribution is always highly requested. Prepare your kody Add the required kodyfire metadata to your package.json Publish to Github Intialize your project as a git repository and push to a public Github repo To do so, kindly follow these steps:- Intitialize a new Github repository and make it public. Open your project root folder locally from terminal and run the following commands:- Link your project to your Github repository. Publish to npm Once you are satisfied with your kody and you would to like to share it with the community. Run the following command. Note: You'll need an NPM account Share with community Congratulation publishing your first kody. Don't forget to share your kody repo link by opening an issue on Kody's github repository. Terminology Kody: Refers to the code generation command-line tool that generates digital artifacts. Artifacts: Refers to the various digital products generated by Kody based on the input provided. Note: Kody uses classical code generation techniques in addition to AI-powered code generation using OpenAI Codex and ChatGPT. Available kodies | Name | Description | | -------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------- | | basic-kodyfire | A general purpose code generator that should handle most of the generation use cases | | typescript-kodyfire | Generate typescript related artifacts | | tsconfig-kodyfire | Generate tsconfig files for your typescript projects | | nextjs-kodyfire | Generate nextJs components and related artifacts | | react-kodyfire | Generate react components | | laravel-kodyfire | Laravel artifacts generation | | uml-kodyfire | Uml diagrams generation using plantuml | | readme-kodyfire | Readme file generation | | word-kodyfire | Generate ms word document based on a template | | pdf-kodyfire | Generate PDF document from HTML templates | | social-image-kodyfire | Generate dynamic images for social sharing based on HTML templates | | social-gif-kodyfire | Generate dynamic gif images for social sharing based on HTML templates | | linkedin-quizzes-kodyfire | Practice Linkedin skill assessement tests from your terminal | | chatgpt-kodyfire | Use chatgpt from the terminal. Allows you provide additional data from various sources (not implemented yet) and export to serveral outputs (markdown only now). | Contributing If you encounter any issues while using Kody or have suggestions for new features, feel free to open an issue or submit a pull request. Please read our contributing guidelines before making contributions. License Kody is MIT licensed.

#github#kodyfire

github

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Softtechhub1•Feb 1, 2025

Mastering-AI-for-Entrepreneurs-9-Free-Courses

Mastering-AI-for-Entrepreneurs-9-Free-Courses Introduction: The Entrepreneur's AI RevolutionArtificial Intelligence (AI) is changing the way we do business. It's not just for tech giants anymore. Small businesses and startups are using AI to work smarter, not harder. As an entrepreneur, you need to understand AI to stay ahead.Why AI is a must-have skill for entrepreneursAI is everywhere. It's in the apps we use, the products we buy, and the services we rely on. Businesses that use AI are seeing big improvements:They're making better decisions with data-driven insightsThey're automating routine tasks, freeing up time for creativityThey're personalizing customer experiences, boosting satisfaction and salesIf you're not using AI, you're falling behind. But here's the good news: you don't need to be a tech wizard to harness the power of AI.Breaking the barriers to AI learningThink AI is too complex? Think again. You don't need a computer science degree to understand and use AI in your business. Many AI tools are designed for non-technical users. They're intuitive and user-friendly.The best part? You can learn about AI for free. There are tons of high-quality courses available at no cost. These courses are designed for busy entrepreneurs like you. They cut through the jargon and focus on practical applications.What to expect from this articleWe've handpicked nine free courses that will turn you into an AI-savvy entrepreneur. Each course is unique, offering different perspectives and skills. We'll cover:What makes each course specialWhat you'll learnHow it applies to your businessWho it's best suited forReady to dive in? Let's explore these game-changing courses that will boost your AI knowledge and give your business an edge.1. Google AI Essentials: A Beginner's Guide to Practical AIWhy This Course Is EssentialGoogle AI Essentials is perfect if you're just starting out. It's designed for people who don't have a tech background. The course focuses on how AI can help you in your day-to-day work, not on complex theories.What You'll LearnThis course is all about making AI work for you. You'll discover how to:Use AI to boost your productivity. Generate ideas, create content, and manage tasks more efficiently.Streamline your workflows. Learn how AI can help with everyday tasks like drafting emails and organizing your schedule.Use AI responsibly. Understand the potential biases in AI and how to use it ethically.Key TakeawaysYou'll earn a certificate from Google. This looks great on your resume or LinkedIn profile.You'll learn how to work alongside AI tools to get better results in your business.You'll gain practical skills you can use right away to improve your work.Get StartedEnroll in Google AI Essentials2. Introduction to Generative AI: A Quick Start for EntrepreneursWhy This Course Works for Busy EntrepreneursThis course is short and sweet. In just 30 minutes, you'll get a solid grasp of generative AI. It's perfect if you're short on time but want to understand the basics.What You'll LearnThe fundamentals of generative AI: what it is, how it works, and its limitsHow generative AI differs from other types of AIReal-world applications of generative AI in businessHow It Helps Your BusinessAfter this course, you'll be able to:Make smarter decisions about using AI tools in your businessSpot opportunities where generative AI could solve problems or create valueUnderstand the potential and limitations of this technologyGet StartedEnroll in Introduction to Generative AI3. Generative AI with Large Language Models: Advanced Skills for EntrepreneursWhy This Course Stands OutThis course digs deeper into the technical side of AI. It's ideal if you have some coding experience and want to understand how AI models work under the hood.What You'll LearnYou'll gain key skills for working with Large Language Models (LLMs):How to gather and prepare data for AI modelsChoosing the right model for your needsEvaluating model performance and improving resultsYou'll also learn about:The architecture behind transformer models (the tech powering many AI tools)Techniques for fine-tuning models to your specific business needsWho Should Take This CourseThis course is best for entrepreneurs who:Have basic Python programming skillsUnderstand the fundamentals of machine learningWant to go beyond using AI tools to actually building and customizing themGet StartedEnroll in Generative AI with Large Language Models4. AI for Everyone by Andrew Ng: Simplifying AI for Business LeadersWhy It's Perfect for BeginnersAndrew Ng is a leading figure in AI education. He's known for making complex topics easy to understand. This course is designed for non-technical learners. You don't need any coding or math skills to benefit from it.What You'll LearnHow AI works at a high levelHow to spot problems in your business that AI can solveWays to assess how AI might impact your business processes and strategiesWhy Entrepreneurs Love This CourseIt explains AI concepts in plain English, without technical jargonYou can complete it in just 8 hours, fitting it into your busy scheduleIt focuses on the business value of AI, not just the technologyGet StartedStart with AI for Everyone on Coursera5. Generative AI: Introduction and ApplicationsWhy This Course Is Ideal for EntrepreneursThis course offers a broad view of generative AI applications. You'll learn about AI in text, image, audio, and more. It's packed with hands-on experience using popular AI tools.What You'll LearnThe basics and history of generative AI technologiesHow different industries are using AI, from marketing to creative projectsPractical skills through labs using tools like ChatGPT, DALL-E, and Stable DiffusionHow It Stands OutYou'll hear from real AI practitioners about their experiencesThe course teaches you how to use generative AI to innovate and improve efficiency in your businessGet StartedEnroll in Generative AI: Introduction and Applications6. Generative AI for Everyone by Andrew Ng: Unlocking ProductivityWhy This Course Is a Must-HaveThis course focuses on using generative AI tools for everyday business tasks. It's all about boosting your productivity and efficiency.What You'll LearnHands-on exercises to integrate AI tools into your daily workReal examples of how businesses are using generative AI to save time and moneyTechniques for prompt engineering to get better results from AI toolsHow It Helps EntrepreneursYou'll learn to automate repetitive tasks, freeing up time for strategic thinkingYou'll discover new ways to use AI tools in your business processesYou'll gain confidence in experimenting with AI to solve business challengesGet StartedGo deeper with DeepLearning.AI7. Generative AI for Business Leaders by LinkedIn LearningWhy This Course Focuses on Business ApplicationsThis course is tailored for leaders who want to integrate AI into their business operations. It provides practical insights for improving workflows and decision-making.What You'll LearnStrategies for using AI to optimize your business operationsHow to save time and resources with AI-powered toolsPractical methods for implementing AI in your company, regardless of sizeKey BenefitsThe course is designed for busy professionals, allowing you to learn at your own paceYou'll gain insights you can apply immediately to your businessIt covers both the potential and the limitations of AI in business settingsGet StartedLevel up on LinkedIn Learning8. AI for Beginners by Microsoft: A Structured Learning PathWhy This Course Builds a Strong AI FoundationMicrosoft's AI for Beginners is a comprehensive 12-week program. It covers core AI concepts in a structured, easy-to-follow format. The course combines theoretical knowledge with hands-on practice through quizzes and labs.What You'll LearnThe basics of AI, machine learning, and data scienceStep-by-step guidance to build a strong knowledge basePractical applications of AI in various business contextsHow to Approach This CourseDedicate 2-3 hours per week to complete the curriculumUse the structured format to gradually build your confidence in AI conceptsApply what you learn to real business scenarios as you progressGet StartedBuild foundations with Microsoft9. AI for Business Specialization by UPenn: Strategic Thinking with AIWhy This Course Is Perfect for Business LeadersThis specialization focuses on AI's transformative impact on core business functions. It covers how AI is changing marketing, finance, and operations.What You'll LearnHow to build an AI strategy tailored to your business needsWays to leverage AI to drive innovation across different departmentsTechniques for integrating AI into your business modelHow to Make the Most of This CourseTake detailed notes on how each module applies to your own business challengesUse the specialization to develop a long-term AI vision for your companyNetwork with other business leaders taking the course to share insights and experiencesGet StartedScale up with UPenn's business focusConclusion: Your Path to Becoming an AI-powered EntrepreneurWe've covered nine fantastic free courses that can transform you into an AI-savvy entrepreneur. Let's recap:Google AI Essentials: Perfect for beginners, focusing on practical AI applications.Introduction to Generative AI: A quick start to understand the basics of generative AI.Generative AI with Large Language Models: For those ready to dive into the technical side.AI for Everyone: A non-technical introduction to AI's business impact.Generative AI: Introduction and Applications: A broad look at generative AI across industries.Generative AI for Everyone: Focused on boosting productivity with AI tools.Generative AI for Business Leaders: Tailored for integrating AI into business operations.AI for Beginners: A structured path to build a strong AI foundation.AI for Business Specialization: Strategic thinking about AI in business functions.Remember, you don't need to tackle all these courses at once. Start small and build your knowledge gradually. Pick the course that aligns best with your current needs and business goals.Embracing AI is not just about staying competitive; it's about opening new doors for innovation and growth. These courses will help you see opportunities where AI can solve problems, improve efficiency, and create value for your business.The AI revolution is happening now. The sooner you start learning, the better positioned you'll be to lead in this new era. Each step you take in understanding AI is a step towards future-proofing your business.So, what are you waiting for? Choose a course, dive in, and start your journey to becoming an AI-powered entrepreneur today. The future of your business may depend on it.MORE ARTICLES FOR YOUHumanizzer Fastpass Bundle – OTO1 to OTO4: Get (Humanizzer + All OTOs) Fastpass for Massive 75% Discount Available Limited-Time OneHumanizzer Review: Build Lifelike Human AI Agents That Talk, Listen & Engage Face-To-Face!—In Your Voice, Just Like You!EasyListDetox App Review: A Windows tool with Giveaway Rights for effortlessly cleaning your email lists of duplicates, invalid, and disposable addresses. Simple, efficient, and time-savingAI Copy Kit Review: Google’s Latest AI Tech Tensorflow (Tf) Create Jaw-Dropping And Advanced Ultra HD Videos, Ultra Shorts, 4K Images, Voiceovers, and Any Other GPT 4-Powered Amazing Content In Minutes Without Any Complicated Tools!From Good to Great: 15 Books to Inspire Personal and Business TransformationFTC Affiliate Commission Disclaimer: Some links in this article may earn us a commission if you make a purchase. This doesn't affect our recommendations.

#github#mastering-ai-for-entrepreneurs-9-free-courses

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Rabbia-Hassan•Jan 27, 2025

Generative-AI-for-Everyone

Generative-AI-for-Everyone Taught by AI genius Andrew NG, this course entails the cutting edge topics such as:- How generative AI works including what it Can and Can't do. Common uses cases such as Reading, Writing, and Chatting. Life Cycle of GenAI projects. Advanced Technology options such as RAG, Fine tunning, and Pre-Training. Implications of GenAI on business and Society. Responsible AI. Instructor: Andrew Ng Solution to Quizzes and Code Activities Week 1 Quiz 1 Week 1 Quiz 2 Week 2 Quiz 1 Week 2 Quiz 2 Week 2 Code Activity 1 Prompting an LLM in code Week 2 Code Activity 2 Reputation monitoring system How to find number of Tokens Week 3 Quiz 1 Week 3 Quiz 2 References Generative AI for Everyone Certificate Generative AI for Everyone

#github#generative-ai-for-everyone

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bogdanmosica•Jan 23, 2025

internet-tools-collection

Internet Tools Collection A collection of tools, website and AI for entrepreneurs, web designers, programmers and for everyone else. Content by category Artificial Intelligence Developers Design Entrepreneur Video Editing Stock videos Stock Photos Stock music Search Engine Optimization Blog Posts Resume Interviews No code website builder No code game builder Side Hustle Browser Extensions Other Students Artificial Intelligence Jasper - The Best AI Writing Assistant [](https://www.jasper.ai/) Create content 5x faster with artificial intelligence. Jasper is the highest quality AI copywriting tool with over 3,000 5-star reviews. Best for writing blog posts, social media content, and marketing copy. AutoDraw [](https://www.autodraw.com/) Fast drawing for everyone. AutoDraw pairs machine learning with drawings from talented artists to help you draw stuff fast. Rytr - Best AI Writer, Content Generator & Writing Assistant [](https://rytr.me/) Rytr is an AI writing assistant that helps you create high-quality content, in just a few seconds, at a fraction of the cost! Neevo - Neevo [](https://www.neevo.ai/) Kinetix Tech [](https://kinetix.tech/) Kinetix is a no-code 3D creation tool powered by Artificial Intelligence. The web-based platform leverages AI motion capture to convert a video into a 3D animation and lets you customize your avatars and environments. We make 3D animation accessible to every creator so they can create engaging stories. LALAL.AI: 100% AI-Powered Vocal and Instrumental Tracks Remover [](https://www.lalal.ai/) Split vocal and instrumental tracks quickly and accurately with LALAL.AI. Upload any audio file and receive high-quality extracted tracks in a few seconds. Copy.ai: Write better marketing copy and content with AI [](https://www.copy.ai/) Get great copy that sells. Copy.ai is an AI-powered copywriter that generates high-quality copy for your business. Get started for free, no credit card required! Marketing simplified! OpenAI [](https://openai.com/) OpenAI is an AI research and deployment company. Our mission is to ensure that artificial general intelligence benefits all of humanity. DALL·E 2 [](https://openai.com/dall-e-2/) DALL·E 2 is a new AI system that can create realistic images and art from a description in natural language. Steve.ai - World’s fastest way to create Videos [](https://www.steve.ai/) Steve.AI is an online Video making software that helps anyone to create Videos and animations in seconds. Octie.ai - Your A.I. ecommerce marketing assistant [](https://octie.ai/) Write emails, product descriptions, and more, with A.I. Created by Octane AI. hypnogram.xyz [](https://hypnogram.xyz/) Generate images from text descriptions using AI FakeYou. Deep Fake Text to Speech. [](https://fakeyou.com/) FakeYou is a text to speech wonderland where all of your dreams come true. Craiyon, formerly DALL-E mini [](https://www.craiyon.com/) Craiyon, formerly DALL-E mini, is an AI model that can draw images from any text prompt! Deck Rocks - Create Pictch Decks [](https://www.deck.rocks/) Writely | Using AI to Improve Your Writing [](https://www.writelyai.com/) Making the art of writing accessible to all Writesonic AI Writer - Best AI Writing Assistant [](https://writesonic.com/) Writesonic is an AI writer that's been trained on top-performing SEO content, high-performing ads, and converting sales copy to help you supercharge your writing and marketing efforts. Smart Copy - AI Copywriting Assistant | Unbounce [](https://unbounce.com/product/smart-copy/) Generate creative AI copy on-the-spot across your favourite tools Synthesia | #1 AI Video Generation Platform [](https://www.synthesia.io/) Create AI videos by simply typing in text. Easy to use, cheap and scalable. Make engaging videos with human presenters — directly from your browser. Free demo. NVIDIA Canvas: Turn Simple Brushstrokes into Realistic Images [](https://www.nvidia.com/en-us/studio/canvas/) Create backgrounds quickly, or speed up your concept exploration so you can spend more time visualizing ideas with the help of NVIDIA Canvas. Hotpot.ai - Hotpot.ai [](https://hotpot.ai/) Hotpot.ai makes graphic design and image editing easy. AI tools allow experts and non-designers to automate tedious tasks while attractive, easy-to-edit templates allow anyone to create device mockups, social media posts, marketing images, app icons, and other work graphics. Klaviyo: Marketing Automation Platform for Email & SMS [](https://www.klaviyo.com/) Klaviyo, an ecommerce marketing automation platform for email marketing and sms syncs your tech stack with your website store to scale your business. Search listening tool for market, customer & content research - AnswerThePublic [](https://answerthepublic.com/) Use our free tool to get instant, raw search insights, direct from the minds of your customers. Upgrade to a paid plan to monitor for new ways that people talk & ask questions about your brand, product or topic. Topic Mojo [](https://topicmojo.com/) Discover unique & newest queries around any topic and find what your customers are searching for. Pulling data from 50+ sources to enhance your topic research. AI Image Enlarger | Enlarge Image Without Losing Quality! [](https://imglarger.com/) AI Image Enlarger is a FREE online image enlarger that could upscale and enhance small images automatically. Make jpg/png pictures big without losing quality. Midjourney [](https://www.midjourney.com/app/) Kaedim - AI for turning 2D images to 3D models [](https://www.kaedim3d.com/webapp) AI for turning 2D images, sketches and photos to 3D models in seconds. Overdub: Ultra realistic text to speech voice cloning - Descript [](https://www.descript.com/overdub) Create a text to speech model of your voice. Try a live demo. Getting Started [](https://magenta.tensorflow.org/get-started) Resources to learn about Magenta Photosonic AI Art Generator | Create Unique Images with AI [](https://photosonic.writesonic.com/) Transform your imagination into stunning digital art with Photosonic - the AI art generator. With its creative suggestions, this Writesonic's AI image generator can help unleash your inner artist and share your creations with the world. Image Computer [](https://image.computer/) Most downloaded Instagram Captions App (+more creator tools) [](https://captionplus.app/) Join 3 Million+ Instagram Creators who use CaptionPlus to find Instagram Captions, Hashtags, Feed Planning, Reel Ideas, IG Story Design and more. Writecream - Best AI Writer & Content Generator - Writecream [](https://www.writecream.com/) Sentence Rewriter is a free tool to reword a sentence, paragraph and even entire essays in a short amount of time. Hypotenuse AI: AI Writing Assistant and Text Generator [](https://www.hypotenuse.ai/) Turn a few keywords into original, insightful articles, product descriptions and social media copy with AI copywriting—all in just minutes. Try it free today. Text to Speach Listnr: Generate realistic Text to Speech voiceovers in seconds [](https://www.listnr.tech/) AI Voiceover Generator with over 600+ voiceovers in 80+ languages, go from Text to Voice in seconds. Get started for Free! Free Text to Speech: Online, App, Software, Commercial license with Natural Sounding Voices. [](https://www.naturalreaders.com/) Free text to speech online app with natural voices, convert text to audio and mp3, for personal and commercial use Developers OverAPI.com | Collecting all the cheat sheets [](https://overapi.com/) OverAPI.com is a site collecting all the cheatsheets,all! Search Engine For Devs [](https://you.com/) Spline - Design tool for 3D web browser experiences [](https://spline.design/) Create web-based 3D browser experiences Image to HTML CSS converter. Convert image to HTML CSS with AI: Fronty [](https://fronty.com/) Fronty - Image to HTML CSS code converter. Convert image to HTML powered by AI. Sketchfab - The best 3D viewer on the web [](https://sketchfab.com/) With a community of over one million creators, we are the world’s largest platform to publish, share, and discover 3D content on web, mobile, AR, and VR. Railway [](https://railway.app/) Railway is an infrastructure platform where you can provision infrastructure, develop with that infrastructure locally, and then deploy to the cloud. JSON Crack - Crack your data into pieces [](https://jsoncrack.com/) Simple visualization tool for your JSON data. No forced structure, paste your JSON and view it instantly. Locofy.ai - ship your products 3-4x faster — with low code [](https://www.locofy.ai/) Turn your designs into production-ready frontend code for mobile apps and web. Ship products 3-4x faster with your existing design tools, tech stacks & workflows. Oh Shit, Git!?! [](https://ohshitgit.com/) Carbon | Create and share beautiful images of your source code [](https://carbon.now.sh/) Carbon is the easiest way to create and share beautiful images of your source code. GPRM : GitHub Profile ReadMe Maker [](https://gprm.itsvg.in/) Best Profile Generator, Create your perfect GitHub Profile ReadMe in the best possible way. Lots of features and tools included, all for free ! HubSpot | Software, Tools, and Resources to Help Your Business Grow Better [](https://www.hubspot.com/) HubSpot’s integrated CRM platform contains the marketing, sales, service, operations, and website-building software you need to grow your business. QuickRef.ME - Quick Reference Cheat Sheet [](https://quickref.me/) Share quick reference and cheat sheet for developers massCode | A free and open source code snippets manager for developers [](https://masscode.io/) Code snippets manager for developers, developed using web technologies. Snyk | Developer security | Develop fast. Stay secure. [](https://snyk.io/) Snyk helps software-driven businesses develop fast and stay secure. Continuously find and fix vulnerabilities for npm, Maven, NuGet, RubyGems, PyPI and more. Developer Roadmaps [](https://roadmap.sh/) Community driven roadmaps, articles, guides, quizzes, tips and resources for developers to learn from, identify their career paths, know what they don't know, find out the knowledge gaps, learn and improve. CSS Generators Get Waves – Create SVG waves for your next design [](https://getwaves.io/) A free SVG wave generator to make unique SVG waves for your next web design. Choose a curve, adjust complexity, randomize! Box Shadows [](https://box-shadow.dev/) Tridiv | CSS 3D Editor [](http://tridiv.com/) Tridiv is a web-based editor for creating 3D shapes in CSS Glassmorphism CSS Generator - Glass UI [](https://ui.glass/generator/) Generate CSS and HTML components using the glassmorphism design specifications based on the Glass UI library. Blobmaker - Make organic SVG shapes for your next design [](https://www.blobmaker.app/) Make organic SVG shapes for your next design. Modify the complexity, contrast, and color, to generate unique SVG blobs every time. Keyframes.app [](https://keyframes.app/) cssFilters.co - Custom and Instagram like photo filters for CSS [](https://www.cssfilters.co/) Visual playground for generating CSS for custom and Instagram like photo filters. Experiment with your own uploaded photo or select one from the Unsplash collection. CSS Animations Animista - CSS Animations on Demand [](https://animista.net/) Animista is a CSS animation library and a place where you can play with a collection of ready-made CSS animations and download only those you will use. Build Internal apps Superblocks | Save 100s of developer hours on internal tools [](https://www.superblocks.com/) Superblocks is the fast, easy and secure way for developers to build custom internal tools fast. Connect your databases & APIs. Drag and drop UI components. Extend with Python or Javascript. Deploy in 1-click. Secure and Monitor using your favorite tools Budibase | Build internal tools in minutes, the easy way [](https://budibase.com/) Budibase is a modern, open source low-code platform for building modern internal applications in minutes. Retool | Build internal tools, remarkably fast. [](https://retool.com/) Retool is the fast way to build internal tools. Drag-and-drop our building blocks and connect them to your databases and APIs to build your own tools, instantly. Connects with Postgres, REST APIs, GraphQL, Firebase, Google Sheets, and more. Built by developers, for developers. Trusted by startups and Fortune 500s. Sign up for free. GitHub Repositories GitHub - vasanthk/how-web-works: What happens behind the scenes when we type www.google.com in a browser? [](https://github.com/vasanthk/how-web-works) What happens behind the scenes when we type www.google.com in a browser? - GitHub - vasanthk/how-web-works: What happens behind the scenes when we type www.google.com in a browser? GitHub - kamranahmedse/developer-roadmap: Interactive roadmaps, guides and other educational content to help developers grow in their careers. [](https://github.com/kamranahmedse/developer-roadmap) Interactive roadmaps, guides and other educational content to help developers grow in their careers. - GitHub - kamranahmedse/developer-roadmap: Interactive roadmaps, guides and other educational content to help developers grow in their careers. GitHub - apptension/developer-handbook: An opinionated guide on how to become a professional Web/Mobile App Developer. [](https://github.com/apptension/developer-handbook) An opinionated guide on how to become a professional Web/Mobile App Developer. - GitHub - apptension/developer-handbook: An opinionated guide on how to become a professional Web/Mobile App Developer. ProfileMe.dev | Create an amazing GitHub profile in minutes [](https://www.profileme.dev/) ProfileMe.dev | Create an amazing GitHub profile in minutes GitHub - Kristories/awesome-guidelines: A curated list of high quality coding style conventions and standards. [](https://github.com/Kristories/awesome-guidelines) A curated list of high quality coding style conventions and standards. - GitHub - Kristories/awesome-guidelines: A curated list of high quality coding style conventions and standards. GitHub - tiimgreen/github-cheat-sheet: A list of cool features of Git and GitHub. [](https://github.com/tiimgreen/github-cheat-sheet) A list of cool features of Git and GitHub. Contribute to tiimgreen/github-cheat-sheet development by creating an account on GitHub. GitHub - andreasbm/web-skills: A visual overview of useful skills to learn as a web developer [](https://github.com/andreasbm/web-skills) A visual overview of useful skills to learn as a web developer - GitHub - andreasbm/web-skills: A visual overview of useful skills to learn as a web developer GitHub - Ebazhanov/linkedin-skill-assessments-quizzes: Full reference of LinkedIn answers 2022 for skill assessments (aws-lambda, rest-api, javascript, react, git, html, jquery, mongodb, java, Go, python, machine-learning, power-point) linkedin excel test lösungen, linkedin machine learning test LinkedIn test questions and answers [](https://github.com/Ebazhanov/linkedin-skill-assessments-quizzes) Full reference of LinkedIn answers 2022 for skill assessments (aws-lambda, rest-api, javascript, react, git, html, jquery, mongodb, java, Go, python, machine-learning, power-point) linkedin excel test lösungen, linkedin machine learning test LinkedIn test questions and answers - GitHub - Ebazhanov/linkedin-skill-assessments-quizzes: Full reference of LinkedIn answers 2022 for skill assessments (aws-lambda, rest-api, javascript, react, git, html, jquery, mongodb, java, Go, python, machine-learning, power-point) linkedin excel test lösungen, linkedin machine learning test LinkedIn test questions and answers Blockchain/Crypto Dashboards [](https://dune.com/) Blockchain ecosystem analytics by and for the community. Explore and share data from Ethereum, xDai, Polygon, Optimism, BSC and Solana for free. Introduction - The Anchor Book v0.24.0 [](https://book.anchor-lang.com/introduction/introduction.html) Crypto & Fiat Exchange Super App | Trade, Save & Spend | hi [](https://hi.com/) Buy, Trade, Send and Earn Crypto & Fiat. Deposit Bitcoin, ETH, USDT and other cryptos and start earning. Get the hi Debit Card and Multi-Currency IBAN Account. Moralis Web3 - Enterprise-Grade Web3 APIs [](https://moralis.io/) Bridge the development gap between Web2 and Web3 with Moralis’ powerful Web3 APIs. Mirror [](https://mirror.xyz/) Built on web3 for web3, Mirror’s robust publishing platform pushes the boundaries of writing online—whether it’s the next big white paper or a weekly community update. Makerdao [](https://blog.makerdao.com/) Sholi — software for Investors & Traders / Sholi MetriX [](https://sholi.io/) Sholi — software for Investors & Traders / Sholi MetriX Stock Trading Quiver Quantitative [](https://www.quiverquant.com/) Quiver Quantitative Chart Prime - The only tool you'll need for trading assets across all markets [](https://chartprime.com/) ChartPrime offers a toolkit that will take your trading game to the next level. Visit our site for a full rundown of features and helpful tutorials. Learning Hacker Rank [](https://www.hackerrank.com/) Coderbyte | Code Screening, Challenges, & Interview Prep [](https://coderbyte.com/) Improve your coding skills with our library of 300+ challenges and prepare for coding interviews with content from leading technology companies. Competitive Programming | Participate & Learn | CodeChef [](https://www.codechef.com/) Learn competitive programming with the help of CodeChef's coding competitions. Take part in these online coding contests to level up your skills Learn to Code - for Free | Codecademy [](https://www.codecademy.com/) Learn the technical skills to get the job you want. Join over 50 million people choosing Codecademy to start a new career (or advance in their current one). Free Code Camp [](https://www.freecodecamp.org/) Learn to Code — For Free Sololearn: Learn to Code [](https://www.sololearn.com/home) Join Now to learn the basics or advance your existing skills Mimo: The coding app you need to learn to code! Python, HTML, JavaScript [](https://getmimo.com/) Join more than 17 million learners worldwide. Learn to code for free. Learn Python, JavaScript, CSS, SQL, HTML, and more with our free code learning app. Free for developers [](https://free-for.dev/#/) Your Career in Web Development Starts Here | The Odin Project [](https://www.theodinproject.com/) The Odin Project empowers aspiring web developers to learn together for free Code Learning Games CheckiO - coding games and programming challenges for beginner and advanced [](https://checkio.org/) CheckiO - coding websites and programming games. Improve your coding skills by solving coding challenges and exercises online with your friends in a fun way. Exchanges experience with other users online through fun coding activities Coding for Kids | Game-Based Programming | CodeMonkey [](https://www.codemonkey.com/) CodeMonkey is a leading coding for kids program. Through its award-winning courses, millions of students learn how to code in real programming languages. Coding Games and Programming Challenges to Code Better [](https://www.codingame.com/) CodinGame is a challenge-based training platform for programmers where you can play with the hottest programming topics. Solve games, code AI bots, learn from your peers, have fun. Learn VIM while playing a game - VIM Adventures [](https://vim-adventures.com/) VIM Adventures is an online game based on VIM's keyboard shortcuts. It's the "Zelda meets text editing" game. So come have some fun and learn some VIM! CodeCombat - Coding games to learn Python and JavaScript [](https://codecombat.com/) Learn typed code through a programming game. Learn Python, JavaScript, and HTML as you solve puzzles and learn to make your own coding games and websites. Design Useberry - Codeless prototype analytics [](https://www.useberry.com/) User testing feedback & rich insights in minutes, not months! Figma: the collaborative interface design tool. [](https://www.figma.com/) Build better products as a team. Design, prototype, and gather feedback all in one place with Figma. Dribbble - Discover the World’s Top Designers & Creative Professionals [](https://dribbble.com/) Find Top Designers & Creative Professionals on Dribbble. We are where designers gain inspiration, feedback, community, and jobs. Your best resource to discover and connect with designers worldwide. Photopea | Online Photo Editor [](https://www.photopea.com/) Photopea Online Photo Editor lets you edit photos, apply effects, filters, add text, crop or resize pictures. Do Online Photo Editing in your browser for free! Toools.design – An archive of 1000+ Design Resources [](https://www.toools.design/) A growing archive of over a thousand design resources, weekly updated for the community. Discover highly useful design tools you never thought existed. All Online Tools in One Box | 10015 Tools [](https://10015.io/) All online tools you need in one box for free. Build anything online with “all-in-one toolbox”. All tools are easy-to-use, blazing fast & free. Phase - Digital Design Reinvented| Phase [](https://phase.com/) Design and prototype websites and apps visually and intuitively, in a new powerful product reworked for the digital age. Animated Backgrounds [](https://animatedbackgrounds.me/) A Collection of 30+ animated backgrounds for websites and blogs.With Animated Backgrounds, set a simple, elegant background animations on your websites and blogs. Trianglify.io · Low Poly Pattern Generator [](https://trianglify.io/) Trianglify.io is a tool for generating low poly triangle patterns that can be used as wallpapers and website assets. Cool Backgrounds [](https://coolbackgrounds.io/) Explore a beautifully curated selection of cool backgrounds that you can add to blogs, websites, or as desktop and phone wallpapers. SVG Repo - Free SVG Vectors and Icons [](https://www.svgrepo.com/) Free Vectors and Icons in SVG format. ✅ Download free mono or multi color vectors for commercial use. Search in 300.000+ Free SVG Vectors and Icons. Microcopy - Short copy text for your website. [](https://www.microcopy.me/) Search micro UX copy text: slogans, headlines, notifications, CTA, error messages, email, account preferences, and much more. 3D icons and icon paks - Free3Dicon [](https://free3dicon.com/) All 3D icons you need in one place. This is a collection of free, beautiful, trending 3D icons, that you can use in any project. Love 3D Icon [](https://free3dicons.com/) Downloads free 3D icons GIMP - GNU Image Manipulation Program [](https://www.gimp.org/) GIMP - The GNU Image Manipulation Program: The Free and Open Source Image Editor blender.org - Home of the Blender project - Free and Open 3D Creation Software [](https://www.blender.org/) The Freedom to Create 3D Design Software | 3D Modeling on the Web | SketchUp [](https://www.sketchup.com/) SketchUp is a premier 3D design software that truly makes 3D modeling for everyone, with a simple to learn yet robust toolset that empowers you to create whatever you can imagine. Free Logo Maker - Create a Logo in Seconds - Shopify [](https://www.shopify.com/tools/logo-maker) Free logo maker tool to generate custom design logos in seconds. This logo creator is built for entrepreneurs on the go with hundreds of templates, free vectors, fonts and icons to design your own logo. The easiest way to create business logos online. All your design tools in one place | Renderforest [](https://www.renderforest.com/) Time to get your brand noticed. Create professional videos, logos, mockups, websites, and graphics — all in one place. Get started now! Prompt Hero [](https://prompthero.com/) Type Scale - A Visual Calculator [](https://type-scale.com/) Preview and choose the right type scale for your project. Experiment with font size, scale and different webfonts. DreamFusion: Text-to-3D using 2D Diffusion [](https://dreamfusion3d.github.io/) DreamFusion: Text-to-3D using 2D Diffusion, 2022. The branding style guidelines documents archive [](https://brandingstyleguides.com/) Welcome to the brand design manual documents directory. Search over our worldwide style assets handpicked collection, access to PDF documents for inspiration. Super designer | Create beautiful designs with a few clicks [](https://superdesigner.co/) Create beautiful designs with a few clicks. Simple design tools to generate unique patterns, backgrounds, 3D shapes, colors & images for social media, websites and more Readymag—a design tool to create websites without coding [](https://readymag.com/) Meet the most elegant, simple and powerful web-tool for designing websites, presentations, portfolios and all kinds of digital publications. ffflux: Online SVG Fluid Gradient Background Generator | fffuel [](https://fffuel.co/ffflux/) SVG generator to make fluid gradient backgrounds that feel organic and motion-like. Perfect to add a feeling of motion and fluidity to your web designs. Generate unique SVG design assets | Haikei [](https://haikei.app/) A web-based design tool to generate unique SVG design assets for websites, social media, blog posts, desktop and mobile wallpapers, posters, and more! Our generators let you discover, customize, randomize, and export generative SVG design assets ready to use with your favorite design tools. UI/UX - Inspirational Free Website Builder Software | 10,000+ Free Templates [](https://nicepage.com/) Nicepage is your website builder software breaking limitations common for website builders with revolutionary freehand positioning. 7000+ Free Templates. Easy Drag-n-Drop. No coding. Mobile-friendly. Clean HTML. Super designer | Create beautiful designs with a few clicks [](https://superdesigner.co/) Create beautiful designs with a few clicks. Simple design tools to generate unique patterns, backgrounds, 3D shapes, colors & images for social media, websites and more Pika – Create beautiful mockups from screenshots [](https://pika.style/) Quickly create beautiful website and device mockup from screenshot. Pika lets you capture website screenshots form URL, add device and browser frames, customize background and more LiveTerm [](https://liveterm.vercel.app/) Minimal Gallery – Web design inspiration [](https://minimal.gallery/) For the love of beautiful, clean and functional websites. Awwwards - Website Awards - Best Web Design Trends [](https://www.awwwards.com/) Awwwards are the Website Awards that recognize and promote the talent and effort of the best developers, designers and web agencies in the world. Design Systems For Figma [](https://www.designsystemsforfigma.com/) A collection of Design Systems for Figma from all over the globe. Superside: Design At Scale For Ambitious Brands [](https://www.superside.com/) We are an always-on design company. Get a team of dedicated designers, speedy turnarounds, magical creative collaboration tech and the top 1% of global talent. UXArchive - Made by Waldo [](https://uxarchive.com/) UXArchive the world's largest library of mobile user flows. Be inspired to design the best user experiences. Search by Muzli [](https://search.muz.li/) Search, discover, test and create beautiful color palettes for your projects Siteinspire | Web Design Inspiration [](https://www.siteinspire.com/) SAVEE [](https://savee.it/) The best way to save and share inspiration. A little corner of the internet to find good landing page copywriting examples [](https://greatlandingpagecopy.com/) A little corner of the internet to find great landing page copywriting examples. The Best Landing Page Examples For Design Inspiration - SaaS Landing Page [](https://saaslandingpage.com/) SaaS Landing Page showcases the best landing page examples created by top-class SaaS companies. Get ideas and inspirations for your next design project. Websites Free templates Premium Bootstrap Themes and Templates: Download @ Creative Tim [](https://www.creative-tim.com/) UI Kits, Templates and Dashboards built on top of Bootstrap, Vue.js, React, Angular, Node.js and Laravel. Join over 2,014,387+ creatives to access all our products! Free Bootstrap Themes, Templates, Snippets, and Guides - Start Bootstrap [](https://startbootstrap.com/) Start Bootstrap develops free to download, open source Bootstrap 5 themes, templates, and snippets and creates guides and tutorials to help you learn more about designing and developing with Bootstrap. Free Website Templates [](https://freewebsitetemplates.com/) Get your free website templates here and use them on your website without needing to link back to us. One Page Love - One Page Website Inspiration and Templates [](https://onepagelove.com/) One Page Love is a One Page website design gallery showcasing the best Single Page websites, templates and resources. Free CSS | 3400 Free Website Templates, CSS Templates and Open Source Templates [](https://www.free-css.com/) Free CSS has 3400 free website templates, all templates are free CSS templates, open source templates or creative commons templates. Free Bootstrap Themes and Website Templates | BootstrapMade [](https://bootstrapmade.com/) At BootstrapMade, we create beautiful website templates and bootstrap themes using Bootstrap, the most popular HTML, CSS and JavaScript framework. Free and Premium Bootstrap Themes, Templates by Themesberg [](https://themesberg.com/) Free and Premium Bootstrap themes, templates, admin dashboards and UI kits used by over 38820 web developers and software companies HTML, Vue.js and React templates for startup landing pages - Cruip [](https://cruip.com/) Cruip is a gallery of premium and free HTML, Vue.js and React templates for startups and SaaS. Free Website Templates Download | WordPress Themes - W3Layouts [](https://w3layouts.com/) Want to download free website templates? W3Layouts WordPress themes and website templates are built with responsive web design techniques. Download now! Free HTML Landing Page Templates and UI Kits | UIdeck [](https://uideck.com/) Free HTML Landing Page Templates, Bootstrap Themes, React Templates, HTML Templates, Tailwind Templates, and UI Kits. Create Online Graphics Snappa - Quick & Easy Graphic Design Software [](https://snappa.com/) Snappa makes it easy to create any type of online graphic. Create & publish images for social media, blogs, ads, and more! Canva [](https://www.canva.com/) Polotno Studio - Make graphical designs [](https://studio.polotno.com) Free online design editor. Create images for social media, youtube previews, facebook covers Free Logo Maker: Design Custom Logos | Adobe Express [](https://www.adobe.com/express/create/logo) The Adobe Express logo maker is instant, intuitive, and intelligent. Use it to generate a wide range of possibilities for your own logo. Photo Editor: Fotor – Free Online Photo Editing & Image Editor [](https://www.fotor.com/) Fotor's online photo editor helps you edit photos with free online photo editing tools. Crop photos, resize images, and add effects/filters, text, and graphics in just a few clicks. Photoshop online has never been easier with Fotor's free online photo editor. VistaCreate – Free Graphic Design Software with 70,000+ Free Templates [](https://create.vista.com/) Looking for free graphic design software? Easily create professional designs with VistaCreate, a free design tool with powerful features and 50K+ ready-made templates Draw Freely | Inkscape [](https://inkscape.org/) Inkscape is professional quality vector graphics software which runs on Linux, Mac OS X and Windows desktop computers. Visual & Video Maker Trusted By 11 Million Users - Piktochart [](https://piktochart.com/) With Piktochart, you can create professional-looking infographics, flyers, posters, charts, videos, and more. No design experience needed. Start for free. The Web's Favorite Online Graphic Design Tool | Stencil [](https://getstencil.com/) Stencil is a fantastically easy-to-use online graphic design tool and image editor built for business owners, social media marketers, and bloggers. Pablo by Buffer - Design engaging images for your social media posts in under 30 seconds [](https://pablo.buffer.com/) Buffer makes it super easy to share any page you're reading. Keep your Buffer topped up and we automagically share them for you through the day. Free Online Graphic Design Software | Create stunning designs in seconds. [](https://desygner.com/) Easy drag and drop graphic design tool for anyone to use with 1000's of ready made templates. Create & print professional business cards, flyers, social posts and more. Color Pallet Color Palettes for Designers and Artists - Color Hunt [](https://colorhunt.co/) Discover the newest hand-picked color palettes of Color Hunt. Get color inspiration for your design and art projects. Coolors - The super fast color palettes generator! [](https://coolors.co/) Generate or browse beautiful color combinations for your designs. Get color palette inspiration from nature - colorpalettes.earth [](https://colorpalettes.earth/) Color palettes inspired by beautiful nature photos Color Palette Generator - Create Beautiful Color Schemes [](https://colors.muz.li/) Search, discover, test and create beautiful color palettes for your projects A Most Useful Color Picker | 0to255 [](https://0to255.com/) Find lighter and darker colors based on any color. Discover why over two million people have used 0to255 to choose colors for their website, logo, room interior, and print design projects. Colour Contrast Checker [](https://colourcontrast.cc/) Check the contrast between different colour combinations against WCAG standards Fonts Google Fonts [](https://fonts.google.com/) Making the web more beautiful, fast, and open through great typography Fonts In Use – Type at work in the real world. [](https://fontsinuse.com/) A searchable archive of typographic design, indexed by typeface, format, and topic. Wordmark - Helps you choose fonts! [](https://wordmark.it/) Wordmark helps you choose fonts by quickly displaying your text with your fonts. OH no Type Company [](https://ohnotype.co/) OH no Type Co. Retail and custom typefaces. Life’s a thrill, fonts are chill! Illustrations Illustrations | unDraw [](https://undraw.co/illustrations) The design project with open-source illustrations for any idea you can imagine and create. Create beautiful websites, products and applications with your color, for free. Design Junction [](https://designjunction.xyz/) Design Junction is a one-stop resource library for Designers and Creatives with curated list of best resources handpicked from around the web Humaaans: Mix-&-Match illustration library [](https://www.humaaans.com/) Mix-&-match illustrations of people with a design library for InVIsion Studio and Sketch. Stubborn - Free Illustrations Generator [](https://stubborn.fun/) Free illustrations generator for Figma and Sketch. Get the opportunity to design your characters using symbols and styles. Open Peeps, Hand-Drawn Illustration Library [](https://www.openpeeps.com/) Open Peeps is a hand-drawn illustration library to create scenes of people. You can use them in product illustration, marketing, comics, product states, user flows, personas, storyboarding, quinceañera invitations, or whatever you want! ⠀ Reshot | Free icons & illustrations [](https://www.reshot.com/) Design freely with instant downloads of curated SVG icons and vector illustrations. All free with commercial licensing. No attribution required. Blush: Illustrations for everyone [](https://blush.design/) Blush makes it easy to add free illustrations to your designs. Play with fully customizable graphics made by artists across the globe. Mockups Angle 4 - 5000+ Device Mockups for Figma, Sketch and XD [](https://angle.sh/) Vector mockups for iPhone, iPad, Android and Mac devices, including the new iPhone 13, Pro, Pro Max and Mini. Perfect for presenting your apps. Huge library of components, compositions, wallpapers and plugins made for Figma, Sketch and XD. Make Mockups, Logos, Videos and Designs in Seconds [](https://placeit.net/) Get unlimited downloads on all our 100K templates! You can make a logo, video, mockup, flyer, business card and social media image in seconds right from your browser. Free and premium tools for graphic designers | Lstore Graphics [](https://www.ls.graphics/) Free and premium mockups, UI/UX tools, scene creators for busy designers Logo Design & Brand Identity Platform for Entrepreneurs | Looka [](https://looka.com/) Logojoy is now Looka! Design a Logo, make a website, and create a Brand Identity you’ll love with the power of Artificial Intelligence. 100% free to use. Create stunning product mockups easily and online - Smartmockups [](https://smartmockups.com/) Smartmockups enables you to create stunning high-resolution mockups right inside your browser within one interface across multiple devices. Previewed - Free mockup generator for your app [](https://previewed.app/) Join Previewed to create stunning 3D image shots and animations for your app. Choose from hundreds of ready made mockups, or create your own. Free Design Software - Graphic Online Maker - Glorify [](https://www.glorify.com/) Create professional and high converting social media posts, ads, infographics, presentations, and more with Glorify, a free design software & graphic maker. Other BuiltWith Technology Lookup [](https://builtwith.com/) Web technology information profiler tool. Find out what a website is built with. Compress JPEG Images Online [](https://compressjpeg.com/) Compress JPEG images and photos for displaying on web pages, sharing on social networks or sending by email. PhotoRoom - Remove Background and Create Product Pictures [](https://www.photoroom.com/) Create product and portrait pictures using only your phone. Remove background, change background and showcase products. Magic Eraser - Remove unwanted things from images in seconds [](https://www.magiceraser.io/) Magic Eraser - Use AI to remove unwanted things from images in seconds. Upload an image, mark the bit you need removed, download the fixed up image. Compressor.io - optimize and compress JPEG photos and PNG images [](https://compressor.io/) Optimize and compress JPEG, PNG, SVG, GIF and WEBP images online. Compress, resize and rename your photos for free. Remove Video Background – Unscreen [](https://www.unscreen.com/) Remove the background of any video - 100% automatically, online & free! Goodbye Greenscreen. Hello Unscreen. Noun Project: Free Icons & Stock Photos for Everything [](https://thenounproject.com/) Noun Project features the most diverse collection of icons and stock photos ever. Download SVG and PNG. Browse over 5 million art-quality icons and photos. Design Principles [](https://principles.design/) An Open Source collection of Design Principles and methods Shapefest™ - A massive library of free 3D shapes [](https://www.shapefest.com/) A massive free library of beautifully rendered 3D shapes. 160,000+ high resolution PNG images in one cohesive library. Learning UX Degreeless.design - Everything I Learned in Design School [](https://degreeless.design/) This is a list of everything I've found useful in my journey of learning design, and an ongoing list of things I think you should read. For budding UX, UI, Interaction, or whatever other title designers. UX Tools | Practical UX skills and tools [](https://uxtools.co/) Lessons and resources from two full-time product designers. Built For Mars [](https://builtformars.com/) On a mission to help the world build better user experiences by demystifying UX. Thousands of hours of research packed into UX case studies. Case Study Club – Curated UX Case Study Gallery [](https://www.casestudy.club/) Case Study Club is the biggest curated gallery of the best UI/UX design case studies. Get inspired by industry-leading designers, openly sharing their UX process. The Guide to Design [](https://start.uxdesign.cc/) A self-guided class to help you get started in UX and answer key questions about craft, design, and career Uxcel - Where design careers are built [](https://app.uxcel.com/explore) Available on any device anywhere in the world, Uxcel is the best way to improve and learn UX design online in just 5 minutes per day. UI & UX Design Tips by Jim Raptis. [](https://www.uidesign.tips/) Learn UI & UX Design with practical byte-sized tips and in-depth articles from Jim Raptis. Entrepreneur Instant Username Search [](https://instantusername.com/#/) Instant Username Search checks out if your username is available on more than 100 social media sites. Results appear instantly as you type. Flourish | Data Visualization & Storytelling [](https://flourish.studio/) Beautiful, easy data visualization and storytelling PiPiADS - #1 TikTok Ads Spy Tool [](https://www.pipiads.com/) PiPiADS is the best tiktok ads spy tool .We provide tiktok advertising,advertising on tiktok,tiktok ads examples,tiktok ads library,tiktok ads best practices,so you can understand the tiktok ads cost and master the tiktok ads 2021 and tiktok ads manager. Minea - The best adspy for product search in ecommerce and dropshipping [](https://en.minea.com/) Minea is the ultimate e-commerce product search tool. Minea tracks all ads on all networks. Facebook Ads, influencer product placements, Snapspy, all networks are tracked. Stop paying adspy 149€ for one network and discover Minea. AdSpy [](https://adspy.com/) Google Trends [](https://trends.google.com/) ScoreApp: Advanced Quiz Funnel Marketing | Make a Quiz Today [](https://www.scoreapp.com/) ScoreApp makes quiz funnel marketing easy, so you can attract relevant warm leads, insightful data and increase your sales. Try for free today Mailmodo - Send Interactive Emails That Drive Conversions [](https://www.mailmodo.com/) Use Mailmodo to create and send interactive emails your customers love. Drive conversions and get better email ROI. Sign up for a free trial now. 185 Top E-Commerce Sites Ranked by User Experience Performance – Baymard Institute [](https://baymard.com/ux-benchmark) See the ranked UX performance of the 185 largest e-commerce sites in the US and Europe. The chart summarizes 50,000+ UX performance ratings. Metricool - Analyze, manage and measure your digital content [](https://metricool.com/) Social media scheduling, web analytics, link in bio and reporting. Metricool is free per live for one brand. START HERE Visualping: #1 Website change detection, monitoring and alerts [](https://visualping.io/) More than 1.5 millions users monitor changes in websites with Visualping, the No1 website change detection, website checker, webpage change monitoring and webpage change detection tool. Gumroad – Sell what you know and see what sticks [](https://gumroad.com/) Gumroad is a powerful, but simple, e-commerce platform. We make it easy to earn your first dollar online by selling digital products, memberships and more. Product Hunt – The best new products in tech. [](https://www.producthunt.com/) Product Hunt is a curation of the best new products, every day. Discover the latest mobile apps, websites, and technology products that everyone's talking about. 12ft Ladder [](https://12ft.io/) Show me a 10ft paywall, I’ll show you a 12ft ladder. namecheckr | Social and Domain Name Availability Search For Brand Professionals [](https://www.namecheckr.com/) Social and Domain Name Availability Search For Brand Professionals Excel AI Formula Generator - Excelformulabot.com [](https://excelformulabot.com/) Transform your text instructions into Excel formulas in seconds with the help of AI. Z-Library [](https://z-lib.org/) Global Print On Demand Platform | Gelato [](https://www.gelato.com/) Create and sell custom products online. With local production in 33 countries, easy integration, and 24/7 customer support, Gelato is an all-in-one platform. Freecycle: Front Door [](https://freecycle.org/) Free eBooks | Project Gutenberg [](https://www.gutenberg.org/) Project Gutenberg is a library of free eBooks. Convertio — File Converter [](https://convertio.co/) Convertio - Easy tool to convert files online. More than 309 different document, image, spreadsheet, ebook, archive, presentation, audio and video formats supported. Namechk [](https://namechk.com/) Crazy Egg Website — Optimization | Heatmaps, Recordings, Surveys & A/B Testing [](https://www.crazyegg.com/) Use Crazy Egg to see what's hot and what's not, and to know what your web visitors are doing with tools, such as heatmaps, recordings, surveys, A/B testing & more. Ifttt [](https://ifttt.com/) Also Asked [](https://alsoasked.com/) Business Name Generator - Easily create Brandable Business Names - Namelix [](https://namelix.com/) Namelix uses artificial intelligence to create a short, brandable business name. Search for domain availability, and instantly generate a logo for your new business Merch Informer [](https://merchinformer.com/) Headline Generator [](https://www.title-generator.com/) Title Generator: create 700 headlines with ONE CLICK: Content Ideas + Catchy Headlines + Ad Campaign E-mail Subject Lines + Emotional Titles. Simple - Efficient - One Click Make [](https://www.make.com/en) Create and add calculator widgets to your website | CALCONIC_ [](https://www.calconic.com/) Web calculator builder empowers you to choose from a pre-made templates or build your own calculator widgets from a scratch without any need of programming knowledge Boost Your Views And Subscribers On YouTube - vidIQ [](https://vidiq.com/) vidIQ helps you acquire the tools and knowledge needed to grow your audience faster on YouTube and beyond. Learn More Last Pass [](https://www.lastpass.com/) Starter Story: Learn How People Are Starting Successful Businesses [](https://www.starterstory.com/) Starter Story interviews successful entrepreneurs and shares the stories behind their businesses. In each interview, we ask how they got started, how they grew, and how they run their business today. How To Say No [](https://www.starterstory.com/how-to-say-no) Saying no is hard, but it's also essential for your sanity. Here are some templates for how to say no - so you can take back your life. Think with Google - Discover Marketing Research & Digital Trends [](https://www.thinkwithgoogle.com/) Uncover the latest marketing research and digital trends with data reports, guides, infographics, and articles from Think with Google. ClickUp™ | One app to replace them all [](https://clickup.com/) Our mission is to make the world more productive. To do this, we built one app to replace them all - Tasks, Docs, Goals, and Chat. 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Video Maker | Create Videos Online | Promo.com [](https://promo.com/) Free customizable video maker to help boost your business. Video creator for ads, social media, product and explainer videos, and for anything else you need! beehiiv — The newsletter platform built for growth [](https://www.beehiiv.com/) Access the best tools available in email, helping your newsletter scale and monetize like never before. GetResponse | Professional Email Marketing for Everyone [](https://www.getresponse.com/) No matter your level of expertise, we have a solution for you. At GetResponse, it's email marketing done right. Start your free account today! Search Email Newsletter Archives : Email Tuna [](https://emailtuna.com/) Explore newsletters without subscribing. Get email design ideas, discount coupon codes and exclusive newsletters deals. Database of email newsletters archived from all over the internet. 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Confetto - Create Instagram content in minutes [](https://www.confet.to/) Confetto is an all-in-one social media marketing tool built for SMBs and Social Media Managers. Confetto helps you create high-quality content for your audience that maximizes your reach and engagement on social media. Design, copy-write, plan and schedule content all in one place. Find email addresses in seconds • Hunter (Email Hunter) [](https://hunter.io/) Hunter is the leading solution to find and verify professional email addresses. Start using Hunter and connect with the people that matter for your business. PlayPhrase.me: Site for cinema archaeologists. [](https://playphrase.me/) Travel and explore the world of cinema. Largest collection of video quotes from movies on the web. #1 Free SEO Tools → SEO Review Tools [](https://www.seoreviewtools.com/) SEO Review Tools: 42+ Free Online SEO Tools build with ❤! → Rank checker → Domain Authority Checker → Keyword Tool → Backlink Checker Podcastle: Seamless Podcast Recording & Editing [](https://podcastle.ai/) Podcastle is the simplest way to create professional-quality podcasts. Record, edit, transcribe, and export your content with the power of AI, in an intuitive web-based platform. Save Ads from TikTok & Facebook Ad Library - Foreplay [](https://www.foreplay.co/) The best way to save ads from TikTok Creative Center and Facebook Ad Library, Organize them into boards and share ad inspiration with your team. Supercharge your creative strategy. 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Castr | #1 Live Video Streaming Solution With Video Hosting [](https://castr.io/) Castr is a live video streaming solution platform that delivers enterprise-grade live videos globally with CDN. Live event streaming, video hosting, pre-recorded live, multi stream – all in one place using Castr. Headliner - Promote your podcast, radio show or blog with video [](https://www.headliner.app/) Easily create videos to promote your podcast, radio show or blog. Share to Instagram, Facebook, Twitter, YouTube, Linkedin and anywhere video lives Create Presentations, Infographics, Design & Video | Visme [](https://www.visme.co/) Create professional presentations, interactive infographics, beautiful design and engaging videos, all in one place. Start using Visme today. Designrr - Create eBooks, Kindle books, Leadmagnets, Flipbooks and Blog posts from your content in 2 minutes [](https://designrr.io/) Upload any web page, MS Word, Video, Podcast or YouTube and it will create a stunning ebook and convert it to pdf, epub, Kindle or Flipbook. Quick and Easy to use. Full Training, 24x7 Support and Facebook Group Included. SwipeWell | Swipe File Software [](https://www.swipewell.app/) The only Chrome extension dedicated to helping you save, organize, and reference marketing examples (so you never feel stumped). Tango | Create how-to guides, in seconds [](https://www.tango.us/) Tango takes the pain out of documenting processes by automatically generating how-to guides while you work. Empower your team to do their best work. Ad Creative Bank [](https://www.theadcreativebank.com/) Get inspired by ads from across industries, learn new best practices, and start thinking creatively about your brand’s digital creative. 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Productivity Temp Mail [](https://temp-mail.org/en/) The Visual Collaboration Platform for Every Team | Miro [](https://miro.com/) Scalable, secure, cross-device and enterprise-ready team collaboration whiteboard for distributed teams. Join 35M+ users from around the world. Grammarly: Free Online Writing Assistant [](https://www.grammarly.com/) Millions trust Grammarly’s free writing app to make their online writing clear and effective. Getting started is simple — download Grammarly’s extension today. Rize · Maximize Your Productivity [](https://rize.io/) Rize is a smart time tracker that improves your focus and helps you build better work habits. Motion | Manage calendars, meetings, projects & tasks in one app [](https://www.usemotion.com/) Automatically prioritize tasks, schedule meetings, and resolve calendar conflicts. Used by over 10k CEOs and professionals to improve focus, get more done, and streamline workday. Notion – One workspace. Every team. [](https://www.notion.so/) We’re more than a doc. Or a table. Customize Notion to work the way you do. Loom: Async Video Messaging for Work | Loom [](https://www.loom.com/) Record your screen, share your thoughts, and get things done faster with async video. Zapier | Automation that moves you forward [](https://zapier.com/) Workflow automation for everyone. Zapier automates your work across 5,000+ app integrations, so you can focus on what matters. Rows — The spreadsheet with superpowers [](https://rows.com/) Combine the power of a spreadsheet with built-in integrations from your business apps. Automate workflows and build tools that make work simpler. Free Online Form Builder | Tally [](https://tally.so/) Tally is the simplest way to create free forms & surveys. Create any type of form in seconds, without knowing how to code, and for free. Highbrow | Learn Something New Every Day. Join for Free! 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PDF Tools Free PDF, Video, Image & Other Online Tools - TinyWow [](https://tinywow.com/) Smallpdf.com - A Free Solution to all your PDF Problems [](https://smallpdf.com/) Smallpdf - the platform that makes it super easy to convert and edit all your PDF files. Solving all your PDF problems in one place - and yes, free. Sejda helps with your PDF tasks [](https://www.sejda.com/) Sejda helps with your PDF tasks. Quick and simple online service, no installation required! Split, merge or convert PDF to images, alternate mix or split scans and many other. iLovePDF | Online PDF tools for PDF lovers [](https://www.ilovepdf.com/) iLovePDF is an online service to work with PDF files completely free and easy to use. Merge PDF, split PDF, compress PDF, office to PDF, PDF to JPG and more! Text rewrite QuillBot [](https://quillbot.com/) Pre Post SEO : Online SEO Tools [](https://www.prepostseo.com/) Free Online SEO Tools: plagiarism checker, grammar checker, image compressor, website seo checker, article rewriter, back link checker Wordtune | Your personal writing assistant & editor [](https://www.wordtune.com/) Wordtune is the ultimate AI writing tool that rewrites, rephrases, and rewords your writing! Trusted by over 1,000,000 users, Wordtune strengthens articles, academic papers, essays, emails and any other online content. Aliexpress alternatives CJdropshipping - Dropshipping from Worldwide to Worldwide! [](https://cjdropshipping.com/) China's reliable eCommerce dropshipping fulfillment supplier, helps small businesses ship worldwide, dropship and fulfillment services that are friendly to start-ups and small businesses, Shopify dropshipping. SaleHoo [](https://www.salehoo.com/) Alibaba.com: Manufacturers, Suppliers, Exporters & Importers from the world's largest online B2B marketplace [](https://www.alibaba.com/) Find quality Manufacturers, Suppliers, Exporters, Importers, Buyers, Wholesalers, Products and Trade Leads from our award-winning International Trade Site. Import & Export on alibaba.com Best Dropshipping Suppliers for US + EU Products | Spocket [](https://www.spocket.co/) Spocket allows you to easily start dropshipping top products from US and EU suppliers. Get started for free and see why Spocket consistently gets 5 stars. Best dropshipping supplier to the US [](https://www.usadrop.com/) THE ONLY AMERICAN-MADE FULFILLMENT CENTER IN CHINA. Our knowledge of the Worldwide dropshipping market and the Chinese Supply-Chain can't be beat! 阿里1688 [](https://www.1688.com/) 阿里巴巴（1688.com）是全球企业间（B2B）电子商务的著名品牌，为数千万网商提供海量商机信息和便捷安全的在线交易市场，也是商人们以商会友、真实互动的社区平台。目前1688.com已覆盖原材料、工业品、服装服饰、家居百货、小商品等12个行业大类，提供从原料--生产--加工--现货等一系列的供应产品和服务 Dropshipping Tools Oberlo | Where Self Made is Made [](https://www.oberlo.com/) Start selling online now with Shopify. All the videos, podcasts, ebooks, and dropshipping tools you'll need to build your online empire. Klaviyo: Marketing Automation Platform for Email & SMS [](https://www.klaviyo.com/) Klaviyo, an ecommerce marketing automation platform for email marketing and sms syncs your tech stack with your website store to scale your business. SMSBump | SMS Marketing E-Commerce App for Shopify [](https://smsbump.com/) SMSBump is an SMS marketing & automation app for Shopify. Segment customers, recover orders, send campaign text messages with a 35%+ click through rate. AfterShip: The #1 Shipment Tracking Platform [](https://www.aftership.com/) Order status lookup, branded tracking page, and multi-carrier tracking API for eCommerce. Supports USPS, FedEx, UPS, and 900+ carriers worldwide. #1 Dropshipping App | Zendrop [](https://zendrop.com/) Start and scale your own dropshipping business with Zendrop. Sell and easily fulfill your orders with the fastest shipping in the industry. Best Dropshipping Suppliers for US + EU Products | Spocket [](https://www.spocket.co/) Spocket allows you to easily start dropshipping top products from US and EU suppliers. Get started for free and see why Spocket consistently gets 5 stars. Video Editing Jitter • The simplest motion design tool on the web. [](https://jitter.video/) Animate your designs easily. Export your creations as videos or GIFs. All in your browser. DaVinci Resolve 18 | Blackmagic Design [](https://www.blackmagicdesign.com/products/davinciresolve) Professional video editing, color correction, visual effects and audio post production all in a single application. Free and paid versions for Mac, Windows and Linux. Online Video Editor | Video Creator | InVideo [](https://invideo.io/) InVideo's Online Video Editor Helps You Make Professional Videos From Premium Templates, Images, And Music. All your video needs in one place | Clipchamp [](https://clipchamp.com/) Fast-forward your creations with our video editing platform. Start with a video template or record your webcam or screen. Get the pro look with filters, transitions, text and more. Then, export in minutes and share in an instant. Descript | All-in-one audio/video editing, as easy as a doc. [](https://www.descript.com/) Record, transcribe, edit, mix, collaborate, and master your audio and video with Descript. Download for free →. Kapwing — Reach more people with your content [](https://www.kapwing.com/) Kapwing is a collaborative, online content creation platform that you can use to edit video and create content. Join over 10 million modern creators who trust Kapwing to create, edit, and grow their content on every channel. Panzoid [](https://panzoid.com/) Powerful, free online apps and community for creating beautiful custom content. Google Web Designer - Home [](https://webdesigner.withgoogle.com/) Kapwing — Reach more people with your content [](https://www.kapwing.com/) Kapwing is a collaborative, online content creation platform that you can use to edit video and create content. Join over 10 million modern creators who trust Kapwing to create, edit, and grow their content on every channel. ClipDrop [](https://clipdrop.co/) Create professional visuals without a photo studio CapCut [](https://www.capcut.com/) CapCut is an all-in-one online video editing software which makes creation, upload & share easier, with frame by frame track editor, cloud drive etc. VEED - Online Video Editor - Video Editing Made Simple [](https://www.veed.io/) Make stunning videos with a single click. Cut, trim, crop, add subtitles and more. Online, no account needed. Try it now, free. VEED Free Video Maker | Create & Edit Your Videos Easily - Animoto [](https://animoto.com/k/welcome) Create, edit, and share videos with our online video maker. Combine your photos, video clips, and music to make quality videos in minutes. Get started free! Runway - Online Video Editor | Everything you need to make content, fast. [](https://runwayml.com/) Discover advanced video editing capabilities to take your creations to the next level. CreatorKit - A.I. video creator for marketers [](https://creatorkit.com/) Create videos with just one click, using our A.I. video editor purpose built for marketers. Create scroll stopping videos, Instagram stories, Ads, Reels, and TikTok videos. Pixar in a Box | Computing | Khan Academy [](https://www.khanacademy.org/computing/pixar) 3D Video Motions Plask - AI Motion Capture and 3D Animation Tool [](https://plask.ai/) Plask is an all-in-one browser-based AI motion capture tool and animation editor that anybody can use, from motion designers to every day content creators. Captions Captions [](https://www.getcaptions.app/) Say hello to Captions, the only camera and editing app that automatically transcribes, captions and clips your talking videos for you. Stock videos Pexels [](https://www.pexels.com/) Pixabay [](https://pixabay.com/) Mixkit - Awesome free assets for your next video project [](https://mixkit.co/) Download Free Stock Video Footage, Stock Music & Premiere Pro Templates for your next video editing project. All assets can be downloaded for free! Free Stock Video Footage HD 4K Download Royalty-Free Clips [](https://www.videvo.net/) Download free stock video footage with over 300,000 video clips in 4K and HD. We also offer a wide selection of music and sound effect files with over 180,000 clips available. Click here to download royalty-free licensing videos, motion graphics, music and sound effects from Videvo today. Free Stock Video Footage HD Royalty-Free Videos Download [](https://mazwai.com/) Download free stock video footage with clips available in HD. Click here to download royalty-free licensing videos from Mazwai now. Royalty Free Stock Video Footage Clips | Vidsplay.com [](https://www.vidsplay.com/) Royalty Free Stock Video Footage Clips Free Stock Video Footage, Royalty Free Videos for Download [](https://coverr.co/) Download royalty free (for personal and commercial use), unique and beautiful video footage for your website or any project. No attribution required. Stock Photos Beautiful Free Images & Pictures | Unsplash [](https://unsplash.com/) Beautiful, free images and photos that you can download and use for any project. Better than any royalty free or stock photos. When we share, everyone wins - Creative Commons [](https://creativecommons.org/) Creative Commons licenses are 20! Honoring 20 years of open sharing using CC licenses, join us in 2022 to celebrate Better Sharing — advancing universal access to knowledge and culture, and fostering creativity, innovation, and collaboration. Help us reach our goal of raising $15 million for a future of Better Sharing.  20 Years of Better … Read More "When we share, everyone wins" Food Pictures • Foodiesfeed • Free Food Photos [](https://www.foodiesfeed.com/) Download 2000+ food pictures ⋆ The best free food photos for commercial use ⋆ CC0 license Free Stock Photos and Images for Websites & Commercial Use [](https://burst.shopify.com/) Browse thousands of beautiful copyright-free images. All our pictures are free to download for personal and commercial use, no attribution required. EyeEm | Authentic Stock Photography and Royalty-Free Images [](https://www.eyeem.com/) Explore high-quality, royalty-free stock photos for commercial use. License individual images or save money with our flexible subscription and image pack plans. picjumbo: Free Stock Photos [](https://picjumbo.com/) Free stock photos and images for your projects and websites.️ Beautiful 100% free high-resolution stock images with no watermark. Free Stock Photos, Images, and Vectors [](https://www.stockvault.net/) 139.738 free stock photos, textures, backgrounds and graphics for your next project. No attribution required. Free Stock Photos, PNGs, Templates & Mockups | rawpixel [](https://www.rawpixel.com/) Free images, PNGs, stickers, backgrounds, wallpapers, graphic templates and PSD mockups. All safe to use with commercial licenses. Free Commercial Stock Photos & Royalty Free Images | PikWizard [](https://pikwizard.com/) Free images, videos & free stock photos. Unlimited downloads ✓ Royalty-free Images ✓Copyright-free for commercial use ✓ No Attribution Required Design Bundles [](https://designbundles.net/) Stock music Royalty Free Music for video creators | Epidemic Sound [](https://www.epidemicsound.com/) Download premium Royalty free Music and SFX! Our free trial gives you access to over 35,000 tracks and 90,000 sound effects for video, streaming and more! Royalty-Free Music & SFX for Video Creators | Artlist [](https://artlist.io/) Explore the ultimate royalty-free music & sound effects catalogs for unlimited use in YouTube videos, social media & films created by inspiring indie artists worldwide. The go-to music licensing choice for all creators Royalty Free Audio Tracks - Envato Elements [](https://elements.envato.com/audio) Download Royalty Free Stock Audio Tracks for your next project from Envato Elements. Premium, High Quality handpicked Audio files ideal for any genre. License popular music for videos • Lickd [](https://lickd.co/) The only place you can license popular music for videos. Access 1M+ mainstream tracks, plus high-quality stock music for content creators NCS (NoCopyrightSounds) - free music for content creators [](https://ncs.io/) NCS is a Record Label dedicated to giving a platform to the next generation of Artists in electronic music, representing genres from house to dubstep via trap, drum & bass, electro pop and more. Search Engine Optimization Keyword Tool For Monthly Search Volume, CPC & Competition [](https://keywordseverywhere.com/) Keywords Everywhere is a browser add-on for Chrome & Firefox that shows search volume, CPC & competition on multiple websites. Semrush - Online Marketing Can Be Easy [](https://www.semrush.com/) Turn the algorithm into a friend. Make your business visible online with 55+ tools for SEO, PPC, content, social media, competitive research, and more. DuckDuckGo — Privacy, simplified. [](https://duckduckgo.com/) The Internet privacy company that empowers you to seamlessly take control of your personal information online, without any tradeoffs. SEO Software for 360° Analysis of Your Website [](https://seranking.com/) Leading SEO software for business owners, agencies, and SEO specialists. Track your rankings, monitor competitors, spot technical errors, and more. Skyrocket your organic traffic with Surfer [](https://surferseo.com/) Use Surfer to research, write, optimize, and audit! Everything you need to create a comprehensive content strategy that yields real results is right here. Ahrefs - SEO Tools & Resources To Grow Your Search Traffic [](https://ahrefs.com/) You don't have to be an SEO pro to rank higher and get more traffic. Join Ahrefs – we're a powerful but easy to learn SEO toolset with a passionate community. Neon Tools [](https://neontools.io/) Google Index Search [](https://lumpysoft.com/) Google Index Search SEO Backlink Checker & Link Building Toolset | Majestic.com [](https://majestic.com/) Develop backlink strategies with our Link Intelligence data, build the strongest SEO backlink campaigns to drive organic traffic and boost your rankings today. 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Fast. Trusted by Top Companies. Free Keyword Research Tool from Wordtracker [](https://www.wordtracker.com/) The best FREE alternative to the Keyword Planner. Use Wordtracker to reveal 1000s of profitable longtail keywords with up to 10,000 results per search Blog Posts The 60 Hottest Front-end Tools of 2021 | CSS-Tricks - CSS-Tricks [](https://css-tricks.com/hottest-front-end-tools-in-2021/) A complete list of the most popular front-end tools in 2021, according to the Web Tools Weekly newsletter. See which resources made the list. Resume ResumeGlow - AI Powered Resume Builder [](https://resumeglow.com/) Get hired fast with a resume that grabs attention. Designed by a team of HR experts and typographers. Customizable templates with more than a million possible Create Your Job-winning Resume - (Free) Resume maker · Resume.io [](https://resume.io/) Free online resume maker, allows you to create a perfect Resume or Cover Letter in 5 minutes. 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Use Unbounce to easily create and optimize landing pages for your small business and boost conversions with AI insights. Low-code Front-end Design & Development Platform | TeleportHQ [](https://teleporthq.io/) Front-end development platform, with a visual builder and headless content modelling capabilities. Static website creation, and UI development tools. Other tools used in no code website MemberSpace - Turn any part of your website into members-only with just a few clicks [](https://www.memberspace.com/) Create memberships on your website for anything you want like courses, video tutorials, member directories, and more while having 100% control over look & feel. Triggre | The number one true no-code platform to run your business [](https://www.triggre.com/) The best no-code platform to create highly advanced business applications in hours, without programming. Try it now for free! 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Automate website actions and repetitive tasks using just your browser, on any website or web app. No Code Browser extensions builder Bildr - Visual Web Development in your Browser [](https://www.bildr.com/) Visually build SaaS products, Chrome extensions, and web3 dApps Other Repurposing content for social media the easy way » Repurpose.io [](https://repurpose.io/) Repurposing content for social media made easy. Automatically repurpose YouTube, TikTok, Lives, Podcasts, and Zoom calls. Try it for FREE. Smart Serials: Your serial numbers database [](https://smartserials.com/) This is your main source of free serial numbers, unlock keys in a clean environment safe to browse by all ages. 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Filter Websites Audio Business Tools Copywriting Design Entertainment Graphics Guides Health Marketing PC Resources Savings SEO Software Travel Video Apply filter Watch Anime Online, Free Anime Streaming Online on Zoro.to Anime Website [](https://zoro.to/) Zoro is a Free anime streaming website which you can watch English Subbed and Dubbed Anime online with No Account and Daily update. WATCH NOW! Animated Drawings [](https://sketch.metademolab.com/) Bring children's drawings to life, by animating characters to move around! Alternativeto [](https://alternativeto.net/) Chatroulette [](https://chatroulette.com/) Random meetings around the world Tiktok Downloader - Download Video tiktok Without Watermark - SnapTik [](https://snaptik.app/en) TikTok Video Downloader - SnapTik.App is one of the best free Download video Tiktok No Watermark tool available online. You can download TikTok video from any device you have. 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#github#internet-tools-collection

youtube

0.274

0.29

Chill Music Lab•Jan 22, 2025

Chill Work Music — Deep Focus Mix for Working, Programming, Coding

This carefully curated mix of tracks is designed specifically to help you focus on programming and coding. Deep, energizing electronic music in genres like chillstep and future garage creates the perfect background for working on complex projects or completing routine tasks. This carefully crafted selection is ideal for creative endeavors, adding atmosphere, enhancing motivation, and setting a positive mood for work or any activity. Thanks to the futuristic atmosphere of this musical accompaniment, you will be able to immerse yourself in the creative process with special depth and inspiration. These tracks help maintain a high level of concentration and productivity, enabling you to achieve maximum results. Discover new horizons of efficiency with our specially curated musical accompaniment. If you enjoyed this video like, comment or subscribe to the channel. 🙏 Join our Discord to contact us, grow, share your goals and find inspiration. Work together to turn ideas into reality. https://discord.gg/Tfe8gnAhYY Genre: Electrtonic Music Style: Chillout, Chillstep, Downtempo, Future Garage Mood: Inspiring, Motivating, Deep Feature: Without Lyrics 📹 Similar videos ► /https://www.youtube.com/playlist?list=PLdE7uo_7KBkf6X1lbOpL3tAWERvlYej2L ► /https://www.youtube.com/playlist?list=PLdE7uo_7KBkeSTmryNClNxUkioFpq3Btx ► /https://www.youtube.com/playlist?list=PLdE7uo_7KBkdbssGgnnIDm3EnE2gmHyEQ ► /https://www.youtube.com/playlist?list=PLdE7uo_7KBkeH0adsnxZupMARfGxY6qik ► /https://www.youtube.com/playlist?list=PLdE7uo_7KBkf0gwWO9-qeu-La5vSJPmPc ► /https://www.youtube.com/playlist?list=PLdE7uo_7KBkdsNAZNbzOUj61OQ5N0Ka26 🎧 Tracklist ► 00:00 Nomyn - Elsewhere ► 03:36 Sappheiros - Stay ► 06:36 Veil - Sorrow ► 09:56 Kazukii - It's Been Awhile ► 11:21 AK - Departure ► 13:57 Lazarus Moment - Reminescence ► 19:29 Nereus - Pathwaysh ► 22:39 Vigo - Bright Light ► 25:57 Osk - Komorebi ► 30:25 Phelian - Wound ► 33:12 4lienetic - If Only ► 35:40 Andy Leech, Victoriya - Aerolith ► 38:39 Bythewvve - Dreams Come True ► 41:01 Faodail - Far From Anyone ► 44:30 Mit-Rich - Timestream ► 46:41 Twisted Psykie - Mourning Glory ► 50:16 Oscuro - Can't Find My Way ► 53:41 Future Skyline - Silent Moon ► 57:45 Hollowz - Children (Cover) ► 1:00:45 Oscuro - Now Or Never ► 1:03:33 Alchemorph - Angel ► 1:06:53 Nikita Mirnyy - Souless ► 1:08:24 Nomyn - Elsewhere ► 1:11:58 Sappheiros - Stay ► 1:14:58 Veil - Sorrow ► 1:18:17 Kazukii - It's Been Awhile ► 1:19:43 AK - Departure ► 1:22:19 Lazarus Moment - Reminescence ► 1:27:50 Nereus - Pathwaysh ► 1:31:00 Vigo - Bright Light ► 1:34:19 Osk - Komorebi ► 1:38:46 Phelian - Wound ► 1:41:33 4lienetic - If Only ► 1:44:02 Andy Leech, Victoriya - Aerolith ► 1:47:00 Bythewvve - Dreams Come True ► 1:49:22 Faodail - Far From Anyone ► 1:52:51 Mit-Rich - Timestream ► 1:55:02 Twisted Psykie - Mourning Glory ► 1:58:38 Oscuro - Can't Find My Way ► 2:02:03 Future Skyline - Silent Moon ► 2:06:07 Hollowz - Children (Cover) ► 2:09:06 Oscuro - Now Or Never ► 2:11:55 Alchemorph - Angel ► 2:15:14 Nikita Mirnyy - Souless ► 2:16:46 Nomyn - Elsewhere ► 2:20:19 Sappheiros - Stay ► 2:23:19 Veil - Sorrow ► 2:26:39 Kazukii - It's Been Awhile ► 2:28:04 AK - Departure ► 2:30:40 Lazarus Moment - Reminescence ► 2:36:11 Nereus - Pathwaysh ► 2:39:21 Vigo - Bright Light #ConcentrationMusic #CodingMusic #FocusMusic

#ConcentrationMusic#CodingMusic#FocusMusic

github

0.457

0.07953823122984799

nahueespinosa•Jan 17, 2025

ai50

My work on CS50’s Introduction to AI with Python https://cs50.harvard.edu/ai/ This course explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, students gain exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence and machine learning as they incorporate them into their own Python programs. By course’s end, students emerge with experience in libraries for machine learning as well as knowledge of artificial intelligence principles that enable them to design intelligent systems of their own. Certificate: https://courses.edx.org/certificates/2ec5ff3f06b24bb595c21e3821591538 Notes I've taken some notes on key concepts and algorithms throughout the lectures for future reference. Lecture 0: Search Concepts Agent: entity that perceives its environment and acts upon that environment. State: a configuration of the agent and its environment. Actions: choices that can be made in a state. Transition model: a description of what state results from performing any applicable action in any state. Path cost: numerical cost associated with a given path. Evaluation function: function that estimates the expected utility of the game from a given state. Algorithms DFS (depth first search): search algorithm that always expands the deepest node in the frontier. BFS (breath first search): search algorithm that always expands the shallowest node in the frontier. Greedy best-first search: search algorithm that expands the node that is closest to the goal, as estimated by an heuristic function h(n). A\* search: search algorithm that expands node with lowest value of the "cost to reach node" plus the "estimated goal cost". Minimax: adversarial search algorithm. Projects Degrees Tic-Tac-Toe Lecture 1: Knowledge Concepts Sentence: an assertion about the world in a knowledge representation language. Knowledge base: a set of sentences known by a knowledge-based agent. Entailment: a entails b if in every model in which sentence a is true, sentence b is also true. Inference: the process of deriving new sentences from old ones. Conjunctive normal form: logical sentence that is a conjunction of clauses. First order logic: Propositional logic. Second order logic: Proposition logic with universal and existential quantification. Algorithms Model checking: enumerate all possible models and see if a proposition is true in every one of them. Conversion to CNF and Inference by resolution Projects Knights Minesweeper Lecture 2: Uncertainty Concepts Unconditional probability: degree of belief in a proposition in the absence of any other evidence. Conditional probability: degree of belief in a proposition given some evidence that has already been revealed. Random variable: a variable in probability theory with a domain of possible values it can take on. Independence: the knowledge that one event occurs does not affect the probability of the other event. Bayes' Rule: P(a) P(b|a) = P(b) P(a|b) Bayesian network: data structure that represents the dependencies among random variables. Markov assumption: the assumption that the current state depends on only a finite fixed number of previous states. Markov chain: a sequence of random variables where the distribution of each variable follows the Markov assumption. Hidden Markov Model: a Markov model for a system with hidden states that generate some observed event. Algorithms Inference by enumeration Sampling Likelihood weighting Projects Heredity PageRank Lecture 3: Optimization Concepts Optimization: choosing the best option from a set of options. Algorithms Local Search Hill climbing steepest-ascent: choose the highest-valued neighbor. stochastic: choose randomly from higher-valued neighbors. first-choice: choose the first higher-valued neighbor. random-restart: conduct hill climbing multiple times. local beam search: chooses the k highest-valued neighbors. Simulated annealing: early on, more likely to accept worse-valued neighbors than the current state. Linear programming Simplex Interior-Point Constraint satisfaction problems Arc consistency: to make X arc-consistent with respect to Y, removing elements from X's domain until every choice for X has a possible choice for Y Backtracking search Projects Crossword Lecture 4: Learning Concepts Supervised learning: given a data set of input-output pairs, learn a function to map inputs to outputs. Classification: supervised learning task of learning a function mapping an input point to a discrete category. Regression: supervised learning task of learning a function mapping and input point to a continuous value. Loss function: function that express how poorly our hypothesis performs (L1, L2). Overfitting: when a model fits too closely to a particular data set and therefore may fail to generalize to future data. Regularization: penalizing hypotheses that are more complex to favor simpler, more general hypotheses. Holdout cross-validation: splitting data into a training set and a test set, such that learning happens on the training set and is evaluated on the test set. k-fold cross-validation: splitting data into k sets, and experimenting k times, using each set as a test set once, and using remaining data as training set. Reinforcement learning: given a set of rewards or punishments, learn what actions to take in the future. Unsupervised learning: given input data without any additional feedback, learn patterns. Clustering: organizing a set of objects into groups in such a way that similar objects tend to be in the same group. Algorithms k-nearest-neighbor classification: given an input, chooses the most common class out of the k nearest data points to that input. Support Vector Machines (SVM) Markov decision process: model for decision-making, representing states, actions and their rewards. Q-learning: method for learning a function Q(s, a), estimate of the value of performing action a in state s. Greedy decision-making epsilon-greedy k-means clustering: clustering data based on repeatedly assigning points to clusters and updating those clusters' centers. Projects Shopping Nim Lecture 5: Neural Networks Concepts Artificial neural network: mathematical model for learning inspired by biological neural networks. Multilayer neural network: artificial neural network with an input layer, an output layer, and at least one hidden layer. Deep neural network: neural network with multiple hidden layer. Dropout: temporarily removing units - selected at random - from a neural network to prevent over-reliance on certain units. Image convolution: applying a filter that adds each pixel value of an image to its neighbors, weighted according to a kernel matrix. Pooling: reducing the size of an input by sampling from regions in the input. Convolutional neural network: neural networks that use convolution, usually for analyzing images. Recurrent neural network: neural network that generates output that feeds back into its own inputs. Algorithms Gradient descent: algorithm for minimizing loss when training neural network. Backpropagation: algorithm for training neural networks with hidden layers. Projects Traffic Lecture 6: Language Concepts Natural language processing n-gram: a continuous sequence of n items inside of a text. Tokenization: the task of splitting a sequence of characters into pieces (tokens). Text Categorization Bag-of-words model: represent text as an unordered collection of words. Information retrieval: the task of finding relevant documents in response to a user query. Topic modeling: models for discovering the topics for a set of documents. Term frequency: number of times a term appears in a document. Function words: words that have little meaning on their own, but are used to grammatically connect other words. Content words: words that carry meaning independently. Inverse document frequency: measure of how common or rare a word is across documents. Information extraction: the task of extracting knowledge from documents. WordNet: a lexical database of semantic relations between words. Word representation: looking for a way to represent the meaning of a word for further processing. one-hot: representation of meaning as a vector with a single 1, and with other values as 0. distribution: representation of meaning distributed across multiple values. Algorithms Markov model applied to language: generating the next word based on the previous words and a probability. Naive Bayes: based on the Bayes' Rule to calculate probability of a text being in a certain category, given it contains specific words. Assuming every word is independent of each other. Additive smoothing: adding a value a to each value in our distribution to smooth the data. Laplace smoothing: adding 1 to each value in our distribution (pretending we've seen each value one more time than we actually have). tf-idf: ranking of what words are important in a document by multiplying term frequency (TF) by inverse document frequency (IDF). Automated template generation: giving AI some terms and let it look into a corpus for patterns where those terms show up together. Then it can use those templates to extract new knowledge from the corpus. word2vec: model for generating word vectors. skip-gram architecture: neural network architecture for predicting context words given a target word. Projects Parser Questions

#github#ai50

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acatai•Jan 16, 2025

Strategy-Card-Game-AI-Competition

Strategy Card Game AI Competition The competition is no longer organized Article about the SCGAI competition history: Summarizing Strategy Card Game AI Competition Jakub Kowalski, Radosław Miernik; IEEE Conference on Games, 2023 [[cite]](https://jakubkowalski.tech/Publications/Kowalski2023SummarizingStrategy.bib) We are organizing a new cardgame-related contest: Tales of Tribute AI Competition !/game artwork/ Getting Started Partially deprecated - work in progress. Online play You can play LOCM 1.5 online, via the CodinGame IDE (click "PREVIEW" to run IDE and see game rules). Source code can be written directly in IDE or you can use CG Sync to synchronize IDE code with your local file Offline referee To run the game engine locally you can use the referee1.5 jar. The most important options are: -p1 where you put command to run your local first player bot -p2 where you put command to run your local second player bot -s (optional) to run server with visualization (at http://localhost:8888/test.html) Thus, example usage is: Running the jar without any options will show the extended readme. Example player algorithms can be found here. Source code of the referee is here. Offline runner To run multiple instances of the game you can use the nim-based runner documented here. Added 1.5 jar to referee1.5-java, moved 1.2 jar from nim directory to java

#github#strategy-card-game-ai-competition

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Cosmic Hippo•Jan 13, 2025

Flow State - Chillstep & Synthwave for Deep Focus | Coding Session

You can get the artwork featured in this video as a digital download on Etsy here: https://www.etsy.com/listing/1858057766/flow-state Enter your flow state with this seamless blend of chillstep and synthwave, crafted for deep focus during coding sessions, studying, or creative projects. These immersive beats and atmospheric melodies are designed to help you stay in the zone, eliminate distractions, and power through your tasks with ease. Perfect for late-night work, programming marathons, or moments when you need clarity and concentration, this playlist will keep you motivated and inspired. Let the combination of chillstep’s relaxing tones and synthwave’s retro-futuristic vibes guide your productivity. If you enjoy this playlist, remember to like, comment, and subscribe for more music. Your support means the world! Tracklist 0:00 Deep in Focus 3:19 Ethereal Flow 6:39 Pulse of Clarity 10:00 Boundless Focus 13:51 Calm Horizons 16:07 Clarity Cascade 20:07 Digital Calm 23:49 Evening Flow 27:27 Flow Patterns 30:27 Infinite Path 33:00 Harmonic Clarity 36:58 Lucid Beats 40:27 Luminous Thoughts 42:59 Momentum 46:02 Sonic Horizon 48:55 Still Momentum 51:27 Tranquil State 54:47 Waves of Productivity 57:37 Refined Energy 01:00:21 Zenith Flow Tags: #flowstate #chillstep #synthwave #codingmusic #focusbeats #deepfocus #productivitymusic #studymusic #workbeats #synthwavevibes #relaxingmusic #codingplaylist #electronicbeats #programmingmusic #codingsession #productivitymusic #chill #nolyrics #instrumental

#flowstate#chillstep#synthwave

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theskeletoncrew•Jan 10, 2025

air-support

!air-support Air Support: Tools for Automating Airdrops of Solana NFTs The Skeleton Crew | Twitter: @skeletoncrewrip | Discord: Skeleton Crew Feeling generous? Your contributions help fund future development. Send tips to our Solana wallet: CH6afYjjydFLPSrfQYEUNCdSNohLCAQV6ir6QnYeZU3t See also: Treat Toolbox, a generative art manager for NFT projects from the Skeleton Crew. Background The Skeleton Crew launched on Oct 1, and has since been delivering daily airdrops of artwork from indie artists, with plans to continue for the entire month of October. In order to execute on this plan, we needed tools that allowed us to automate the process. This repository is the result of that effort, which we now share with you in the hopes of more teams spending less time giving themselves Carpal tunnel syndrome doing all of this manually inside of Phantom :) IMPORTANT - Before you Start Creating and sending NFTs in bulk comes with costs. On Solana, the costs are significantly better than some other chains. BUT, it's a good idea to try a drop on devnet first to be sure you understand the fees involved. We assume no responsibility for any costs incurred through the use of these tools. Use at your own risk. Getting Started In order to use Air Support, you will need to install and configure the current version of Metaplex. We run this locally with some customizations for speed (ex. hardcoding some metadata which is common across all of our drops). Also, have a look at the configuration options at the top of the Makefile. At minimum, you'll need to specify paths to Metaplex, your keyfile, and an RPC Host. It's highly recommended that you use a third-party RPC provider to perform large airdrops. DROP is a name for a set of airdrops; in our case we numbered these 1-31 for each day in October. TYPE is a name for a single airdropped item that's part of a drop; in our case we had a "trick" and a "treat" as part of each drop, sometimes even "trick1", "trick2"... etc. The name will be "token" by default, and is used to prefix log files in each step below. For the generate step to work, you will need to build Metaplex's rust tools. Inside metaplex/rust, run: You will also need a few other pieces of software installed, including: gshuf: brew install coreutils jq: brew install jq How to Use Air Support Prerequisites: follow all steps in the Getting Started section above. Then, the basic workflow looks something like this: 📇 prepare: Collect a list of token mint addresses, for which the holders of those tokens represent a community you wish to airdrop to. This is sometimes done by providing your Candy Machine address to https://tools.abstratica.art. Store this in the air support root directory as token-mint-addresses.json. ✍️ record: run this to fetch the wallet addresses of all users that hold the tokens, and don't have them listed on a secondary exchange. The goal here is to avoid sending airdrops to exchanges where they may not be recoverable. Note: As of now, Air Support can only identify tokens listed on Digital Eyes, Magic Eden, Solanart, and Alpha.art. FTX and Solsea use unique addresses for escrow wallets. The command below will fetch the addresses and store them in airdrops/1/token-holders.log. 🎨 create: Start Metaplex, and use it to create your Master Edition NFT with a limited supply (the number of airdrops you want to send). 🖨 generate: run this to generate prints of the Master Edition. These will be stored in the wallet associated with the keys you specify as options. The below command would create 500 prints of the Master with mint address RPdCMRxBx4YPcJv6HUb2S5zHGJcDrDrZszUNNGmLwfT. 🏅 choose: run this next to decide who will receive the airdrop. Important to note that if 2 tokens are owned by the same wallet, by design they have twice the chance to receive an airdrop as someone with only 1 token when using this script to pick recipients. If you have 10,000 token owners recorded as not listed on marketplaces in step 2, and 500 airdrops to send, this will randomly select 500 of those recorded tokens. 📬 distribute: the last step is to send the airdrops out. This script will run through the addresses generated in step 4 and the recipients chosen in step 5 and send airdrops 1-by-1. It is possible that failures will occur. Logs are saved during the process in a {NAME}_sent.log file. Because distribution happens line-by-line, it is safe to rerun the script again to attempt to correct failures. You can also check your wallet to see that all tokens have been distributed. (Note that your Master edition will still remain as only prints are recorded to be sent in step 4. You can keep these for yourself or a community vault.) There is also an optional STARTINDEX param that can be used if you need to restart a distribution from somewhere in the middle. 🔥 burn: if you realize you made a mistake on your Master NFT, but only after you went ahead and started printing a bunch of editions, this command will automate the process of sending those costly mistakes to the Solana incinerator. There is also an optional STARTINDEX param that can be used if you need to restart a distribution from somewhere in the middle. Other Tips Transparency is key when running airdrop campaigns to your communities. In an ideal world, where we had more than 24 hours between our launch and the start of our month of airdrops, we might have attempted to bring some or all of these processes on-chain. The next best thing we could offer is a transparency repo, where we publish the daily receipts of our airdrops, to make it easy for our community to investigate the drops on the blockchain if they feel the desire to do so. Our tools give you the receipts as output to do the same if you wish. You can have a look at that repo here: https://github.com/theskeletoncrew/airdrop-transparency Acknowledgements The record step utilizes code created by the Exiled Apes organization, shared under an Apache License, originally found here: https://github.com/exiled-apes/exiled-holders

#github#air-support

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Cosmic Hippo•Dec 24, 2024

Coding Session in the Snowy Mountains - Chillstep & Chillwave for Winter Focus

The image featured in this video is available as a digital print on Etsy: https://www.etsy.com/listing/1834213950/coding-session-in-the-snowy-mountains Escape to a serene winter retreat. This playlist weaves together calming chillstep and atmospheric chillwave beats, creating the perfect environment for productivity and inspiration amidst a snowy landscape. Imagine coding in a cozy cabin, surrounded by towering, snow-covered peaks and the crisp, silent air of the mountains. The music mirrors the peaceful energy of the scene, helping you stay focused while coding, studying, or simply reflecting on creative projects. Whether you're tackling late-night tasks or enjoying a quiet moment of clarity, this mix is your ultimate companion for deep concentration and relaxation. Tune in, let the winter vibes surround you, and find your flow amidst the snow. Tracklist 0:00 Icy Reverie 3:38 Glacial Flow 5:20 Alpine Reflections 9:21 Glacial Glow 12:56 Frozen Tranquility 15:53 Blizzard Beats 19:39 White Mirage 23:30 Frosted Threads 27:32 Frozen Focus 30:59 Wandering Stars 33:25 Whispering Pines 37:14 Beneath the Frost 40:56 Falling Flurries 44:46 Frost and Firelight 47:18 Pinewood Echoes 50:40 Snowbound Serenity Tags: #CodingMusic #Chillstep #Chillwave #WinterFocus #SnowyMountains #StudyBeats #AmbientMusic #DeepFocus #WinterVibes #RelaxingBeats #ProductivityMusic #Christmas #codingsession #cosyatmosphere #cozybeats Disclaimer: This music has been created with the help of AI tools.

#CodingMusic#Chillstep#Chillwave

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trokas•Nov 20, 2024

ai_primer

Welcome to AI primer course INTERACTIVE BOOK LINK Main aim of this course is to give you enough information so that you can start exploring field of AI on your own and maybe even start searching for DS role. We have only 5 main chapters and one bonus lecture to cover. Unsupervised learning SVD (Singular Value Decomposition) - it’s a good tool to introduce both technical tools we will be working with as well as giving us a glimpse at unsupervised learning. Supervised learning RF (Random Forests) - one of the first “silver bullets” out there. Our discussion will also cover Shannon’s work on entropy as it’s one of the key ingredients. Deep learning DNN (Deep Neural Networks) - we will build our own Perceptron from scratch, thus focusing on gradient descent and backprop on the way. By changing activation function logistic regression will be introduced and finally we will explore what a stack of layers (deep NN) can offer. CNN (Convolutional Neural Networks) - even though different techniques come and go in deep learning world I strongly believe that CNN’s will be around for quite some time to come. We will use them not only for images, but also for time series prediction. Attention - powerful idea that stands behind Transformers and one of the enablers for GPT-3, DALL-E 2 and others. Reinforcement Learning (bonus lecture) TD (Temporal Difference) - one of the core principles in reinforcement learning. We will apply it to play tic-tac-toe. Also we will cover following toolset, which hopefully will be useful for your future projects: numpy (mainly in SVD and FCN lectures) - will help us store vectors, matrices and perform operations on them. matplotlib (in all lectures) - nice and simple plotting lib. scikit-learn - ML library. pandas (mainly in RF lecture) - structured way of looking at tabular data. PyTorch (FCN and CNN lectures) - simple deep learning library based on tensorflow. git (final project) - version control tool. Toolset will be presented only in lectures, thus it’s up to you to learn them on your own if you do not plan to attend. There are a lot of resources, but I highly suggest to read intros in corresponding docs. What to expect from a single lecture? There will be no clear distinction between theory and practice, thus you should have your PC ready for small assignments that you will encounter on the way. Most important material will be listed here, but during lectures you will hear and see a lot of complementary material. Each lecture will end with a list of resources (some of them mandatory). We will start a new lecture with a recap of what was done last time and discussion regarding mentioned resources in the hope to deepen understanding in the subject and inspire you to search for sources and publications yourself. Launching notebooks You can launch notebooks while in interactive book by simply pressing the rocket logo and choosing Colab. To get faster run times click Runtime and Change runtime type, then select GPU or TPU. If necessary you can install missing packages by running !pip install [package name] directly in the notebook. NOTE: Colab will not save your changes between sessions! Download the notebook or save a copy in Google Drive before closing the browser. If you want to open notebooks locally (for a quick preview) you might find nteract useful. As an alternative you can use non free, but cheap options like Jarvislabs or Paperspace. Actually Paperspace has free GPU option, but often it is not available. (re)Sources Each chapter will have a list of resources, but for now I highly recommend to start listening/watching following resources on your spare time: Data Skeptic podcast Artificial Intelligence podcast Two Minute Papers youtube channel If I had to recommend a single book for beginner it will be this one - Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition.

#github#ai_primer

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Cosmic Hippo•Nov 11, 2024

3 A.M Coding Session - Chillstep Beats to Keep You Going

The image featured in this video is available as a digital print on Etsy: https://www.etsy.com/listing/1816366216/3-am-coding-session Welcome to the ultimate late-night programming session! Designed for night owls and dedicated developers, this video is perfect for those quiet hours of coding, debugging, and designing when focus comes naturally. These chillstep beats offer the ideal background for cyber work, programming projects, or any creative task that requires steady concentration at 3 A.M. With a smooth blend of atmospheric chillstep and ambient sounds, this playlist creates a deep, immersive vibe that enhances both productivity and relaxation. Perfect for all-nighters, marathon work sessions, or simply winding down with a productive beat, this is the music to keep you motivated without distraction. The tranquil sounds help you get lost in your work, making each line of code, creative idea, or cyber project flow effortlessly. Let these chillstep beats guide you through late-night moments of intense problem-solving and brainstorming. Find your rhythm, and stay grounded, inspired, and focused in the calm of the night. Tracklist 0:00 Code Flow 3:59 Afloat in Dreams 7:41 Code Dream 11:07 Cosmic Code 15:08 Digital Daydream 18:27 Digital Trance 21:54 Ethereal Daydream 25:55 Ethereal Dream 29:55 Floating on Stardust 32:36 Flowing Codes 36:36 Infinite Flow 40:36 Neon Dreams 44:36 Quantum Blanket 48:38 Serene State 52:39 Waves of Focus Ideal for: Coding, programming, and cyber projects Deep work and all-nighters Studying, journaling, or creative flow Late-night relaxation and concentration Take a deep breath, settle into the rhythm, and let this 3 A.M coding session carry you through. You’ve got this! Tags: #3am #programmingbeats #CyberFocus #codingmusic #chillstepmusic #ProductiveFlow #ambientfocus #codingsession #allnighter #latenightwork #programming #workbeats #chessmusic #studymusic #tradingmusic #codingmusic #gamingmusic Disclaimer: This music has been created with the help of AI tools.

#3am#programmingbeats#CyberFocus

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Intellipaat•Aug 24, 2024

Best Programming Language For AI in 2024 | Intellipaat #Shorts #AI #Python

Curious about the Best Programming Language for AI in 2024? 🤖 In this #Shorts video, we explore the top language you should learn if you want to dive into the world of Artificial Intelligence. Whether you’re just starting out or looking to expand your skills, understanding the best tools for AI development is crucial. Watch to find out why Python continues to dominate the AI landscape and what makes it the go-to choice for developers. #BestProgrammingLanguageForAI #AI #Python #ArtificialIntelligence #ShortsVideo #ShortsFeed #ShortsFeedVideo #ShortsFeedViral #Intellipaat ✅ What makes Python the best programming language for AI in 2024? Python is considered the best programming language for AI in 2024 due to its simplicity, extensive libraries, and active community support. Its libraries like TensorFlow, PyTorch, and scikit-learn make it easier to implement complex algorithms and work with large datasets. Additionally, Python's readability and flexibility make it a favorite among developers working on AI projects, enabling rapid prototyping and development. ✅ Why is choosing the right programming language important for AI development? Choosing the right programming language is crucial for AI development because it impacts the efficiency and scalability of your projects. The right language should offer powerful tools, libraries, and frameworks that simplify AI tasks like data processing, machine learning, and natural language processing. Python, for instance, excels in these areas, making it the preferred choice for AI and ensuring that your projects are built on a solid, efficient foundation.

#Shorts#BestProgrammingLanguageForAI#AI

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Chill Music Lab•Aug 19, 2024

Music for Work — Deep Focus Mix for Programming, Coding

This carefully curated mix of tracks is designed specifically to help you focus on programming and coding. Dark and cyber electronic music in genres like chillstep and future garage will create the perfect background for working on complex projects or completing routine tasks. Thanks to the futuristic atmosphere of this musical accompaniment, you will be able to immerse yourself in the creative process with special depth and inspiration. These tracks will help you maintain a high level of concentration and productivity to achieve maximum results. Discover new horizons of efficiency with our specially curated musical accompaniment 🎯Tips for Deep Focus and Concentration: Movement Meditation: Try practicing movement meditation, such as yoga, tai chi, or walking in a natural environment. This will help clear your mind, improve focus, and reduce stress levels. Polyphasic Sleep: Explore polyphasic sleep methods, which involve taking short periods of sleep throughout the day to enhance your concentration and productivity. Some people find that it allows them to reduce overall sleep time and remain more alert during the day. Color Therapy: Use colors to manage your mood and energy levels. For example, different shades of blue and green can promote calmness and focus, while bright colors like orange or red can stimulate activity and energy. Nature Contemplation Practice: Spend time outdoors, immersing yourself in the natural beauty and sounds of the environment. This can help calm your mind, reduce stress, and increase concentration. Music therapy with our Chill Music Lab playlists: Listen to our playlists or radio, which include relaxing and focusing tracks. Such music can help improve concentration and create a calm working atmosphere for your goals. If you enjoyed this video like, comment or subscribe to the channel. 🙏 Join our English-speaking Discord to get in contact with us and fellow music lovers. ❤️ https://discord.gg/5p8D8GdVfp Genre: Electronic Music Style: Future Garage, Chillstep Mood: Cyber, Deep, Atmospheric Feature: No prominent lyrics 📹 Similar videos ► /https://www.youtube.com/playlist?list=PLdE7uo_7KBkf6X1lbOpL3tAWERvlYej2L ► /https://www.youtube.com/playlist?list=PLdE7uo_7KBkeSTmryNClNxUkioFpq3Btx ► /https://www.youtube.com/playlist?list=PLdE7uo_7KBkdbssGgnnIDm3EnE2gmHyEQ ► /https://www.youtube.com/playlist?list=PLdE7uo_7KBkeH0adsnxZupMARfGxY6qik ► /https://www.youtube.com/playlist?list=PLdE7uo_7KBkf0gwWO9-qeu-La5vSJPmPc ► /https://www.youtube.com/playlist?list=PLdE7uo_7KBkdsNAZNbzOUj61OQ5N0Ka26 🎧 Tracklist ► 00:00 Blackbird - 2 Silhouette ► 03:11 Etsu - Kyouka ► 06:44 Arda Leen - Last Party Loving You, Kissing You ► 09:00 Nightblure - Left Behind ► 12:17 Lazarus Moment - In A Cabin By The Lake ► 17:10 madebytaylor - Distant w/ Zyphyr ► 19:47 Atleast We Dream - Whisper ► 22:06 Shibire - Solitude ► 24:38 Lazarus Moment - Sand Ghosts ► 28:57 Aurum - Spacesounds ► 31:49 Suerre - In Pursuit ► 34:50 Veil - Far Away ► 37:09 Kazukii - Surrender ► 39:38 Lazarus Moment - Unforgiven ► 42:56 Smokefishe - Children ► 44:23 Souns - Sun Inside the Sun (Synkro Remix) ► 50:35 Lazarus Moment - Vagrant ► 56:28 Future Skyline - Silent Moon ► 1:00:14 Infinitum - Reborn ► 1:02:55 Arnyd - Singularity ► 1:07:03 Code of Kasilid - 187 ► 1:10:34 Foxer - You ► 1:13:33 Quallm - Rain ► 1:15:12 Airshade - Maybe (Instrumental) ► 1:17:41 Fugue - Drowsiness ► 1:20:46 Oscuro - Without Your Love ► 1:23:06 Honeyruin - Let It Take You ► 1:24:46 Blackbird - 2 Silhouette ► 1:27:54 Etsu - Kyouka ► 1:31:27 Arda Leen - Last Party Loving You, Kissing You ► 1:33:43 Nightblure - Left Behind ► 1:37:00 Lazarus Moment - In A Cabin By The Lake ► 1:41:53 madebytaylor - Distant w/ Zyphyr ► 1:44:30 Atleast We Dream - Whisper ► 1:46:49 Shibire - Solitude ► 1:49:21 Lazarus Moment - Sand Ghosts ► 1:53:40 Aurum - Spacesounds ► 1:56:32 Suerre - In Pursuit ► 1:59:33 Veil - Far Away ► 2:01:52 Kazukii - Surrender ► 2:04:21 Lazarus Moment - Unforgiven ► 2:07:39 Smokefishe - Children ► 2:09:06 Souns - Sun Inside the Sun (Synkro Remix) ► 2:15:18 Lazarus Moment - Vagrant ► 2:21:11 Future Skyline - Silent Moon ► 2:24:57 Infinitum - Reborn ► 2:27:38 Arnyd - Singularity ► 2:31:46 Code of Kasilid - 187 ► 2:35:17 Foxer - You ► 2:38:16 Quallm - Rain ► 2:39:55 Airshade - Maybe (Instrumental) ► 2:42:24 Fugue - Drowsiness ► 2:45:29 Oscuro - Without Your Love ► 2:47:49 Honeyruin - Let It Take You ► 2:49:29 Blackbird - 2 Silhouette #WorkMusic #FocusMusic #CodingMusic

#WorkMusic#FocusMusic#CodingMusic

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Chill Music Lab•Jul 17, 2024

Chill Work Music — Deep Focus and Productivity Mix for Programming, Coding

This carefully curated mix of tracks is specifically designed to help you focus on work and be productive. Music in genres like chillstep, future garage, and chill electronic will create the perfect background for tackling complex projects or routine tasks. Perfect as a programming music and for intense coding sessions. Thanks to the relaxing atmosphere of this musical accompaniment, you will be able to immerse yourself in the creative process with special concentration and inspiration. These tracks will help you maintain a high level of attention and productivity to achieve maximum results. Discover new horizons of efficiency with our playlist! 🎯 Tips for Chill and Productive Work: Using Artificial Intelligence: Utilize AI tools to automate routine tasks. This will allow you to focus on more creative and complex aspects of your work. Gratitude Journal: At the end of each workday, write down three things you are grateful for. This will help you end the day on a positive note and reduce stress. Experiment with Rhythms: Try working at different times of the day. You might find that your productivity is significantly higher at night than during the day. Change of Scenery: If you feel you're losing concentration, try changing your workspace. Sit in a different chair, move to another room, or even go outside if possible. Music therapy with our Chill Music Lab playlists: Listen to our playlists or radio, which include relaxing and focusing tracks. Such music can help improve concentration and create a calm working atmosphere for your goals. If you enjoyed this video like, comment or subscribe to the channel. 🙏 Join our English-speaking Discord to get in contact with us and fellow music lovers. ❤️ https://discord.gg/5p8D8GdVfp Genre: Electronic Music Style: Chillstep, Future Garage Mood: Cyber, Deep, Atmospheric Feature: No prominent lyrics 📹 Similar videos ► https://www.youtube.com/playlist?list=PLdE7uo_7KBkf6X1lbOpL3tAWERvlYej2L ► https://www.youtube.com/playlist?list=PLdE7uo_7KBkeSTmryNClNxUkioFpq3Btx ► https://www.youtube.com/playlist?list=PLdE7uo_7KBkdbssGgnnIDm3EnE2gmHyEQ ► https://www.youtube.com/playlist?list=PLdE7uo_7KBkeH0adsnxZupMARfGxY6qik ► https://www.youtube.com/playlist?list=PLdE7uo_7KBkf0gwWO9-qeu-La5vSJPmPc ► https://www.youtube.com/playlist?list=PLdE7uo_7KBkdsNAZNbzOUj61OQ5N0Ka26 🎧 Tracklist ► 00:00 Arnydmusic - Polaris ► 03:22 Arnyd - Hypernova ► 06:58 Neskre - Saviour ► 10:24 Exal & SkyFlair - Afterlife ► 14:11 Warmth - Solstice (Aurora Principle Remix) ► 18:06 Himalia - Growing Upwards. ► 24:26 Lonely Bird - Foggy Night ► 27:19 F0x3r - Precious Little Things ► 31:09 Deadfeelings - Melancholia ► 34:42 AK - Gone ► 37:51 Skandition - Chasing A Dream ► 43:18 Foxer - You ► 46:51 4lienetic - If Only ► 49:35 Tecnosine - Capacious ► 52:36 Vonnboyd - Lost without you ► 55:16 Blackbird - Love In Purple ► 59:21 Infinitum - Reborn ► 1:02:42 Future Skyline - Silent Moon ► 1:07:12 Code of Kasilid - Proto ► 1:11:11 AK - We're Older Now ► 1:14:12 Iketa - Under ► 1:16:42 Yzuva - Forget ► 1:20:22 Direct - Millions ► 1:25:20 Lazarus Moment - Forests Calling ► 1:28:51 Hystvme - Dream ► 1:31:32 Synthetic Epiphany - Infinite ► 1:34:56 Turno - Nocturno ► 1:37:13 4Lienetic - The Most Painful Goodbye #WorkMusic #FocusMusic #ChillMusic

#WorkMusic#FocusMusic#ChillMusic

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Chill Music Lab•Jun 13, 2024

Music for Work — Deep Focus Mix for Programming, Coding

This carefully curated mix of tracks is designed specifically to help you focus on programming and coding. Dark and cyber electronic music in genres like chillstep and future garage will create the perfect background for working on complex projects or completing routine tasks. Thanks to the futuristic atmosphere of this musical accompaniment, you will be able to immerse yourself in the creative process with special depth and inspiration. These tracks will help you maintain a high level of concentration and productivity to achieve maximum results. Discover new horizons of efficiency with our specially curated musical accompaniment 🎯Tips for Deep Focus and Concentration: Movement Meditation: Try practicing movement meditation, such as yoga, tai chi, or walking in a natural environment. This will help clear your mind, improve focus, and reduce stress levels. Polyphasic Sleep: Explore polyphasic sleep methods, which involve taking short periods of sleep throughout the day to enhance your concentration and productivity. Some people find that it allows them to reduce overall sleep time and remain more alert during the day. Color Therapy: Use colors to manage your mood and energy levels. For example, different shades of blue and green can promote calmness and focus, while bright colors like orange or red can stimulate activity and energy. Nature Contemplation Practice: Spend time outdoors, immersing yourself in the natural beauty and sounds of the environment. This can help calm your mind, reduce stress, and increase concentration. Music therapy with our Chill Music Lab playlists: Listen to our playlists or radio, which include relaxing and focusing tracks. Such music can help improve concentration and create a calm working atmosphere for your goals. If you enjoyed this video like, comment or subscribe to the channel. 🙏 Join our English-speaking Discord to get in contact with us and fellow music lovers. ❤️ https://discord.gg/5p8D8GdVfp Genre: Electronic Music Style: Future Garage, Chillstep Mood: Cyber, Deep, Atmospheric Feature: No prominent lyrics 📹 Similar videos ► https://www.youtube.com/playlist?list=PLdE7uo_7KBkf6X1lbOpL3tAWERvlYej2L ► https://www.youtube.com/playlist?list=PLdE7uo_7KBkeSTmryNClNxUkioFpq3Btx ► https://www.youtube.com/playlist?list=PLdE7uo_7KBkdbssGgnnIDm3EnE2gmHyEQ ► https://www.youtube.com/playlist?list=PLdE7uo_7KBkeH0adsnxZupMARfGxY6qik ► https://www.youtube.com/playlist?list=PLdE7uo_7KBkf0gwWO9-qeu-La5vSJPmPc ► https://www.youtube.com/playlist?list=PLdE7uo_7KBkdsNAZNbzOUj61OQ5N0Ka26 🎧 Tracklist ► 00:00 Aurum - Spacesounds ► 03:10 Arnyd - Hypernova ► 06:38 Future Skyline - Silent Moon ► 10:58 Lazarus Moment - In A Cabin By The Lake ► 16:07 Nikita Mirnyy - Souless ► 17:31 Arnyd - Impermanence ► 21:00 Lazarus Moment - Reminescence ► 26:48 4lienetic - If Only ► 29:45 Lazarus Moment - Unforgiven ► 33:28 Etsu - Kyouka ► 37:15 Ecepta - By My Side ► 39:45 Etsu - Falling Apart ► 42:45 Rtik - Lone Voices ► 45:23 Hydrecta - Memories ► 49:40 Lazarus Moment - Vagrant ► 56:06 Spaceouters - Ma ► 1:00:18 Blackbird - Music Is Dead ► 1:03:02 Etsu - Auspice ► 1:05:59 Kasper Klick - Salvation ► 1:10:20 Solarwood - Afterglow ► 1:13:31 Etsuchill - Selcouth ► 1:17:45 Longinus - Indigo ► 1:20:15 Foxer - You ► 1:23:41 Temporal, Tulpa - Once Upon A Time #WorkMusic #FocusMusic #CodingMusic

#WorkMusic#FocusMusic#CodingMusic

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Shaw Talebi•Jan 20, 2024

AI for Business: A (non-technical) introduction

🗞️ Get exclusive access to AI resources and project ideas: https://the-data-entrepreneurs.kit.com/shaw 🧑‍🎓 Build practical AI projects with me in just 6 weeks: https://maven.com/shaw-talebi/ai-builders-bootcamp -- While everyone seems to be talking about AI these days, it may not be entirely clear how to use it to drive value in business. Here, I give a non-technical introduction to AI and share tips on how to use it in practice. 📰 Read more: https://medium.com/towards-data-science/ai-machine-learning-for-business-24bcf9754038 References [2] DALLE-2 [3] Canva [4] Royal Society. (2017). (rep.). Machine learning: the power and promise of computers that learn by example. Retrieved January 20, 2024, from https://royalsociety.org/~/media/policy/projects/machine-learning/publications/machine-learning-report.pdf. Introduction - 0:00 Outline - 0:37 What is AI? - 1:14 Intelligence in action - 2:13 Models - 3:37 2 Types of Models - 4:28 Machine Learning - 5:26 Example: Credit Decisioning - 7:22 5 Rules of Thumb for Using AI - 9:28 1) Focus on problems, not technologies - 10:10 2) Apply to problems you solve repeatedly - 12:47 3) "70% Solution" is good enough - 14:44 4) 1 failure doesn't erase 9 successes - 15:30 5) Start simple, fast, and easy - 16:17 Key Terms & Recap - 19:10 What's next? - 19:59 Homepage: https://www.shawhintalebi.com/

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Tina Huang•Dec 30, 2023

How To Self Study AI FAST

Want to get ahead in your career using AI? Join my FREE workshop: https://www.lonelyoctopus.com/workshop Head to http://brilliant.org/TinaHuang/ to get started for free with Brilliant's interactive lessons. The first 200 people will also get 20% off an annual membership. A video to learn AI skills for my short attention span friends who keep giving up on learning this field. ✉️ NEWSLETTER: https://tinahuang.substack.com/ It's about learning, coding, and generally how to get your sh*t together c: 🤖 AI Lunch & Learn series: https://www.lonelyoctopus.com/email-signup It's a FREE weekly 1hr livestream about AI & tech topics eg. how to build a GPT, how to build AI products, jobs in the era of AI etc. 🐙 Lonely Octopus: https://www.lonelyoctopus.com/ Check it out if you're interested in learning AI & data skill, then applying them to real freelance projects! 🤝 Business Inqueries: https://tally.so/r/mRDV99 🖱️Links mentioned in video ======================== Freecode camp for python: https://www.youtube.com/watch?v=zOjov-2OZ0E Python book: https://automatetheboringstuff.com/ Introduction to AI: https://www.youtube.com/watch?v=zjkBMFhNj_g Prompt engineering course: https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/ Josh Starmer: https://www.youtube.com/@statquest/ Math for Machine Learning: https://imp.i384100.net/math-for-ml Stanford Statistics: https://www.coursera.org/learn/stanford-statistics Brilliant Neural Network course: https://brilliant.org/courses/intro-neural-networks/ Brilliant Intermediate Deep Learning course: https://brilliant.org/courses/artificial-neural-networks/ Deep Learning Course: https://www.youtube.com/watch?v=zxagGtF9MeU&list=PLblh5JKOoLUIxGDQs4LFFD--41Vzf-ME1 Deep Learning Specialization: https://www.coursera.org/specializations/deep-learning Computer Vision Specialization: https://www.coursera.org/learn/introduction-computer-vision-watson-opencv Natural Language Processing Specialization: https://www.coursera.org/specializations/natural-language-processing Beginner project with starter code: https://github.com/fiverrhellotinah/youtubeproject 🔗Affiliates ======================== My SQL for data science interviews course (10 full interviews): https://365datascience.com/learn-sql-for-data-science-interviews/ 365 Data Science: https://365datascience.pxf.io/WD0za3 (link for 57% discount for their complete data science training) Check out StrataScratch for data science interview prep: https://stratascratch.com/?via=tina 🎥 My filming setup ======================== 📷 camera: https://amzn.to/3LHbi7N 🎤 mic: https://amzn.to/3LqoFJb 🔭 tripod: https://amzn.to/3DkjGHe 💡 lights: https://amzn.to/3LmOhqk ⏰Timestamps ======================== 00:00 intro 📲Socials ======================== instagram: https://www.instagram.com/hellotinah/ linkedin: https://www.linkedin.com/in/tinaw-h/ discord: https://discord.gg/5mMAtprshX 🎥Other videos you might be interested in ======================== How I consistently study with a full time job: https://www.youtube.com/watch?v=INymz5VwLmk How I would learn to code (if I could start over): https://www.youtube.com/watch?v=MHPGeQD8TvI&t=84s 🐈‍⬛🐈‍⬛About me ======================== Hi, my name is Tina and I'm an ex-Meta data scientist turned internet person! 📧Contact ======================== youtube: youtube comments are by far the best way to get a response from me! linkedin: https://www.linkedin.com/in/tinaw-h/ email for business inquiries only: hellotinah@gmail.com ======================== Some links are affiliate links and I may receive a small portion of sales price at no cost to you. I really appreciate your support in helping improve this channel! :)

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Thebbie-A•Dec 21, 2023

responsible-ai-hub

Responsible AI Hub Welcome to the Responsible AI Hub for Developers with all levels of expertise in AI and Machine Learning. This is a dedicated space to help the community discover relevant training resources and events to learn about Responsible AI. View Hub Website You can visit the hosted Responsible AI Hub site to learn about upcoming training events, or to explore self-guided workshops to skill up on topics like: The Responsible AI Dashboard Azure Content Safety Azure Prompt Flow Build & Preview Site Want to contribute content? Start by making sure you can build and preview the site in a relevant development environment. The project is instrumented with a dev container, making it easy to launch using either Github Codespaces (in the cloud) or Docker Desktop (in your local device). The project is built using the Docusaurus 3 static site generator. Once the container is running, use these commands to build and preview the site: You should see something like this: You can now open the browser to that URL to see the site in preview mode. As you make changes to the content, the site preview will automatically refresh to show those updates. To learn more about how the website is configured and structured, see the Docusaurus documentation. Provide Feedback Have comments or questions? Post an Issue to let us know how we can improve the content to support you better, on your learning journey. TODO 🚧 Updating SUPPORT.MD as required Review security processes in SECURITY.MD Contributing This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com. When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA. This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments. Trademarks This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.

#github#responsible-ai-hub

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Dave Ebbelaar•Aug 4, 2023

How I'd Learn AI in 2025 (if I could start over)

Here's the roadmap that I would follow to learn artificial intelligence (AI). 📚 Get the FREE roadmap here ➡️ https://bit.ly/data-alchemy Already got tech skills and want to start as a freelancer? 🛠️ Let me show you how: https://www.datalumina.com/data-freelancer?utmsource=youtube&utmmedium=video&utmcampaign=youtubevideotraffic&utmcontent=How%20I%27d%20Learn%20AI%20in%202024%20%28if%20I%20could%20start%20over%29 ⏱️ Timestamps 00:00 Introduction 00:34 Why learn AI? 01:28 Code vs. Low/No-code approach 02:27 Misunderstandings about AI 03:27 Ask yourself this question 04:19 What makes this approach different 05:42 Step 1: Set up your environment 06:54 Step 2: Learn Python and key libraries 08:02 Step 3: Learn Git and GitHub Basics 08:35 Step 4: Work on projects and portfolio 13:12 Step 5: Specialize and share knowledge 14:31 Step 6: Continue to learn and upskill 15:39 Step 7: Monetize your skills 16:53: What is Data Alchemy? 🛠️ Explore ProjectPro https://bit.ly/3q837w8 👋🏻 About Me Hey there! I'm Dave, an AI Engineer and the founder of Datalumina, where our mission is to facilitate entrepreneurial and technological proficiency in professionals and businesses. Through my videos here on this channel, my posts on LinkedIn, and courses on Skool, I share practical strategies and tools to navigate the complexities of data, artificial intelligence, and entrepreneurship. ✔️ How I manage my business and dev projects https://try.web.clickup.com/datalumina 📥 Datalumina's Newsletter https://www.datalumina.com/newsletter #ai #roadmap #datalumina 📌 Video Description In this video, Dave shares a comprehensive and actionable roadmap for anyone looking to start their journey into the exciting world of artificial intelligence (AI) in 2024. Whether you're a complete beginner or someone looking to pivot your career towards AI, this video lays out a step-by-step guide that demystifies the process of learning AI from the ground up. Dave highlights the significance of AI in today's tech landscape and addresses common misconceptions that newcomers might have. With a focus on practical learning, the video emphasizes the importance of choosing between a code-centric or a low/no-code approach, making AI accessible to a broader audience. Dave's unique approach involves asking a critical question that shapes the learning path, ensuring that viewers embark on a journey tailored to their goals and interests. The roadmap detailed in the video covers essential steps such as setting up your learning environment, mastering Python and key libraries crucial for AI, understanding the basics of Git and GitHub, and the importance of working on projects to build a strong portfolio. Dave also talks about the importance of specialization and the continuous process of learning and upskilling in fields like generative AI, large language models, chatbots, and machine learning. Furthermore, Dave shares insights on how to monetize your AI skills, turning your passion into a profession. The video concludes with an introduction to Data Alchemy, a concept that encapsulates the transformative power of AI knowledge. For those eager to dive into the AI world, Dave offers a free roadmap accessible through the link provided in the video description. This invaluable resource serves as a compass for navigating the complexities of AI learning, making it an essential watch for anyone interested in artificial intelligence, machine learning, and related technologies.

#ai#roadmap#datalumina

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Tyler Germain | AI Automation•Jul 21, 2023

Delivering Automations to Clients (How I Do It)

In this video, I explain how to deliver AI Automation Services to your clients. Wether you are building a custom chatbot using Botpress & Stack AI, or you are building custom business process automations with Make.com, this video has you covered. Service delivery is a crucial component of running a respectable agency, and perfecting it will help you scale your business. ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ ✅ How to Deliver Services for AI Automation Agencies ✅ Subscribe to Stay Up to Date @tylergermain 🤙🏻 ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Instagram: https://www.instagram.com/itstylergermain Twitter: https://twitter.com/itstylergermain LinkedIn: https://www.linkedin.com/in/tylergermain Business Email: tyler@automationunlocked.co ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ I’m Tyler Germain, the creator of this channel, dedicated to AI automation. I’m sharing knowledge on starting, growing, and managing my automation agency, Automation Unlocked. I worked in the field of data analytics for years, before ultimately quitting my job to pursue AI automation full time. On this channel, I offer insights on securing clients, selling services, and even delving into real-life client projects. I teach viewers the skills to utilize powerful tools like make.com and zapier, as well as a bit of no-code magic. Join me on my channel to unlock the secrets of AI automation and discover a world of endless possibilities. ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Links: Botpress 👉 https://botpress.com/ Make.com 👉 https://www.make.com/en/register?pc=automationunlocked Automation Unlocked Memberships 👉 https://www.automationunlocked.co ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Join the AI Automation Community 🤘 https://discord.gg/YQzppBm5Vf ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Work with Our Team 🦾 https://www.automationunlocked.co/agency

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Stewart Gauld•May 26, 2023

How to use Copy.ai | Best AI writing software for small business (Copy.ai tutorial)

In this AI writing software tutorial, I share how to use Copy AI to save your business time and money in 2023. AI writing software or AI writing assistance is growing at an exponential rate. One of the most popular AI tools that leverage Open AI is Copy AI. Rather than spending hours manually creating written content for blogs, social media, emails, reports and more, you can leverage the support of AI. AI allows you to create unique and personalised content in minutes. With the support of Copy AI, you can multiply the speed of your writing activities and process. 👉 Get started with Copy AI here (My favourite AI writer) ➜ https://www.copy.ai/?via=stewart-gauld *(This Copy AI link is an affiliate link, which means we will get a commission if you upgrade to a paid plan (with no extra cost to you) through this link, and this helps support our channel so we thank you in advance!) ► Looking for a simple, understandable and actionable road map for setting up your small business online? Start here and get our all-in-one small business playbook 📚: 👉 https://godigitalnow.store/products/go-digital-now-the-ultimate-small-business-playbook-ebook ► Here are some relevant resources to help you in your business journey with AI: Check out our top 6 AI writing software here: https://stewartgauld.com/best-ai-writing-software/ Read my complete Copy AI review article here: https://stewartgauld.com/copy-ai-review/ Learn how to use ChatGPT for business here: https://youtu.be/d8RnjRshcE8 Read about my top 11 AI tools for small businesses: https://stewartgauld.com/best-ai-tools/ ► Today we navigate through the below chapters for this Copy AI tutorial: 0:00 Intro 01:37 Getting started 02:36 Copy AI pricing 03:22 Copy AI dashboard 03:58 Templates 04:36 Chat by Copy AI 04:59 Prompt ideas and templates 06:08 Content editor 06:58 How to create a blog with AI 10:06 Optimize with chat AI 10:57 Copy AI tools 11:38 Managing projects 12:04 How to use templates 13:34 Outro ► Are you interested in joining our small business community? Join us to receive actionable tips, tutorials and tools to grow your small business online (Subscribe to our email list) or join our exclusive community here: https://mailchi.mp/71ac3fcdbfdf/stewart-gauld Let me know if you found this Copy AI tutorial helpful. Also, if you require any help or support, make sure to get in touch with us today. Thanks for watching and enjoy! #AI #AIwritingsoftware #copyai

#AI#AIwritingsoftware#copyai

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Jensen Tung•May 11, 2023

I Tried Selling AI Art Prints On Etsy For 30 Days

🖼️ Sell physical art with Printful: https://www.printful.com/a/jensentung 💰 Use Elevate.Store & save $1600 when starting your business. Insane deals on Canva Pro, Wix, Mailchimp, Vistaprint, .Store Domain Name & many more: https://www.elevate.store/jensentung/1 LEARN MORE 💸 Check out my top 25 business ideas to start: https://stan.store/jensentung 👋 Book a 1:1 consulting call with me: https://stan.store/jensentung/p/call-8bmh6 STOCKS AND CRYPTO 📈 Where I invest in stocks, ETFs, and HISAs: https://interactivebrokers.com/mkt/?src=JensenNPY&url=%2Fen%2Fwhyib%2Foverview.php 💵 Where I buy Bitcoin & Crypto: https://coinbase-consumer.sjv.io/LX7Kba 💰 How I store my Bitcoin & Crypto: https://bit.ly/3NWBKfS ENTREPRENEURSHIP 💻 Shopify (Get FREE trial): https://shopify.pxf.io/PO0gYQ 👕 Printful (Print-On-Demand): https://www.printful.com/a/jensentung 🟢 Fiverr: https://go.fiverr.com/visit/?bta=574619&brand=fiverrcpa 🖼️ Kittl (Use code JENSENYT for 25% off all plans): https://kittl.pxf.io/21AZ2O ETSY 🎨 Etsy (Get 40 FREE listings): https://etsy.me/433Ombp 🐝 Everbee (Etsy Keyword Research): https://everbee.io/?via=jensen FILMMAKING 🎥 All my video gear and equipment: https://kit.co/JensenTung 🎵 Where I get my music: https://fm.pxf.io/jensenfreetrial 📝 How I take notes & manage projects: https://affiliate.notion.so/s7g8vhkbmnjf SOCIALS 🟡 Instagram: https://instagram.com/jensentung ⚪ TikTok: https://tiktok.com/@jensentung 🔵 Facebook: https://facebook.com/JensenTungOfficial 🟣 Twitch: https://twitch.tv/jensentung ⚫ Website: https://jensentung.com/ DISCLAIMER The inclusion of Interactive Brokers’ (IBKR) name, logo or weblinks is present pursuant to an advertising arrangement only. IBKR is not a contributor, reviewer, provider or sponsor of content published on this site, and is not responsible for the accuracy of any products or services discussed. VIDEO SUMMARY In this video, I share with you my experience of trying to sell physical AI art prints through Print On Demand on Etsy for 30 days. I talk about the different strategies I tried, the results I got, and what I learned. STARRING 👤 Jensen Tung: https://www.instagram.com/jensentung WEBSITES & TOOLS MENTIONED 👉 Midjourney: https://www.midjourney.com 👉 NightCafe: https://creator.nightcafe.studio/?code=JENSEN 👉 Stable Diffusion: https://youtu.be/pA-njBq1qmM 👉 Etsy (Get 40 FREE listings): https://etsy.me/433Ombp 👉 Everbee (eRank alternative that I now use): https://www.everbee.io/?via=jensen 👉 Printful: https://www.printful.com/a/jensentung 👉 Gigapixel AI: https://topazlabs.com/ref/1998

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tinkerhub•Jan 8, 2023

Practical-AI-Bootcamp

Practical AI Bootcamp Practical AI Bootcamp by TinkerHub Foundation. Here you will learn how to build good AI products. This learning program cover the following. Finding the right machine learning model for a problem Building responsible AI - Bias and other issues How to train a good machine learning model - how to tune hyperparams Transfer Learning - where, when and how to use ? Speed and performance Wraping and hosting machine learning models On device machine learning Some tools and tricks Participants criteria Should know OOP and python Should know git and github Should know basic machine learning (different categories of ML, what is training ? What is testing ? What is dataset..etc) All the resources to get you started with the program is given in the resources folder. You can learn it and finish the task for joining the program! Join the program This bootcamp need you to have the following skills Python Github Machine learning There is a task for you in the tasks folder. Finish the task in a private repo. Give Gopikrishnan Sasikumar access to the private repo. Fill this form We will let you know if you are selected Program schedule This is a 2 week Bootcamp. There will be 1 hour sessions every Monday, Wednesday, Friday and Sunday. There will be tasks to do every other days. Day 1 (Aug 18) Finding the right machine learning model for a problem Should I use machine learning for this problem ? What kind of ML task is this ? Machine learning or deep learning ? Day 2 (Aug 19) Building responsible AI - Bias and other issues Bias Accountability and explainability Reproducability Robustness Privacy Day 3 (Aug 23) Dataset and performance Data prep Data reading Data Augumentation Day 4 (Aug 25) Techniques in training AI models How to find the right learning rate ? Effect of batch size Epochs and early stop Day 5 (Aug 27) Transfer learning where when and how to use Day 6 (Aug 29) Wraping and hosting machine learning models Building a micro service Making the model as an API Hosting and serving Day 7 (Aug 31) On device machine learning Techniques to make models small TensorFlow lite PyTorch quantisation Day 8 (Sep 02) Some tools and tricks Installation Finding models Data Privacy Cloud APIs and frameworks Projects (Sep 03 to Sep 09) You and your fellow teammates will be doing a project based on what you learnt through out the bootcamp

#github#practical-ai-bootcamp