Startup

Day 1 of building an AI SaaS to $10k mo

#saas #ai #chatgpt #developer #developers #coders #builders #software #softwaredeveloper #softwareengineer #founder #founders #entrepreneur #entrepreneurship #startup #startuplife #startupbusiness #leanstartup

How I Code Profitable Apps SOLO (no wasted time / beginner friendly / with AI)

Check out Scrimba – my preferred platform for learning to code (get an extra 20% off Pro with my links): AI Engineer Path: https://scrimba.com/the-ai-engineer-path-c02v?via=edmundyong Frontend Developer Career Path: https://scrimba.com/the-frontend-developer-career-path-c0j?via=edmundyong All Courses: https://scrimba.com/courses?via=edmundyong ===== Join Startup Club - A community for solo makers: https://discord.gg/YFPJQRBTrA Mobbin - A library of design inspiration for your apps: https://mobbin.com/?via=edmund Try my Startup (Easy Folders): https://chromewebstore.google.com/detail/chatgpt-folders-search-pr/gdocioajfidpnaejbgmbnkflgmppibfe?utm_source=youtube Socials: https://www.instagram.com/e.yongg/ https://www.twitter.com/edmund_io/ ===== Wishing all you happy holidays 🎄🎅 Sharing a general roadmap on how I approach coding apps that earn money. Resources used in this video (let me know if I am missing any): https://roadmap.sh/ https://dev.to/rowsanali/do-you-have-shiny-object-syndrome-as-a-dev-4ld7 https://longform.asmartbear.com/slc/ https://www.getbeamer.com/blog/customer-feedback-management-startups https://x.com/namyakhann/status/1863525098529194293 https://x.com/namyakhann/status/1861816326496399830 ===== 00:00 - Intro 00:46 - The mindset you need to adopt 01:23 - Setting clear goals (seriously) 02:51 - The building phase 05:34 - The marketing phase 06:25 - The iterating phase ===== #SeoulVlog #dayinthelife #korean #koreanvlog #startups #SeoulLife #indiehackers #DigitalNomad #softwareengineer #softwaredeveloper #codingvlog #solotravel #solopreneur #startupvlog

Hot Take: Not all your startups need AI forced into them

I'm a final year Computer Engineering student, hence applying for jobs all around. There's this particular trend I've noticed with startups that are coming up these days. That is, even for the absolute basic stuff they'll use 'AI', and they'll think they built something 'revolutionary'. No. You're breaking your product in ways you don't realise. An example, that even some well established companies are guilty of: AI Chatbots You absolutely don't need them and it's an entire gimmick. If you really wanna implement a chatbot, connect the user to an actual person on your end, which I think is not possible if you're at a 'startup' stage. You'll need employees who can handle user queries in real time. If the user really is stuck let them use the 'Contact Us' page. A really close relative of mine is very vocal about the frustration he faces whenever he tries to use the AI Chatbot on any well known e-com website. The only case for AI Chatbot that makes sense is when it's directing the customer to an actual customer support rep if none of the AI's solutions is working for the customer. Even then, implementing a search page for FAQ is extremely easy and user friendly. Another example: AI Interviewer I recently interviewed for a startup, and their whole interviewing process was AI'zed?!?! No real person at the other end, I was answering to their questions which were in video format. They even had a 'mascot' / 'AI interviewer' avatar designed by an AI (AI-ception???). This mascot just text-to-speech'ed all the questions for me to rewind and hear what I missed again. And I had to record video and audio to answer these questions on their platform itself. The entire interview process just could've been a questionnaire, or if you're really concerned on the integrity of the interviewee, just take a few minutes out of your oh-so-busy schedule as a startup owner. Atleast for hiring employees who would make the most impact on your product going ahead. I say the most impact, because (atleast as a developer) the work done by these employees would define how robust your product is, and/or how easily other features can be integrated into the codebase. Trust me, refactoring code later on would only cost you time and money. These resources would rather be more useful in other departments of your startup. The only use case for an AI Interviewer I see is for preparing for an actual interview, provided that feedback is given to the user at the earliest, which you don't need to worry about as a startup owner. So yeah, you're probably better off without integrating AI in your product. Thank you for reading. TLDR; The title; I know AI is the new thing and gets everyone drooling and all, but for the love of God, just focus on what your startup does best and put real people behind it; Integrating AI without human intervention is as good as a broken product; Do your hiring yourself, or through real people, emphasizing on the fact that the people you hire at an early stage will define your growth ahead;

Are no-code tools actually slowing down startups?

I've noticed a concerning trend: while no-code tools promise quick launches for startups, many founders end up rebuilding their entire product from scratch later. Meanwhile, established platforms for startups like Zapier are doubling their prices, pushing teams to reconsider their stack entirely. What I'm seeing in the market: Bubble/FlutterFlow promise "build Uber in weeks" Make. com offers Zapier various automation features, but they are expensive, which made me look for alternatives. AI tools like Cursor + Claude claim to replace coding skills Traditional coding still takes 6+ months to learn basics Successful startups often rebuild their no-code MVPs Here's what makes me wonder: are we choosing speed over sustainability? Edit: I'm so happy that my post sparked so many beautiful minds. I'm going to process all the insights you've shared and publish on my subreddit, because one just can't lose it on the Internet

My startup does $740k on no code. I have other ideas - should I build those on no code or use AI?

Hey yall - I'm the founder of a startup in the HR tech space. We rely almost entirely on no code (mainly bubble). Bubble can do 90% of what we need it to and then I can use third party tools like supabase for the remaining 10% I love Bubble because I don't know how to code but it still lets me build apps. I'm seeing a lot of action in the ai space about how AI makes coding super easy, and I'm wondering if folks think this is a good time to finally learn to code. I'm HUGE on no code, but I've also seen its limits (bubble specifically sucks at handling complex calculations and the way it handles load is pretty wonky). I've been wanting to learn to code for some time now but it never felt worth it since, again, bubble can get me 90% there. But now it looks like I can use AI to get 100% there What do yall think? Worth dropping no code to learn to code with AI? Or is AI not as advanced at coding as folks make it seem?

160 of Y Combinators 229 Startup Cohort are AI Startups with and 75% of the Cohort has 0 revenue

Y Combinator (YC), one of the most prestigious startup accelerators in the world, has just unveiled its latest batch of innovative startups, providing key insights into what the future might hold. Y Combinators Summer 2023 Batch In a recent post by Garry Tan, YC's president, Tan offers a nostalgic look back at his first YC Demo Day in 2008, where he, as a budding entrepreneur, pitched his startup. Now, fifteen years later, he's at the helm, proudly launching the 37th Demo Day, this time for the Summer 2023 batch. Tan proudly declares this batch as one of YC's most impressive yet, emphasizing the deep technical talent of the participants. From a staggering pool of over 24,000 applications, only 229 startups were chosen, making this one of the most competitive batches to date. This batch marks a number of firsts and solidifies several rising trends within the startups landscape. 75% of these companies began their YC journey with zero revenue, and 81% hadn't raised any funding before joining the accelerator. YC's decision to focus on early-stage startups this round signals their commitment to nurturing raw, untapped potential. A Return to Face-to-Face Interaction After three years, YC has brought back the in-person Demo Day format, allowing startups, investors, and mentors to connect directly. While the virtual format has its merits, there's an unmistakable magic in the YC Demo Day room, filled with anticipation, hope, and innovation. AI Takes Center Stage Artificial Intelligence is the standout sector in the Summer 2023 batch. With recent advancements making waves across various industries, there's arguably no better time to launch an AI-focused startup, and no better platform than YC to foster its growth. This signals a clear trend in the startup investing and venture capital space: AI is just getting started. Of the entire Summer 2023 batch, 160 out of the entire 229 Summer 2023 batch that are utilizing or implementing artificial intelligence in some capacity. This means over 2 out of every 3 startups accepted is focused on artificial intelligence in some capacity. Some of the startups include: Quill AI: Automating the job of a financial analyst Fiber AI: Automating prospecting and outbound marketing Reworkd AI: Open Source Zapier of AI Agents Watto AI: AI-powered McKinsey-quality reports in seconds Agentive: AI-powered auditing platform Humanlike: Replace your call center with voice bots that sound human Greenlite: AI compliance team for fintech and banking atla: AI assistants to help in-house lawyers answer legal questions Studdy: An AI Match tutor Glade: League of Legends with AI-generated maps and gameplay and literally over 100 others. As you can see, there's a startup covering nearly every sector of AI in the new batch. YC By The Numbers YC continues to grow as a community. The accelerator now boasts over 10,000 founders spanning more than 4,500 startups. The success stories are impressive: over 350 startups valued at over $150 million and 90 valued at more than $1 billion. The unicorn creation rate of 5% is truly unparalleled in the industry. To cater to the ever-growing community, YC has added more full-time Group Partners than ever. This includes industry veterans such as Tom Blomfield, co-founder of billion-dollar startups GoCardless and Monzo, and YC alumni like Wayne Crosby (Zenter) and Emmett Shear (Twitch). YC Core Values YC's commitment to diversity is evident in the demographics of the S23 batch. They've also spotlighted the industries these startups operate in, with 70% in B2B SaaS/Enterprise, followed by fintech, healthcare, consumer, and proptech/industrials. Garry Tan emphasizes three core tenets for YC investors: to act ethically, to make decisions swiftly, and to commit long-term. He underlines the importance of the YC community, urging investors to provide valuable introductions and guidance to founders. The Road Ahead With YC's track record and the promise shown by the Summer 2023 batch, the future of the startup ecosystem looks promising. As always, YC remains at the forefront, championing innovation and shaping the next generation of global startups. Original Post: https://www.democratizing.finance/post/take-a-peek-into-the-future-with-y-combinators-finalized-summer-2023-batch

How a founder built a B2B AI startup to serve with 65+ global brands (including Fortune500 companies) (I will not promote)

AI Palette is an AI-driven platform that helps food and beverage companies predict emerging product trends. I had the opportunity recently to sit down with the founder to get his advice on building an AI-first startup, which he'll be going through in this post. (I will not promote) About AI Palette: Co-founders: >!2 (Somsubhra GanChoudhuri, Himanshu Upreti)!!100+!!$12.7M USD!!AI-powered predictive analytics for the CPG (Consumer Packaged Goods) industry!!Signed first paying customer in the first year!!65+ global brands, including Cargill, Diageo, Ajinomoto, Symrise, Mondelez, and L’Oréal, use AI Palette!!Every new product launched has secured a paying client within months!!Expanded into Beauty & Personal Care (BPC), onboarding one of India’s largest BPC companies within weeks!!Launched multiple new product lines in the last two years, creating a unified suite for brand innovation!Identify the pain points in your industry for ideas* When I was working in the flavour and fragrance industry, I noticed a major issue CPG companies faced: launching a product took at least one to two years. For instance, if a company decided today to launch a new juice, it wouldn’t hit the market until 2027. This long timeline made it difficult to stay relevant and on top of trends. Another big problem I noticed was that companies relied heavily on market research to determine what products to launch. While this might work for current consumer preferences, it was highly inefficient since the product wouldn’t actually reach the market for several years. By the time the product launched, the consumer trends had already shifted, making that research outdated. That’s where AI can play a crucial role. Instead of looking at what consumers like today, we realised that companies should use AI to predict what they will want next. This allows businesses to create products that are ahead of the curve. Right now, the failure rate for new product launches is alarmingly high, with 8 out of 10 products failing. By leveraging AI, companies can avoid wasting resources on products that won’t succeed, leading to better, more successful launches. Start by talking to as many industry experts as possible to identify the real problems When we first had the idea for AI Palette, it was just a hunch, a gut feeling—we had no idea whether people would actually pay for it. To validate the idea, we reached out to as many people as we could within the industry. Since our focus area was all about consumer insights, we spoke to professionals in the CPG sector, particularly those in the insights departments of CPG companies. Through these early conversations, we began to see a common pattern emerge and identified the exact problem we wanted to solve. Don’t tell people what you’re building—listen to their frustrations and challenges first. Going into these early customer conversations, our goal was to listen and understand their challenges without telling them what we were trying to build. This is crucial as it ensures that you can gather as much data about the problem to truly understand it and that you aren't biasing their answers by showing your solution. This process helped us in two key ways: First, it validated that there was a real problem in the industry through the number of people who spoke about experiencing the same problem. Second, it allowed us to understand the exact scale and depth of the problem—e.g., how much money companies were spending on consumer research, what kind of tools they were currently using, etc. Narrow down your focus to a small, actionable area to solve initially. Once we were certain that there was a clear problem worth solving, we didn’t try to tackle everything at once. As a small team of two people, we started by focusing on a specific area of the problem—something big enough to matter but small enough for us to handle. Then, we approached customers with a potential solution and asked them for feedback. We learnt that our solution seemed promising, but we wanted to validate it further. If customers are willing to pay you for the solution, it’s a strong validation signal for market demand. One of our early customer interviewees even asked us to deliver the solution, which we did manually at first. We used machine learning models to analyse the data and presented the results in a slide deck. They paid us for the work, which was a critical moment. It meant we had something with real potential, and we had customers willing to pay us before we had even built the full product. This was the key validation that we needed. By the time we were ready to build the product, we had already gathered crucial insights from our early customers. We understood the specific information they wanted and how they wanted the results to be presented. This input was invaluable in shaping the development of our final product. Building & Product Development Start with a simple concept/design to validate with customers before building When we realised the problem and solution, we began by designing the product, but not by jumping straight into coding. Instead, we created wireframes and user interfaces using tools like InVision and Figma. This allowed us to visually represent the product without the need for backend or frontend development at first. The goal was to showcase how the product would look and feel, helping potential customers understand its value before we even started building. We showed these designs to potential customers and asked for feedback. Would they want to buy this product? Would they pay for it? We didn’t dive into actual development until we found a customer willing to pay a significant amount for the solution. This approach helped us ensure we were on the right track and didn’t waste time or resources building something customers didn’t actually want. Deliver your solution using a manual consulting approach before developing an automated product Initially, we solved problems for customers in a more "consulting" manner, delivering insights manually. Recall how I mentioned that when one of our early customer interviewees asked us to deliver the solution, we initially did it manually by using machine learning models to analyse the data and presenting the results to them in a slide deck. This works for the initial stages of validating your solution, as you don't want to invest too much time into building a full-blown MVP before understanding the exact features and functionalities that your users want. However, after confirming that customers were willing to pay for what we provided, we moved forward with actual product development. This shift from a manual service to product development was key to scaling in a sustainable manner, as our building was guided by real-world feedback and insights rather than intuition. Let ongoing customer feedback drive iteration and the product roadmap Once we built the first version of the product, it was basic, solving only one problem. But as we worked closely with customers, they requested additional features and functionalities to make it more useful. As a result, we continued to evolve the product to handle more complex use cases, gradually developing new modules based on customer feedback. Product development is a continuous process. Our early customers pushed us to expand features and modules, from solving just 20% of their problems to tackling 50–60% of their needs. These demands shaped our product roadmap and guided the development of new features, ultimately resulting in a more complete solution. Revenue and user numbers are key metrics for assessing product-market fit. However, critical mass varies across industries Product-market fit (PMF) can often be gauged by looking at the size of your revenue and the number of customers you're serving. Once you've reached a certain critical mass of customers, you can usually tell that you're starting to hit product-market fit. However, this critical mass varies by industry and the type of customers you're targeting. For example, if you're building an app for a broad consumer market, you may need thousands of users. But for enterprise software, product-market fit may be reached with just a few dozen key customers. Compare customer engagement and retention with other available solutions on the market for product-market fit Revenue and the number of customers alone isn't always enough to determine if you're reaching product-market fit. The type of customer and the use case for your product also matter. The level of engagement with your product—how much time users are spending on the platform—is also an important metric to track. The more time they spend, the more likely it is that your product is meeting a crucial need. Another way to evaluate product-market fit is by assessing retention, i.e whether users are returning to your platform and relying on it consistently, as compared to other solutions available. That's another key indication that your solution is gaining traction in the market. Business Model & Monetisation Prioritise scalability Initially, we started with a consulting-type model where we tailor-made specific solutions for each customer use-case we encountered and delivered the CPG insights manually, but we soon realized that this wasn't scalable. The problem with consulting is that you need to do the same work repeatedly for every new project, which requires a large team to handle the workload. That is not how you sustain a high-growth startup. To solve this, we focused on building a product that would address the most common problems faced by our customers. Once built, this product could be sold to thousands of customers without significant overheads, making the business scalable. With this in mind, we decided on a SaaS (Software as a Service) business model. The benefit of SaaS is that once you create the software, you can sell it to many customers without adding extra overhead. This results in a business with higher margins, where the same product can serve many customers simultaneously, making it much more efficient than the consulting model. Adopt a predictable, simplistic business model for efficiency. Look to industry practices for guidance When it came to monetisation, we considered the needs of our CPG customers, who I knew from experience were already accustomed to paying annual subscriptions for sales databases and other software services. We decided to adopt the same model and charge our customers an annual upfront fee. This model worked well for our target market, aligning with industry standards and ensuring stable, recurring revenue. Moreover, our target CPG customers were already used to this business model and didn't have to choose from a huge variety of payment options, making closing sales a straightforward and efficient process. Marketing & Sales Educate the market to position yourself as a thought leader When we started, AI was not widely understood, especially in the CPG industry. We had to create awareness around both AI and its potential value. Our strategy focused on educating potential users and customers about AI, its relevance, and why they should invest in it. This education was crucial to the success of our marketing efforts. To establish credibility, we adopted a thought leadership approach. We wrote blogs on the importance of AI and how it could solve problems for CPG companies. We also participated in events and conferences to demonstrate our expertise in applying AI to the industry. This helped us build our brand and reputation as leaders in the AI space for CPG, and word-of-mouth spread as customers recognized us as the go-to company for AI solutions. It’s tempting for startups to offer products for free in the hopes of gaining early traction with customers, but this approach doesn't work in the long run. Free offerings don’t establish the value of your product, and customers may not take them seriously. You should always charge for pilots, even if the fee is minimal, to ensure that the customer is serious about potentially working with you, and that they are committed and engaged with the product. Pilots/POCs/Demos should aim to give a "flavour" of what you can deliver A paid pilot/POC trial also gives you the opportunity to provide a “flavour” of what your product can deliver, helping to build confidence and trust with the client. It allows customers to experience a detailed preview of what your product can do, which builds anticipation and desire for the full functionality. During this phase, ensure your product is built to give them a taste of the value you can provide, which sets the stage for a broader, more impactful adoption down the line. Fundraising & Financial Management Leverage PR to generate inbound interest from VCs When it comes to fundraising, our approach was fairly traditional—we reached out to VCs and used connections from existing investors to make introductions. However, looking back, one thing that really helped us build momentum during our fundraising process was getting featured in Tech in Asia. This wasn’t planned; it just so happened that Tech in Asia was doing a series on AI startups in Southeast Asia and they reached out to us for an article. During the interview, they asked if we were fundraising, and we mentioned that we were. As a result, several VCs we hadn’t yet contacted reached out to us. This inbound interest was incredibly valuable, and we found it far more effective than our outbound efforts. So, if you can, try to generate some PR attention—it can help create inbound interest from VCs, and that interest is typically much stronger and more promising than any outbound strategies because they've gone out of their way to reach out to you. Be well-prepared and deliberate about fundraising. Keep trying and don't lose heart When pitching to VCs, it’s crucial to be thoroughly prepared, as you typically only get one shot at making an impression. If you mess up, it’s unlikely they’ll give you a second chance. You need to have key metrics at your fingertips, especially if you're running a SaaS company. Be ready to answer questions like: What’s your retention rate? What are your projections for the year? How much will you close? What’s your average contract value? These numbers should be at the top of your mind. Additionally, fundraising should be treated as a structured process, not something you do on the side while juggling other tasks. When you start, create a clear plan: identify 20 VCs to reach out to each week. By planning ahead, you’ll maintain momentum and speed up the process. Fundraising can be exhausting and disheartening, especially when you face multiple rejections. Remember, you just need one investor to say yes to make it all worthwhile. When using funds, prioritise profitability and grow only when necessary. Don't rely on funding to survive. In the past, the common advice for startups was to raise money, burn through it quickly, and use it to boost revenue numbers, even if that meant operating at a loss. The idea was that profitability wasn’t the main focus, and the goal was to show rapid growth for the next funding round. However, times have changed, especially with the shift from “funding summer” to “funding winter.” My advice now is to aim for profitability as soon as possible and grow only when it's truly needed. For example, it’s tempting to hire a large team when you have substantial funds in the bank, but ask yourself: Do you really need 10 new hires, or could you get by with just four? Growing too quickly can lead to unnecessary expenses, so focus on reaching profitability as soon as possible, rather than just inflating your team or burn rate. The key takeaway is to spend your funds wisely and only when absolutely necessary to reach profitability. You want to avoid becoming dependent on future VC investments to keep your company afloat. Instead, prioritize reaching break-even as quickly as you can, so you're not reliant on external funding to survive in the long run. Team-Building & Leadership Look for complementary skill sets in co-founders When choosing a co-founder, it’s important to find someone with a complementary skill set, not just someone you’re close to. For example, I come from a business and commercial background, so I needed someone with technical expertise. That’s when I found my co-founder, Himanshu, who had experience in machine learning and AI. He was a great match because his technical knowledge complemented my business skills, and together we formed a strong team. It might seem natural to choose your best friend as your co-founder, but this can often lead to conflict. Chances are, you and your best friend share similar interests, skills, and backgrounds, which doesn’t bring diversity to the table. If both of you come from the same industry or have the same strengths, you may end up butting heads on how things should be done. Having diverse skill sets helps avoid this and fosters a more collaborative working relationship. Himanshu (left) and Somsubhra (right) co-founded AI Palette in 2018 Define roles clearly to prevent co-founder conflict To avoid conflict, it’s essential that your roles as co-founders are clearly defined from the beginning. If your co-founder and you have distinct responsibilities, there is no room for overlap or disagreement. This ensures that both of you can work without stepping on each other's toes, and there’s mutual respect for each other’s expertise. This is another reason as to why it helps to have a co-founder with a complementary skillset to yours. Not only is having similar industry backgrounds and skillsets not particularly useful when building out your startup, it's also more likely to lead to conflicts since you both have similar subject expertise. On the other hand, if your co-founder is an expert in something that you're not, you're less likely to argue with them about their decisions regarding that aspect of the business and vice versa when it comes to your decisions. Look for employees who are driven by your mission, not salary For early-stage startups, the first hires are crucial. These employees need to be highly motivated and excited about the mission. Since the salary will likely be low and the work demanding, they must be driven by something beyond just the paycheck. The right employees are the swash-buckling pirates and romantics, i.e those who are genuinely passionate about the startup’s vision and want to be part of something impactful beyond material gains. When employees are motivated by the mission, they are more likely to stick around and help take the startup to greater heights. A litmus test for hiring: Would you be excited to work with them on a Sunday? One of the most important rounds in the hiring process is the culture fit round. This is where you assess whether a candidate shares the same values as you and your team. A key question to ask yourself is: "Would I be excited to work with this person on a Sunday?" If there’s any doubt about your answer, it’s likely not a good fit. The idea is that you want employees who align with the company's culture and values and who you would enjoy collaborating with even outside of regular work hours. How we structure the team at AI Palette We have three broad functions in our organization. The first two are the big ones: Technical Team – This is the core of our product and technology. This team is responsible for product development and incorporating customer feedback into improving the technology Commercial Team – This includes sales, marketing, customer service, account managers, and so on, handling everything related to business growth and customer relations. General and Administrative Team – This smaller team supports functions like finance, HR, and administration. As with almost all businesses, we have teams that address the two core tasks of building (technical team) and selling (commercial team), but given the size we're at now, having the administrative team helps smoothen operations. Set broad goals but let your teams decide on execution What I've done is recruit highly skilled people who don't need me to micromanage them on a day-to-day basis. They're experts in their roles, and as Steve Jobs said, when you hire the right person, you don't have to tell them what to do—they understand the purpose and tell you what to do. So, my job as the CEO is to set the broader goals for them, review the plans they have to achieve those goals, and periodically check in on progress. For example, if our broad goal is to meet a certain revenue target, I break it down across teams: For the sales team, I’ll look at how they plan to hit that target—how many customers they need to sell to, how many salespeople they need, and what tactics and strategies they plan to use. For the technical team, I’ll evaluate our product offerings—whether they think we need to build new products to attract more customers, and whether they think it's scalable for the number of customers we plan to serve. This way, the entire organization's tasks are cascaded in alignment with our overarching goals, with me setting the direction and leaving the details of execution to the skilled team members that I hire.

Joined an AI Startup with Ex-ShipStation Team - Need Tips on Finding Early Users

Hey Reddit, My name’s Welcome (Yes, that’s really my name), and I’ve been in tech for most of my career, mostly at bigger companies with established brands and resources. But recently, I decided to join a small startup called BotDojo. It’s my first time being part of a small team, and it’s been a pretty eye-opening experience so far. But, like with anything new, I’ve hit a few bumps along the way, and I’m hoping you all might have some advice. A little backstory: BotDojo was started by some of the engineers who used to work together at ShipStation. After ShipStation sold, they spent some time experimenting with AI but kept running into the same problems—having to patch together tools, getting inconsistent results, handling data ingestion, and struggling to track performance. So, they decided to build a platform to help developers build, test, and deploy AI solutions. Since I came on board, my focus has been on finding early users, and it’s been a mixed bag of wins and frustrations. We’ve got a solid group of people using the free version (which is great), but only a few have upgraded to the paid plan so far (ranging from startups to large enterprises). The cool thing is that those who have become paying customers absolutely love the product. It’s just been hard getting more people to that point. We’ve tried a bunch of things: Attending industry events, doing cold email outreach, running social ads (the usual stuff). And while we’ve seen some interest, we’re running into a few challenges:   Learning curve: The software is really powerful, but it takes a week or two for users to really see what it can do. Without a dedicated sales team to walk them through it, it’s been tough getting people to stick around long enough to see the value. Standing out is hard: The AI space is super crowded right now. I think a lot of people see “AI tool” and assume it’s just like everything else out there (even though BotDojo has some awesome features that really set it apart).  Sign-ups, but limited engagement: We’re on a freemium model to make it easy for people to try it out, but that also means we get a lot of bots and people who sign up but don’t really dive in. So, I thought I’d reach out here and see if anyone has been through this early stage before. How did you manage to break through and find those first paying users who really saw the value in what you were building?  Are there any strategies, communities, or tactics that worked particularly well for you? And if you had to do it all over again, what would you focus on? I figure I’m not the only one trying to navigate these waters, so I’m hoping this can be a helpful thread for others too. Thanks so much for reading, and I’d be super grateful for any advice or insights you can share! 🙏

Behind the scene : fundraising pre-seed of an AI startup

A bit of feedback from our journey at our AI startup. We started prototyping stuff around agentic AI last winter with very cool underlying tech research based on some academic papers (I can send you links if you're interested in LLM orchestration). I'm a serial entrepreneur with 2x exits, nothing went fancy but enough to keep going into the next topic. This time, running an AI project has been a bit different and unique due to the huge interest around the topic. Here are a few insights. Jan \~ Mar: Research Nothing was serious, just a side project with a friend on weekends (the guy became our lead SWE). Market was promising and we had the convinction that our tech can be game changer in computer systems workflows. March \~ April: Market Waking Up Devin published their pre-seed $20m fundraising led by Founders Fund; they paved the market with legitimacy. I decided to launch some coffee meetings with a few angels in my network. Interest confirmed. Back to work on some more serious early prototyping; hard work started here. April \~ May: YC S24 (Fail) Pumped up by our prospective angels and the market waking up on the agentic topic, I applied to YC as a solo founder (was still looking for funds and co-founders). Eventually got rejected (no co-founder and not US-based). May \~ July: VC Dance (Momentum 1) Almost randomly at the same time we got rejected from YC, I got introduced to key members of the VC community by one of our prospective angels. Interest went crazy... tons of calls. Brace yourself here, we probably met 30\~40 funds (+ angels). Got strong interests from 4\~5 of them (3 to 5 meetings each), ultimately closed 1 and some interests which might convert later in the next stage. The legend of AI being hype is true. Majority of our calls went only by word of mouth, lots of inbounds, people even not having the deck would book us a call in the next 48h after saying hi. Also lots of "tourists," just looking because of AI but with no strong opinion on the subject to move further. The hearsay about 90% rejection is true. You'll have a lot of nos, ending some days exhausted and unmotivated. End July: Closing, the Hard Part The VC roadshow is kind of an art you need to master. You need to keep momentum high enough and looking over-subscribed. Good pre-seed VC deals are over-competitive, and good funds only focus on them; they will have opportunities to catch up on lost chances at the seed stage later. We succeeded (arduously) to close our 18\~24mo budget with 1 VC, a few angels, and some state-guaranteed debt. Cash in bank just on time for payday in August (don't under-estimate time of processing) Now: Launching and Prepping the Seed Round We're now in our first weeks of go-to-market with a lot of uncertainty but a very ambitious plan ahead. The good part of having met TONS of VCs during the pre-seed roadshow is that we met probably our future lead investors in these. What would look like a loss of time in the initial pre-seed VC meetings has been finally very prolific, helping us to refine our strategy, assessing more in-depth the market (investors have a lot of insights, they meet a lot of people... that's their full-time job). We now have clear milestones and are heading to raise our seed round by end of year/Q1 if stars stay aligned :) Don't give up, the show must go on.

What if one founder in every startup became an influencer in their space? Will they bring in more business? (I will not promote)

I quit my last startup literally a week ago and I am already starting something new. I raised $2.7 million for my last company. But I feel like that the distribution was not strong enough. Hence I failed. Now as a second time founder distribution is the first thing I am going to focus on. So I have started doing physical events. The first event is IRL in San Francisco. I am inviting the CEO of Product Hunt for a fireside. The event is about how Ai agent founders can launch on Product Hunt. I will also start making videos everyday until the event to promote myself as a influencer in the space of AI agents + Product launches. Then use this momentum and newly created distribution to launch my first Ai agent. Let's see if the strategy works. I will keep people here posted!

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!

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?

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!

New to Startups; Where do I start?

I have an idea for an specialized AI based software system in a particular market that I think, if done well, could be a very helpful and lucrative software/AI (both for its owners as well as its users). It hasn't been properly implemented into any form that I or my associates have been able to find and I believe that now is the perfect time to start its development. I'm an entrepreneur, have started several successful companies over the years and am well experienced in all things business. But, none of my companies have involved creating a brand new product or would fall into the "Startup" category. It's a whole new world to me. That being said, I'm not sure what the proper steps are to make this idea come to fruition and am hoping for a point in the right direction. How do people usually go from idea to launch? I imagine there are 2 distinct things I need right now, funding for the project and a partner to help create the software. Step 1 would be the partner. For this partner, I'm not sure where to start to find this person. I'd imagine I need someone that's experienced in machine learning, AI engineering, software development, programming, etc. Or a combination of people with those skills. Since none of my companies are startup or tech based, I don't have connections to anyone with those skills. If I go around looking for a partner with those skills, I'll surely need to explain my idea to them and will need to be able to protect my idea before hand. Do I copyright it? Make them sign an NDA? What's common business practice? Where do I go to look for a partner with those skills? For funding, I can fund the initial stages of the project for a handful of months. From there, I'd like to find some kind of investment. But that sounds like a bridge to cross when I get further down that road. Looking forward to starting down this road and hopefully making something that benefits and pushes forward this new world of AI!

Hot Take: Not all your startups need AI forced into them

I'm a final year Computer Engineering student, hence applying for jobs all around. There's this particular trend I've noticed with startups that are coming up these days. That is, even for the absolute basic stuff they'll use 'AI', and they'll think they built something 'revolutionary'. No. You're breaking your product in ways you don't realise. An example, that even some well established companies are guilty of: AI Chatbots You absolutely don't need them and it's an entire gimmick. If you really wanna implement a chatbot, connect the user to an actual person on your end, which I think is not possible if you're at a 'startup' stage. You'll need employees who can handle user queries in real time. If the user really is stuck let them use the 'Contact Us' page. A really close relative of mine is very vocal about the frustration he faces whenever he tries to use the AI Chatbot on any well known e-com website. The only case for AI Chatbot that makes sense is when it's directing the customer to an actual customer support rep if none of the AI's solutions is working for the customer. Even then, implementing a search page for FAQ is extremely easy and user friendly. Another example: AI Interviewer I recently interviewed for a startup, and their whole interviewing process was AI'zed?!?! No real person at the other end, I was answering to their questions which were in video format. They even had a 'mascot' / 'AI interviewer' avatar designed by an AI (AI-ception???). This mascot just text-to-speech'ed all the questions for me to rewind and hear what I missed again. And I had to record video and audio to answer these questions on their platform itself. The entire interview process just could've been a questionnaire, or if you're really concerned on the integrity of the interviewee, just take a few minutes out of your oh-so-busy schedule as a startup owner. Atleast for hiring employees who would make the most impact on your product going ahead. I say the most impact, because (atleast as a developer) the work done by these employees would define how robust your product is, and/or how easily other features can be integrated into the codebase. Trust me, refactoring code later on would only cost you time and money. These resources would rather be more useful in other departments of your startup. The only use case for an AI Interviewer I see is for preparing for an actual interview, provided that feedback is given to the user at the earliest, which you don't need to worry about as a startup owner. So yeah, you're probably better off without integrating AI in your product. Thank you for reading. TLDR; The title; I know AI is the new thing and gets everyone drooling and all, but for the love of God, just focus on what your startup does best and put real people behind it; Integrating AI without human intervention is as good as a broken product; Do your hiring yourself, or through real people, emphasizing on the fact that the people you hire at an early stage will define your growth ahead;

AI will obsolete most young vertical SAAS startups, I will not promote

This is an unpopular opinion, but living in New York City and working with a ton of vertical SaaS startups, meaning basically database wrapper startups that engineer workflows for specific industries and specific users, what they built was at one point in time kind of innovative, or their edge was the fact that they built these like very specific workflows. And so a lot of venture capital and seed funding has gone into these types of startups. But with AI, those database wrapper startups are basically obsolete. I personally feel like all of these companies are going to have to shift like quickly to AI or watch all of their edge and what value they bring to the table absolutely evaporate. It's something that I feel like it's not currently being priced in and no one really knows how to price, but it's going to be really interesting to watch as more software becomes generated and workflows get generated. I’m not saying these companies are worth nothing, but their products need to be completely redone EDIT: for people not understanding: The UX is completely different from traditional vertical saas. Also in real world scenarios, AI does not call the same APIs as the front end. The data handling and validation is different. It’s 50% rebuild. Then add in the technical debt, the fact that they might need a different tech stack to build agents correctly, different experience in their engineers. the power struggles that occur inside companies that need a huge change like this could tank the whole thing alone. It can be done, but these companies are vulnerable. The edge they have is working with existing customers to get it right. But they basically blew millions on a tech implementation that’s not as relevant going forwards. Investors maybe better served putting money into a fresh cap table

Lessons from 139 YC AI startups (S23)

YC's Demo Day was last week, and with it comes another deluge of AI companies. A record-breaking 139 startups were in some way related to AI or ML - up from 112 in the last batch. Here are 5 of my biggest takeaways: AI is (still) eating the world. It's remarkable how diverse the industries are - over two dozen verticals were represented, from materials science to social media to security. However, the top four categories were: AI Ops: Tooling and platforms to help companies deploy working AI models. We'll discuss more below, but AI Ops has become a huge category, primarily focused on LLMs and taming them for production use cases. Developer Tools: Apps, plugins, and SDKs making it easier to write code. There were plenty of examples of integrating third-party data, auto-generating code/tests, and working with agents/chatbots to build and debug code. Healthcare + Biotech: It seems like healthcare has a lot of room for automation, with companies working on note-taking, billing, training, and prescribing. And on the biotech side, there are some seriously cool companies building autonomous surgery robots and at-home cancer detection. Finance + Payments: Startups targeting banks, fintechs, and compliance departments. This was a wide range of companies, from automated collections to AI due diligence to "Copilot for bankers." Those four areas covered over half of the startups. The first two make sense: YC has always filtered for technical founders, and many are using AI to do what they know - improve the software developer workflow. But it's interesting to see healthcare and finance not far behind. Previously, I wrote: Large enterprises, healthcare, and government are not going to send sensitive data to OpenAI. This leaves a gap for startups to build on-premise, compliant \[LLMs\] for these verticals. And we're now seeing exactly that - LLMs focused on healthcare and finance and AI Ops companies targeting on-prem use cases. It also helps that one of the major selling points of generative AI right now is cost-cutting - an enticing use case for healthcare and finance. Copilots are king. In the last batch, a lot of startups positioned themselves as "ChatGPT for X," with a consumer focus. It seems the current trend, though, is "Copilot for X" - B2B AI assistants to help you do everything from KYC checks to corporate event planning to chip design to negotiate contracts. Nearly two dozen companies were working on some sort of artificial companion for businesses - and a couple for consumers. It's more evidence for the argument that AI will not outright replace workers - instead, existing workers will collaborate with AI to be more productive. And as AI becomes more mainstream, this trend of making specialized tools for specific industries or tasks will only grow. That being said - a Bing-style AI that lives in a sidebar and is only accessible via chat probably isn't the most useful form factor for AI. But until OpenAI, Microsoft, and Google change their approach (or until another company steps up), we'll probably see many more Copilots. AI Ops is becoming a key sector. "AI Ops" has been a term for only a few years. "LLM Ops" has existed for barely a year. And yet, so many companies are focused on training, fine-tuning, deploying, hosting, and post-processing LLMs it's quickly becoming a critical piece of the AI space. It's a vast industry that's sprung up seemingly overnight, and it was pretty interesting to see some of the problems being solved at the bleeding edge. For example: Adding context to language models with as few as ten samples. Pausing and moving training runs in real-time. Managing training data ownership and permissions. Faster vector databases. Fine-tuning models with synthetic data. But as much ~~hype~~ enthusiasm and opportunity as there might be, the size of the AI Ops space also shows how much work is needed to really productionalize LLMs and other models. There are still many open questions about reliability, privacy, observability, usability, and safety when it comes to using LLMs in the wild. Who owns the model? Does it matter? Nine months ago, anyone building an LLM company was doing one of three things: Training their own model from scratch. Fine-tuning a version of GPT-3. Building a wrapper around ChatGPT. Thanks to Meta, the open-source community, and the legions of competitors trying to catch up to OpenAI, there are now dozens of ways to integrate LLMs. However, I found it interesting how few B2B companies mentioned whether or not they trained their own model. If I had to guess, I'd say many are using ChatGPT or a fine-tuned version of Llama 2. But it raises an interesting question - if the AI provides value, does it matter if it's "just" ChatGPT behind the scenes? And once ChatGPT becomes fine-tuneable, when (if ever) will startups decide to ditch OpenAI and use their own model instead? "AI" isn't a silver bullet. At the end of the day, perhaps the biggest lesson is that "AI" isn't a magical cure-all - you still need to build a defensible company. At the beginning of the post-ChatGPT hype wave, it seemed like you just had to say "we're adding AI" to raise your next round or boost your stock price. But competition is extremely fierce. Even within this batch, there were multiple companies with nearly identical pitches, including: Solving customer support tickets. Negotiating sales contracts. Writing drafts of legal documents. Building no-code LLM workflows. On-prem LLM deployment. Automating trust and safety moderation. As it turns out, AI can be a competitive advantage, but it can't make up for a bad business. The most interesting (and likely valuable) companies are the ones that take boring industries and find non-obvious use cases for AI. In those cases, the key is having a team that can effectively distribute a product to users, with or without AI. Where we’re headed I'll be honest - 139 companies is a lot. In reviewing them all, there were points where it just felt completely overwhelming. But after taking a step back, seeing them all together paints an incredibly vivid picture of the current AI landscape: one that is diverse, rapidly evolving, and increasingly integrated into professional and personal tasks. These startups aren't just building AI for the sake of technology or academic research, but are trying to address real-world problems. Technology is always a double-edged sword - and some of the startups felt a little too dystopian for my taste - but I'm still hopeful about AI's ability to improve productivity and the human experience.

Early stage startups do outreach and skip content marketing, did that change recently?

Since venturing into the startup world in 2021 as a technical solo founder, I always had to pursue advice on the non-technical stuff such as marketing. When starting out as a boostrapped project, it's commonly adviced that you should focus on direct outreach and skip any SEO/Content Marketing work. But I'm wondering if this memo changed recently due to all the advancements in AI and text generation. What took 10 hours to write can now take 3 with the right automations setup. So if this change means that it would be good idea for founders to semi-automate their content marketing even in the very early stages. To be clear, by early stage I mean you have a few users and maybe handful of customers not a blank zero. Also by 'content automation' I wouldn't solely rely on ChatGPT to generate my blogs, but it for sure can help me with research, ideation, drafting, and reviewing. What do you guys think?

The #1 Mistake Entrepreneurs Make – And How to Avoid It Before Launching Your Startup (I will not promote)

Why So Many Startups Fail Before They Even Begin A huge number of early-stage entrepreneurs fall into the same trap: They get excited about their idea but don’t validate if the market actually wants it. They invest time and money building something nobody will pay for. They focus on execution first instead of proof of demand. By the time reality hits, they’ve burned months (or years) and thousands of dollars—just to learn the hard way that they started wrong. How do we prevent this? What’s the best way to validate a business idea before wasting resources? According to your unique experiences, what’s the Smartest Way to Validate a Startup Idea? I’ve been working on new approaches to validation that combine: ✔ AI-powered feasibility analysis to assess business potential instantly. ✔ Market research frameworks that help test demand before launching. ✔ Lean execution models that prevent unnecessary risk. But I want to hear from you: How did YOU validate your startup idea before launching? What methods have actually worked to confirm demand? What’s one mistake you made in your early-stage startup that others can avoid? The best insights come from real founders who’ve been in the trenches. Let’s break down what actually works. Drop your thoughts below! 👇 (I will not promote).

Seeking Non-Technical Co-founder for Enterprise AI Startup in NJ

Hey r/startups community! I'm diving deep into the world of AI and building a tool that's tailor-made for enterprises dealing with fragmented data. If you're someone with: A track record in enterprise sales, Solid ties with VCs and angel investors, and A vast network in the industry, ... then we might just make the perfect team! A bit about me: I have a background in software development, am a published AI researcher, and have hands-on experience in product creation. Let's join forces and turn this concept into a game-changer. Interested? DM me your details or drop a comment. Excited to see where this journey takes us!

Startup founders, tell me what's the job you would like to get done in your startup but you are too busy to repeat or start it?

I've been focusing on LLM-based agent development for the past two years and am now building a related startup. I want to validate my startup ideas and would especially like to hear feedback from entrepreneurs. What are the repetitive tasks in your startup journey that need to be completed but you're too busy to handle or initiate? I can develop specialized intelligent agents to solve these issues for you. Whether it's workflow automation or more advanced agent AI applications, whatever problems you want to solve, I'm willing to try and help. For just a small fee (provided it's truly an important problem worth solving), I can customize solutions for you. Following the principle of "do things that don't scale," I'm more focused on helping entrepreneurs quickly and effectively address their pain points. Through this approach, we can explore together the practical value of AI agents in startup scenarios and gradually improve product functionality. Feel free to share any specific business scenarios or needs you have. Let's discuss how AI agents can truly help entrepreneurs improve their work efficiency.

16 years old and thinking about creating a startup

Hi to everyone, this is my first post on Reddit and r/Startups. Sorry in advance if there is any mistake. I'm 16 years old, and I'm already planning to create my startup. Growing up in the digital age has given me both inspiration and doubts. On one side, you hear advice like, “You need connections with powerful people to succeed.” On the other, there are stories of founders coming from poverty and now leading billion-dollar companies.That really sucks. I'm here because I believe this community offers honest and grounded insights. So you can analyze, I leave you my goals. I accept all the advice you have. I’ll finish high school in two years while using my free time to learn about AI, programming, agile methods, and business basics. After that, I plan to pursue a Systems Engineering degree, even though I’ve debated skipping university. My older siblings convinced me it’s worth it for the professional and technical foundation. During college, I aim to freelance, save money, and build connections with entrepreneurs and developers. Beyond that, my 15-year plan includes working in tech companies to gain experience, creating an MVP for my startup, and securing funding through investors or incubators. I want to solve real-world problems using tools that feel future-proof. While I sometimes feel behind, I’m determined to catch up and take advantage of the opportunities ahead. I know the startup journey is uncertain—like a vulnerable animal facing competition, funding issues, and market challenges. But I’m ready to adapt as my vision evolves. Like for example the time. Obviously I would like to keep it exactly but you never know what can happen along the way. I’d love to hear your thoughts or advice. Thanks in advance, and I apologize if anything is unclear

Behind the scene : fundraising pre-seed of an AI startup

A bit of feedback from our journey at our AI startup. We started prototyping stuff around agentic AI last winter with very cool underlying tech research based on some academic papers (I can send you links if you're interested in LLM orchestration). I'm a serial entrepreneur with 2x exits, nothing went fancy but enough to keep going into the next topic. This time, running an AI project has been a bit different and unique due to the huge interest around the topic. Here are a few insights. Jan \~ Mar: Research Nothing was serious, just a side project with a friend on weekends (the guy became our lead SWE). Market was promising and we had the convinction that our tech can be game changer in computer systems workflows. March \~ April: Market Waking Up Devin published their pre-seed $20m fundraising led by Founders Fund; they paved the market with legitimacy. I decided to launch some coffee meetings with a few angels in my network. Interest confirmed. Back to work on some more serious early prototyping; hard work started here. April \~ May: YC S24 (Fail) Pumped up by our prospective angels and the market waking up on the agentic topic, I applied to YC as a solo founder (was still looking for funds and co-founders). Eventually got rejected (no co-founder and not US-based). May \~ July: VC Dance (Momentum 1) Almost randomly at the same time we got rejected from YC, I got introduced to key members of the VC community by one of our prospective angels. Interest went crazy... tons of calls. Brace yourself here, we probably met 30\~40 funds (+ angels). Got strong interests from 4\~5 of them (3 to 5 meetings each), ultimately closed 1 and some interests which might convert later in the next stage. The legend of AI being hype is true. Majority of our calls went only by word of mouth, lots of inbounds, people even not having the deck would book us a call in the next 48h after saying hi. Also lots of "tourists," just looking because of AI but with no strong opinion on the subject to move further. The hearsay about 90% rejection is true. You'll have a lot of nos, ending some days exhausted and unmotivated. End July: Closing, the Hard Part The VC roadshow is kind of an art you need to master. You need to keep momentum high enough and looking over-subscribed. Good pre-seed VC deals are over-competitive, and good funds only focus on them; they will have opportunities to catch up on lost chances at the seed stage later. We succeeded (arduously) to close our 18\~24mo budget with 1 VC, a few angels, and some state-guaranteed debt. Cash in bank just on time for payday in August (don't under-estimate time of processing) Now: Launching and Prepping the Seed Round We're now in our first weeks of go-to-market with a lot of uncertainty but a very ambitious plan ahead. The good part of having met TONS of VCs during the pre-seed roadshow is that we met probably our future lead investors in these. What would look like a loss of time in the initial pre-seed VC meetings has been finally very prolific, helping us to refine our strategy, assessing more in-depth the market (investors have a lot of insights, they meet a lot of people... that's their full-time job). We now have clear milestones and are heading to raise our seed round by end of year/Q1 if stars stay aligned :) Don't give up, the show must go on.

How do I, as a Data Scientist, get familiar with the business knowledge and jargon I need to start my AI startup and sell my product?

Here’s my situation: I'm a Data Scientist working on an MVP, almost ready to launch, called Attendify. It simplifies the attendance process in schools—no more teachers calling out names and sending records to principals. Instead, teachers create an account, upload student images, and start a live camera feed, all through the app. The app detects students using deep learning, stores everything in a database, and automates the process. Plus, I plan to add features based on school needs and pitch it directly to them. Now, I’m looking to establish a proper AI startup with a website and digital presence, but I'm lacking in business knowledge, especially the lingo like "B2B" and other terms. My goal is to be a sole founder with an online workforce. Can anyone recommend a cost-effective course (maybe on Coursera) that covers the basics I need to know to effectively talk with clients and understand the business side of things? Attendify is just one of them, I might create more in the future so I'm going towards SaaS side I was thinking about Entrepreneurship Specialization by University of Pennsylvania on Coursera if anyone got any feedback on it

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!

36 startup ideas found by analyzing podcasts (problem, solution & source episode)

Hey, I've been a bit of a podcast nerd for a long time. Around a year ago I began experimenting with transcription of podcasts for a SaaS I was running. I realized pretty quickly that there's a lot of knowledge and value in podcast discussions that is for all intents and purposes entirely unsearchable or discoverable to most people. I ended up stopping work on that SaaS product (party for lack of product/market fit, and partly because podcasting was far more interesting), and focusing on the podcast technology full-time instead. I'm a long-time lurker and poster of r/startups and thought this would make for some interesting content and inspiration for folks. Given I'm in this space, have millions of transcripts, and transcribe thousands daily... I've been exploring fun ways to expose some of the interesting knowledge and conversations taking place that utilize our own data/API. I'm a big fan of the usual startup podcasts (My First Million, Greg Isenberg, etc. etc.) and so I built an automation that turns all of the startup ideas discussed into a weekly email digest. I always struggle to listen to as many episodes as I'd actually like to, so I thought I'd summarise the stuff I care about instead (startup opportunities being discussed). I thought it would be interesting to post some of the ideas extracted so far. They range from being completely whacky and blue sky, to pretty boring but realistic. A word of warning before anyone complains – this is a big mixture of tech, ai, non-tech, local services, etc. ideas: Some of the ideas are completely mundane, but realistic (e.g. local window cleaning service) Some of the ideas are completely insane, blue sky, but sound super interesting Here's the latest 36 ideas: |Idea Name|Problem|Solution|Source| |:-|:-|:-|:-| |SalesForce-as-a-Service - White Label Enterprise Sales Teams|White-label enterprise sales teams for B2B SaaS. Companies need sales but can't hire/train. Recruit retail sellers, train for tech, charge 30% of deals closed.|Create a white-label enterprise sales team by recruiting natural salespeople from retail and direct sales backgrounds (e.g. mall kiosks, cutco knives). Train them specifically in B2B SaaS sales techniques and processes. Offer this trained sales force to tech companies on a contract basis.|My First Million - "Life Hacks From The King of Introverts + 7 Business Ideas| |TechButler - Mobile Device Maintenance Service|Mobile tech maintenance service. Clean/optimize devices, improve WiFi, basic support. $100/visit to homes. Target affluent neighborhoods.|Mobile tech support service providing in-home device cleaning, optimization, and setup. Focus on common issues like WiFi improvement, device maintenance, and basic tech support.|My First Million - "Life Hacks From The King of Introverts + 7 Business Ideas| |MemoryBox - At-Home Video Digitization Service|Door-to-door VHS conversion service. Parents have boxes of old tapes. Pick up, digitize, deliver. $30/tape with minimum order. Going extinct.|Door-to-door VHS to digital conversion service that handles everything from pickup to digital delivery. Make it extremely convenient for customers to preserve their memories.|My First Million - "Life Hacks From The King of Introverts + 7 Business Ideas| |Elite Match Ventures - Success-Based Luxury Matchmaking|High-end matchmaking for 50M+ net worth individuals. Only charge $1M+ when they get married. No upfront fees. Extensive vetting process.|Premium matchmaking service exclusively for ultra-high net worth individuals with a pure contingency fee model - only get paid ($1M+) upon successful marriage. Focus on quality over quantity with extensive vetting and personalized matching.|My First Million - "Life Hacks From The King of Introverts + 7 Business Ideas| |LocalHost - Simple Small Business Websites|Simple WordPress sites for local businesses. $50/month includes hosting, updates, security. Target restaurants and shops. Recurring revenue play.|Simplified web hosting and WordPress management service targeting local small businesses. Focus on basic sites with standard templates, ongoing maintenance, and reliable support for a fixed monthly fee.|My First Million - "Life Hacks From The King of Introverts + 7 Business Ideas| |VoiceJournal AI - Voice-First Smart Journaling|Voice-to-text journaling app with AI insights. 8,100 monthly searches. $15/month subscription. Partners with journaling YouTubers.|AI-powered journaling app that combines voice recording, transcription, and intelligent insights. Users can speak their thoughts, which are automatically transcribed and analyzed for patterns, emotions, and actionable insights.|Where It Happens - "7 $1M+ AI startup ideas you can launch tomorrow with $0"| |AIGenAds - AI-Generated UGC Content Platform|AI platform turning product briefs into UGC-style video ads. Brands spending $500/video for human creators. Generate 100 variations for $99/month.|AI platform that generates UGC-style video ads using AI avatars and scripting. System would allow rapid generation of multiple ad variations at a fraction of the cost. Platform would use existing AI avatar technology combined with script generation to create authentic-looking testimonial-style content.|Where It Happens - "7 $1M+ AI startup ideas you can launch tomorrow with $0"| |InfographAI - Automated Infographic Generation Platform|AI turning blog posts into branded infographics. Marketers spending hours on design. $99/month unlimited generation.|AI-powered platform that automatically converts blog posts and articles into visually appealing infographics. System would analyze content, extract key points, and generate professional designs using predefined templates and brand colors.|Where It Happens - "7 $1M+ AI startup ideas you can launch tomorrow with $0"| |KidFinance - Children's Financial Education Entertainment|Children's media franchise teaching financial literacy. Former preschool teacher creating 'Dora for money'. Books, videos, merchandise potential.|Character-driven financial education content for kids, including books, videos, and potentially TV show. Focus on making money concepts fun and memorable.|The Side Hustle Show - "How a Free Challenge Turned Into a $500,000 a Year Business (Greatest Hits)"| |FinanceTasker - Daily Financial Task Challenge|Free 30-day financial challenge with daily action items. People overwhelmed by money management. Makes $500k/year through books, speaking, and premium membership.|A free 30-day financial challenge delivering one simple, actionable task per day via email. Each task includes detailed scripts and instructions. Participants join a Facebook community for support and accountability. The program focuses on quick wins to build momentum. Automated delivery allows scaling.|The Side Hustle Show - "How a Free Challenge Turned Into a $500,000 a Year Business (Greatest Hits)"| |FinanceAcademy - Expert Financial Training Platform|Premium financial education platform. $13/month for expert-led courses and live Q&As. 4000+ members generating $40k+/month.|Premium membership site with expert-led courses, live Q&As, and community support. Focus on specific topics like real estate investing, business creation, and advanced money management.|The Side Hustle Show - "How a Free Challenge Turned Into a $500,000 a Year Business (Greatest Hits)"| |SecurityFirst Compliance - Real Security + Compliance Platform|Security-first compliance platform built by hackers. Companies spending $50k+ on fake security. Making $7M/year showing why current solutions don't work.|A compliance platform built by security experts that combines mandatory compliance requirements with real security measures. The solution includes hands-on security testing, expert guidance, and a focus on actual threat prevention rather than just documentation. It merges traditional compliance workflows with practical security implementations.|In the Pit with Cody Schneider| |LinkedInbound - Automated Professional Visibility Engine|LinkedIn automation for inbound job offers. Professionals spending hours on manual outreach. $99/month per job seeker.|Automated system for creating visibility and generating inbound interest on LinkedIn through coordinated profile viewing and engagement. Uses multiple accounts to create visibility patterns that trigger curiosity and inbound messages.|In the Pit with Cody Schneider| |ConvoTracker - Community Discussion Monitoring Platform|Community discussion monitoring across Reddit, Twitter, HN. Companies missing sales opportunities. $499/month per brand tracked.|Comprehensive monitoring system that tracks competitor mentions and industry discussions across multiple platforms (Reddit, Twitter, Hacker News, etc.) with automated alerts and engagement suggestions.|In the Pit with Cody Schneider| |ContentAds Pro - Smart Display Ad Implementation|Display ad implementation service for content creators. Bloggers losing thousands in ad revenue monthly. Makes $3-5k per site setup plus ongoing optimization fees.|Implementation of professional display advertising through networks like Mediavine that specialize in optimizing ad placement and revenue while maintaining user experience. Include features like turning off ads for email subscribers and careful placement to minimize impact on core metrics.|The Side Hustle Show - "636: Is Business Coaching Worth It? A Look Inside the last 12 months of Side Hustle Nation"| |MoneyAppReviews - Professional Side Hustle App Testing|Professional testing service for money-making apps. People wasting time on low-paying apps. Makes $20k/month from affiliate commissions and ads.|Professional app testing service that systematically reviews money-making apps and creates detailed, honest reviews including actual earnings data, time investment, and practical tips.|The Side Hustle Show - "636: Is Business Coaching Worth It? A Look Inside the last 12 months of Side Hustle Nation"| |LightPro - Holiday Light Installation Service|Professional Christmas light installation service. Homeowners afraid of ladders. $500-2000 per house plus storage.|Professional Christmas light installation service targeting residential and commercial properties. Full-service offering including design, installation, maintenance, removal and storage. Focus on safety and premium aesthetic results.|The Side Hustle Show - "639: 30 Ways to Make Extra Money for the Holidays"| |FocusMatch - Research Participant Marketplace|Marketplace connecting companies to paid research participants. Companies spending weeks finding people. $50-150/hour per study.|Online platform connecting companies directly with paid research participants. Participants create detailed profiles and get matched to relevant studies. Companies get faster access to their target demographic while participants earn money sharing opinions.|The Side Hustle Show - "639: 30 Ways to Make Extra Money for the Holidays"| |SolarShine Pro - Specialized Solar Panel Cleaning Service|Solar panel cleaning service using specialized equipment. Panels lose 50% efficiency when dirty. $650 per job, automated scheduling generates $18k/month from repeat customers.|Professional solar panel cleaning service using specialized deionized water system and European cleaning equipment. Includes automated 6-month scheduling, professional liability coverage, and warranty-safe cleaning processes. Service is bundled with inspection and performance monitoring.|The UpFlip Podcast - "156. $18K/Month with This ONE Service — Niche Business Idea"| |ExteriorCare Complete - One-Stop Exterior Maintenance Service|One-stop exterior home cleaning service (solar, windows, gutters, bird proofing). Automated scheduling. $650 average ticket. 60% repeat customers on 6-month contracts.|All-in-one exterior cleaning service offering comprehensive maintenance packages including solar, windows, gutters, roof cleaning and bird proofing. Single point of contact, consistent quality, and automated scheduling for all services.|The UpFlip Podcast - "156. $18K/Month with This ONE Service — Niche Business Idea"| |ContentMorph - Automated Cross-Platform Content Adaptation|AI platform converting blog posts into platform-optimized social content. Marketing teams spending 5hrs/post on manual adaptation. $199/mo per brand with 50% margins.|An AI-powered platform that automatically transforms long-form content (blog posts, podcasts, videos) into platform-specific formats (Instagram reels, TikToks, tweets). The system would preserve brand voice while optimizing for each platform's unique requirements and best practices.|Entrepreneurs on Fire - "Digital Threads: The Entrepreneur Playbook for Digital-First Marketing with Neal Schaffer"| |MarketerMatch - Verified Digital Marketing Talent Marketplace|Marketplace for pre-vetted digital marketing specialists. Entrepreneurs spending 15hrs/week on marketing tasks. Platform takes 15% commission averaging $900/month per active client.|A specialized marketplace exclusively for digital marketing professionals, pre-vetted for specific skills (video editing, social media, SEO, etc.). Platform includes skill verification, portfolio review, and specialization matching.|Entrepreneurs on Fire - "Digital Threads: The Entrepreneur Playbook for Digital-First Marketing with Neal Schaffer"| |Tiger Window Cleaning - Premium Local Window Service|Local window cleaning service targeting homeowners. Traditional companies charging 2x market rate. Making $10k/month from $200 initial investment.|Local window cleaning service combining competitive pricing ($5/pane), excellent customer service, and quality guarantees. Uses modern tools like water-fed poles for efficiency. Implements systematic approach to customer communication and follow-up.|The Side Hustle Show - "630: How this College Student’s Side Hustle Brings in $10k a Month"| |RealViz3D - Real Estate Visualization Platform|3D visualization service turning architectural plans into photorealistic renderings for real estate agents. Agents struggling with unbuilt property sales. Making $30-40k/year per operator.|Professional 3D modeling and rendering service that creates photorealistic visualizations of properties before they're built or renovated. The service transforms architectural plans into immersive 3D representations that show lighting, textures, and realistic details. This helps potential buyers fully understand and connect with the space before it physically exists.|Side Hustle School - "#2861 - TBT: An Architect’s Side Hustle in 3D Real Estate Modeling"| |Somewhere - Global Talent Marketplace|Platform connecting US companies with vetted overseas talent. Tech roles costing $150k locally filled for 50% less. Grew from $15M to $52M valuation in 9 months.|Platform connecting US companies with pre-vetted overseas talent at significantly lower rates while maintaining high quality. Handles payments, contracts, and quality assurance to remove friction from global hiring.|My First Million - "I Lost Everything Twice… Then Made $26M In 18 Months| |GymLaunch - Rapid Gym Turnaround Service|Consultants flying to struggling gyms to implement proven member acquisition systems. Gym owners lacking sales expertise. Made $100k in first 21 days.|Expert consultants fly in to implement proven member acquisition systems, train staff, and rapidly fill gyms with new members. The service combines sales training, marketing automation, and proven conversion tactics to transform struggling gyms into profitable businesses within weeks.|My First Million - "I Lost Everything Twice… Then Made $26M In 18 Months| |PublishPlus - Publishing Backend Monetization|Backend monetization system for publishing companies. One-time customers becoming recurring revenue. Grew business from $2M to $110M revenue.|Add complementary backend products and services to increase customer lifetime value. Develop software tools and additional services that natural extend from initial publishing product. Focus on high-margin recurring revenue streams.|My First Million - "I Lost Everything Twice… Then Made $26M In 18 Months| |WelcomeBot - Automated Employee Onboarding Platform|Automated employee welcome platform. HR teams struggling with consistent onboarding. $99/month per 100 employees.|An automated onboarding platform that creates personalized welcome experiences through pre-recorded video messages, scheduled check-ins, and automated swag delivery. The platform would ensure consistent high-quality onboarding regardless of timing or location.|Entrepreneurs on Fire - "Free Training on Building Systems and Processes to Scale Your Business with Chris Ronzio: An EOFire Classic from 2021"| |ProcessBrain - Business Knowledge Documentation Platform|SaaS platform turning tribal knowledge into documented processes. Business owners spending hours training new hires. $199/month per company.|A software platform that makes it easy to document and delegate business processes and procedures. The platform would include templates, guided documentation flows, and tools to easily share and update procedures. It would help businesses create a comprehensive playbook of their operations.|Entrepreneurs on Fire - "Free Training on Building Systems and Processes to Scale Your Business with Chris Ronzio: An EOFire Classic from 2021"| |TradeMatch - Modern Manufacturing Job Marketplace|Modern job board making manufacturing sexy again. Factory jobs paying $40/hr but can't recruit. $500 per successful referral.|A specialized job marketplace and recruitment platform focused exclusively on modern manufacturing and trade jobs. The platform would combine TikTok-style content marketing, referral programs, and modern UX to make manufacturing jobs appealing to Gen Z and young workers. Would leverage existing $500 referral fees and industry demand.|My First Million - "He Sold His Company For $15M, Then Got A Job At McDonald’s"| |GroundLevel - Executive Immersion Program|Structured program putting CEOs in front-line jobs. Executives disconnected from workers. $25k per placement.|A structured program that places executives and founders in front-line jobs (retail, warehouse, service) for 2-4 weeks with documentation and learning framework. Similar to Scott Heiferman's McDonald's experience but productized.|My First Million - "He Sold His Company For $15M, Then Got A Job At McDonald’s"| |OneStepAhead - Micro-Mentorship Marketplace|Marketplace for 30-min mentorship calls with people one step ahead. Professionals seeking specific guidance. Takes 15% of session fees.|MicroMentor Marketplace - Platform connecting people with mentors who are just one step ahead in their journey for focused, affordable micro-mentorship sessions.|Entrepreneurs on Fire - "How to Create an Unbroken Business with Michael Unbroken: An EOFire Classic from 2021"| |VulnerableLeader - Leadership Authenticity Training Platform|Leadership vulnerability training platform. Leaders struggling with authentic communication. $2k/month per company subscription.|Leadership Vulnerability Platform - A digital training platform combining assessment tools, guided exercises, and peer support to help leaders develop authentic communication skills. The platform would include real-world scenarios, video coaching, and measurable metrics for tracking leadership growth through vulnerability.|Entrepreneurs on Fire - "How to Create an Unbroken Business with Michael Unbroken: An EOFire Classic from 2021"| |NetworkAI - Smart Network Intelligence Platform|AI analyzing your network to find hidden valuable connections. Professionals missing opportunities in existing contacts. $49/month per user.|AI Network Navigator - Smart tool that analyzes your professional network across platforms, identifies valuable hidden connections, and suggests specific actionable ways to leverage relationships for mutual benefit.|Entrepreneurs on Fire - "How to Create an Unbroken Business with Michael Unbroken: An EOFire Classic from 2021"| |Porch Pumpkins - Seasonal Decoration Service|Full-service porch pumpkin decoration. Homeowners spend $300-1350 per season. One operator making $1M in 8 weeks seasonal revenue.|Full-service seasonal porch decoration service focused on autumn/Halloween, including design, installation, maintenance, and removal. Offering premium curated pumpkin arrangements with various package tiers.|My First Million - "The guy who gets paid $80K/yr to do nothing"| |Silent Companion - Professional Presence Service|Professional silent companions for lonely people. Huge problem in Japan/globally. $68/session, $80k/year per companion. Non-sexual, just presence.|A professional companion service where individuals can rent a non-judgmental, quiet presence for various activities. The companion provides silent company without the pressure of conversation or social performance. They accompany clients to events, meals, or just sit quietly together.|My First Million - "The guy who gets paid $80K/yr to do nothing"| Hope this is useful. If anyone would like to ensure I include any particular podcasts or episodes etc. in future posts, very happy to do so. I'll generally send \~5 ideas per week in a short weekly digest format (you can see the format I'd usually use in here: podcastmarketwatch.beehiiv.com). I find it mindblowing that the latest models with large context windows make it even possible to analyze full transcripts at such scale. It's a very exciting time we're living through! Would love some feedback on this stuff, happy to iterate and improve the analysis/ideas... or create a new newsletter on a different topic if anyone would like. Cheers!

Voice AI Isn’t Just for Big Brands – Here’s How Startups Can Use It (I will not promote)

When you think about Voice AI, it’s easy to picture massive companies like Amazon or Google pouring millions into complex systems. But it isn’t just for the big guys anymore. Startups can use it too, and it doesn’t have to cost a fortune. Why Startups Should Care About Voice AI Voice AI used to be expensive and complicated, but that’s changed a lot. Today, even small startups can use it to save time, cut costs, and make customers happier—all without needing a massive budget. If you think that repetitive tasks are eating up your team’s time, or if customers are getting frustrated by slow responses, Voice AI can help. And it’s not just for call centers or tech giants. Startups can benefit from it just as much, if not more. 3 Practical Ways Startups Can Use Voice AI Automated Scheduling and Appointment Setting Whether it’s booking meetings, setting reminders, or rescheduling, Voice AI can handle it all. This is especially useful for service-based startups, like healthcare clinics, legal firms, or consulting agencies. Answering Frequently Asked Questions (FAQs) Every startup gets repetitive questions—“What are your hours?” “What’s your refund policy?” Instead of answering the same things over and over, Voice AI can automate it. Order Tracking and Status Updates For e-commerce startups, Voice AI can provide real-time order updates without involving a human. Customers get quick answers, and your team can focus on more important tasks. Simple Workflow: How It Works Customer Initiates Call Customer calls the business for scheduling, FAQs, or order updates. Voice AI Answers AI responds with a natural, human-like voice. AI Handles the Request Schedules appointments, answers FAQs, or provides order updates. Integration and Confirmation Syncs with calendars or order management systems. Confirms booking or provides tracking info. Call Ends Customer gets what they need without waiting. Team stays focused on higher-priority tasks. If the fear is that Voice AI will sound robotic or annoy customers, it’s worth reconsidering. Today’s tech is way more natural and human-like than it used to be. You can use free trial of platforms like Retell AI or Play AI or Bland AI (I will not promote) Would it make sense for your startup to try Voice AI?

Online Reputation AI - Startup got stuck

Hi, I‘m one of 3 co-founders of a startup that built an AI-driven SaaS and App product this year. We‘re coming from an SaaS background, two of us senior developers (in the 3% of highest earning freelancers in Germany) and expert in our fields. The third is a seasoned sales strategist. We have a minor 4th co-founder (legal advisor). The company is self-funded, no investors. Our tech is owned by us, built by us and the product was already operational after a few months. We basically solve three data science/NLP issues in a generalized way: understand customer feedback to improve your business. Analyzes online review with context and explains it with a drill down, aggregation, charts (AI insights, timeframe reports); evidence driven, agentic LLM and ETL processes drive this. respond to customer feedback, half-automated, human in the loop, but AI supported. In the tone of your brand, any language. And context-aware, with your customer support signature etc. competitor analysis. Because we do 1 for you, we can do 1. for all of your competitors and compare the results, yielding insights like „oh, this happens to everyone in November to December, so I should focus on something else“ — etc. Now, after a huge sales effort we got only one paying customer. This customer is petty happy with the product. They tell us that they use our product daily, it‘s better than all the other solutions out there (better than TrustYou, etc.) However, after cold calling/emailing hundreds of leads, we almost always hear that „what we have is good enough“. Or that they don‘t have budget. I‘m the introverted tech part of the startup. I‘m good with algorithms. Give me any tech issue and I will solve it for you quickly and efficiently. I make stuff work. But with my startups I never had commercial luck. People always tell me about my stellar potential, because I can build things almost nobody else can. I come from a poor families background, worked my way up the very hard way. I just love tech and programming. I wrote a book for O’Reilly once. I‘m not doing bad economically, but I‘m probably not the best sales person. After founding a few startups with amazing tech, people using the products and loving them, but no commercial success, I truly question myself and if I‘m just unlucky with the fact that I‘m located in Europe, targeting the wrong industries, or are just unlucky somehow? I won‘t blame my co-founders here. They definitely did the best they could. I‘m just a bit resignated. I recently thought about valuing my own lifetime more and only building software for myself anymore. Basically not focusing on what problems other people face and trying to solve them, but solely focusing on what I enjoy doing most — e.g. coding algorithms for a music visualizer. Because in the end, my time is my most valuable resource. If I waste any second on something that isn‘t contributing to „my life“ and how I define success, then it would be a rather stupid deed? I don‘t want to derail too much here. I‘m confused and seeking for advice. Burn me if you like, but please be aware that you are talking to a broadly educated nerd.

How a founder built a B2B AI startup to serve with 65+ global brands (including Fortune500 companies) (I will not promote)

AI Palette is an AI-driven platform that helps food and beverage companies predict emerging product trends. I had the opportunity recently to sit down with the founder to get his advice on building an AI-first startup, which he'll be going through in this post. (I will not promote) About AI Palette: Co-founders: >!2 (Somsubhra GanChoudhuri, Himanshu Upreti)!!100+!!$12.7M USD!!AI-powered predictive analytics for the CPG (Consumer Packaged Goods) industry!!Signed first paying customer in the first year!!65+ global brands, including Cargill, Diageo, Ajinomoto, Symrise, Mondelez, and L’Oréal, use AI Palette!!Every new product launched has secured a paying client within months!!Expanded into Beauty & Personal Care (BPC), onboarding one of India’s largest BPC companies within weeks!!Launched multiple new product lines in the last two years, creating a unified suite for brand innovation!Identify the pain points in your industry for ideas* When I was working in the flavour and fragrance industry, I noticed a major issue CPG companies faced: launching a product took at least one to two years. For instance, if a company decided today to launch a new juice, it wouldn’t hit the market until 2027. This long timeline made it difficult to stay relevant and on top of trends. Another big problem I noticed was that companies relied heavily on market research to determine what products to launch. While this might work for current consumer preferences, it was highly inefficient since the product wouldn’t actually reach the market for several years. By the time the product launched, the consumer trends had already shifted, making that research outdated. That’s where AI can play a crucial role. Instead of looking at what consumers like today, we realised that companies should use AI to predict what they will want next. This allows businesses to create products that are ahead of the curve. Right now, the failure rate for new product launches is alarmingly high, with 8 out of 10 products failing. By leveraging AI, companies can avoid wasting resources on products that won’t succeed, leading to better, more successful launches. Start by talking to as many industry experts as possible to identify the real problems When we first had the idea for AI Palette, it was just a hunch, a gut feeling—we had no idea whether people would actually pay for it. To validate the idea, we reached out to as many people as we could within the industry. Since our focus area was all about consumer insights, we spoke to professionals in the CPG sector, particularly those in the insights departments of CPG companies. Through these early conversations, we began to see a common pattern emerge and identified the exact problem we wanted to solve. Don’t tell people what you’re building—listen to their frustrations and challenges first. Going into these early customer conversations, our goal was to listen and understand their challenges without telling them what we were trying to build. This is crucial as it ensures that you can gather as much data about the problem to truly understand it and that you aren't biasing their answers by showing your solution. This process helped us in two key ways: First, it validated that there was a real problem in the industry through the number of people who spoke about experiencing the same problem. Second, it allowed us to understand the exact scale and depth of the problem—e.g., how much money companies were spending on consumer research, what kind of tools they were currently using, etc. Narrow down your focus to a small, actionable area to solve initially. Once we were certain that there was a clear problem worth solving, we didn’t try to tackle everything at once. As a small team of two people, we started by focusing on a specific area of the problem—something big enough to matter but small enough for us to handle. Then, we approached customers with a potential solution and asked them for feedback. We learnt that our solution seemed promising, but we wanted to validate it further. If customers are willing to pay you for the solution, it’s a strong validation signal for market demand. One of our early customer interviewees even asked us to deliver the solution, which we did manually at first. We used machine learning models to analyse the data and presented the results in a slide deck. They paid us for the work, which was a critical moment. It meant we had something with real potential, and we had customers willing to pay us before we had even built the full product. This was the key validation that we needed. By the time we were ready to build the product, we had already gathered crucial insights from our early customers. We understood the specific information they wanted and how they wanted the results to be presented. This input was invaluable in shaping the development of our final product. Building & Product Development Start with a simple concept/design to validate with customers before building When we realised the problem and solution, we began by designing the product, but not by jumping straight into coding. Instead, we created wireframes and user interfaces using tools like InVision and Figma. This allowed us to visually represent the product without the need for backend or frontend development at first. The goal was to showcase how the product would look and feel, helping potential customers understand its value before we even started building. We showed these designs to potential customers and asked for feedback. Would they want to buy this product? Would they pay for it? We didn’t dive into actual development until we found a customer willing to pay a significant amount for the solution. This approach helped us ensure we were on the right track and didn’t waste time or resources building something customers didn’t actually want. Deliver your solution using a manual consulting approach before developing an automated product Initially, we solved problems for customers in a more "consulting" manner, delivering insights manually. Recall how I mentioned that when one of our early customer interviewees asked us to deliver the solution, we initially did it manually by using machine learning models to analyse the data and presenting the results to them in a slide deck. This works for the initial stages of validating your solution, as you don't want to invest too much time into building a full-blown MVP before understanding the exact features and functionalities that your users want. However, after confirming that customers were willing to pay for what we provided, we moved forward with actual product development. This shift from a manual service to product development was key to scaling in a sustainable manner, as our building was guided by real-world feedback and insights rather than intuition. Let ongoing customer feedback drive iteration and the product roadmap Once we built the first version of the product, it was basic, solving only one problem. But as we worked closely with customers, they requested additional features and functionalities to make it more useful. As a result, we continued to evolve the product to handle more complex use cases, gradually developing new modules based on customer feedback. Product development is a continuous process. Our early customers pushed us to expand features and modules, from solving just 20% of their problems to tackling 50–60% of their needs. These demands shaped our product roadmap and guided the development of new features, ultimately resulting in a more complete solution. Revenue and user numbers are key metrics for assessing product-market fit. However, critical mass varies across industries Product-market fit (PMF) can often be gauged by looking at the size of your revenue and the number of customers you're serving. Once you've reached a certain critical mass of customers, you can usually tell that you're starting to hit product-market fit. However, this critical mass varies by industry and the type of customers you're targeting. For example, if you're building an app for a broad consumer market, you may need thousands of users. But for enterprise software, product-market fit may be reached with just a few dozen key customers. Compare customer engagement and retention with other available solutions on the market for product-market fit Revenue and the number of customers alone isn't always enough to determine if you're reaching product-market fit. The type of customer and the use case for your product also matter. The level of engagement with your product—how much time users are spending on the platform—is also an important metric to track. The more time they spend, the more likely it is that your product is meeting a crucial need. Another way to evaluate product-market fit is by assessing retention, i.e whether users are returning to your platform and relying on it consistently, as compared to other solutions available. That's another key indication that your solution is gaining traction in the market. Business Model & Monetisation Prioritise scalability Initially, we started with a consulting-type model where we tailor-made specific solutions for each customer use-case we encountered and delivered the CPG insights manually, but we soon realized that this wasn't scalable. The problem with consulting is that you need to do the same work repeatedly for every new project, which requires a large team to handle the workload. That is not how you sustain a high-growth startup. To solve this, we focused on building a product that would address the most common problems faced by our customers. Once built, this product could be sold to thousands of customers without significant overheads, making the business scalable. With this in mind, we decided on a SaaS (Software as a Service) business model. The benefit of SaaS is that once you create the software, you can sell it to many customers without adding extra overhead. This results in a business with higher margins, where the same product can serve many customers simultaneously, making it much more efficient than the consulting model. Adopt a predictable, simplistic business model for efficiency. Look to industry practices for guidance When it came to monetisation, we considered the needs of our CPG customers, who I knew from experience were already accustomed to paying annual subscriptions for sales databases and other software services. We decided to adopt the same model and charge our customers an annual upfront fee. This model worked well for our target market, aligning with industry standards and ensuring stable, recurring revenue. Moreover, our target CPG customers were already used to this business model and didn't have to choose from a huge variety of payment options, making closing sales a straightforward and efficient process. Marketing & Sales Educate the market to position yourself as a thought leader When we started, AI was not widely understood, especially in the CPG industry. We had to create awareness around both AI and its potential value. Our strategy focused on educating potential users and customers about AI, its relevance, and why they should invest in it. This education was crucial to the success of our marketing efforts. To establish credibility, we adopted a thought leadership approach. We wrote blogs on the importance of AI and how it could solve problems for CPG companies. We also participated in events and conferences to demonstrate our expertise in applying AI to the industry. This helped us build our brand and reputation as leaders in the AI space for CPG, and word-of-mouth spread as customers recognized us as the go-to company for AI solutions. It’s tempting for startups to offer products for free in the hopes of gaining early traction with customers, but this approach doesn't work in the long run. Free offerings don’t establish the value of your product, and customers may not take them seriously. You should always charge for pilots, even if the fee is minimal, to ensure that the customer is serious about potentially working with you, and that they are committed and engaged with the product. Pilots/POCs/Demos should aim to give a "flavour" of what you can deliver A paid pilot/POC trial also gives you the opportunity to provide a “flavour” of what your product can deliver, helping to build confidence and trust with the client. It allows customers to experience a detailed preview of what your product can do, which builds anticipation and desire for the full functionality. During this phase, ensure your product is built to give them a taste of the value you can provide, which sets the stage for a broader, more impactful adoption down the line. Fundraising & Financial Management Leverage PR to generate inbound interest from VCs When it comes to fundraising, our approach was fairly traditional—we reached out to VCs and used connections from existing investors to make introductions. However, looking back, one thing that really helped us build momentum during our fundraising process was getting featured in Tech in Asia. This wasn’t planned; it just so happened that Tech in Asia was doing a series on AI startups in Southeast Asia and they reached out to us for an article. During the interview, they asked if we were fundraising, and we mentioned that we were. As a result, several VCs we hadn’t yet contacted reached out to us. This inbound interest was incredibly valuable, and we found it far more effective than our outbound efforts. So, if you can, try to generate some PR attention—it can help create inbound interest from VCs, and that interest is typically much stronger and more promising than any outbound strategies because they've gone out of their way to reach out to you. Be well-prepared and deliberate about fundraising. Keep trying and don't lose heart When pitching to VCs, it’s crucial to be thoroughly prepared, as you typically only get one shot at making an impression. If you mess up, it’s unlikely they’ll give you a second chance. You need to have key metrics at your fingertips, especially if you're running a SaaS company. Be ready to answer questions like: What’s your retention rate? What are your projections for the year? How much will you close? What’s your average contract value? These numbers should be at the top of your mind. Additionally, fundraising should be treated as a structured process, not something you do on the side while juggling other tasks. When you start, create a clear plan: identify 20 VCs to reach out to each week. By planning ahead, you’ll maintain momentum and speed up the process. Fundraising can be exhausting and disheartening, especially when you face multiple rejections. Remember, you just need one investor to say yes to make it all worthwhile. When using funds, prioritise profitability and grow only when necessary. Don't rely on funding to survive. In the past, the common advice for startups was to raise money, burn through it quickly, and use it to boost revenue numbers, even if that meant operating at a loss. The idea was that profitability wasn’t the main focus, and the goal was to show rapid growth for the next funding round. However, times have changed, especially with the shift from “funding summer” to “funding winter.” My advice now is to aim for profitability as soon as possible and grow only when it's truly needed. For example, it’s tempting to hire a large team when you have substantial funds in the bank, but ask yourself: Do you really need 10 new hires, or could you get by with just four? Growing too quickly can lead to unnecessary expenses, so focus on reaching profitability as soon as possible, rather than just inflating your team or burn rate. The key takeaway is to spend your funds wisely and only when absolutely necessary to reach profitability. You want to avoid becoming dependent on future VC investments to keep your company afloat. Instead, prioritize reaching break-even as quickly as you can, so you're not reliant on external funding to survive in the long run. Team-Building & Leadership Look for complementary skill sets in co-founders When choosing a co-founder, it’s important to find someone with a complementary skill set, not just someone you’re close to. For example, I come from a business and commercial background, so I needed someone with technical expertise. That’s when I found my co-founder, Himanshu, who had experience in machine learning and AI. He was a great match because his technical knowledge complemented my business skills, and together we formed a strong team. It might seem natural to choose your best friend as your co-founder, but this can often lead to conflict. Chances are, you and your best friend share similar interests, skills, and backgrounds, which doesn’t bring diversity to the table. If both of you come from the same industry or have the same strengths, you may end up butting heads on how things should be done. Having diverse skill sets helps avoid this and fosters a more collaborative working relationship. Himanshu (left) and Somsubhra (right) co-founded AI Palette in 2018 Define roles clearly to prevent co-founder conflict To avoid conflict, it’s essential that your roles as co-founders are clearly defined from the beginning. If your co-founder and you have distinct responsibilities, there is no room for overlap or disagreement. This ensures that both of you can work without stepping on each other's toes, and there’s mutual respect for each other’s expertise. This is another reason as to why it helps to have a co-founder with a complementary skillset to yours. Not only is having similar industry backgrounds and skillsets not particularly useful when building out your startup, it's also more likely to lead to conflicts since you both have similar subject expertise. On the other hand, if your co-founder is an expert in something that you're not, you're less likely to argue with them about their decisions regarding that aspect of the business and vice versa when it comes to your decisions. Look for employees who are driven by your mission, not salary For early-stage startups, the first hires are crucial. These employees need to be highly motivated and excited about the mission. Since the salary will likely be low and the work demanding, they must be driven by something beyond just the paycheck. The right employees are the swash-buckling pirates and romantics, i.e those who are genuinely passionate about the startup’s vision and want to be part of something impactful beyond material gains. When employees are motivated by the mission, they are more likely to stick around and help take the startup to greater heights. A litmus test for hiring: Would you be excited to work with them on a Sunday? One of the most important rounds in the hiring process is the culture fit round. This is where you assess whether a candidate shares the same values as you and your team. A key question to ask yourself is: "Would I be excited to work with this person on a Sunday?" If there’s any doubt about your answer, it’s likely not a good fit. The idea is that you want employees who align with the company's culture and values and who you would enjoy collaborating with even outside of regular work hours. How we structure the team at AI Palette We have three broad functions in our organization. The first two are the big ones: Technical Team – This is the core of our product and technology. This team is responsible for product development and incorporating customer feedback into improving the technology Commercial Team – This includes sales, marketing, customer service, account managers, and so on, handling everything related to business growth and customer relations. General and Administrative Team – This smaller team supports functions like finance, HR, and administration. As with almost all businesses, we have teams that address the two core tasks of building (technical team) and selling (commercial team), but given the size we're at now, having the administrative team helps smoothen operations. Set broad goals but let your teams decide on execution What I've done is recruit highly skilled people who don't need me to micromanage them on a day-to-day basis. They're experts in their roles, and as Steve Jobs said, when you hire the right person, you don't have to tell them what to do—they understand the purpose and tell you what to do. So, my job as the CEO is to set the broader goals for them, review the plans they have to achieve those goals, and periodically check in on progress. For example, if our broad goal is to meet a certain revenue target, I break it down across teams: For the sales team, I’ll look at how they plan to hit that target—how many customers they need to sell to, how many salespeople they need, and what tactics and strategies they plan to use. For the technical team, I’ll evaluate our product offerings—whether they think we need to build new products to attract more customers, and whether they think it's scalable for the number of customers we plan to serve. This way, the entire organization's tasks are cascaded in alignment with our overarching goals, with me setting the direction and leaving the details of execution to the skilled team members that I hire.

After building an AI Co-founder to solve my startup struggles, I realized we might be onto something bigger. What problems would you want YOUR AI Co-founder to solve?

A few days ago, I shared my entrepreneurial journey and the endless loop of startup struggles I was facing. The response from the community was overwhelming, and it validated something I had stumbled upon while trying to solve my own problems. In just a matter of days, we've built out the core modules I initially used for myself, deep market research capabilities, automated outreach systems, and competitor analysis. It's surreal to see something born out of personal frustration turning into a tool that others might actually find valuable. But here's where it gets interesting (and where I need your help). While we're actively onboarding users for our alpha test, I can't shake the feeling that we're just scratching the surface. We've built what helped me, but what would help YOU? When you're lying awake at 3 AM, stressed about your startup, what tasks do you wish you could delegate to an AI co-founder who actually understands context and can take meaningful action? Of course, it's not a replacement for an actual AI cofounder, but using our prior entrepreneurial experience and conversations with other folks, we understand that OUTREACH and SALES might actually be a big problem statement we can go deeper on as it naturally helps with the following: Idea Validation - Testing your assumptions with real customers before building Pricing strategy - Understanding what the market is willing to pay Product strategy - Getting feedback on features and roadmap Actually revenue - Converting conversations into real paying customers I'm not asking you to imagine some sci-fi scenario, we've already built modules that can: Generate comprehensive 20+ page market analysis reports with actionable insights Handle customer outreach Monitor competitors and target accounts, tracking changes in their strategy Take supervised actions based on the insights gathered (Manual effort is required currently) But what else should it do? What would make you trust an AI co-founder with parts of your business? Or do you think this whole concept is fundamentally flawed? I'm committed to building this the right way, not just another AI tool or an LLM Wrapper, but an agentic system that can understand your unique challenges and work towards overcoming them. Whether you think this is revolutionary or ridiculous, I want to hear your honest thoughts. But more importantly, I want to hear your unfiltered feedback in the comments. What would make this truly valuable for YOU? Edit 1: The AI cofounder will take no equity in your startup.

I will not promote — just need advice from fellow startup entrepreneurs

Hey everyone, I’m now based in the San Francisco Bay Area. I’m starting a development agency to help early-stage startups and small businesses turn ideas into real products. Whether it’s building an MVP, scaling an existing app, or providing a dedicated offshore team — that’s the direction I’m heading. Quick background on me: I work with: • Frontend: React.js, Next.js, React Native • Backend: Node.js, Express, Supabase, Firebase • Databases: MongoDB, PostgreSQL • AI/API Integrations: OpenAI and others • DevOps/Automation: n8n, serverless tools, cloud platforms I’ve built and designed dozens of products, and now I have a small but strong team of 5 devs/designers in India. We can scale fast and deliver at Indian pricing — but that’s not my pitch here. I’m not here to promote. I’m genuinely looking for feedback and ideas from others doing similar things. If you’re running a dev agency, working with offshore teams, or supporting early-stage founders, I’d love to know: • What’s working for you in terms of client acquisition? • How are you building trust in the early stages? • Are there any strategies or lessons you wish you knew when starting? Also, if this aligns with something you’re building and you’re open to collaboration, I’m all ears. Let’s connect and share what’s working. Appreciate any thoughts, and happy to answer any questions too!

Startup founders, tell me what's the job you would like to get done in your startup but you are too busy to repeat or start it?

I've been focusing on LLM-based agent development for the past two years and am now building a related startup. I want to validate my startup ideas and would especially like to hear feedback from entrepreneurs. What are the repetitive tasks in your startup journey that need to be completed but you're too busy to handle or initiate? I can develop specialized intelligent agents to solve these issues for you. Whether it's workflow automation or more advanced agent AI applications, whatever problems you want to solve, I'm willing to try and help. For just a small fee (provided it's truly an important problem worth solving), I can customize solutions for you. Following the principle of "do things that don't scale," I'm more focused on helping entrepreneurs quickly and effectively address their pain points. Through this approach, we can explore together the practical value of AI agents in startup scenarios and gradually improve product functionality. Feel free to share any specific business scenarios or needs you have. Let's discuss how AI agents can truly help entrepreneurs improve their work efficiency.

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!

Feeling stuck—built a startup, got rejected from YC & IVI, met smarter people, and now I don’t know what to do. ( i will not promote ) I will not promote

I will not promote I don’t even know where to start, but I just feel completely stuck right now. I’m 20 years oldI don’t even know where to start, but I just feel completely stuck right now. I’m 20 years old, have been grinding non-stop for months, and it feels like I have nothing to show for it. I built an AI agent that automates workflows for businesses. I can build tech, but I can’t sell. That’s been my biggest realization recently—I thought building would be enough, but it’s not. I need customers, I need a co-founder, I need to figure out the business side… and I have no idea how. I applied to YC, IVI at ISB, and EF, met a lot of insanely smart people—some were impressed with me and my work, but they were wiser, more experienced, and honestly, just better at all of this than I am. It made me realize how much I don’t know. I got rejected from YC & IVI. 💔 YC didn’t even give much feedback—just a standard rejection. 💔 IVI told me: “You're too young, you need more experience, and you should work with a team before trying to start something.” That hit me hard. I had already been struggling to find a co-founder, and this just made me wonder if I even belong in this space yet. The Frustrating Part? I KNOW my tool Has a Unique Edge. I’m not just another AI automation tool—I know my tool has a strong USP that competitors lack. It has the potential to be an AI employee for businesses, not just another workflow tool. But I still haven’t built the “perfect product” I originally envisioned. And that’s what’s eating at me. I see what it COULD be, but I haven’t made it happen yet. At the same time, the competition in the AI agent space is exploding. YC-backed companies are working on AI agent startups. OpenAI is making huge progress with Operator. Competitors are moving fast, while I feel stuck. I’ve delayed development because I’m unsure whether to double down, pivot, or just move on entirely. Where I’m Stuck Right Now 🔹 Do I keep pushing and try to crack sales somehow? 🔹 Do I join a startup as a founding engineer to get experience, make connections, and learn sales before trying again? 🔹 Do I move to Bangalore, meet founders, and figure out what’s next? 🔹 Do I pivot to something nicher instead of competing in the AI agent race? If so, how do I even find a niche worth pursuing? 🔹 Do I even belong in startups? Or am I just forcing something that’s not working? I feel stuck in a weird middle zone where I’m not a beginner, but I’m also not successful. I’ve done enough to see what’s possible, but not enough to make it real. Every rejection makes me question if I’m even on the right path. I don’t know if I’m posting this for advice or just to get it out of my system. Maybe both. Has anyone else felt like this before? If you’ve been in this situation—how did you figure out whether to keep going or move on? TL;DR: I’m 20, built an AI agent for automating workflows, got rejected from YC & IVI, met insanely smart and experienced people, realized I can build tech but can’t sell, struggling to find a co-founder, AI agent competition is growing, delaying development, confused about the future—don’t know whether to double down, pivot, or move on. The frustrating part? I\ know I have a unique edge that others lack, but I still haven’t built the perfect product I originally envisioned.* edit: removed the tool's name

AI Business Ideas for Startups: What’s the Next Big Opportunity? (I will not promote)

AI is reshaping industries, and startups that leverage it right are gaining massive traction. But with so many possibilities, where should new founders focus? Some promising AI startup ideas right now: AI-powered automation – Replacing repetitive tasks in finance, HR, and legal AI chatbots & assistants – Personalized customer support, lead gen, and virtual agents AI-driven analytics – Helping businesses make smarter decisions with real-time insights AI content generation – Scaling marketing, SEO, and creative work effortlessly AI healthcare solutions – Virtual diagnostics, mental health support, and remote monitoring But here’s the challenge—building an AI startup isn’t just about tech, it’s about solving real problems. If you were launching an AI startup today, what problem would you solve?

What I Learned from a Failed Startup: Seeking Advice on Engineering, Co-Founder Agreements & Execution (i will not promote)

Hey everyone! Long-time lurker, first-time founder here. I’m reaching out to get feedback on a recent startup experience—what went wrong, what I could have done better, and how I should approach future opportunities. The Background There were three founders in this venture: • Founder A (CEO, 50%) – The product/growth guy who identified the problem space. • Founder B (Me, CTO, 37.5%) – A software engineer with a software dev shop and multiple clients. I wanted to diversify into building my own products but am not inherently a “product person.” • Founder C (COO, 12.5%) – Brought into the mix by Founder A, with the goal of leveraging his network for traction once the product was built. The idea was to create Product X, a solution targeting the SMB space while competitors were moving upmarket. It wasn’t revolutionary—more of a strategic market play. The Initial Plan & My Role • Founder A would define and prioritize product specs, guiding what needed to be built. • I (Founder B) didn’t have time to code myself, so I allocated engineers from my dev shop (which I personally paid for). My stake was adjusted from 32.5% to 37.5% to reflect this contribution. • Founder C was more of an observer early on, planning to help with traction once we had a product ready. We agreed on a 1-year cliff and a 4-year vesting schedule for equity. Where Things Started to Go Wrong • Lack of a Clear Product Roadmap – Founder A was very focused on getting something built fast, but we never signed off on a structured roadmap or milestones. I underestimated the complexity of what was actually needed for customer conversations. • Engineering Expectations vs. Reality – The team (one part-time lead + two full-time juniors from my dev shop) faced early feedback that development was too slow. In response, I ramped up the lead to full-time and added a part-time PM. But Founder A continued pushing for speed, despite real hurdles (OAuth integrations, etc.). • Shifting MVP Goalposts – Midway, Founder A concluded that an MVP wouldn’t cut it—we needed a more complete product to be competitive. This meant more engineering, more delays, and more of my own money spent on development. The Breaking Point Near the 1-year vesting mark, we had an opportunity: a paying client willing to fund an app. I didn’t have devs on the bench, so I asked Founder A to hold off our project briefly while I hired more engineers to avoid stalling either effort. This was the final straw. Founder A (with Founder C somewhat aligned) decided the arrangement wasn’t working—citing past disagreements and the “slowness” issue. The decision was made to end the partnership. Now, Founder A, as majority holder, is requesting a full handover of the code, Founder C is indifferent, and all engineering costs I covered are essentially lost. Key Takeaways (So Far) Crystal-Clear Agreements Upfront – A formalized product roadmap and timeline should’ve been locked in from day one. Business Needs > Engineering Standards – I wanted to build something solid and scalable, but in an early-stage startup, speed to market is king. This was before AI tools became mainstream, so our approach wasn’t as optimized. Don’t Overextend Without Protection – I personally financed all engineering, but without clear safeguards, that investment became a sunk cost. Expenses Must Be Distributed – I was solely covering engineering salaries, which created an imbalance in financial risk. Future partnerships should ensure costs are shared proportionally, rather than one person shouldering the burden. Where I Need Advice Looking back, I want to improve as an engineer, CEO, and co-founder. • What should I have done differently in structuring this partnership? • How do you balance engineering quality with the startup need for speed? • As a dev shop owner, how can I better navigate equity deals where I’m also bringing in engineering resources? I really appreciate everyone who went through this long post and provide any insights from founders, engineers, or anyone who has been in a similar situation. Thanks for reading! ===================================================================== For readers who might be thinking what set this type of expectation? Because I had a dev shop and I thought my co-founders will be understanding of my business circumstance and I was a bit trigger to build a product with a C-exec team, I gave the impression of "unlimited" engineering which I later realized down the line that it was not feasible for me. Something I learned that I have to be more careful with and set expectations accordingly from the very beginning. And from the feedback of the commenters here, I am much more aware what I should offer and how to set expectations, esp. in the early stages of execution. So thank you all! 🙏🏾 EDIT: I would like to thank everyone who contributed to this thread. You not only helped me but future founders who are considering to get into the startup scene!

Hot Take: Not all your startups need AI forced into them

I'm a final year Computer Engineering student, hence applying for jobs all around. There's this particular trend I've noticed with startups that are coming up these days. That is, even for the absolute basic stuff they'll use 'AI', and they'll think they built something 'revolutionary'. No. You're breaking your product in ways you don't realise. An example, that even some well established companies are guilty of: AI Chatbots You absolutely don't need them and it's an entire gimmick. If you really wanna implement a chatbot, connect the user to an actual person on your end, which I think is not possible if you're at a 'startup' stage. You'll need employees who can handle user queries in real time. If the user really is stuck let them use the 'Contact Us' page. A really close relative of mine is very vocal about the frustration he faces whenever he tries to use the AI Chatbot on any well known e-com website. The only case for AI Chatbot that makes sense is when it's directing the customer to an actual customer support rep if none of the AI's solutions is working for the customer. Even then, implementing a search page for FAQ is extremely easy and user friendly. Another example: AI Interviewer I recently interviewed for a startup, and their whole interviewing process was AI'zed?!?! No real person at the other end, I was answering to their questions which were in video format. They even had a 'mascot' / 'AI interviewer' avatar designed by an AI (AI-ception???). This mascot just text-to-speech'ed all the questions for me to rewind and hear what I missed again. And I had to record video and audio to answer these questions on their platform itself. The entire interview process just could've been a questionnaire, or if you're really concerned on the integrity of the interviewee, just take a few minutes out of your oh-so-busy schedule as a startup owner. Atleast for hiring employees who would make the most impact on your product going ahead. I say the most impact, because (atleast as a developer) the work done by these employees would define how robust your product is, and/or how easily other features can be integrated into the codebase. Trust me, refactoring code later on would only cost you time and money. These resources would rather be more useful in other departments of your startup. The only use case for an AI Interviewer I see is for preparing for an actual interview, provided that feedback is given to the user at the earliest, which you don't need to worry about as a startup owner. So yeah, you're probably better off without integrating AI in your product. Thank you for reading. TLDR; The title; I know AI is the new thing and gets everyone drooling and all, but for the love of God, just focus on what your startup does best and put real people behind it; Integrating AI without human intervention is as good as a broken product; Do your hiring yourself, or through real people, emphasizing on the fact that the people you hire at an early stage will define your growth ahead;

I Built a Startup at 18, Shut It Down at 21, and Now I Feel Lost at 22 - What Should I Do?

Here’s My Story: At 18, during my first year of studying computer engineering, I founded my first tech startup. For three years, I wore every hat, programming, marketing, sales, design, HR, product management, and even handling finances. It was a crash course in business and tech, and I learned more than I ever thought possible. By the time I shut it down in 2023, we had 8,000 app downloads, lots of positive feedback, and promising metrics. But despite the progress, I had to let it go (there might be a possibility of launching it again since I didn't shut it because it was a failure). Now at 22, I’m Lost. I’ve realized how much I want to master everything related to business and tech, but I feel scattered. When I tried creating content on LinkedIn, I wrote posts about AI, programming, HR, funding, and product management, and I have more drafts waiting. One day I want to start another startup. The next, I dream of becoming an AI product manager or just finding a stable job. With graduation coming next summer, I’m struggling to focus and figure out which path to take. I Need Your Advice. If you’ve been through something similar or have guidance for someone at this crossroads, I’d love to hear your thoughts. How do I find clarity and decide which direction to pursue? i will not promote

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!

AI Business Ideas for Startups: What’s the Next Big Opportunity? (I will not promote)

AI is reshaping industries, and startups that leverage it right are gaining massive traction. But with so many possibilities, where should new founders focus? Some promising AI startup ideas right now: AI-powered automation – Replacing repetitive tasks in finance, HR, and legal AI chatbots & assistants – Personalized customer support, lead gen, and virtual agents AI-driven analytics – Helping businesses make smarter decisions with real-time insights AI content generation – Scaling marketing, SEO, and creative work effortlessly AI healthcare solutions – Virtual diagnostics, mental health support, and remote monitoring But here’s the challenge—building an AI startup isn’t just about tech, it’s about solving real problems. If you were launching an AI startup today, what problem would you solve?

After building an AI Co-founder to solve my startup struggles, I realized we might be onto something bigger. What problems would you want YOUR AI Co-founder to solve?

A few days ago, I shared my entrepreneurial journey and the endless loop of startup struggles I was facing. The response from the community was overwhelming, and it validated something I had stumbled upon while trying to solve my own problems. In just a matter of days, we've built out the core modules I initially used for myself, deep market research capabilities, automated outreach systems, and competitor analysis. It's surreal to see something born out of personal frustration turning into a tool that others might actually find valuable. But here's where it gets interesting (and where I need your help). While we're actively onboarding users for our alpha test, I can't shake the feeling that we're just scratching the surface. We've built what helped me, but what would help YOU? When you're lying awake at 3 AM, stressed about your startup, what tasks do you wish you could delegate to an AI co-founder who actually understands context and can take meaningful action? Of course, it's not a replacement for an actual AI cofounder, but using our prior entrepreneurial experience and conversations with other folks, we understand that OUTREACH and SALES might actually be a big problem statement we can go deeper on as it naturally helps with the following: Idea Validation - Testing your assumptions with real customers before building Pricing strategy - Understanding what the market is willing to pay Product strategy - Getting feedback on features and roadmap Actually revenue - Converting conversations into real paying customers I'm not asking you to imagine some sci-fi scenario, we've already built modules that can: Generate comprehensive 20+ page market analysis reports with actionable insights Handle customer outreach Monitor competitors and target accounts, tracking changes in their strategy Take supervised actions based on the insights gathered (Manual effort is required currently) But what else should it do? What would make you trust an AI co-founder with parts of your business? Or do you think this whole concept is fundamentally flawed? I'm committed to building this the right way, not just another AI tool or an LLM Wrapper, but an agentic system that can understand your unique challenges and work towards overcoming them. Whether you think this is revolutionary or ridiculous, I want to hear your honest thoughts. But more importantly, I want to hear your unfiltered feedback in the comments. What would make this truly valuable for YOU? Edit 1: The AI cofounder will take no equity in your startup.

What I Learned from a Failed Startup: Seeking Advice on Engineering, Co-Founder Agreements & Execution (i will not promote)

Hey everyone! Long-time lurker, first-time founder here. I’m reaching out to get feedback on a recent startup experience—what went wrong, what I could have done better, and how I should approach future opportunities. The Background There were three founders in this venture: • Founder A (CEO, 50%) – The product/growth guy who identified the problem space. • Founder B (Me, CTO, 37.5%) – A software engineer with a software dev shop and multiple clients. I wanted to diversify into building my own products but am not inherently a “product person.” • Founder C (COO, 12.5%) – Brought into the mix by Founder A, with the goal of leveraging his network for traction once the product was built. The idea was to create Product X, a solution targeting the SMB space while competitors were moving upmarket. It wasn’t revolutionary—more of a strategic market play. The Initial Plan & My Role • Founder A would define and prioritize product specs, guiding what needed to be built. • I (Founder B) didn’t have time to code myself, so I allocated engineers from my dev shop (which I personally paid for). My stake was adjusted from 32.5% to 37.5% to reflect this contribution. • Founder C was more of an observer early on, planning to help with traction once we had a product ready. We agreed on a 1-year cliff and a 4-year vesting schedule for equity. Where Things Started to Go Wrong • Lack of a Clear Product Roadmap – Founder A was very focused on getting something built fast, but we never signed off on a structured roadmap or milestones. I underestimated the complexity of what was actually needed for customer conversations. • Engineering Expectations vs. Reality – The team (one part-time lead + two full-time juniors from my dev shop) faced early feedback that development was too slow. In response, I ramped up the lead to full-time and added a part-time PM. But Founder A continued pushing for speed, despite real hurdles (OAuth integrations, etc.). • Shifting MVP Goalposts – Midway, Founder A concluded that an MVP wouldn’t cut it—we needed a more complete product to be competitive. This meant more engineering, more delays, and more of my own money spent on development. The Breaking Point Near the 1-year vesting mark, we had an opportunity: a paying client willing to fund an app. I didn’t have devs on the bench, so I asked Founder A to hold off our project briefly while I hired more engineers to avoid stalling either effort. This was the final straw. Founder A (with Founder C somewhat aligned) decided the arrangement wasn’t working—citing past disagreements and the “slowness” issue. The decision was made to end the partnership. Now, Founder A, as majority holder, is requesting a full handover of the code, Founder C is indifferent, and all engineering costs I covered are essentially lost. Key Takeaways (So Far) Crystal-Clear Agreements Upfront – A formalized product roadmap and timeline should’ve been locked in from day one. Business Needs > Engineering Standards – I wanted to build something solid and scalable, but in an early-stage startup, speed to market is king. This was before AI tools became mainstream, so our approach wasn’t as optimized. Don’t Overextend Without Protection – I personally financed all engineering, but without clear safeguards, that investment became a sunk cost. Expenses Must Be Distributed – I was solely covering engineering salaries, which created an imbalance in financial risk. Future partnerships should ensure costs are shared proportionally, rather than one person shouldering the burden. Where I Need Advice Looking back, I want to improve as an engineer, CEO, and co-founder. • What should I have done differently in structuring this partnership? • How do you balance engineering quality with the startup need for speed? • As a dev shop owner, how can I better navigate equity deals where I’m also bringing in engineering resources? I really appreciate everyone who went through this long post and provide any insights from founders, engineers, or anyone who has been in a similar situation. Thanks for reading! ===================================================================== For readers who might be thinking what set this type of expectation? Because I had a dev shop and I thought my co-founders will be understanding of my business circumstance and I was a bit trigger to build a product with a C-exec team, I gave the impression of "unlimited" engineering which I later realized down the line that it was not feasible for me. Something I learned that I have to be more careful with and set expectations accordingly from the very beginning. And from the feedback of the commenters here, I am much more aware what I should offer and how to set expectations, esp. in the early stages of execution. So thank you all! 🙏🏾 EDIT: I would like to thank everyone who contributed to this thread. You not only helped me but future founders who are considering to get into the startup scene!

Honest thoughts on our startup

Hey y'all, we've recently started a marking push, overhauled the website, and our machine learning team has done drastic work to improve the reviews & auto pr descriptions. That being said, would love some candid feedback if you or your team members would consider using a tool like ours and if not, why not. Our company is Korbit AI and we do context based, AI code reviews and automated PR descriptions. While we have been getting more traction since some of the updates and larger companies are starting to sign up, I would really appreciate some honest opinions on why you \wouldn't\ try a tool like this in hopes we can use that info to alter marketing etc. This is not meant to be a promotion post, and legitimately looking to learn from other people rocking startups trying to build up a larger customer base etc. Any feedback is very much appreciated Thank you in advanced, Riley

Feeling stuck—built a startup, got rejected from YC & IVI, met smarter people, and now I don’t know what to do. ( i will not promote ) I will not promote

I will not promote I don’t even know where to start, but I just feel completely stuck right now. I’m 20 years oldI don’t even know where to start, but I just feel completely stuck right now. I’m 20 years old, have been grinding non-stop for months, and it feels like I have nothing to show for it. I built an AI agent that automates workflows for businesses. I can build tech, but I can’t sell. That’s been my biggest realization recently—I thought building would be enough, but it’s not. I need customers, I need a co-founder, I need to figure out the business side… and I have no idea how. I applied to YC, IVI at ISB, and EF, met a lot of insanely smart people—some were impressed with me and my work, but they were wiser, more experienced, and honestly, just better at all of this than I am. It made me realize how much I don’t know. I got rejected from YC & IVI. 💔 YC didn’t even give much feedback—just a standard rejection. 💔 IVI told me: “You're too young, you need more experience, and you should work with a team before trying to start something.” That hit me hard. I had already been struggling to find a co-founder, and this just made me wonder if I even belong in this space yet. The Frustrating Part? I KNOW my tool Has a Unique Edge. I’m not just another AI automation tool—I know my tool has a strong USP that competitors lack. It has the potential to be an AI employee for businesses, not just another workflow tool. But I still haven’t built the “perfect product” I originally envisioned. And that’s what’s eating at me. I see what it COULD be, but I haven’t made it happen yet. At the same time, the competition in the AI agent space is exploding. YC-backed companies are working on AI agent startups. OpenAI is making huge progress with Operator. Competitors are moving fast, while I feel stuck. I’ve delayed development because I’m unsure whether to double down, pivot, or just move on entirely. Where I’m Stuck Right Now 🔹 Do I keep pushing and try to crack sales somehow? 🔹 Do I join a startup as a founding engineer to get experience, make connections, and learn sales before trying again? 🔹 Do I move to Bangalore, meet founders, and figure out what’s next? 🔹 Do I pivot to something nicher instead of competing in the AI agent race? If so, how do I even find a niche worth pursuing? 🔹 Do I even belong in startups? Or am I just forcing something that’s not working? I feel stuck in a weird middle zone where I’m not a beginner, but I’m also not successful. I’ve done enough to see what’s possible, but not enough to make it real. Every rejection makes me question if I’m even on the right path. I don’t know if I’m posting this for advice or just to get it out of my system. Maybe both. Has anyone else felt like this before? If you’ve been in this situation—how did you figure out whether to keep going or move on? TL;DR: I’m 20, built an AI agent for automating workflows, got rejected from YC & IVI, met insanely smart and experienced people, realized I can build tech but can’t sell, struggling to find a co-founder, AI agent competition is growing, delaying development, confused about the future—don’t know whether to double down, pivot, or move on. The frustrating part? I\ know I have a unique edge that others lack, but I still haven’t built the perfect product I originally envisioned.* edit: removed the tool's name

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!

AI Startups: Are We in a Gold Rush or a Bubble? (I will not promote)

AI is everywhere—from automating workflows to building fully autonomous agents, startups are racing to integrate AI into everything. Funding is still flowing, and AI-powered tools are disrupting multiple industries. But here’s the reality: not all AI startups will survive. Many are just wrappers around existing models, offering little real innovation. The cost of running AI at scale is massive, and monetization is still a challenge. The ones that will last? Startups solving real problems, not just riding the hype. I see AI as a long-term game, but not every startup will make it. The ones that truly integrate AI as a value-add, rather than just a marketing buzzword, will thrive. What do you think—is AI the future, or are we in for a reality check? (I will not promote)

Are no-code tools actually slowing down startups?

I've noticed a concerning trend: while no-code tools promise quick launches for startups, many founders end up rebuilding their entire product from scratch later. Meanwhile, established platforms for startups like Zapier are doubling their prices, pushing teams to reconsider their stack entirely. What I'm seeing in the market: Bubble/FlutterFlow promise "build Uber in weeks" Make. com offers Zapier various automation features, but they are expensive, which made me look for alternatives. AI tools like Cursor + Claude claim to replace coding skills Traditional coding still takes 6+ months to learn basics Successful startups often rebuild their no-code MVPs Here's what makes me wonder: are we choosing speed over sustainability? Edit: I'm so happy that my post sparked so many beautiful minds. I'm going to process all the insights you've shared and publish on my subreddit, because one just can't lose it on the Internet

What Does “Building a Community” Actually Mean for a Startup?

I’ve talked to a lot of founders, and almost everyone gives the same advice: “Build your product and do sales at the same time. Also, build a community alongside it.” I get the first part. Shipping and selling together makes sense. But the “community building” part? That’s where things get blurry for me. Does community building mean posting regular updates on Twitter or LinkedIn? Does it mean making Instagram reels about the product? Or is it more about actually talking to potential customers one-on-one? When people say “build a community,” do they mean creating a place where users can interact with each other or just a way to keep them engaged with the product? The reason I’m asking is that I see different approaches everywhere. Some founders document their startup journey on social media, and that seems to attract an audience. Others focus on getting early users into a private group (Discord, Slack, or WhatsApp) and nurturing relationships there. And then there are those who take a totally different approach—like building in public, sharing code, or offering free tools to bring people in. For my startup, I’m trying to figure out what community building should look like in 2025. The startup landscape has changed drastically in the past year, especially with AI and automation becoming more mainstream. Founders no longer have time to manually interact with every user. So what’s the new way of doing this? What’s working for early-stage startups today? I’d love to hear thoughts from fellow founders. What does “community” actually mean in today’s world, and what’s the best way to build one?

Month 2 of building my startup after being laid off - $200 in revenue and 4 (actual) paying customers

In September 2024, I got laid off from my Silicon Valley job. It fucking sucked. I took a day to be sad, then got to work - I'm not one to wallow, I prefer action. Updated my resume, hit up my network, started interviewing. During this time, I had a realization - I'm tired of depending on a single income stream. I needed to diversify. Then it hit me: I literally work with RAG (retrieval augmented generation) in AI. Why not use this knowledge to help small businesses reduce their customer service load and boost sales? One month later, Answer HQ 0.5 (the MVP) was in the hands of our first users (shoutout to these alpha testers - their feedback shaped everything). By month 2, Answer HQ 1.0 launched with four paying customers, and growing. You're probably thinking - great, another chatbot. Yes, Answer HQ is a chatbot at its core. But here's the difference: it actually works. Our paying customers are seeing real results in reducing support load, plus it has something unique - it actively drives sales by turning customer questions into conversions. How? The AI doesn't just answer questions, it naturally recommends relevant products and content (blogs, social media, etc). Since I'm targeting small business owners (who usually aren't tech wizards) and early startups, Answer HQ had to be dead simple to set up. Here's my onboarding process - just 4 steps. I've checked out competitors like Intercom and Crisp, and I can say this: if my non-tech fiancée can set up an assistant on her blog in minutes, anyone can. Key learnings so far: Building in public is powerful. I shared my journey on Threads and X, and the support for a solo founder has been amazing. AI dev tools (Cursor, Claude Sonnet 3.5) have made MVP development incredibly accessible. You can get a working prototype frontend ready in days. I don't see how traditional no-code tools can survive in this age. But.. for a production-ready product? You still need dev skills and background. Example: I use Redis for super-fast loading of configs and themes. An AI won't suggest this optimization unless you know to ask for it. Another example: Cursor + Sonnet 3.5 struggles with code bases with many files and dependencies. It will change things you don't want it to change. Unless you can read code + understand it + know what needs to be changed and not changed, you'll easily run into upper limits of what prompting alone can do. I never mention "artificial intelligence" "AI" "machine learning" or any of these buzzwords once in my copy in my landing page, docs, product, etc. There is no point. Your customers do not care that something has AI in it. AI is not the product. Solving their pain points and problems is the product. AI is simply a tool of many tools like databases, APIs, caching, system design, etc. Early on, I personally onboarded every user through video calls. Time-consuming? Yes. But it helped me deeply understand their pain points and needs. I wasn't selling tech - I was showing them solutions to their problems. Tech stack: NextJS/React/Tailwind/shadcn frontend, Python FastAPI backend. Using Supabase Postgres, Upstash Redis, and Pinecone for different data needs. Hosted on Vercel and Render.com. Customer growth: Started with one alpha tester who saw such great results (especially in driving e-commerce sales) that he insisted on paying for a full year to keep me motivated. This led to two monthly customers, then a fourth annual customer after I raised prices. My advisor actually pushed me to raise prices again, saying I was undercharging for the value provided. I have settled on my final pricing now. I am learning so much. Traditionally, I have a software development and product management background. I am weak in sales and marketing. Building that app, designing the architecture, talking to customers, etc, these are all my strong suits. I enjoy doing it too. But now I need to improve on my ability to market the startup and really start learning things like SEO, content marketing, cold outreach, etc. I enjoying learning new skills. Happy to answer any questions about the journey so far!

Growing an AI startup

Hi everyone! I'm currently navigating the waters of building out an AI startup, specifically one that leverages the LLMS to enhance business operations—our platform, Invisioned, aims to customize AI solutions for businesses looking to innovate their processes. We have Agents for various use cases and our own proprietary data. Given the complexity and fast-paced nature of AI technologies, I'm grappling with several challenges, particularly around scaling effectively since it’s subscription based. How did you get your first subscriber? I would love to hear from fellow entrepreneurs who have faced similar hurdles. What strategies did you find most effective in scaling your tech startup? Any insights or resources you could share would be greatly appreciated!. Thanks in advance.

Funny story about a misinformed startup founder...

So I do artificial intelligence/ML for clients on a by-project basis. I was contacted by an entrepreneur yesterday, and the pitch was too good not to share: The person was founding a software startup. Neither he nor the other cofounder are "tech people," and so they know nothing about software. He wanted me to come onboard as the "technical" founder, because I can do AI and they want to put AI into their software product. He did not know anything about AI, so he wanted me to tell him what it is and what it could do for their software. I got the vibe that he had just heard about this magical thing called AI on the news, and without any understanding of it, thought "that would be cool to have." They had no funding. I offered a suggestion that could make their product idea successful, and they rejected it because it would be too hard. I politely declined, and told them to contact me when they would like to hire me to build something for them.

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

How I Made $0 in One Month with My AI Startup

How I Made $0 in One Month with My AI Startup Step 1: Built an AI writing tool. Step 2: Expected users to magically appear. Step 3: Checked my analytics—still just me and my mom using it. Step 4: Did what every founder does—refreshed the dashboard 47 times a day. Step 5: Launched on Product Hunt. Got 12 upvotes. Half were from my alternate accounts. Step 6: Thought about running ads. Remembered I had no budget. Step 7: Scrolled Twitter for "growth hacks." Implemented none. Step 8: Posted on LinkedIn. My post got 2 likes—one from a bot. Step 9: Checked my Stripe account. Still $0. Moral of the story? Just launching isn’t enough. If you’re building an AI tool (or anything), people won’t magically show up—you need to market, iterate, and actually talk to users. Speaking of AI tools, I ended up building Panda AI Studio because AI tools should be easy to use, not a headache. No complicated prompts, no figuring out how to "talk" to AI. Just 200+ tools that do what you need for writing, content, marketing, job hunting, and more. If that sounds like something you’d actually use, check it out. Maybe I’ll finally break the $1 mark next month. 😅

New Year Resolution: I Will Generate Some Viable SaaS Ideas AND Help You Become a Brand New AI Startup Founder Within 7 Days

Over the Christmas period, I conceived and debuted on some reddit communities, The 7-Day Startup Challenge. The feedback I got from the various communities have been nothing short of fantastic! The 7-Day Startup Challenge simply means leveraging the power of no code platforms like Bubble, Flutterflow, Glide, Thunkable, Softr etc. along with AI APIs to build a functioning MicroSaaS/SaaS within 7 days. I can tailor this around your interests or hobbies so you are more passionate about your new startup. Whether you're a startup novice or a veteran, I am happy to work with you every step of the way. I will work with you from validating and refining your idea(s) to building and publishing your app! I can even work with you on a viable marketing strategy that will help fetch your new startup some revenue within the next 10 to 45 days. Here's what I will provide as part of The 7-Day Startup Challenge A fully validated and refined version of your idea described in technical terms in a shared document A startup name, domain and logo (if you don't have one already) A landing page to capture pre-sign ups, generate some early buzz and index your app on search engines Figma files showing the design of your app(s) Web app (dependent on whether your startup idea requires a web app or a mobile app instead)) iOS app (dependent on whether your startup idea requires a web app or a mobile app instead) Android app (dependent on whether your startup idea requires a web app or a mobile app instead) 1-month of in scope support to fix any bugs and address any issues An outlined marketing strategy you can implement to grow your startup both short and long term. As per tentative timelines, you can expect the following deliverables on schedule Day 1: Secure digital assets such as domain name, hosting, logo etc.; deliver validated and refined version of your startup idea Day 2-3: Landing page & Figma files Day 1-5/6: Build your apps (web app and/or iOS and Android app) Day 6: Evaluations and review if necessary; demo day Day 7: Live launch on web; publish on Android and iOS app stores PS: For more sophisticated ideas (non MicroSaaS), kindly allow approx. 30 days for delivery. I can be as hands on or hands off as you wish. Meaning I can do all the work whilst you sit back and wait for the results OR I can work with you every step of the way to deliver on your demands. For high potential startup ideas, I can partner with you long term to build them out together. I have to be selective because I'm unable to partner together on every single idea out there. Outside of a partnership, all the digital assets (startup name, logo, web app, mobile app etc.) are 100% owned by you. If building an AI SaaS startup via the outlined strategy sounds intriguing enough to you, feel free to send me a DM with any questions you have!

I am building my agency to help founders build AI startups after 2 successful AI SaaS exits and 4 failures

Hey everyone, I have been building AI products before ChatGPT was launched. In these years, I have managed to launch, scale and exit 2 SaaS products successfully. Today I am launching a new service offering - Query Labs - Helping you build AI agents for your startups. Like all my previous products, I will be building this in public and share my learning along the way. Here's what I have built so far : Microsponsors ( Fail ) My first product ever. I tried to create a marketplace for newsletter writers to find sponsorship opportunity. Got a few very big newsletter listed on the marketplace as well. However, building marketplace is tough. I found it very difficult to bring in sponsors. Ended up shutting it down, AI Query (Exit - Pre revenue ) It was the second half of 2022 and GPT-3 was the most advance AI on the market. I decided to build a tool that can help developers and non-technical folks write SQL queries by just asking in plain english. I got my first taste of success with this. Had a decent offer even before I figured out monetisation. Accepted the offer to focus on my next product which had already started gaining traction AI Excel Bot ( Exit - Revenue Generating ) AI Excel Bot was my wild success. I had worked hard on the SEO for the site, along with the UI / UX to make it the best AI to write excel formulas and general excel task. There was already a large competitor in the market. However, the reality is that you don't need to be the top player. There is always room for multiple players to survive in a large market. You just need to find the good differentiating factor For AI Excel Bot, the differentiator was the chrome extension, that helped users access it anywhere on the internet. Scaled the product to more than 40k users at the time of exit. However, in the end I decided to exit and focus on my software service business that needed more time. Tutore AI ( Fail ) I wanted to build something useful for students to help them learn better. Tutore was my idea to build AI tools for students. I did launch quickly with multiple tools. However, wasn't motivated enough to continue with the grind. I have decided to sell the product. Have had some meetings with potential buyers but didn't agree on price. Prompt Hackers ( 1k users but no revenue ) Prompt Hackers is a directory of AI prompts for all the use cases you can image. I focused a lot on bringing traffic and newsletter subscription from the day 1. I have never had a problem bringing initial set of users to my products. Prompt Hackers was getting close to 20k page views a month. At the same time we had close to 1k newsletter subscribers. Since our target customers were people choosing to use ChatGPT / Bard instead of some specific software for their task, I built a Prompt Generation and Prompt Optimisation AI. Along with this I also created features to build private prompt library. To make the experience even better, I launched a Chrome Extension that helps users access the prompt generation AI and their prompt library while using ChatGPT. However, I couldn't figure out monetisation. I still get close to 4k page views per month with no marketing at all. There are users who use the AI tools and the prompt library feature daily. But, since I couldn't figure out monetisation, I decided to not put time into the project. There you go. These are all the products I have built in the last 3 years. I have been heavy investing myself in the latest tech in LLMs and AI agents. I know the biggest challenge for AI founders is the AI agents and backend pipelines. That's why I am launching Query Labs. To help you build the best AI implementation for your innovative AI startup. I would love to hear feedback from the community. I will be sharing my learning with my new service along the way. Thanks!

I built an ai startup, got paying customers—then made it free because it felt like a scam

A few months ago, I launched my first AI startup, AppScriptor. It was an AI-powered tool that could generate mobile apps automatically. At first, I was thrilled—I got a lot of reactions, paying customers, and people excited about the idea. But the more I worked on it, the more I realized a major flaw: real-time app previews were nearly impossible. Users wanted instant updates and fast iterations, but the technology just wasn’t there yet. I felt guilty charging people for something that wasn’t delivering the experience they expected. It wasn’t bad, but it wasn’t good enough. I didn’t want to keep collecting payments while knowing I couldn’t improve it quickly enough—so I made it completely free. Eventually, I shut it down. That experience taught me a crucial lesson: people don’t just want an MVP; they want something useful. That’s why I built StarterPilot—an AI-driven platform that helps entrepreneurs validate ideas, generate branding assets, and build landing pages effortlessly. This time, I’m making sure it actually delivers. What’s the hardest part of launching a startup for you? Would love to hear your experiences!

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.

[N] No-code AI: Former Microsoft and Salesforce execs reveal new ‘machine teaching’ startup Intelus - Build your model and label at the same time.

Hi! We have opened up our new labeling AI tool to try for free. Their team pioneered the machine teaching concept and the approach allows you to automatically select and generate labels and create a model for labeling more data at the same time. Try it and let us know what you think. \-Intelus.ai GeekWire: https://www.geekwire.com/2021/no-code-ai-former-microsoft-and-salesforce-execs-reveal-new-machine-teaching-startup-intelus/

[N] How Stability AI’s Founder Tanked His Billion-Dollar Startup

forbes article: https://www.forbes.com/sites/kenrickcai/2024/03/29/how-stability-ais-founder-tanked-his-billion-dollar-startup/ archive no paywall: https://archive.is/snbeV How Stability AI’s Founder Tanked His Billion-Dollar Startup Mar 29, 2024 Stability AI founder Emad Mostaque took the stage last week at the Terranea Resort in Palos Verdes, California to roaring applause and an introduction from an AI-generated Aristotle who announced him as “a modern Prometheus” with “the astuteness of Athena and the vision of Daedalus.” “Under his stewardship, AI becomes the Herculean force poised to vanquish the twin serpents of illness and ailment and extend the olive branch of longevity,” the faux Aristotle proclaimed. “I think that’s the best intro I’ve ever had,” Mostaque said. But behind Mostaque's hagiographic introduction lay a grim and fast metastasizing truth. Stability, once one of AI’s buzziest startups, was floundering. It had been running out of money for months and Mostaque had been unable to secure enough additional funding. It had defaulted on payments to Amazon whose cloud service undergirded Stability’s core offerings. The star research team behind its flagship text-to-image generator Stable Diffusion had tendered their resignations just three days before — as Forbes would first report — and other senior leaders had issued him an ultimatum: resign, or we walk too. Still, onstage before a massive audience of peers and acolytes, Mostaque talked a big game. “AI is jet planes for the mind,” he opined. “AI is our collective intelligence. It's the human Colossus.” He claimed a new, faster version of the Stable Diffusion image generator released earlier this month could generate “200 cats with hats per second.” But later, when he was asked about Stability’s financial model, Mostaque fumbled. “I can’t say that publicly,” he replied. “But it’s going well. We’re ahead of forecast.” Four days later, Mostaque stepped down as CEO of Stability, as Forbes first reported. In a post to X, the service formerly known as Twitter, he claimed he’d voluntarily abdicated his role to decentralize “the concentration of power in AI.” But sources told Forbes that was hardly the case. Behind the scenes, Mostaque had fought to maintain his position and control despite mounting pressure externally and internally to step down. Company documents and interviews with 32 current and former employees, investors, collaborators and industry observers suggest his abrupt exit was the result of poor business judgment and wild overspending that undermined confidence in his vision and leadership, and ultimately kneecapped the company. Mostaque, through his attorneys, declined to comment on record on a detailed list of questions about the reporting in this story. But in an email to Forbes earlier this week he broadly disputed the allegations. “Nobody tells you how hard it is to be a CEO and there are better CEOs than me to scale a business,” he said in a statement. “I am not sure anyone else would have been able to build and grow the research team to build the best and most widely used models out there and I’m very proud of the team there. I look forward to moving onto the next problem to handle and hopefully move the needle.” In an emailed statement, Christian Laforte and Shan Shan Wong, the interim co-CEOs who replaced Mostaque, said, "the company remains focused on commercializing its world leading technology” and providing it “to partners across the creative industries." After starting Stability in 2019, Mostaque built the company into an early AI juggernaut by seizing upon a promising research project that would become Stable Diffusion and funding it into a business reality. The ease with which the software generated detailed images from the simplest text prompts immediately captivated the public: 10 million people used it on any given day, the company told Forbes in early 2023. For some true believers, Mostaque was a crucial advocate for open-source AI development in a space dominated by the closed systems of OpenAI, Google and Anthropic. But his startup’s rise to one of the buzziest in generative AI was in part built on a series of exaggerations and misleading claims, as Forbes first reported last year (Mostaque disputed some points at the time). And they continued after he raised $100 million at a $1 billion valuation just days after launching Stable Diffusion in 2022. His failure to deliver on an array of grand promises, like building bespoke AI models for nation states, and his decision to pour tens of millions into research without a sustainable business plan, eroded Stability’s foundations and jeopardized its future. "He was just giving shit away,” one former employee told Forbes. “That man legitimately wanted to transform the world. He actually wanted to train AI models for kids in Malawi. Was it practical? Absolutely not." By October 2023, Stability would have less than $4 million left in the bank, according to an internal memo prepared for a board meeting and reviewed by Forbes. And mounting debt, including months of overdue Amazon Web Services payments, had already left it in the red. To avoid legal penalties for skipping Americans staff’s payroll, the document explained, the London-based startup was considering delaying tax payments to the U.K. government. It was Stability’s armada of GPUs, the wildly powerful and equally expensive chips undergirding AI, that were so taxing the company’s finances. Hosted by AWS, they had long been one of Mostaque’s bragging points; he often touted them as one of the world’s 10 largest supercomputers. They were responsible for helping Stability’s researchers build and maintain one of the top AI image generators, as well as break important new ground on generative audio, video and 3D models. “Undeniably, Stability has continued to ship a lot of models,” said one former employee. “They may not have profited off of it, but the broader ecosystem benefitted in a huge, huge way.” But the costs associated with so much compute were now threatening to sink the company. According to an internal October financial forecast seen by Forbes, Stability was on track to spend $99 million on compute in 2023. It noted as well that Stability was “underpaying AWS bills for July (by $1M)” and “not planning to pay AWS at the end of October for August usage ($7M).” Then there were the September and October bills, plus $1 million owed to Google Cloud and $600,000 to GPU cloud data center CoreWeave. (Amazon, Google and CoreWeave declined to comment.) With an additional $54 million allocated to wages and operating expenses, Stability’s total projected costs for 2023 were $153 million. But according to its October financial report, its projected revenue for the calendar year was just $11 million. Stability was on track to lose more money per month than it made in an entire year. The company’s dire financial position had thoroughly soured Stability’s current investors, including Coatue, which had invested tens of millions in the company during its $101 million funding round in 2022. In the middle of 2023, Mostaque agreed to an independent audit after Coatue raised a series of concerns, according to a source with direct knowledge of the matter. The outcome of the investigation is unclear. Coatue declined to comment. Within a week of an early October board meeting where Mostaque shared that financial forecast, Lightspeed Venture Partners, another major investor, sent a letter to the board urging them to sell the company. The distressing numbers had “severely undermined” the firm’s confidence in Mostaque’s ability to lead the company. “In particular, we are surprised and deeply concerned by a cash position just now disclosed to us that is inconsistent with prior discussions on this topic,” Lightspeed’s general counsel Brett Nissenberg wrote in the letter, a copy of which was viewed by Forbes. “Lightspeed believes that the company is not likely financeable on terms that would assure the company’s long term sound financial position.” (Lightspeed declined a request for comment.) The calls for a sale led Stability to quietly begin looking for a buyer. Bloomberg reported in November that Stability approached AI startups Cohere and Jasper to gauge their interest. Stability denied this, and Jasper CEO Timothy Young did the same when reached for comment by Forbes. A Cohere representative declined to comment. But one prominent AI company confirmed that Mostaque’s representatives had reached out to them to test the waters. Those talks did not advance because “the numbers didn’t add up,” this person, who declined to be named due to the confidential nature of the talks, told Forbes. Stability also tried to court Samsung as a buyer, going so far as to redecorate its office in advance of a planned meeting with the Korean electronics giant. (Samsung said that it invested in Stability in 2023 and that it does not comment on M&A discussions.) Coatue had been calling for Mostaque’s resignation for months, according to a source with direct knowledge. But it and other investors were unable to oust him because he was the company’s majority shareholder. When they tried a different tact by rallying other investors to offer him a juicy equity package to resign, Mostaque refused, said two sources. By October, Coatue and Lightspeed had had enough. Coatue left the board and Lightspeed resigned its observer seat. “Emad infuriated our initial investors so much it’s just making it impossible for us to raise more money under acceptable terms,” one current Stability executive told Forbes. The early months of 2024 saw Stability’s already precarious position eroding further still. Employees were quietly laid off. Three people in a position to know estimated that at least 10% of staff were cut. And cash reserves continued to dwindle. Mostaque mentioned a lifeline at the October board meeting: $95 million in tentative funding from new investors, pending due diligence. But in the end, only a fraction of it was wired, two sources say, much of it from Intel, which Forbes has learned invested $20 million, a fraction of what was reported. (Intel did not return a request for comment by publication time.) Two hours after Forbes broke the news of Mostaque’s plans to step down as CEO, Stability issued a press release confirming his resignation. Chief operating officer Wong and chief technology officer Laforte have taken over in the interim. Mostaque, who said on X that he still owns a majority of the company, also stepped down from the board, which has now initiated a search for a permanent CEO. There is a lot of work to be done to turn things around, and very little time in which to do it. Said the current Stability executive, “There’s still a possibility of a turnaround story, but the odds drop by the day.” In July of 2023, Mostaque still thought he could pull it off. Halfway through the month, he shared a fundraising plan with his lieutenants. It was wildly optimistic, detailing the raise of $500 million in cash and another $750 million in computing facilities from marquee investors like Nvidia, Google, Intel and the World Bank (Nvidia and Google declined comment. Intel did not respond. The World Bank said it did not invest in Stability). In a Slack message reviewed by Forbes, Mostaque said Google was “willing to move fast” and the round was “likely to be oversubscribed.” It wasn’t. Three people with direct knowledge of these fundraising efforts told Forbes that while there was some interest in Stability, talks often stalled when it came time to disclose financials. Two of them noted that earlier in the year, Mostaque had simply stopped engaging with VCs who asked for numbers. Only one firm invested around that time: actor Ashton Kutcher’s Sound Ventures, which invested $35 million in the form of a convertible SAFE note during the second quarter, according to an internal document. (Sound Ventures did not respond to a request for comment.) And though he’d managed to score a meeting with Nvidia and its CEO Jensen Huang, it ended in disaster, according to two sources. “Under Jensen's microscopic questions, Emad just fell apart,” a source in position to know told Forbes. Huang quickly concluded Stability wasn’t ready for an investment from Nvidia, the sources said. Mostaque told Forbes in an email that he had not met with Huang since 2022, except to say “hello and what’s up a few times after.” His July 2023 message references a plan to raise $150 million from Nvidia. (Nvidia declined to comment.) After a June Forbes investigation citing more than 30 sources revealed Mostaque’s history of misleading claims, Mostaque struggled to raise funding, a Stability investor told Forbes. (Mostaque disputed the story at the time and called it "coordinated lies" in his email this week to Forbes). Increasingly, investors scrutinized his assertions and pressed for data. And Young, now the CEO of Jasper, turned down a verbal offer to be Stability’s president after reading the article, according to a source with direct knowledge of the matter. The collapse of the talks aggravated the board and other executives, who had hoped Young would compensate for the sales and business management skills that Mostaque lacked, according to four people in a position to know. (Young declined to comment.) When Stability’s senior leadership convened in London for the CogX conference in September, the financing had still not closed. There, a group of executives confronted Mostaque asking questions about the company’s cash position and runway, according to three people with direct knowledge of the incident. They did not get the clarity they’d hoped for. By October, Mostaque had reduced his fundraising target by more than 80%. The months that followed saw a steady drumbeat of departures — general counsel Adam Avrunin, vice presidents Mike Melnicki, Ed Newton-Rex and Joe Penna, chief people officer Ozden Onder — culminating in the demoralizing March exit of Stable Diffusion’s primary developers Robin Rombach, Andreas Blattmann, Patrick Esser and Dominik Lorenz. Rombach, who led the team, had been angling to leave for months, two sources said, first threatening to resign last summer because of the fundraising failures. Others left over concerns about cash flow, as well as liabilities — including what four people described as Mostaque’s lax approach to ensuring that Stability products could not be used to produce child sexual abuse imagery. “Stability AI is committed to preventing the misuse of AI and prohibits the use of our image models and services for unlawful activity, including attempts to edit or create CSAM,” Ella Irwin, senior vice president of integrity, said in a statement. Newton-Rex told Forbes he resigned because he disagreed with Stability’s position that training AI on copyrighted work without consent is fair use. Melnicki and Penna declined to comment. Avrunin and Onder could not be reached for comment. None of the researchers responded to requests for comment. The Stable Diffusion researchers’ departure as a cohort says a lot about the state of Stability AI. The company’s researchers were widely viewed as its crown jewels, their work subsidized with a firehose of pricey compute power that was even extended to people outside the company. Martino Russi, an artificial intelligence researcher, told Forbes that though he was never formally employed by Stability, the company provided him a “staggering” amount of compute between January and April 2023 to play around with developing an AI video generator that Stability might someday use. “It was Candy Land or Coney Island,” said Russi, who estimates that his experiment, which was ultimately shelved, cost the company $2.5 million. Stable Diffusion was simultaneously Stability’s marquee product and its existential cash crisis. One current employee described it to Forbes as “a giant vacuum that absorbed everything: money, compute, people.” While the software was widely used, with Mostaque claiming downloads reaching into the hundreds of millions, Stability struggled to translate that wild success into revenue. Mostaque knew it could be done — peers at Databricks, Elastic and MongoDB had all turned a free product into a lucrative business — he just couldn’t figure out how. His first attempt was Stability’s API, which allowed paying customers to integrate Stable Diffusion into their own products. In early 2023, a handful of small companies, like art generator app NightCafe and presentation software startup Tome, signed on, according to four people with knowledge of the deals. But Stability’s poor account management services soured many, and in a matter of months NightCafe and Tome canceled their contracts, three people said. NightCafe founder Angus Russell told Forbes that his company switched to a competitor which “offered much cheaper inference costs and a broader service.” Tome did not respond to a request for comment. Meanwhile, Mostaque’s efforts to court larger companies like Samsung and Snapchat were failing, according to five people familiar with the effort. Canva, which was already one of the heaviest users of open-sourced Stable Diffusion, had multiple discussions with Stability, which was angling for a contract it hoped would generate several millions in annual revenue. But the deal never materialized, four sources said. “These three companies wanted and needed us,” one former employee told Forbes. “They would have been the perfect customers.” (Samsung, Snap and Canva declined to comment.) “It’s not that there was not an appetite to pay Stability — there were tons of companies that would have that wanted to,” the former employee said. “There was a huge opportunity and demand, but just a resistance to execution.” Mostaque’s other big idea was to provide governments with bespoke national AI models that would invigorate their economies and citizenry. “Emad envisions a world where AI through 100 national models serves not as a tool of the few, but as a benefactor to all promising to confront great adversaries, cancer, autism, and the sands of time itself,” the AI avatar of Aristotle said in his intro at the conference. Mostaque told several prospective customers that he could deliver such models within 60 days — an untenable timeline, according to two people in position to know. Stability attempted to develop a model for the Singaporean government over the protestation of employees who questioned its technical feasibility, three sources familiar with the effort told Forbes. But it couldn’t pull it off and Singapore never became a customer. (The government of Singapore confirmed it did not enter into a deal with Stability, but declined to answer additional questions.) As Stability careened from one new business idea to another, resources were abruptly reallocated and researchers reassigned. The whiplash shifts in a largely siloed organization demoralized and infuriated employees. “There were ‘urgent’ things, ‘urgent urgent’ things and ‘most urgent,’” one former employee complained. “None of these things seem important if everything is important.” Another former Stability executive was far more pointed in their assessment. “Emad is the most disorganized leader I have ever worked with in my career,” this person told Forbes. “He has no vision, and changes directions every week, often based on what he sees on Twitter.” In a video interview posted shortly before this story was published, Mostaque explained his leadership style: “I'm particularly great at taking creatives, developers, researchers, others, and achieving their full potential in designing systems. But I should not be dealing with, you know, HR and operations and business development and other elements. There are far better people than me to do that.” By December 2023, Stability had partially abandoned its open-source roots and announced that any commercial use of Stable Diffusion would cost customers at least $20 per month (non-commercial and research use of Stable Diffusion would remain free). But privately, Stability was considering a potentially more lucrative source of revenue: reselling the compute it was leasing from providers like AWS, according to six people familiar with the effort. Though it was essentially GPU arbitrage, Stability framed the strategy to investors as a “managed services” offering. Its damning October financial report projected optimistically that such an offering would bring in $139 million in 2024 — 98% of its revenue. Multiple employees at the time told Forbes they feared reselling compute, even if the company called it “managed services,” would violate the terms of Stability’s contract with AWS. Amazon declined to comment. “The line internally was that we are not reselling compute,” one former employee said. “This was some of the dirtiest feeling stuff.” Stability also discussed reselling a cluster of Nvidia A100 chips, leased via CoreWeave, to the venture capital firm Andreessen Horowitz, three sources said. “It was under the guise of managed services, but there wasn’t any management happening,” one of these people told Forbes. Andreessen Horowitz and CoreWeave declined to comment. Stability did not respond to questions about if it plans to continue this strategy now that Mostaque is out of the picture. Regardless, interim co-CEOs Wong and Laforte are on a tight timeline to clean up his mess. Board chairman Jim O’Shaughnessy said in a statement that he was confident the pair “will adeptly steer the company forward in developing and commercializing industry-leading generative AI products.” But burn continues to far outpace revenue. The Financial Times reported Friday that the company made $5.4 million of revenue in February, against $8 million in costs. Several sources said there are ongoing concerns about making payroll for the roughly 150 remaining employees. Leadership roles have gone vacant for months amid the disarray, leaving the company increasingly directionless. Meanwhile, a potentially catastrophic legal threat looms over the company: A trio of copyright infringement lawsuits brought by Getty Images and a group of artists in the U.S. and U.K., who claim Stability illegally used their art and photography to train the AI models powering Stable Diffusion. A London-based court has already rejected the company’s bid to throw out one of the lawsuits on the basis that none of its researchers were based in the U.K. And Stability’s claim that Getty’s Delaware lawsuit should be blocked because it's a U.K.-based company was rejected. (Stability did not respond to questions about the litigation.) AI-related copyright litigation “could go on for years,” according to Eric Goldman, a law professor at Santa Clara University. He told Forbes that though plaintiffs suing AI firms face an uphill battle overcoming the existing legal precedent on copyright infringement, the quantity of arguments available to make are virtually inexhaustible. “Like in military theory, if there’s a gap in your lines, that’s where the enemy pours through — if any one of those arguments succeeds, it could completely change the generative AI environment,” he said. “In some sense, generative AI as an industry has to win everything.” Stability, which had more than $100 million in the bank just a year and a half ago, is in a deep hole. Not only does it need more funding, it needs a viable business model — or a buyer with the vision and chops to make it successful in a fast-moving and highly competitive sector. At an all hands meeting this past Monday, Stability’s new leaders detailed a path forward. One point of emphasis: a plan to better manage resources and expenses, according to one person in attendance. It’s a start, but Mostaque’s meddling has left them with little runway to execute. His resignation, though, has given some employees hope. “A few people are 100% going to reconsider leaving after today,” said one current employee. “And the weird gloomy aura of hearing Emad talking nonsense for an hour is gone.” Shortly before Mostaque resigned, one current Stability executive told Forbes that they were optimistic his departure could make Stability appealing enough to receive a small investment or sale to a friendly party. “There are companies that have raised hundreds of millions of dollars that have much less intrinsic value than Stability,” the person said. “A white knight may still appear.”

[N] How Stability AI’s Founder Tanked His Billion-Dollar Startup

forbes article: https://www.forbes.com/sites/kenrickcai/2024/03/29/how-stability-ais-founder-tanked-his-billion-dollar-startup/ archive no paywall: https://archive.is/snbeV How Stability AI’s Founder Tanked His Billion-Dollar Startup Mar 29, 2024 Stability AI founder Emad Mostaque took the stage last week at the Terranea Resort in Palos Verdes, California to roaring applause and an introduction from an AI-generated Aristotle who announced him as “a modern Prometheus” with “the astuteness of Athena and the vision of Daedalus.” “Under his stewardship, AI becomes the Herculean force poised to vanquish the twin serpents of illness and ailment and extend the olive branch of longevity,” the faux Aristotle proclaimed. “I think that’s the best intro I’ve ever had,” Mostaque said. But behind Mostaque's hagiographic introduction lay a grim and fast metastasizing truth. Stability, once one of AI’s buzziest startups, was floundering. It had been running out of money for months and Mostaque had been unable to secure enough additional funding. It had defaulted on payments to Amazon whose cloud service undergirded Stability’s core offerings. The star research team behind its flagship text-to-image generator Stable Diffusion had tendered their resignations just three days before — as Forbes would first report — and other senior leaders had issued him an ultimatum: resign, or we walk too. Still, onstage before a massive audience of peers and acolytes, Mostaque talked a big game. “AI is jet planes for the mind,” he opined. “AI is our collective intelligence. It's the human Colossus.” He claimed a new, faster version of the Stable Diffusion image generator released earlier this month could generate “200 cats with hats per second.” But later, when he was asked about Stability’s financial model, Mostaque fumbled. “I can’t say that publicly,” he replied. “But it’s going well. We’re ahead of forecast.” Four days later, Mostaque stepped down as CEO of Stability, as Forbes first reported. In a post to X, the service formerly known as Twitter, he claimed he’d voluntarily abdicated his role to decentralize “the concentration of power in AI.” But sources told Forbes that was hardly the case. Behind the scenes, Mostaque had fought to maintain his position and control despite mounting pressure externally and internally to step down. Company documents and interviews with 32 current and former employees, investors, collaborators and industry observers suggest his abrupt exit was the result of poor business judgment and wild overspending that undermined confidence in his vision and leadership, and ultimately kneecapped the company. Mostaque, through his attorneys, declined to comment on record on a detailed list of questions about the reporting in this story. But in an email to Forbes earlier this week he broadly disputed the allegations. “Nobody tells you how hard it is to be a CEO and there are better CEOs than me to scale a business,” he said in a statement. “I am not sure anyone else would have been able to build and grow the research team to build the best and most widely used models out there and I’m very proud of the team there. I look forward to moving onto the next problem to handle and hopefully move the needle.” In an emailed statement, Christian Laforte and Shan Shan Wong, the interim co-CEOs who replaced Mostaque, said, "the company remains focused on commercializing its world leading technology” and providing it “to partners across the creative industries." After starting Stability in 2019, Mostaque built the company into an early AI juggernaut by seizing upon a promising research project that would become Stable Diffusion and funding it into a business reality. The ease with which the software generated detailed images from the simplest text prompts immediately captivated the public: 10 million people used it on any given day, the company told Forbes in early 2023. For some true believers, Mostaque was a crucial advocate for open-source AI development in a space dominated by the closed systems of OpenAI, Google and Anthropic. But his startup’s rise to one of the buzziest in generative AI was in part built on a series of exaggerations and misleading claims, as Forbes first reported last year (Mostaque disputed some points at the time). And they continued after he raised $100 million at a $1 billion valuation just days after launching Stable Diffusion in 2022. His failure to deliver on an array of grand promises, like building bespoke AI models for nation states, and his decision to pour tens of millions into research without a sustainable business plan, eroded Stability’s foundations and jeopardized its future. "He was just giving shit away,” one former employee told Forbes. “That man legitimately wanted to transform the world. He actually wanted to train AI models for kids in Malawi. Was it practical? Absolutely not." By October 2023, Stability would have less than $4 million left in the bank, according to an internal memo prepared for a board meeting and reviewed by Forbes. And mounting debt, including months of overdue Amazon Web Services payments, had already left it in the red. To avoid legal penalties for skipping Americans staff’s payroll, the document explained, the London-based startup was considering delaying tax payments to the U.K. government. It was Stability’s armada of GPUs, the wildly powerful and equally expensive chips undergirding AI, that were so taxing the company’s finances. Hosted by AWS, they had long been one of Mostaque’s bragging points; he often touted them as one of the world’s 10 largest supercomputers. They were responsible for helping Stability’s researchers build and maintain one of the top AI image generators, as well as break important new ground on generative audio, video and 3D models. “Undeniably, Stability has continued to ship a lot of models,” said one former employee. “They may not have profited off of it, but the broader ecosystem benefitted in a huge, huge way.” But the costs associated with so much compute were now threatening to sink the company. According to an internal October financial forecast seen by Forbes, Stability was on track to spend $99 million on compute in 2023. It noted as well that Stability was “underpaying AWS bills for July (by $1M)” and “not planning to pay AWS at the end of October for August usage ($7M).” Then there were the September and October bills, plus $1 million owed to Google Cloud and $600,000 to GPU cloud data center CoreWeave. (Amazon, Google and CoreWeave declined to comment.) With an additional $54 million allocated to wages and operating expenses, Stability’s total projected costs for 2023 were $153 million. But according to its October financial report, its projected revenue for the calendar year was just $11 million. Stability was on track to lose more money per month than it made in an entire year. The company’s dire financial position had thoroughly soured Stability’s current investors, including Coatue, which had invested tens of millions in the company during its $101 million funding round in 2022. In the middle of 2023, Mostaque agreed to an independent audit after Coatue raised a series of concerns, according to a source with direct knowledge of the matter. The outcome of the investigation is unclear. Coatue declined to comment. Within a week of an early October board meeting where Mostaque shared that financial forecast, Lightspeed Venture Partners, another major investor, sent a letter to the board urging them to sell the company. The distressing numbers had “severely undermined” the firm’s confidence in Mostaque’s ability to lead the company. “In particular, we are surprised and deeply concerned by a cash position just now disclosed to us that is inconsistent with prior discussions on this topic,” Lightspeed’s general counsel Brett Nissenberg wrote in the letter, a copy of which was viewed by Forbes. “Lightspeed believes that the company is not likely financeable on terms that would assure the company’s long term sound financial position.” (Lightspeed declined a request for comment.) The calls for a sale led Stability to quietly begin looking for a buyer. Bloomberg reported in November that Stability approached AI startups Cohere and Jasper to gauge their interest. Stability denied this, and Jasper CEO Timothy Young did the same when reached for comment by Forbes. A Cohere representative declined to comment. But one prominent AI company confirmed that Mostaque’s representatives had reached out to them to test the waters. Those talks did not advance because “the numbers didn’t add up,” this person, who declined to be named due to the confidential nature of the talks, told Forbes. Stability also tried to court Samsung as a buyer, going so far as to redecorate its office in advance of a planned meeting with the Korean electronics giant. (Samsung said that it invested in Stability in 2023 and that it does not comment on M&A discussions.) Coatue had been calling for Mostaque’s resignation for months, according to a source with direct knowledge. But it and other investors were unable to oust him because he was the company’s majority shareholder. When they tried a different tact by rallying other investors to offer him a juicy equity package to resign, Mostaque refused, said two sources. By October, Coatue and Lightspeed had had enough. Coatue left the board and Lightspeed resigned its observer seat. “Emad infuriated our initial investors so much it’s just making it impossible for us to raise more money under acceptable terms,” one current Stability executive told Forbes. The early months of 2024 saw Stability’s already precarious position eroding further still. Employees were quietly laid off. Three people in a position to know estimated that at least 10% of staff were cut. And cash reserves continued to dwindle. Mostaque mentioned a lifeline at the October board meeting: $95 million in tentative funding from new investors, pending due diligence. But in the end, only a fraction of it was wired, two sources say, much of it from Intel, which Forbes has learned invested $20 million, a fraction of what was reported. (Intel did not return a request for comment by publication time.) Two hours after Forbes broke the news of Mostaque’s plans to step down as CEO, Stability issued a press release confirming his resignation. Chief operating officer Wong and chief technology officer Laforte have taken over in the interim. Mostaque, who said on X that he still owns a majority of the company, also stepped down from the board, which has now initiated a search for a permanent CEO. There is a lot of work to be done to turn things around, and very little time in which to do it. Said the current Stability executive, “There’s still a possibility of a turnaround story, but the odds drop by the day.” In July of 2023, Mostaque still thought he could pull it off. Halfway through the month, he shared a fundraising plan with his lieutenants. It was wildly optimistic, detailing the raise of $500 million in cash and another $750 million in computing facilities from marquee investors like Nvidia, Google, Intel and the World Bank (Nvidia and Google declined comment. Intel did not respond. The World Bank said it did not invest in Stability). In a Slack message reviewed by Forbes, Mostaque said Google was “willing to move fast” and the round was “likely to be oversubscribed.” It wasn’t. Three people with direct knowledge of these fundraising efforts told Forbes that while there was some interest in Stability, talks often stalled when it came time to disclose financials. Two of them noted that earlier in the year, Mostaque had simply stopped engaging with VCs who asked for numbers. Only one firm invested around that time: actor Ashton Kutcher’s Sound Ventures, which invested $35 million in the form of a convertible SAFE note during the second quarter, according to an internal document. (Sound Ventures did not respond to a request for comment.) And though he’d managed to score a meeting with Nvidia and its CEO Jensen Huang, it ended in disaster, according to two sources. “Under Jensen's microscopic questions, Emad just fell apart,” a source in position to know told Forbes. Huang quickly concluded Stability wasn’t ready for an investment from Nvidia, the sources said. Mostaque told Forbes in an email that he had not met with Huang since 2022, except to say “hello and what’s up a few times after.” His July 2023 message references a plan to raise $150 million from Nvidia. (Nvidia declined to comment.) After a June Forbes investigation citing more than 30 sources revealed Mostaque’s history of misleading claims, Mostaque struggled to raise funding, a Stability investor told Forbes. (Mostaque disputed the story at the time and called it "coordinated lies" in his email this week to Forbes). Increasingly, investors scrutinized his assertions and pressed for data. And Young, now the CEO of Jasper, turned down a verbal offer to be Stability’s president after reading the article, according to a source with direct knowledge of the matter. The collapse of the talks aggravated the board and other executives, who had hoped Young would compensate for the sales and business management skills that Mostaque lacked, according to four people in a position to know. (Young declined to comment.) When Stability’s senior leadership convened in London for the CogX conference in September, the financing had still not closed. There, a group of executives confronted Mostaque asking questions about the company’s cash position and runway, according to three people with direct knowledge of the incident. They did not get the clarity they’d hoped for. By October, Mostaque had reduced his fundraising target by more than 80%. The months that followed saw a steady drumbeat of departures — general counsel Adam Avrunin, vice presidents Mike Melnicki, Ed Newton-Rex and Joe Penna, chief people officer Ozden Onder — culminating in the demoralizing March exit of Stable Diffusion’s primary developers Robin Rombach, Andreas Blattmann, Patrick Esser and Dominik Lorenz. Rombach, who led the team, had been angling to leave for months, two sources said, first threatening to resign last summer because of the fundraising failures. Others left over concerns about cash flow, as well as liabilities — including what four people described as Mostaque’s lax approach to ensuring that Stability products could not be used to produce child sexual abuse imagery. “Stability AI is committed to preventing the misuse of AI and prohibits the use of our image models and services for unlawful activity, including attempts to edit or create CSAM,” Ella Irwin, senior vice president of integrity, said in a statement. Newton-Rex told Forbes he resigned because he disagreed with Stability’s position that training AI on copyrighted work without consent is fair use. Melnicki and Penna declined to comment. Avrunin and Onder could not be reached for comment. None of the researchers responded to requests for comment. The Stable Diffusion researchers’ departure as a cohort says a lot about the state of Stability AI. The company’s researchers were widely viewed as its crown jewels, their work subsidized with a firehose of pricey compute power that was even extended to people outside the company. Martino Russi, an artificial intelligence researcher, told Forbes that though he was never formally employed by Stability, the company provided him a “staggering” amount of compute between January and April 2023 to play around with developing an AI video generator that Stability might someday use. “It was Candy Land or Coney Island,” said Russi, who estimates that his experiment, which was ultimately shelved, cost the company $2.5 million. Stable Diffusion was simultaneously Stability’s marquee product and its existential cash crisis. One current employee described it to Forbes as “a giant vacuum that absorbed everything: money, compute, people.” While the software was widely used, with Mostaque claiming downloads reaching into the hundreds of millions, Stability struggled to translate that wild success into revenue. Mostaque knew it could be done — peers at Databricks, Elastic and MongoDB had all turned a free product into a lucrative business — he just couldn’t figure out how. His first attempt was Stability’s API, which allowed paying customers to integrate Stable Diffusion into their own products. In early 2023, a handful of small companies, like art generator app NightCafe and presentation software startup Tome, signed on, according to four people with knowledge of the deals. But Stability’s poor account management services soured many, and in a matter of months NightCafe and Tome canceled their contracts, three people said. NightCafe founder Angus Russell told Forbes that his company switched to a competitor which “offered much cheaper inference costs and a broader service.” Tome did not respond to a request for comment. Meanwhile, Mostaque’s efforts to court larger companies like Samsung and Snapchat were failing, according to five people familiar with the effort. Canva, which was already one of the heaviest users of open-sourced Stable Diffusion, had multiple discussions with Stability, which was angling for a contract it hoped would generate several millions in annual revenue. But the deal never materialized, four sources said. “These three companies wanted and needed us,” one former employee told Forbes. “They would have been the perfect customers.” (Samsung, Snap and Canva declined to comment.) “It’s not that there was not an appetite to pay Stability — there were tons of companies that would have that wanted to,” the former employee said. “There was a huge opportunity and demand, but just a resistance to execution.” Mostaque’s other big idea was to provide governments with bespoke national AI models that would invigorate their economies and citizenry. “Emad envisions a world where AI through 100 national models serves not as a tool of the few, but as a benefactor to all promising to confront great adversaries, cancer, autism, and the sands of time itself,” the AI avatar of Aristotle said in his intro at the conference. Mostaque told several prospective customers that he could deliver such models within 60 days — an untenable timeline, according to two people in position to know. Stability attempted to develop a model for the Singaporean government over the protestation of employees who questioned its technical feasibility, three sources familiar with the effort told Forbes. But it couldn’t pull it off and Singapore never became a customer. (The government of Singapore confirmed it did not enter into a deal with Stability, but declined to answer additional questions.) As Stability careened from one new business idea to another, resources were abruptly reallocated and researchers reassigned. The whiplash shifts in a largely siloed organization demoralized and infuriated employees. “There were ‘urgent’ things, ‘urgent urgent’ things and ‘most urgent,’” one former employee complained. “None of these things seem important if everything is important.” Another former Stability executive was far more pointed in their assessment. “Emad is the most disorganized leader I have ever worked with in my career,” this person told Forbes. “He has no vision, and changes directions every week, often based on what he sees on Twitter.” In a video interview posted shortly before this story was published, Mostaque explained his leadership style: “I'm particularly great at taking creatives, developers, researchers, others, and achieving their full potential in designing systems. But I should not be dealing with, you know, HR and operations and business development and other elements. There are far better people than me to do that.” By December 2023, Stability had partially abandoned its open-source roots and announced that any commercial use of Stable Diffusion would cost customers at least $20 per month (non-commercial and research use of Stable Diffusion would remain free). But privately, Stability was considering a potentially more lucrative source of revenue: reselling the compute it was leasing from providers like AWS, according to six people familiar with the effort. Though it was essentially GPU arbitrage, Stability framed the strategy to investors as a “managed services” offering. Its damning October financial report projected optimistically that such an offering would bring in $139 million in 2024 — 98% of its revenue. Multiple employees at the time told Forbes they feared reselling compute, even if the company called it “managed services,” would violate the terms of Stability’s contract with AWS. Amazon declined to comment. “The line internally was that we are not reselling compute,” one former employee said. “This was some of the dirtiest feeling stuff.” Stability also discussed reselling a cluster of Nvidia A100 chips, leased via CoreWeave, to the venture capital firm Andreessen Horowitz, three sources said. “It was under the guise of managed services, but there wasn’t any management happening,” one of these people told Forbes. Andreessen Horowitz and CoreWeave declined to comment. Stability did not respond to questions about if it plans to continue this strategy now that Mostaque is out of the picture. Regardless, interim co-CEOs Wong and Laforte are on a tight timeline to clean up his mess. Board chairman Jim O’Shaughnessy said in a statement that he was confident the pair “will adeptly steer the company forward in developing and commercializing industry-leading generative AI products.” But burn continues to far outpace revenue. The Financial Times reported Friday that the company made $5.4 million of revenue in February, against $8 million in costs. Several sources said there are ongoing concerns about making payroll for the roughly 150 remaining employees. Leadership roles have gone vacant for months amid the disarray, leaving the company increasingly directionless. Meanwhile, a potentially catastrophic legal threat looms over the company: A trio of copyright infringement lawsuits brought by Getty Images and a group of artists in the U.S. and U.K., who claim Stability illegally used their art and photography to train the AI models powering Stable Diffusion. A London-based court has already rejected the company’s bid to throw out one of the lawsuits on the basis that none of its researchers were based in the U.K. And Stability’s claim that Getty’s Delaware lawsuit should be blocked because it's a U.K.-based company was rejected. (Stability did not respond to questions about the litigation.) AI-related copyright litigation “could go on for years,” according to Eric Goldman, a law professor at Santa Clara University. He told Forbes that though plaintiffs suing AI firms face an uphill battle overcoming the existing legal precedent on copyright infringement, the quantity of arguments available to make are virtually inexhaustible. “Like in military theory, if there’s a gap in your lines, that’s where the enemy pours through — if any one of those arguments succeeds, it could completely change the generative AI environment,” he said. “In some sense, generative AI as an industry has to win everything.” Stability, which had more than $100 million in the bank just a year and a half ago, is in a deep hole. Not only does it need more funding, it needs a viable business model — or a buyer with the vision and chops to make it successful in a fast-moving and highly competitive sector. At an all hands meeting this past Monday, Stability’s new leaders detailed a path forward. One point of emphasis: a plan to better manage resources and expenses, according to one person in attendance. It’s a start, but Mostaque’s meddling has left them with little runway to execute. His resignation, though, has given some employees hope. “A few people are 100% going to reconsider leaving after today,” said one current employee. “And the weird gloomy aura of hearing Emad talking nonsense for an hour is gone.” Shortly before Mostaque resigned, one current Stability executive told Forbes that they were optimistic his departure could make Stability appealing enough to receive a small investment or sale to a friendly party. “There are companies that have raised hundreds of millions of dollars that have much less intrinsic value than Stability,” the person said. “A white knight may still appear.”

[N] How Stability AI’s Founder Tanked His Billion-Dollar Startup

forbes article: https://www.forbes.com/sites/kenrickcai/2024/03/29/how-stability-ais-founder-tanked-his-billion-dollar-startup/ archive no paywall: https://archive.is/snbeV How Stability AI’s Founder Tanked His Billion-Dollar Startup Mar 29, 2024 Stability AI founder Emad Mostaque took the stage last week at the Terranea Resort in Palos Verdes, California to roaring applause and an introduction from an AI-generated Aristotle who announced him as “a modern Prometheus” with “the astuteness of Athena and the vision of Daedalus.” “Under his stewardship, AI becomes the Herculean force poised to vanquish the twin serpents of illness and ailment and extend the olive branch of longevity,” the faux Aristotle proclaimed. “I think that’s the best intro I’ve ever had,” Mostaque said. But behind Mostaque's hagiographic introduction lay a grim and fast metastasizing truth. Stability, once one of AI’s buzziest startups, was floundering. It had been running out of money for months and Mostaque had been unable to secure enough additional funding. It had defaulted on payments to Amazon whose cloud service undergirded Stability’s core offerings. The star research team behind its flagship text-to-image generator Stable Diffusion had tendered their resignations just three days before — as Forbes would first report — and other senior leaders had issued him an ultimatum: resign, or we walk too. Still, onstage before a massive audience of peers and acolytes, Mostaque talked a big game. “AI is jet planes for the mind,” he opined. “AI is our collective intelligence. It's the human Colossus.” He claimed a new, faster version of the Stable Diffusion image generator released earlier this month could generate “200 cats with hats per second.” But later, when he was asked about Stability’s financial model, Mostaque fumbled. “I can’t say that publicly,” he replied. “But it’s going well. We’re ahead of forecast.” Four days later, Mostaque stepped down as CEO of Stability, as Forbes first reported. In a post to X, the service formerly known as Twitter, he claimed he’d voluntarily abdicated his role to decentralize “the concentration of power in AI.” But sources told Forbes that was hardly the case. Behind the scenes, Mostaque had fought to maintain his position and control despite mounting pressure externally and internally to step down. Company documents and interviews with 32 current and former employees, investors, collaborators and industry observers suggest his abrupt exit was the result of poor business judgment and wild overspending that undermined confidence in his vision and leadership, and ultimately kneecapped the company. Mostaque, through his attorneys, declined to comment on record on a detailed list of questions about the reporting in this story. But in an email to Forbes earlier this week he broadly disputed the allegations. “Nobody tells you how hard it is to be a CEO and there are better CEOs than me to scale a business,” he said in a statement. “I am not sure anyone else would have been able to build and grow the research team to build the best and most widely used models out there and I’m very proud of the team there. I look forward to moving onto the next problem to handle and hopefully move the needle.” In an emailed statement, Christian Laforte and Shan Shan Wong, the interim co-CEOs who replaced Mostaque, said, "the company remains focused on commercializing its world leading technology” and providing it “to partners across the creative industries." After starting Stability in 2019, Mostaque built the company into an early AI juggernaut by seizing upon a promising research project that would become Stable Diffusion and funding it into a business reality. The ease with which the software generated detailed images from the simplest text prompts immediately captivated the public: 10 million people used it on any given day, the company told Forbes in early 2023. For some true believers, Mostaque was a crucial advocate for open-source AI development in a space dominated by the closed systems of OpenAI, Google and Anthropic. But his startup’s rise to one of the buzziest in generative AI was in part built on a series of exaggerations and misleading claims, as Forbes first reported last year (Mostaque disputed some points at the time). And they continued after he raised $100 million at a $1 billion valuation just days after launching Stable Diffusion in 2022. His failure to deliver on an array of grand promises, like building bespoke AI models for nation states, and his decision to pour tens of millions into research without a sustainable business plan, eroded Stability’s foundations and jeopardized its future. "He was just giving shit away,” one former employee told Forbes. “That man legitimately wanted to transform the world. He actually wanted to train AI models for kids in Malawi. Was it practical? Absolutely not." By October 2023, Stability would have less than $4 million left in the bank, according to an internal memo prepared for a board meeting and reviewed by Forbes. And mounting debt, including months of overdue Amazon Web Services payments, had already left it in the red. To avoid legal penalties for skipping Americans staff’s payroll, the document explained, the London-based startup was considering delaying tax payments to the U.K. government. It was Stability’s armada of GPUs, the wildly powerful and equally expensive chips undergirding AI, that were so taxing the company’s finances. Hosted by AWS, they had long been one of Mostaque’s bragging points; he often touted them as one of the world’s 10 largest supercomputers. They were responsible for helping Stability’s researchers build and maintain one of the top AI image generators, as well as break important new ground on generative audio, video and 3D models. “Undeniably, Stability has continued to ship a lot of models,” said one former employee. “They may not have profited off of it, but the broader ecosystem benefitted in a huge, huge way.” But the costs associated with so much compute were now threatening to sink the company. According to an internal October financial forecast seen by Forbes, Stability was on track to spend $99 million on compute in 2023. It noted as well that Stability was “underpaying AWS bills for July (by $1M)” and “not planning to pay AWS at the end of October for August usage ($7M).” Then there were the September and October bills, plus $1 million owed to Google Cloud and $600,000 to GPU cloud data center CoreWeave. (Amazon, Google and CoreWeave declined to comment.) With an additional $54 million allocated to wages and operating expenses, Stability’s total projected costs for 2023 were $153 million. But according to its October financial report, its projected revenue for the calendar year was just $11 million. Stability was on track to lose more money per month than it made in an entire year. The company’s dire financial position had thoroughly soured Stability’s current investors, including Coatue, which had invested tens of millions in the company during its $101 million funding round in 2022. In the middle of 2023, Mostaque agreed to an independent audit after Coatue raised a series of concerns, according to a source with direct knowledge of the matter. The outcome of the investigation is unclear. Coatue declined to comment. Within a week of an early October board meeting where Mostaque shared that financial forecast, Lightspeed Venture Partners, another major investor, sent a letter to the board urging them to sell the company. The distressing numbers had “severely undermined” the firm’s confidence in Mostaque’s ability to lead the company. “In particular, we are surprised and deeply concerned by a cash position just now disclosed to us that is inconsistent with prior discussions on this topic,” Lightspeed’s general counsel Brett Nissenberg wrote in the letter, a copy of which was viewed by Forbes. “Lightspeed believes that the company is not likely financeable on terms that would assure the company’s long term sound financial position.” (Lightspeed declined a request for comment.) The calls for a sale led Stability to quietly begin looking for a buyer. Bloomberg reported in November that Stability approached AI startups Cohere and Jasper to gauge their interest. Stability denied this, and Jasper CEO Timothy Young did the same when reached for comment by Forbes. A Cohere representative declined to comment. But one prominent AI company confirmed that Mostaque’s representatives had reached out to them to test the waters. Those talks did not advance because “the numbers didn’t add up,” this person, who declined to be named due to the confidential nature of the talks, told Forbes. Stability also tried to court Samsung as a buyer, going so far as to redecorate its office in advance of a planned meeting with the Korean electronics giant. (Samsung said that it invested in Stability in 2023 and that it does not comment on M&A discussions.) Coatue had been calling for Mostaque’s resignation for months, according to a source with direct knowledge. But it and other investors were unable to oust him because he was the company’s majority shareholder. When they tried a different tact by rallying other investors to offer him a juicy equity package to resign, Mostaque refused, said two sources. By October, Coatue and Lightspeed had had enough. Coatue left the board and Lightspeed resigned its observer seat. “Emad infuriated our initial investors so much it’s just making it impossible for us to raise more money under acceptable terms,” one current Stability executive told Forbes. The early months of 2024 saw Stability’s already precarious position eroding further still. Employees were quietly laid off. Three people in a position to know estimated that at least 10% of staff were cut. And cash reserves continued to dwindle. Mostaque mentioned a lifeline at the October board meeting: $95 million in tentative funding from new investors, pending due diligence. But in the end, only a fraction of it was wired, two sources say, much of it from Intel, which Forbes has learned invested $20 million, a fraction of what was reported. (Intel did not return a request for comment by publication time.) Two hours after Forbes broke the news of Mostaque’s plans to step down as CEO, Stability issued a press release confirming his resignation. Chief operating officer Wong and chief technology officer Laforte have taken over in the interim. Mostaque, who said on X that he still owns a majority of the company, also stepped down from the board, which has now initiated a search for a permanent CEO. There is a lot of work to be done to turn things around, and very little time in which to do it. Said the current Stability executive, “There’s still a possibility of a turnaround story, but the odds drop by the day.” In July of 2023, Mostaque still thought he could pull it off. Halfway through the month, he shared a fundraising plan with his lieutenants. It was wildly optimistic, detailing the raise of $500 million in cash and another $750 million in computing facilities from marquee investors like Nvidia, Google, Intel and the World Bank (Nvidia and Google declined comment. Intel did not respond. The World Bank said it did not invest in Stability). In a Slack message reviewed by Forbes, Mostaque said Google was “willing to move fast” and the round was “likely to be oversubscribed.” It wasn’t. Three people with direct knowledge of these fundraising efforts told Forbes that while there was some interest in Stability, talks often stalled when it came time to disclose financials. Two of them noted that earlier in the year, Mostaque had simply stopped engaging with VCs who asked for numbers. Only one firm invested around that time: actor Ashton Kutcher’s Sound Ventures, which invested $35 million in the form of a convertible SAFE note during the second quarter, according to an internal document. (Sound Ventures did not respond to a request for comment.) And though he’d managed to score a meeting with Nvidia and its CEO Jensen Huang, it ended in disaster, according to two sources. “Under Jensen's microscopic questions, Emad just fell apart,” a source in position to know told Forbes. Huang quickly concluded Stability wasn’t ready for an investment from Nvidia, the sources said. Mostaque told Forbes in an email that he had not met with Huang since 2022, except to say “hello and what’s up a few times after.” His July 2023 message references a plan to raise $150 million from Nvidia. (Nvidia declined to comment.) After a June Forbes investigation citing more than 30 sources revealed Mostaque’s history of misleading claims, Mostaque struggled to raise funding, a Stability investor told Forbes. (Mostaque disputed the story at the time and called it "coordinated lies" in his email this week to Forbes). Increasingly, investors scrutinized his assertions and pressed for data. And Young, now the CEO of Jasper, turned down a verbal offer to be Stability’s president after reading the article, according to a source with direct knowledge of the matter. The collapse of the talks aggravated the board and other executives, who had hoped Young would compensate for the sales and business management skills that Mostaque lacked, according to four people in a position to know. (Young declined to comment.) When Stability’s senior leadership convened in London for the CogX conference in September, the financing had still not closed. There, a group of executives confronted Mostaque asking questions about the company’s cash position and runway, according to three people with direct knowledge of the incident. They did not get the clarity they’d hoped for. By October, Mostaque had reduced his fundraising target by more than 80%. The months that followed saw a steady drumbeat of departures — general counsel Adam Avrunin, vice presidents Mike Melnicki, Ed Newton-Rex and Joe Penna, chief people officer Ozden Onder — culminating in the demoralizing March exit of Stable Diffusion’s primary developers Robin Rombach, Andreas Blattmann, Patrick Esser and Dominik Lorenz. Rombach, who led the team, had been angling to leave for months, two sources said, first threatening to resign last summer because of the fundraising failures. Others left over concerns about cash flow, as well as liabilities — including what four people described as Mostaque’s lax approach to ensuring that Stability products could not be used to produce child sexual abuse imagery. “Stability AI is committed to preventing the misuse of AI and prohibits the use of our image models and services for unlawful activity, including attempts to edit or create CSAM,” Ella Irwin, senior vice president of integrity, said in a statement. Newton-Rex told Forbes he resigned because he disagreed with Stability’s position that training AI on copyrighted work without consent is fair use. Melnicki and Penna declined to comment. Avrunin and Onder could not be reached for comment. None of the researchers responded to requests for comment. The Stable Diffusion researchers’ departure as a cohort says a lot about the state of Stability AI. The company’s researchers were widely viewed as its crown jewels, their work subsidized with a firehose of pricey compute power that was even extended to people outside the company. Martino Russi, an artificial intelligence researcher, told Forbes that though he was never formally employed by Stability, the company provided him a “staggering” amount of compute between January and April 2023 to play around with developing an AI video generator that Stability might someday use. “It was Candy Land or Coney Island,” said Russi, who estimates that his experiment, which was ultimately shelved, cost the company $2.5 million. Stable Diffusion was simultaneously Stability’s marquee product and its existential cash crisis. One current employee described it to Forbes as “a giant vacuum that absorbed everything: money, compute, people.” While the software was widely used, with Mostaque claiming downloads reaching into the hundreds of millions, Stability struggled to translate that wild success into revenue. Mostaque knew it could be done — peers at Databricks, Elastic and MongoDB had all turned a free product into a lucrative business — he just couldn’t figure out how. His first attempt was Stability’s API, which allowed paying customers to integrate Stable Diffusion into their own products. In early 2023, a handful of small companies, like art generator app NightCafe and presentation software startup Tome, signed on, according to four people with knowledge of the deals. But Stability’s poor account management services soured many, and in a matter of months NightCafe and Tome canceled their contracts, three people said. NightCafe founder Angus Russell told Forbes that his company switched to a competitor which “offered much cheaper inference costs and a broader service.” Tome did not respond to a request for comment. Meanwhile, Mostaque’s efforts to court larger companies like Samsung and Snapchat were failing, according to five people familiar with the effort. Canva, which was already one of the heaviest users of open-sourced Stable Diffusion, had multiple discussions with Stability, which was angling for a contract it hoped would generate several millions in annual revenue. But the deal never materialized, four sources said. “These three companies wanted and needed us,” one former employee told Forbes. “They would have been the perfect customers.” (Samsung, Snap and Canva declined to comment.) “It’s not that there was not an appetite to pay Stability — there were tons of companies that would have that wanted to,” the former employee said. “There was a huge opportunity and demand, but just a resistance to execution.” Mostaque’s other big idea was to provide governments with bespoke national AI models that would invigorate their economies and citizenry. “Emad envisions a world where AI through 100 national models serves not as a tool of the few, but as a benefactor to all promising to confront great adversaries, cancer, autism, and the sands of time itself,” the AI avatar of Aristotle said in his intro at the conference. Mostaque told several prospective customers that he could deliver such models within 60 days — an untenable timeline, according to two people in position to know. Stability attempted to develop a model for the Singaporean government over the protestation of employees who questioned its technical feasibility, three sources familiar with the effort told Forbes. But it couldn’t pull it off and Singapore never became a customer. (The government of Singapore confirmed it did not enter into a deal with Stability, but declined to answer additional questions.) As Stability careened from one new business idea to another, resources were abruptly reallocated and researchers reassigned. The whiplash shifts in a largely siloed organization demoralized and infuriated employees. “There were ‘urgent’ things, ‘urgent urgent’ things and ‘most urgent,’” one former employee complained. “None of these things seem important if everything is important.” Another former Stability executive was far more pointed in their assessment. “Emad is the most disorganized leader I have ever worked with in my career,” this person told Forbes. “He has no vision, and changes directions every week, often based on what he sees on Twitter.” In a video interview posted shortly before this story was published, Mostaque explained his leadership style: “I'm particularly great at taking creatives, developers, researchers, others, and achieving their full potential in designing systems. But I should not be dealing with, you know, HR and operations and business development and other elements. There are far better people than me to do that.” By December 2023, Stability had partially abandoned its open-source roots and announced that any commercial use of Stable Diffusion would cost customers at least $20 per month (non-commercial and research use of Stable Diffusion would remain free). But privately, Stability was considering a potentially more lucrative source of revenue: reselling the compute it was leasing from providers like AWS, according to six people familiar with the effort. Though it was essentially GPU arbitrage, Stability framed the strategy to investors as a “managed services” offering. Its damning October financial report projected optimistically that such an offering would bring in $139 million in 2024 — 98% of its revenue. Multiple employees at the time told Forbes they feared reselling compute, even if the company called it “managed services,” would violate the terms of Stability’s contract with AWS. Amazon declined to comment. “The line internally was that we are not reselling compute,” one former employee said. “This was some of the dirtiest feeling stuff.” Stability also discussed reselling a cluster of Nvidia A100 chips, leased via CoreWeave, to the venture capital firm Andreessen Horowitz, three sources said. “It was under the guise of managed services, but there wasn’t any management happening,” one of these people told Forbes. Andreessen Horowitz and CoreWeave declined to comment. Stability did not respond to questions about if it plans to continue this strategy now that Mostaque is out of the picture. Regardless, interim co-CEOs Wong and Laforte are on a tight timeline to clean up his mess. Board chairman Jim O’Shaughnessy said in a statement that he was confident the pair “will adeptly steer the company forward in developing and commercializing industry-leading generative AI products.” But burn continues to far outpace revenue. The Financial Times reported Friday that the company made $5.4 million of revenue in February, against $8 million in costs. Several sources said there are ongoing concerns about making payroll for the roughly 150 remaining employees. Leadership roles have gone vacant for months amid the disarray, leaving the company increasingly directionless. Meanwhile, a potentially catastrophic legal threat looms over the company: A trio of copyright infringement lawsuits brought by Getty Images and a group of artists in the U.S. and U.K., who claim Stability illegally used their art and photography to train the AI models powering Stable Diffusion. A London-based court has already rejected the company’s bid to throw out one of the lawsuits on the basis that none of its researchers were based in the U.K. And Stability’s claim that Getty’s Delaware lawsuit should be blocked because it's a U.K.-based company was rejected. (Stability did not respond to questions about the litigation.) AI-related copyright litigation “could go on for years,” according to Eric Goldman, a law professor at Santa Clara University. He told Forbes that though plaintiffs suing AI firms face an uphill battle overcoming the existing legal precedent on copyright infringement, the quantity of arguments available to make are virtually inexhaustible. “Like in military theory, if there’s a gap in your lines, that’s where the enemy pours through — if any one of those arguments succeeds, it could completely change the generative AI environment,” he said. “In some sense, generative AI as an industry has to win everything.” Stability, which had more than $100 million in the bank just a year and a half ago, is in a deep hole. Not only does it need more funding, it needs a viable business model — or a buyer with the vision and chops to make it successful in a fast-moving and highly competitive sector. At an all hands meeting this past Monday, Stability’s new leaders detailed a path forward. One point of emphasis: a plan to better manage resources and expenses, according to one person in attendance. It’s a start, but Mostaque’s meddling has left them with little runway to execute. His resignation, though, has given some employees hope. “A few people are 100% going to reconsider leaving after today,” said one current employee. “And the weird gloomy aura of hearing Emad talking nonsense for an hour is gone.” Shortly before Mostaque resigned, one current Stability executive told Forbes that they were optimistic his departure could make Stability appealing enough to receive a small investment or sale to a friendly party. “There are companies that have raised hundreds of millions of dollars that have much less intrinsic value than Stability,” the person said. “A white knight may still appear.”

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!

[N] AI Robotics startup Covariant (founded by Peter Chen, Pieter Abbeel, other Berkeley / ex-OpenAI folks) just raised $40M in Series B funding round. “Covariant has recently seen increased usage from clients hoping to avoid supply chain disruption due to the coronavirus pandemic.”

h/t their announcement, VB and WSJ article: Logistics AI Startup Covariant Reaps $40 Million in Funding Round Company plans to explore uses of machine learning for automation beyond warehouse operations Artificial-intelligence robotics startup Covariant raised $40 million to expand its logistics automation technology to new industries and ramp up hiring, the company said Wednesday. The Berkeley, Calif.-based company makes AI software that it says helps warehouse robots pick objects at a faster rate than human workers, with a roughly 95% accuracy rate. Covariant is working with Austrian logistics-automation company Knapp AG and the robotics business of Swiss industrial conglomerate ABB Ltd., which provide hardware such as robot arms or conveyor belts to pair with the startup’s technology platform. “What we’ve built is a universal brain for robotic manipulation tasks,” Covariant co-founder and Chief Executive Peter Chen said in an interview. “We provide the software, they provide the rest of the systems.” Logistics-sector appetite for such technology is growing as distribution and fulfillment operations that have relied on human labor look to speed output and meet rising digital commerce demand. The coronavirus pandemic has accelerated that interest as businesses have sought to adjust their operations to volatile swings in consumer demand and to new restrictions, such as spacing workers further apart to guard against contagion. That has provided a bright spot for some technology startups even as many big backers scale back venture-capital spending. Last month logistics delivery platform Bringg said it raised $30 million in a Series D funding round, for example, as demand for home delivery of food, household goods and e-commerce staples soared among homebound consumers. Covariant’s Series B round brings the company’s total funding to $67 million. New investor Index Ventures led the round, with participation from existing investor Amplify Partners and new investors including Radical Ventures. Mr. Chen said the funding will be used to explore the technology’s potential application in other markets such as manufacturing, recycling or agriculture “where there are repetitive manual processes.” Covariant also plans to hire more engineering and other staff, he said. Covariant was founded in 2017 and now has about 50 employees. The company’s technology uses camera systems to capture images of objects, and artificial intelligence to analyze objects and how to pick them up. Machine learning helps Covariant-powered robots learn from experience. The startup’s customers include a German electrical supplies wholesaler that uses the technology to control a mechanical arm that picks out orders of circuit boards, switches and other goods.

[N] AI Robotics startup Covariant (founded by Peter Chen, Pieter Abbeel, other Berkeley / ex-OpenAI folks) just raised $40M in Series B funding round. “Covariant has recently seen increased usage from clients hoping to avoid supply chain disruption due to the coronavirus pandemic.”

h/t their announcement, VB and WSJ article: Logistics AI Startup Covariant Reaps $40 Million in Funding Round Company plans to explore uses of machine learning for automation beyond warehouse operations Artificial-intelligence robotics startup Covariant raised $40 million to expand its logistics automation technology to new industries and ramp up hiring, the company said Wednesday. The Berkeley, Calif.-based company makes AI software that it says helps warehouse robots pick objects at a faster rate than human workers, with a roughly 95% accuracy rate. Covariant is working with Austrian logistics-automation company Knapp AG and the robotics business of Swiss industrial conglomerate ABB Ltd., which provide hardware such as robot arms or conveyor belts to pair with the startup’s technology platform. “What we’ve built is a universal brain for robotic manipulation tasks,” Covariant co-founder and Chief Executive Peter Chen said in an interview. “We provide the software, they provide the rest of the systems.” Logistics-sector appetite for such technology is growing as distribution and fulfillment operations that have relied on human labor look to speed output and meet rising digital commerce demand. The coronavirus pandemic has accelerated that interest as businesses have sought to adjust their operations to volatile swings in consumer demand and to new restrictions, such as spacing workers further apart to guard against contagion. That has provided a bright spot for some technology startups even as many big backers scale back venture-capital spending. Last month logistics delivery platform Bringg said it raised $30 million in a Series D funding round, for example, as demand for home delivery of food, household goods and e-commerce staples soared among homebound consumers. Covariant’s Series B round brings the company’s total funding to $67 million. New investor Index Ventures led the round, with participation from existing investor Amplify Partners and new investors including Radical Ventures. Mr. Chen said the funding will be used to explore the technology’s potential application in other markets such as manufacturing, recycling or agriculture “where there are repetitive manual processes.” Covariant also plans to hire more engineering and other staff, he said. Covariant was founded in 2017 and now has about 50 employees. The company’s technology uses camera systems to capture images of objects, and artificial intelligence to analyze objects and how to pick them up. Machine learning helps Covariant-powered robots learn from experience. The startup’s customers include a German electrical supplies wholesaler that uses the technology to control a mechanical arm that picks out orders of circuit boards, switches and other goods.

[N] How Stability AI’s Founder Tanked His Billion-Dollar Startup

forbes article: https://www.forbes.com/sites/kenrickcai/2024/03/29/how-stability-ais-founder-tanked-his-billion-dollar-startup/ archive no paywall: https://archive.is/snbeV How Stability AI’s Founder Tanked His Billion-Dollar Startup Mar 29, 2024 Stability AI founder Emad Mostaque took the stage last week at the Terranea Resort in Palos Verdes, California to roaring applause and an introduction from an AI-generated Aristotle who announced him as “a modern Prometheus” with “the astuteness of Athena and the vision of Daedalus.” “Under his stewardship, AI becomes the Herculean force poised to vanquish the twin serpents of illness and ailment and extend the olive branch of longevity,” the faux Aristotle proclaimed. “I think that’s the best intro I’ve ever had,” Mostaque said. But behind Mostaque's hagiographic introduction lay a grim and fast metastasizing truth. Stability, once one of AI’s buzziest startups, was floundering. It had been running out of money for months and Mostaque had been unable to secure enough additional funding. It had defaulted on payments to Amazon whose cloud service undergirded Stability’s core offerings. The star research team behind its flagship text-to-image generator Stable Diffusion had tendered their resignations just three days before — as Forbes would first report — and other senior leaders had issued him an ultimatum: resign, or we walk too. Still, onstage before a massive audience of peers and acolytes, Mostaque talked a big game. “AI is jet planes for the mind,” he opined. “AI is our collective intelligence. It's the human Colossus.” He claimed a new, faster version of the Stable Diffusion image generator released earlier this month could generate “200 cats with hats per second.” But later, when he was asked about Stability’s financial model, Mostaque fumbled. “I can’t say that publicly,” he replied. “But it’s going well. We’re ahead of forecast.” Four days later, Mostaque stepped down as CEO of Stability, as Forbes first reported. In a post to X, the service formerly known as Twitter, he claimed he’d voluntarily abdicated his role to decentralize “the concentration of power in AI.” But sources told Forbes that was hardly the case. Behind the scenes, Mostaque had fought to maintain his position and control despite mounting pressure externally and internally to step down. Company documents and interviews with 32 current and former employees, investors, collaborators and industry observers suggest his abrupt exit was the result of poor business judgment and wild overspending that undermined confidence in his vision and leadership, and ultimately kneecapped the company. Mostaque, through his attorneys, declined to comment on record on a detailed list of questions about the reporting in this story. But in an email to Forbes earlier this week he broadly disputed the allegations. “Nobody tells you how hard it is to be a CEO and there are better CEOs than me to scale a business,” he said in a statement. “I am not sure anyone else would have been able to build and grow the research team to build the best and most widely used models out there and I’m very proud of the team there. I look forward to moving onto the next problem to handle and hopefully move the needle.” In an emailed statement, Christian Laforte and Shan Shan Wong, the interim co-CEOs who replaced Mostaque, said, "the company remains focused on commercializing its world leading technology” and providing it “to partners across the creative industries." After starting Stability in 2019, Mostaque built the company into an early AI juggernaut by seizing upon a promising research project that would become Stable Diffusion and funding it into a business reality. The ease with which the software generated detailed images from the simplest text prompts immediately captivated the public: 10 million people used it on any given day, the company told Forbes in early 2023. For some true believers, Mostaque was a crucial advocate for open-source AI development in a space dominated by the closed systems of OpenAI, Google and Anthropic. But his startup’s rise to one of the buzziest in generative AI was in part built on a series of exaggerations and misleading claims, as Forbes first reported last year (Mostaque disputed some points at the time). And they continued after he raised $100 million at a $1 billion valuation just days after launching Stable Diffusion in 2022. His failure to deliver on an array of grand promises, like building bespoke AI models for nation states, and his decision to pour tens of millions into research without a sustainable business plan, eroded Stability’s foundations and jeopardized its future. "He was just giving shit away,” one former employee told Forbes. “That man legitimately wanted to transform the world. He actually wanted to train AI models for kids in Malawi. Was it practical? Absolutely not." By October 2023, Stability would have less than $4 million left in the bank, according to an internal memo prepared for a board meeting and reviewed by Forbes. And mounting debt, including months of overdue Amazon Web Services payments, had already left it in the red. To avoid legal penalties for skipping Americans staff’s payroll, the document explained, the London-based startup was considering delaying tax payments to the U.K. government. It was Stability’s armada of GPUs, the wildly powerful and equally expensive chips undergirding AI, that were so taxing the company’s finances. Hosted by AWS, they had long been one of Mostaque’s bragging points; he often touted them as one of the world’s 10 largest supercomputers. They were responsible for helping Stability’s researchers build and maintain one of the top AI image generators, as well as break important new ground on generative audio, video and 3D models. “Undeniably, Stability has continued to ship a lot of models,” said one former employee. “They may not have profited off of it, but the broader ecosystem benefitted in a huge, huge way.” But the costs associated with so much compute were now threatening to sink the company. According to an internal October financial forecast seen by Forbes, Stability was on track to spend $99 million on compute in 2023. It noted as well that Stability was “underpaying AWS bills for July (by $1M)” and “not planning to pay AWS at the end of October for August usage ($7M).” Then there were the September and October bills, plus $1 million owed to Google Cloud and $600,000 to GPU cloud data center CoreWeave. (Amazon, Google and CoreWeave declined to comment.) With an additional $54 million allocated to wages and operating expenses, Stability’s total projected costs for 2023 were $153 million. But according to its October financial report, its projected revenue for the calendar year was just $11 million. Stability was on track to lose more money per month than it made in an entire year. The company’s dire financial position had thoroughly soured Stability’s current investors, including Coatue, which had invested tens of millions in the company during its $101 million funding round in 2022. In the middle of 2023, Mostaque agreed to an independent audit after Coatue raised a series of concerns, according to a source with direct knowledge of the matter. The outcome of the investigation is unclear. Coatue declined to comment. Within a week of an early October board meeting where Mostaque shared that financial forecast, Lightspeed Venture Partners, another major investor, sent a letter to the board urging them to sell the company. The distressing numbers had “severely undermined” the firm’s confidence in Mostaque’s ability to lead the company. “In particular, we are surprised and deeply concerned by a cash position just now disclosed to us that is inconsistent with prior discussions on this topic,” Lightspeed’s general counsel Brett Nissenberg wrote in the letter, a copy of which was viewed by Forbes. “Lightspeed believes that the company is not likely financeable on terms that would assure the company’s long term sound financial position.” (Lightspeed declined a request for comment.) The calls for a sale led Stability to quietly begin looking for a buyer. Bloomberg reported in November that Stability approached AI startups Cohere and Jasper to gauge their interest. Stability denied this, and Jasper CEO Timothy Young did the same when reached for comment by Forbes. A Cohere representative declined to comment. But one prominent AI company confirmed that Mostaque’s representatives had reached out to them to test the waters. Those talks did not advance because “the numbers didn’t add up,” this person, who declined to be named due to the confidential nature of the talks, told Forbes. Stability also tried to court Samsung as a buyer, going so far as to redecorate its office in advance of a planned meeting with the Korean electronics giant. (Samsung said that it invested in Stability in 2023 and that it does not comment on M&A discussions.) Coatue had been calling for Mostaque’s resignation for months, according to a source with direct knowledge. But it and other investors were unable to oust him because he was the company’s majority shareholder. When they tried a different tact by rallying other investors to offer him a juicy equity package to resign, Mostaque refused, said two sources. By October, Coatue and Lightspeed had had enough. Coatue left the board and Lightspeed resigned its observer seat. “Emad infuriated our initial investors so much it’s just making it impossible for us to raise more money under acceptable terms,” one current Stability executive told Forbes. The early months of 2024 saw Stability’s already precarious position eroding further still. Employees were quietly laid off. Three people in a position to know estimated that at least 10% of staff were cut. And cash reserves continued to dwindle. Mostaque mentioned a lifeline at the October board meeting: $95 million in tentative funding from new investors, pending due diligence. But in the end, only a fraction of it was wired, two sources say, much of it from Intel, which Forbes has learned invested $20 million, a fraction of what was reported. (Intel did not return a request for comment by publication time.) Two hours after Forbes broke the news of Mostaque’s plans to step down as CEO, Stability issued a press release confirming his resignation. Chief operating officer Wong and chief technology officer Laforte have taken over in the interim. Mostaque, who said on X that he still owns a majority of the company, also stepped down from the board, which has now initiated a search for a permanent CEO. There is a lot of work to be done to turn things around, and very little time in which to do it. Said the current Stability executive, “There’s still a possibility of a turnaround story, but the odds drop by the day.” In July of 2023, Mostaque still thought he could pull it off. Halfway through the month, he shared a fundraising plan with his lieutenants. It was wildly optimistic, detailing the raise of $500 million in cash and another $750 million in computing facilities from marquee investors like Nvidia, Google, Intel and the World Bank (Nvidia and Google declined comment. Intel did not respond. The World Bank said it did not invest in Stability). In a Slack message reviewed by Forbes, Mostaque said Google was “willing to move fast” and the round was “likely to be oversubscribed.” It wasn’t. Three people with direct knowledge of these fundraising efforts told Forbes that while there was some interest in Stability, talks often stalled when it came time to disclose financials. Two of them noted that earlier in the year, Mostaque had simply stopped engaging with VCs who asked for numbers. Only one firm invested around that time: actor Ashton Kutcher’s Sound Ventures, which invested $35 million in the form of a convertible SAFE note during the second quarter, according to an internal document. (Sound Ventures did not respond to a request for comment.) And though he’d managed to score a meeting with Nvidia and its CEO Jensen Huang, it ended in disaster, according to two sources. “Under Jensen's microscopic questions, Emad just fell apart,” a source in position to know told Forbes. Huang quickly concluded Stability wasn’t ready for an investment from Nvidia, the sources said. Mostaque told Forbes in an email that he had not met with Huang since 2022, except to say “hello and what’s up a few times after.” His July 2023 message references a plan to raise $150 million from Nvidia. (Nvidia declined to comment.) After a June Forbes investigation citing more than 30 sources revealed Mostaque’s history of misleading claims, Mostaque struggled to raise funding, a Stability investor told Forbes. (Mostaque disputed the story at the time and called it "coordinated lies" in his email this week to Forbes). Increasingly, investors scrutinized his assertions and pressed for data. And Young, now the CEO of Jasper, turned down a verbal offer to be Stability’s president after reading the article, according to a source with direct knowledge of the matter. The collapse of the talks aggravated the board and other executives, who had hoped Young would compensate for the sales and business management skills that Mostaque lacked, according to four people in a position to know. (Young declined to comment.) When Stability’s senior leadership convened in London for the CogX conference in September, the financing had still not closed. There, a group of executives confronted Mostaque asking questions about the company’s cash position and runway, according to three people with direct knowledge of the incident. They did not get the clarity they’d hoped for. By October, Mostaque had reduced his fundraising target by more than 80%. The months that followed saw a steady drumbeat of departures — general counsel Adam Avrunin, vice presidents Mike Melnicki, Ed Newton-Rex and Joe Penna, chief people officer Ozden Onder — culminating in the demoralizing March exit of Stable Diffusion’s primary developers Robin Rombach, Andreas Blattmann, Patrick Esser and Dominik Lorenz. Rombach, who led the team, had been angling to leave for months, two sources said, first threatening to resign last summer because of the fundraising failures. Others left over concerns about cash flow, as well as liabilities — including what four people described as Mostaque’s lax approach to ensuring that Stability products could not be used to produce child sexual abuse imagery. “Stability AI is committed to preventing the misuse of AI and prohibits the use of our image models and services for unlawful activity, including attempts to edit or create CSAM,” Ella Irwin, senior vice president of integrity, said in a statement. Newton-Rex told Forbes he resigned because he disagreed with Stability’s position that training AI on copyrighted work without consent is fair use. Melnicki and Penna declined to comment. Avrunin and Onder could not be reached for comment. None of the researchers responded to requests for comment. The Stable Diffusion researchers’ departure as a cohort says a lot about the state of Stability AI. The company’s researchers were widely viewed as its crown jewels, their work subsidized with a firehose of pricey compute power that was even extended to people outside the company. Martino Russi, an artificial intelligence researcher, told Forbes that though he was never formally employed by Stability, the company provided him a “staggering” amount of compute between January and April 2023 to play around with developing an AI video generator that Stability might someday use. “It was Candy Land or Coney Island,” said Russi, who estimates that his experiment, which was ultimately shelved, cost the company $2.5 million. Stable Diffusion was simultaneously Stability’s marquee product and its existential cash crisis. One current employee described it to Forbes as “a giant vacuum that absorbed everything: money, compute, people.” While the software was widely used, with Mostaque claiming downloads reaching into the hundreds of millions, Stability struggled to translate that wild success into revenue. Mostaque knew it could be done — peers at Databricks, Elastic and MongoDB had all turned a free product into a lucrative business — he just couldn’t figure out how. His first attempt was Stability’s API, which allowed paying customers to integrate Stable Diffusion into their own products. In early 2023, a handful of small companies, like art generator app NightCafe and presentation software startup Tome, signed on, according to four people with knowledge of the deals. But Stability’s poor account management services soured many, and in a matter of months NightCafe and Tome canceled their contracts, three people said. NightCafe founder Angus Russell told Forbes that his company switched to a competitor which “offered much cheaper inference costs and a broader service.” Tome did not respond to a request for comment. Meanwhile, Mostaque’s efforts to court larger companies like Samsung and Snapchat were failing, according to five people familiar with the effort. Canva, which was already one of the heaviest users of open-sourced Stable Diffusion, had multiple discussions with Stability, which was angling for a contract it hoped would generate several millions in annual revenue. But the deal never materialized, four sources said. “These three companies wanted and needed us,” one former employee told Forbes. “They would have been the perfect customers.” (Samsung, Snap and Canva declined to comment.) “It’s not that there was not an appetite to pay Stability — there were tons of companies that would have that wanted to,” the former employee said. “There was a huge opportunity and demand, but just a resistance to execution.” Mostaque’s other big idea was to provide governments with bespoke national AI models that would invigorate their economies and citizenry. “Emad envisions a world where AI through 100 national models serves not as a tool of the few, but as a benefactor to all promising to confront great adversaries, cancer, autism, and the sands of time itself,” the AI avatar of Aristotle said in his intro at the conference. Mostaque told several prospective customers that he could deliver such models within 60 days — an untenable timeline, according to two people in position to know. Stability attempted to develop a model for the Singaporean government over the protestation of employees who questioned its technical feasibility, three sources familiar with the effort told Forbes. But it couldn’t pull it off and Singapore never became a customer. (The government of Singapore confirmed it did not enter into a deal with Stability, but declined to answer additional questions.) As Stability careened from one new business idea to another, resources were abruptly reallocated and researchers reassigned. The whiplash shifts in a largely siloed organization demoralized and infuriated employees. “There were ‘urgent’ things, ‘urgent urgent’ things and ‘most urgent,’” one former employee complained. “None of these things seem important if everything is important.” Another former Stability executive was far more pointed in their assessment. “Emad is the most disorganized leader I have ever worked with in my career,” this person told Forbes. “He has no vision, and changes directions every week, often based on what he sees on Twitter.” In a video interview posted shortly before this story was published, Mostaque explained his leadership style: “I'm particularly great at taking creatives, developers, researchers, others, and achieving their full potential in designing systems. But I should not be dealing with, you know, HR and operations and business development and other elements. There are far better people than me to do that.” By December 2023, Stability had partially abandoned its open-source roots and announced that any commercial use of Stable Diffusion would cost customers at least $20 per month (non-commercial and research use of Stable Diffusion would remain free). But privately, Stability was considering a potentially more lucrative source of revenue: reselling the compute it was leasing from providers like AWS, according to six people familiar with the effort. Though it was essentially GPU arbitrage, Stability framed the strategy to investors as a “managed services” offering. Its damning October financial report projected optimistically that such an offering would bring in $139 million in 2024 — 98% of its revenue. Multiple employees at the time told Forbes they feared reselling compute, even if the company called it “managed services,” would violate the terms of Stability’s contract with AWS. Amazon declined to comment. “The line internally was that we are not reselling compute,” one former employee said. “This was some of the dirtiest feeling stuff.” Stability also discussed reselling a cluster of Nvidia A100 chips, leased via CoreWeave, to the venture capital firm Andreessen Horowitz, three sources said. “It was under the guise of managed services, but there wasn’t any management happening,” one of these people told Forbes. Andreessen Horowitz and CoreWeave declined to comment. Stability did not respond to questions about if it plans to continue this strategy now that Mostaque is out of the picture. Regardless, interim co-CEOs Wong and Laforte are on a tight timeline to clean up his mess. Board chairman Jim O’Shaughnessy said in a statement that he was confident the pair “will adeptly steer the company forward in developing and commercializing industry-leading generative AI products.” But burn continues to far outpace revenue. The Financial Times reported Friday that the company made $5.4 million of revenue in February, against $8 million in costs. Several sources said there are ongoing concerns about making payroll for the roughly 150 remaining employees. Leadership roles have gone vacant for months amid the disarray, leaving the company increasingly directionless. Meanwhile, a potentially catastrophic legal threat looms over the company: A trio of copyright infringement lawsuits brought by Getty Images and a group of artists in the U.S. and U.K., who claim Stability illegally used their art and photography to train the AI models powering Stable Diffusion. A London-based court has already rejected the company’s bid to throw out one of the lawsuits on the basis that none of its researchers were based in the U.K. And Stability’s claim that Getty’s Delaware lawsuit should be blocked because it's a U.K.-based company was rejected. (Stability did not respond to questions about the litigation.) AI-related copyright litigation “could go on for years,” according to Eric Goldman, a law professor at Santa Clara University. He told Forbes that though plaintiffs suing AI firms face an uphill battle overcoming the existing legal precedent on copyright infringement, the quantity of arguments available to make are virtually inexhaustible. “Like in military theory, if there’s a gap in your lines, that’s where the enemy pours through — if any one of those arguments succeeds, it could completely change the generative AI environment,” he said. “In some sense, generative AI as an industry has to win everything.” Stability, which had more than $100 million in the bank just a year and a half ago, is in a deep hole. Not only does it need more funding, it needs a viable business model — or a buyer with the vision and chops to make it successful in a fast-moving and highly competitive sector. At an all hands meeting this past Monday, Stability’s new leaders detailed a path forward. One point of emphasis: a plan to better manage resources and expenses, according to one person in attendance. It’s a start, but Mostaque’s meddling has left them with little runway to execute. His resignation, though, has given some employees hope. “A few people are 100% going to reconsider leaving after today,” said one current employee. “And the weird gloomy aura of hearing Emad talking nonsense for an hour is gone.” Shortly before Mostaque resigned, one current Stability executive told Forbes that they were optimistic his departure could make Stability appealing enough to receive a small investment or sale to a friendly party. “There are companies that have raised hundreds of millions of dollars that have much less intrinsic value than Stability,” the person said. “A white knight may still appear.”

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

Raised $450k for my startup, here are the lessons I've learned along the way

2021 has been a pretty amazing year for Omnisearch. Having started initial work on Omnisearch at the end of 2020, we entered the new year with a working MVP yet no revenue, no significant partnerships, and no funding. Fast forward to the end of 2021, and we now have fantastic revenue growth, a partnership with a public company, and a far more powerful, complete and polished product. But one milestone really changed Omnisearch’s trajectory: our $450,000 USD pre-seed round by GoAhead Ventures. In this post I want to share the story of how it came about and offer a couple of takeaways to keep in mind when preparing for fundraising. ​ The story Contrary to most advice, my co-founder Matej and I didn’t allocate a specific time to switch to “fundraising mode” but rather talked to investors on an ongoing basis. It was a bit of a distraction from working on the product, but on the positive side we were able to constantly get feedback on the idea, pitch, go-to-market strategy and hiring, as well as hearing investors’ major concerns sooner rather than later. That being said, our six-month long fundraising efforts weren’t yielding results - we talked to about twenty investors, mostly angels or smaller funds, with no success. The feedback was generally of the “too early for us” variety (since we were still pre-revenue), with additional questions about our go-to-market strategy and ideal customer persona. The introduction to our eventual investors, California-based GoAhead Ventures, came through a friend who had pitched them previously. We wrote a simple blurb and sent our pitch deck. We then went through GoAhead’s hyper-efficient screening process, consisting of a 30-minute call, a recorded three-minute pitch, and filling out a simple Google doc. Throughout the whole process, the GoAhead team left an awesome impression thanks to their knowledge of enterprise software and their responsiveness. They ended up investing and the whole deal was closed within two weeks, which is super fast even by Silicon Valley standards. While our fundraising experience is a single data point and your case might be different, here are the key takeaways from our journey. ​ Perseverance wins: Like I said above, we talked to about twenty investors before we closed our round. Getting a series of “no”s sucks, but we took the feedback seriously and tried to prepare better for questions that caught us off guard. But we persevered, keeping in mind that from a bird’s eye perspective it’s an amazing time to be building startups and raising funds. Focus on traction: Sounds pretty obvious, right? The truth is, though, that even a small amount of revenue is infinitely better than none at all. One of the major differences between our eventual successful investor pitch and the earlier ones was that we had actual paying customers, though our MRR was low. This allows you to talk about customers in the present tense, showing there’s actual demand for your product and making the use cases more tangible. And ideally, highlight a couple of customer testimonials to boost your credibility. Have a demo ready: In Omnisearch’s case, the demo was oftentimes the best received part of the pitch or call. We’d show investors the live demo, and for bonus points even asked them to choose a video from YouTube and then try searching through it. This always had a “wow” effect on prospective investors and made the subsequent conversation more exciting and positive. Accelerators: Accelerators like Y Combinator or Techstars can add enormous value to a startup, especially in the early stages. And while it’s a great idea to apply, don’t rely on them too heavily. Applications happen only a few times a year, and you should have a foolproof fundraising plan in case you don’t get in. In our case, we just constantly looked for investors who were interested in our space (defined as enterprise SaaS more broadly), using LinkedIn, AngelList, and intros from our own network. Practice the pitch ad nauseam: Pitching is tough to get right even for seasoned pros, so it pays to practice as often as possible. We took every opportunity to perfect the pitch: attending meetups and giving the thirty-second elevator pitch to other attendees over beer and pizza, participating in startup competitions, going to conferences and exhibiting at our own booth, attending pre-accelerator programs, and pitching to friends who are in the startup world. Show an understanding of the competition: Frankly, this was one of the strongest parts of our pitch and investor conversations. If you’re in a similar space to ours, Gartner Magic Quadrants and Forrester Waves are an awesome resource, as well as sites like AlternativeTo or Capterra and G2. By thoroughly studying these resources we gained a great understanding of the industry landscape and were able to articulate our differentiation more clearly and succinctly. Presenting this visually in a coordinate system or a feature grid is, from our experience, even more effective. Remember it’s just the beginning! Getting your first round of funding is just the beginning of the journey, so it’s important to avoid euphoria and get back to building and selling the product as soon as possible. While securing funding enables you to scale the team, and is a particular relief if the founders had worked without a salary, the end goal is still to build a big, profitable, and overall awesome startup.

How a Small Startup in Asia Secured a Contract with the US Department of Homeland Security

Uzair Javaid, a Ph.D. with a passion for data privacy, co-founded Betterdata to tackle one of AI's most pressing challenges: protecting privacy while enabling innovation. Recently, Betterdata secured a lucrative contract with the US Department of Homeland Security, 1 of only 4 companies worldwide to do so and the only one in Asia. Here's how he did it: The Story So what's your story? I grew up in Peshawar, Pakistan, excelling in coding despite studying electrical engineering. Inspired by my professors, I set my sights on studying abroad and eventually earned a Ph.D. scholarship at NUS Singapore, specializing in data security and privacy. During my research, I ethically hacked Ethereum and published 15 papers—three times the requirement. While wrapping up my Ph.D., I explored startup ideas and joined Entrepreneur First, where I met Kevin Yee. With his expertise in generative models and mine in privacy, we founded Betterdata. Now, nearly three years in, we’ve secured a major contract with the U.S. Department of Homeland Security—one of only four companies globally and the only one from Asia. The Startup In a nutshell, what does your startup do? Betterdata is a startup that uses AI and synthetic data generation to address two major challenges: data privacy and the scarcity of high-quality data for training AI models. By leveraging generative models and privacy-enhancing technologies, Betterdata enables businesses, such as banks, to use customer data without breaching privacy regulations. The platform trains AI on real data, learns its patterns, and generates synthetic data that mimics the real thing without containing any personal or sensitive information. This allows companies to innovate and develop AI solutions safely and ethically, all while tackling the growing need for diverse, high-quality data in AI development. How did you conduct ideation and validation for your startup? The initial idea for Betterdata came from personal experience. During my Ph.D., I ethically hacked Ethereum’s blockchain, exposing flaws in encryption-based data sharing. This led me to explore AI-driven deep synthesis technology—similar to deepfakes but for structured data privacy. With GDPR impacting 28M+ businesses, I saw a massive opportunity to help enterprises securely share data while staying compliant. To validate the idea, I spoke to 50 potential customers—a number that strikes the right balance. Some say 100, but that’s impractical for early-stage founders. At 50, patterns emerge: if 3 out of 10 mention the same problem, and this repeats across 50, you have 10–15 strong signals, making it a solid foundation for an MVP. Instead of outbound sales, which I dislike, we used three key methods: Account-Based Marketing (ABM)—targeting technically savvy users with solutions for niche problems, like scaling synthetic data for banks. Targeted Content Marketing—regular customer conversations shaped our thought leadership and outreach. Raising Awareness Through Partnerships—collaborating with NUS, Singapore’s PDPC, and Plug and Play to build credibility and educate the market. These strategies attracted serious customers willing to pay, guiding Betterdata’s product development and market fit. How did you approach the initial building and ongoing product development? In the early stages, we built synthetic data generation algorithms and a basic UI for proof-of-concept, using open-source datasets to engage with banks. We quickly learned that banks wouldn't share actual customer data due to privacy concerns, so we had to conduct on-site installations and gather feedback to refine our MVP. Through continuous consultation with customers, we discovered real enterprise data posed challenges, such as missing values, which led us to adapt our prototype accordingly. This iterative approach of listening to customer feedback and observing their usage allowed us to improve our product, enhance UX, and address unmet needs while building trust and loyalty. Working closely with our customers also gives us a data advantage. Our solution’s effectiveness depends on customer data, which we can't fully access, but bridging this knowledge gap gives us a competitive edge. The more customers we test on, the more our algorithms adapt to diverse use cases, making it harder for competitors to replicate our insights. My approach to iteration is simple: focus solely on customer feedback and ignore external noise like trends or advice. The key question for the team is: which customer is asking for this feature or solution? As long as there's a clear answer, we move forward. External influences, such as AI hype, often bring more confusion than clarity. True long-term success comes from solving real customer problems, not chasing trends. Customers may not always know exactly what they want, but they understand their problems. Our job is to identify these problems and solve them in innovative ways. While customers may suggest specific features, we stay focused on solving the core issue rather than just fulfilling their exact requests. The idea aligns with the quote often attributed to Henry Ford: "If I asked people what they wanted, they would have said faster horses." The key is understanding their problems, not just taking requests at face value. How do you assess product-market fit? To assess product-market fit, we track two key metrics: Customers' Willingness to Pay: We measure both the quantity and quality of meetings with potential customers. A high number of meetings with key decision-makers signals genuine interest. At Betterdata, we focused on getting meetings with people in banks and large enterprises to gauge our product's resonance with the target market. How Much Customers Are Willing to Pay: We monitor the price customers are willing to pay, especially in the early stages. For us, large enterprises, like banks, were willing to pay a premium for our synthetic data platform due to the growing need for privacy tech. This feedback guided our product refinement and scaling strategy. By focusing on these metrics, we refined our product and positioned it for scaling. What is your business model? We employ a structured, phase-driven approach for out business model, as a B2B startup. I initially struggled with focusing on the core value proposition in sales, often becoming overly educational. Eventually, we developed a product roadmap with models that allowed us to match customer needs to specific offerings and justify our pricing. Our pricing structure includes project-based pilots and annual contracts for successful deployments. At Betterdata, our customer engagement unfolds across three phases: Phase 1: Trial and Benchmarking \- We start with outreach and use open-source datasets to showcase results, offering customers a trial period to evaluate the solution. Phase 2: Pilot or PoC \- After positive trial results, we conduct a PoC or pilot using the customer’s private data, with the understanding that successful pilots lead to an annual contract. Phase 3: Multi-Year Contracts \- Following a successful pilot, we transition to long-term commercial contracts, focusing on multi-year agreements to ensure stability and ongoing partnerships. How do you do marketing for your brand? We take a non-conventional approach to marketing, focusing on answering one key question: Which customers are willing to pay, and how much? This drives our messaging to show how our solution meets their needs. Our strategy centers around two main components: Building a network of lead magnets \- These are influential figures like senior advisors, thought leaders, and strategic partners. Engaging with institutions like IMDA, SUTD, and investors like Plug and Play helps us gain access to the right people and foster warm introductions, which shorten our sales cycle and ensure we’re reaching the right audience. Thought leadership \- We build our brand through customer traction, technology evidence, and regulatory guidelines. This helps us establish credibility in the market and position ourselves as trusted leaders in our field. This holistic approach has enabled us to navigate diverse market conditions in Asia and grow our B2B relationships. By focusing on these areas, we drive business growth and establish strong trust with stakeholders. What's your advice for fundraising? Here are my key takeaways for other founders when it comes to fundraising: Fundraise When You Don’t Need To We closed our seed round in April 2023, a time when we weren't actively raising. Founders should always be in fundraising mode, even when they're not immediately in need of capital. Don’t wait until you have only a few months of runway left. Keep the pipeline open and build relationships. When the timing is right, execution becomes much easier. For us, our investment came through a combination of referrals and inbound interest. Even our lead investor initially rejected us, but after re-engaging, things eventually fell into place. It’s crucial to stay humble, treat everyone with respect, and maintain those relationships for when the time is right. Be Mindful of How You Present Information When fundraising, how you present information matters a lot. We created a comprehensive, easily digestible investment memo, hosted on Notion, which included everything an investor might need—problem, solution, market, team, risks, opportunities, and data. The goal was for investors to be able to get the full picture within 30 minutes without chasing down extra details. We also focused on making our financial model clear and meaningful, even though a 5-year forecast might be overkill at the seed stage. The key was clarity and conciseness, and making it as easy as possible for investors to understand the opportunity. I learned that brevity and simplicity are often the best ways to make a memorable impact. For the pitch itself, keep it simple and focus on 4 things: problem, solution, team, and market. If you can summarize each of these clearly and concisely, you’ll have a compelling pitch. Later on, you can expand into market segments, traction, and other metrics, but for seed-stage, focus on those four areas, and make sure you’re strong in at least three of them. If you do, you'll have a compelling case. How do you run things day-to-day? i.e what's your operational workflow and team structure? Here's an overview of our team structure and process: Internally: Our team is divided into two main areas: backend (internal team) and frontend (market-facing team). There's no formal hierarchy within the backend team. We all operate as equals, defining our goals based on what needs to be developed, assigning tasks, and meeting weekly to share updates and review progress. The focus is on full ownership of tasks and accountability for getting things done. I also contribute to product development, identifying challenges and clearing obstacles to help the team move forward. Backend Team: We approach tasks based on the scope defined by customers, with no blame or hierarchy. It's like a sports team—sometimes someone excels, and other times they struggle, but we support each other and move forward together. Everyone has the creative freedom to work in the way that suits them best, but we establish regular meetings and check-ins to ensure alignment and progress. Frontend Team: For the market-facing side, we implement a hierarchy because the market expects this structure. If I present myself as "CEO," it signals authority and credibility. This distinction affects how we communicate with the market and how we build our brand. The frontend team is split into four main areas: Business Product (Software Engineering) Machine Learning Engineering R&D The C-suite sits at the top, followed by team leads, and then the executors. We distill market expectations into actionable tasks, ensuring that everyone is clear on their role and responsibilities. Process: We start by receiving market expectations and defining tasks based on them. Tasks are assigned to relevant teams, and execution happens with no communication barriers between team members. This ensures seamless collaboration and focused execution. The main goal is always effectiveness—getting things done efficiently while maintaining flexibility in how individuals approach their work. In both teams, there's an emphasis on accountability, collaboration, and clear communication, but the structure varies according to the nature of the work and external expectations.

How a founder built a B2B AI startup to serve with 65+ global brands (including Fortune500 companies)

AI Palette is an AI-driven platform that helps food and beverage companies predict emerging product trends. I had the opportunity recently to sit down with the founder to get his advice on building an AI-first startup, which he'll be going through in this post. About AI Palette: Co-founders: >!2 (Somsubhra GanChoudhuri, Himanshu Upreti)!!100+!!$12.7M USD!!AI-powered predictive analytics for the CPG (Consumer Packaged Goods) industry!!Signed first paying customer in the first year!!65+ global brands, including Cargill, Diageo, Ajinomoto, Symrise, Mondelez, and L’Oréal, use AI Palette!!Every new product launched has secured a paying client within months!!Expanded into Beauty & Personal Care (BPC), onboarding one of India’s largest BPC companies within weeks!!Launched multiple new product lines in the last two years, creating a unified suite for brand innovation!Identify the pain points in your industry for ideas* When I was working in the flavour and fragrance industry, I noticed a major issue CPG companies faced: launching a product took at least one to two years. For instance, if a company decided today to launch a new juice, it wouldn’t hit the market until 2027. This long timeline made it difficult to stay relevant and on top of trends. Another big problem I noticed was that companies relied heavily on market research to determine what products to launch. While this might work for current consumer preferences, it was highly inefficient since the product wouldn’t actually reach the market for several years. By the time the product launched, the consumer trends had already shifted, making that research outdated. That’s where AI can play a crucial role. Instead of looking at what consumers like today, we realised that companies should use AI to predict what they will want next. This allows businesses to create products that are ahead of the curve. Right now, the failure rate for new product launches is alarmingly high, with 8 out of 10 products failing. By leveraging AI, companies can avoid wasting resources on products that won’t succeed, leading to better, more successful launches. Start by talking to as many industry experts as possible to identify the real problems When we first had the idea for AI Palette, it was just a hunch, a gut feeling—we had no idea whether people would actually pay for it. To validate the idea, we reached out to as many people as we could within the industry. Since our focus area was all about consumer insights, we spoke to professionals in the CPG sector, particularly those in the insights departments of CPG companies. Through these early conversations, we began to see a common pattern emerge and identified the exact problem we wanted to solve. Don’t tell people what you’re building—listen to their frustrations and challenges first. Going into these early customer conversations, our goal was to listen and understand their challenges without telling them what we were trying to build. This is crucial as it ensures that you can gather as much data about the problem to truly understand it and that you aren't biasing their answers by showing your solution. This process helped us in two key ways: First, it validated that there was a real problem in the industry through the number of people who spoke about experiencing the same problem. Second, it allowed us to understand the exact scale and depth of the problem—e.g., how much money companies were spending on consumer research, what kind of tools they were currently using, etc. Narrow down your focus to a small, actionable area to solve initially. Once we were certain that there was a clear problem worth solving, we didn’t try to tackle everything at once. As a small team of two people, we started by focusing on a specific area of the problem—something big enough to matter but small enough for us to handle. Then, we approached customers with a potential solution and asked them for feedback. We learnt that our solution seemed promising, but we wanted to validate it further. If customers are willing to pay you for the solution, it’s a strong validation signal for market demand. One of our early customer interviewees even asked us to deliver the solution, which we did manually at first. We used machine learning models to analyse the data and presented the results in a slide deck. They paid us for the work, which was a critical moment. It meant we had something with real potential, and we had customers willing to pay us before we had even built the full product. This was the key validation that we needed. By the time we were ready to build the product, we had already gathered crucial insights from our early customers. We understood the specific information they wanted and how they wanted the results to be presented. This input was invaluable in shaping the development of our final product. Building & Product Development Start with a simple concept/design to validate with customers before building When we realised the problem and solution, we began by designing the product, but not by jumping straight into coding. Instead, we created wireframes and user interfaces using tools like InVision and Figma. This allowed us to visually represent the product without the need for backend or frontend development at first. The goal was to showcase how the product would look and feel, helping potential customers understand its value before we even started building. We showed these designs to potential customers and asked for feedback. Would they want to buy this product? Would they pay for it? We didn’t dive into actual development until we found a customer willing to pay a significant amount for the solution. This approach helped us ensure we were on the right track and didn’t waste time or resources building something customers didn’t actually want. Deliver your solution using a manual consulting approach before developing an automated product Initially, we solved problems for customers in a more "consulting" manner, delivering insights manually. Recall how I mentioned that when one of our early customer interviewees asked us to deliver the solution, we initially did it manually by using machine learning models to analyse the data and presenting the results to them in a slide deck. This works for the initial stages of validating your solution, as you don't want to invest too much time into building a full-blown MVP before understanding the exact features and functionalities that your users want. However, after confirming that customers were willing to pay for what we provided, we moved forward with actual product development. This shift from a manual service to product development was key to scaling in a sustainable manner, as our building was guided by real-world feedback and insights rather than intuition. Let ongoing customer feedback drive iteration and the product roadmap Once we built the first version of the product, it was basic, solving only one problem. But as we worked closely with customers, they requested additional features and functionalities to make it more useful. As a result, we continued to evolve the product to handle more complex use cases, gradually developing new modules based on customer feedback. Product development is a continuous process. Our early customers pushed us to expand features and modules, from solving just 20% of their problems to tackling 50–60% of their needs. These demands shaped our product roadmap and guided the development of new features, ultimately resulting in a more complete solution. Revenue and user numbers are key metrics for assessing product-market fit. However, critical mass varies across industries Product-market fit (PMF) can often be gauged by looking at the size of your revenue and the number of customers you're serving. Once you've reached a certain critical mass of customers, you can usually tell that you're starting to hit product-market fit. However, this critical mass varies by industry and the type of customers you're targeting. For example, if you're building an app for a broad consumer market, you may need thousands of users. But for enterprise software, product-market fit may be reached with just a few dozen key customers. Compare customer engagement and retention with other available solutions on the market for product-market fit Revenue and the number of customers alone isn't always enough to determine if you're reaching product-market fit. The type of customer and the use case for your product also matter. The level of engagement with your product—how much time users are spending on the platform—is also an important metric to track. The more time they spend, the more likely it is that your product is meeting a crucial need. Another way to evaluate product-market fit is by assessing retention, i.e whether users are returning to your platform and relying on it consistently, as compared to other solutions available. That's another key indication that your solution is gaining traction in the market. Business Model & Monetisation Prioritise scalability Initially, we started with a consulting-type model where we tailor-made specific solutions for each customer use-case we encountered and delivered the CPG insights manually, but we soon realized that this wasn't scalable. The problem with consulting is that you need to do the same work repeatedly for every new project, which requires a large team to handle the workload. That is not how you sustain a high-growth startup. To solve this, we focused on building a product that would address the most common problems faced by our customers. Once built, this product could be sold to thousands of customers without significant overheads, making the business scalable. With this in mind, we decided on a SaaS (Software as a Service) business model. The benefit of SaaS is that once you create the software, you can sell it to many customers without adding extra overhead. This results in a business with higher margins, where the same product can serve many customers simultaneously, making it much more efficient than the consulting model. Adopt a predictable, simplistic business model for efficiency. Look to industry practices for guidance When it came to monetisation, we considered the needs of our CPG customers, who I knew from experience were already accustomed to paying annual subscriptions for sales databases and other software services. We decided to adopt the same model and charge our customers an annual upfront fee. This model worked well for our target market, aligning with industry standards and ensuring stable, recurring revenue. Moreover, our target CPG customers were already used to this business model and didn't have to choose from a huge variety of payment options, making closing sales a straightforward and efficient process. Marketing & Sales Educate the market to position yourself as a thought leader When we started, AI was not widely understood, especially in the CPG industry. We had to create awareness around both AI and its potential value. Our strategy focused on educating potential users and customers about AI, its relevance, and why they should invest in it. This education was crucial to the success of our marketing efforts. To establish credibility, we adopted a thought leadership approach. We wrote blogs on the importance of AI and how it could solve problems for CPG companies. We also participated in events and conferences to demonstrate our expertise in applying AI to the industry. This helped us build our brand and reputation as leaders in the AI space for CPG, and word-of-mouth spread as customers recognized us as the go-to company for AI solutions. It’s tempting for startups to offer products for free in the hopes of gaining early traction with customers, but this approach doesn't work in the long run. Free offerings don’t establish the value of your product, and customers may not take them seriously. You should always charge for pilots, even if the fee is minimal, to ensure that the customer is serious about potentially working with you, and that they are committed and engaged with the product. Pilots/POCs/Demos should aim to give a "flavour" of what you can deliver A paid pilot/POC trial also gives you the opportunity to provide a “flavour” of what your product can deliver, helping to build confidence and trust with the client. It allows customers to experience a detailed preview of what your product can do, which builds anticipation and desire for the full functionality. During this phase, ensure your product is built to give them a taste of the value you can provide, which sets the stage for a broader, more impactful adoption down the line. Fundraising & Financial Management Leverage PR to generate inbound interest from VCs When it comes to fundraising, our approach was fairly traditional—we reached out to VCs and used connections from existing investors to make introductions. However, looking back, one thing that really helped us build momentum during our fundraising process was getting featured in Tech in Asia. This wasn’t planned; it just so happened that Tech in Asia was doing a series on AI startups in Southeast Asia and they reached out to us for an article. During the interview, they asked if we were fundraising, and we mentioned that we were. As a result, several VCs we hadn’t yet contacted reached out to us. This inbound interest was incredibly valuable, and we found it far more effective than our outbound efforts. So, if you can, try to generate some PR attention—it can help create inbound interest from VCs, and that interest is typically much stronger and more promising than any outbound strategies because they've gone out of their way to reach out to you. Be well-prepared and deliberate about fundraising. Keep trying and don't lose heart When pitching to VCs, it’s crucial to be thoroughly prepared, as you typically only get one shot at making an impression. If you mess up, it’s unlikely they’ll give you a second chance. You need to have key metrics at your fingertips, especially if you're running a SaaS company. Be ready to answer questions like: What’s your retention rate? What are your projections for the year? How much will you close? What’s your average contract value? These numbers should be at the top of your mind. Additionally, fundraising should be treated as a structured process, not something you do on the side while juggling other tasks. When you start, create a clear plan: identify 20 VCs to reach out to each week. By planning ahead, you’ll maintain momentum and speed up the process. Fundraising can be exhausting and disheartening, especially when you face multiple rejections. Remember, you just need one investor to say yes to make it all worthwhile. When using funds, prioritise profitability and grow only when necessary. Don't rely on funding to survive. In the past, the common advice for startups was to raise money, burn through it quickly, and use it to boost revenue numbers, even if that meant operating at a loss. The idea was that profitability wasn’t the main focus, and the goal was to show rapid growth for the next funding round. However, times have changed, especially with the shift from “funding summer” to “funding winter.” My advice now is to aim for profitability as soon as possible and grow only when it's truly needed. For example, it’s tempting to hire a large team when you have substantial funds in the bank, but ask yourself: Do you really need 10 new hires, or could you get by with just four? Growing too quickly can lead to unnecessary expenses, so focus on reaching profitability as soon as possible, rather than just inflating your team or burn rate. The key takeaway is to spend your funds wisely and only when absolutely necessary to reach profitability. You want to avoid becoming dependent on future VC investments to keep your company afloat. Instead, prioritize reaching break-even as quickly as you can, so you're not reliant on external funding to survive in the long run. Team-Building & Leadership Look for complementary skill sets in co-founders When choosing a co-founder, it’s important to find someone with a complementary skill set, not just someone you’re close to. For example, I come from a business and commercial background, so I needed someone with technical expertise. That’s when I found my co-founder, Himanshu, who had experience in machine learning and AI. He was a great match because his technical knowledge complemented my business skills, and together we formed a strong team. It might seem natural to choose your best friend as your co-founder, but this can often lead to conflict. Chances are, you and your best friend share similar interests, skills, and backgrounds, which doesn’t bring diversity to the table. If both of you come from the same industry or have the same strengths, you may end up butting heads on how things should be done. Having diverse skill sets helps avoid this and fosters a more collaborative working relationship. Himanshu (left) and Somsubhra (right) co-founded AI Palette in 2018 Define roles clearly to prevent co-founder conflict To avoid conflict, it’s essential that your roles as co-founders are clearly defined from the beginning. If your co-founder and you have distinct responsibilities, there is no room for overlap or disagreement. This ensures that both of you can work without stepping on each other's toes, and there’s mutual respect for each other’s expertise. This is another reason as to why it helps to have a co-founder with a complementary skillset to yours. Not only is having similar industry backgrounds and skillsets not particularly useful when building out your startup, it's also more likely to lead to conflicts since you both have similar subject expertise. On the other hand, if your co-founder is an expert in something that you're not, you're less likely to argue with them about their decisions regarding that aspect of the business and vice versa when it comes to your decisions. Look for employees who are driven by your mission, not salary For early-stage startups, the first hires are crucial. These employees need to be highly motivated and excited about the mission. Since the salary will likely be low and the work demanding, they must be driven by something beyond just the paycheck. The right employees are the swash-buckling pirates and romantics, i.e those who are genuinely passionate about the startup’s vision and want to be part of something impactful beyond material gains. When employees are motivated by the mission, they are more likely to stick around and help take the startup to greater heights. A litmus test for hiring: Would you be excited to work with them on a Sunday? One of the most important rounds in the hiring process is the culture fit round. This is where you assess whether a candidate shares the same values as you and your team. A key question to ask yourself is: "Would I be excited to work with this person on a Sunday?" If there’s any doubt about your answer, it’s likely not a good fit. The idea is that you want employees who align with the company's culture and values and who you would enjoy collaborating with even outside of regular work hours. How we structure the team at AI Palette We have three broad functions in our organization. The first two are the big ones: Technical Team – This is the core of our product and technology. This team is responsible for product development and incorporating customer feedback into improving the technology Commercial Team – This includes sales, marketing, customer service, account managers, and so on, handling everything related to business growth and customer relations. General and Administrative Team – This smaller team supports functions like finance, HR, and administration. As with almost all businesses, we have teams that address the two core tasks of building (technical team) and selling (commercial team), but given the size we're at now, having the administrative team helps smoothen operations. Set broad goals but let your teams decide on execution What I've done is recruit highly skilled people who don't need me to micromanage them on a day-to-day basis. They're experts in their roles, and as Steve Jobs said, when you hire the right person, you don't have to tell them what to do—they understand the purpose and tell you what to do. So, my job as the CEO is to set the broader goals for them, review the plans they have to achieve those goals, and periodically check in on progress. For example, if our broad goal is to meet a certain revenue target, I break it down across teams: For the sales team, I’ll look at how they plan to hit that target—how many customers they need to sell to, how many salespeople they need, and what tactics and strategies they plan to use. For the technical team, I’ll evaluate our product offerings—whether they think we need to build new products to attract more customers, and whether they think it's scalable for the number of customers we plan to serve. This way, the entire organization's tasks are cascaded in alignment with our overarching goals, with me setting the direction and leaving the details of execution to the skilled team members that I hire.

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.

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

From research paper to a tech startup - help!

Hi! I'm a CS master student that loves being creative. I’ve always wanted to start a business. I have gotten offers to join other startups when I took my bachelors, but personally I never believed in the startups, so I’ve always ended up politely declining on any startup offers. But my master thesis idea is very intriguing. However, I still feel very lost. I can’t even think of any good company names, or where I would even find enthusiastic co founders.  My master thesis as an AI startup with large potential. As of today, I have not started on the product itself. I will write a paper on the product, and finish the thesis in August 2026. My supervisor suggested that this is a good startup idea, and has a large market potential. I want to try. I’ve written about my goals, milestones, and some questions. Feel free to help me in any way, by answering my questions below. Goal:  Learn about startups and non-technical part of it (business, finance, sales, etc) (I'm clueless here) Build the business part time Try and fail Milestones Complete my paper on the product Create MVP for customers to test Validate idea and check market Find company name, acquire domain and launch SaaS  Get feedback, do networking and improve the product Join a Startup Lab and find Cofounders. The following roles would need to be filled  CEO (Me, Vision and tech expert) COO (Business strategy, operations, and scaling.),  CMO (marketing and sales responsible, working to acquire new business) CPO (Product design, user experience, and frontend development)  Formally create the company, divide shares, hold weekend work meeting, pick company name (again) Goal: create product for an industry (the product can be tailored to different industries) and get the first clients. Work that needs to be done: Tech: Create the product for the industry  COO: pitching competitions, define the sales pitch, and how to price the product CMO: find out how marketing should be done, and what companies to contact for demo CMO: design company logo, design web page for business usage, create front page of the website  Growth + Profits Questions Between now, and until I have the working demo, what should I do with my time? I have courses where I learn technical skills for the company. It does not make sense to create the website for the product, when I don't know how the user would interact with the product.  Should I start the company even before the product is made? (While I'm a student and working on the paper) How can I acquire non-technical skills for running a business? I prefer reading books. How can I learn about software companies (practical skills)? For example: How to lower hosting costs?  How to price a product for customers and a product for business? (Software contracts) How to guarantee  privacy when it comes to business documents?  I’m planning on searching for co-founders, after I have validated the idea myself. Should I instead find co founders before I have even created the product? (with no guarantee that there would even be a product?) Should I try to make the product without co-founders? (This is my first startup, so it might tank within the first few months) Any experience with starting a software business while working full time? Thank you for all the help!

Struggling to launch your startup because of tech barriers? I want to help build your MVP—free.

Hey fellow entrepreneurs, I've been noticing a common struggle—so many great startup ideas never get off the ground because of technical roadblocks. Finding a technical co-founder is tough, hiring devs is expensive, and learning to code takes time most founders don’t have. I’m working on a tool that helps non-technical founders turn their ideas into real, functional web apps using AI. But instead of building in isolation, I want to test it in real startup conditions—which means helping actual entrepreneurs like you bring their MVPs to life. Here’s the deal: I’ll build an MVP for free—no catch, no hidden agenda. I just want to test my platform with real use cases and learn from your feedback. If you’ve been sitting on an idea but haven’t executed because of technical hurdles, I’d love to try building a first version for you. Drop your idea below in this format: What’s your project about? (e.g., “a platform connecting indie artists with brands”) What are the key features? (e.g., “artist profiles, project bidding, contract management”) I’ll pick some of the most popular ideas and try to generate an MVP using my tool. Whether or not it works perfectly, we’ll both learn something valuable—and hopefully, you’ll have a solid starting point to iterate on. Looking forward to hearing your ideas! Let’s see what we can build together. — Clément

How a Small Startup in Asia Secured a Contract with the US Department of Homeland Security

Uzair Javaid, a Ph.D. with a passion for data privacy, co-founded Betterdata to tackle one of AI's most pressing challenges: protecting privacy while enabling innovation. Recently, Betterdata secured a lucrative contract with the US Department of Homeland Security, 1 of only 4 companies worldwide to do so and the only one in Asia. Here's how he did it: The Story So what's your story? I grew up in Peshawar, Pakistan, excelling in coding despite studying electrical engineering. Inspired by my professors, I set my sights on studying abroad and eventually earned a Ph.D. scholarship at NUS Singapore, specializing in data security and privacy. During my research, I ethically hacked Ethereum and published 15 papers—three times the requirement. While wrapping up my Ph.D., I explored startup ideas and joined Entrepreneur First, where I met Kevin Yee. With his expertise in generative models and mine in privacy, we founded Betterdata. Now, nearly three years in, we’ve secured a major contract with the U.S. Department of Homeland Security—one of only four companies globally and the only one from Asia. The Startup In a nutshell, what does your startup do? Betterdata is a startup that uses AI and synthetic data generation to address two major challenges: data privacy and the scarcity of high-quality data for training AI models. By leveraging generative models and privacy-enhancing technologies, Betterdata enables businesses, such as banks, to use customer data without breaching privacy regulations. The platform trains AI on real data, learns its patterns, and generates synthetic data that mimics the real thing without containing any personal or sensitive information. This allows companies to innovate and develop AI solutions safely and ethically, all while tackling the growing need for diverse, high-quality data in AI development. How did you conduct ideation and validation for your startup? The initial idea for Betterdata came from personal experience. During my Ph.D., I ethically hacked Ethereum’s blockchain, exposing flaws in encryption-based data sharing. This led me to explore AI-driven deep synthesis technology—similar to deepfakes but for structured data privacy. With GDPR impacting 28M+ businesses, I saw a massive opportunity to help enterprises securely share data while staying compliant. To validate the idea, I spoke to 50 potential customers—a number that strikes the right balance. Some say 100, but that’s impractical for early-stage founders. At 50, patterns emerge: if 3 out of 10 mention the same problem, and this repeats across 50, you have 10–15 strong signals, making it a solid foundation for an MVP. Instead of outbound sales, which I dislike, we used three key methods: Account-Based Marketing (ABM)—targeting technically savvy users with solutions for niche problems, like scaling synthetic data for banks. Targeted Content Marketing—regular customer conversations shaped our thought leadership and outreach. Raising Awareness Through Partnerships—collaborating with NUS, Singapore’s PDPC, and Plug and Play to build credibility and educate the market. These strategies attracted serious customers willing to pay, guiding Betterdata’s product development and market fit. How did you approach the initial building and ongoing product development? In the early stages, we built synthetic data generation algorithms and a basic UI for proof-of-concept, using open-source datasets to engage with banks. We quickly learned that banks wouldn't share actual customer data due to privacy concerns, so we had to conduct on-site installations and gather feedback to refine our MVP. Through continuous consultation with customers, we discovered real enterprise data posed challenges, such as missing values, which led us to adapt our prototype accordingly. This iterative approach of listening to customer feedback and observing their usage allowed us to improve our product, enhance UX, and address unmet needs while building trust and loyalty. Working closely with our customers also gives us a data advantage. Our solution’s effectiveness depends on customer data, which we can't fully access, but bridging this knowledge gap gives us a competitive edge. The more customers we test on, the more our algorithms adapt to diverse use cases, making it harder for competitors to replicate our insights. My approach to iteration is simple: focus solely on customer feedback and ignore external noise like trends or advice. The key question for the team is: which customer is asking for this feature or solution? As long as there's a clear answer, we move forward. External influences, such as AI hype, often bring more confusion than clarity. True long-term success comes from solving real customer problems, not chasing trends. Customers may not always know exactly what they want, but they understand their problems. Our job is to identify these problems and solve them in innovative ways. While customers may suggest specific features, we stay focused on solving the core issue rather than just fulfilling their exact requests. The idea aligns with the quote often attributed to Henry Ford: "If I asked people what they wanted, they would have said faster horses." The key is understanding their problems, not just taking requests at face value. How do you assess product-market fit? To assess product-market fit, we track two key metrics: Customers' Willingness to Pay: We measure both the quantity and quality of meetings with potential customers. A high number of meetings with key decision-makers signals genuine interest. At Betterdata, we focused on getting meetings with people in banks and large enterprises to gauge our product's resonance with the target market. How Much Customers Are Willing to Pay: We monitor the price customers are willing to pay, especially in the early stages. For us, large enterprises, like banks, were willing to pay a premium for our synthetic data platform due to the growing need for privacy tech. This feedback guided our product refinement and scaling strategy. By focusing on these metrics, we refined our product and positioned it for scaling. What is your business model? We employ a structured, phase-driven approach for out business model, as a B2B startup. I initially struggled with focusing on the core value proposition in sales, often becoming overly educational. Eventually, we developed a product roadmap with models that allowed us to match customer needs to specific offerings and justify our pricing. Our pricing structure includes project-based pilots and annual contracts for successful deployments. At Betterdata, our customer engagement unfolds across three phases: Phase 1: Trial and Benchmarking \- We start with outreach and use open-source datasets to showcase results, offering customers a trial period to evaluate the solution. Phase 2: Pilot or PoC \- After positive trial results, we conduct a PoC or pilot using the customer’s private data, with the understanding that successful pilots lead to an annual contract. Phase 3: Multi-Year Contracts \- Following a successful pilot, we transition to long-term commercial contracts, focusing on multi-year agreements to ensure stability and ongoing partnerships. How do you do marketing for your brand? We take a non-conventional approach to marketing, focusing on answering one key question: Which customers are willing to pay, and how much? This drives our messaging to show how our solution meets their needs. Our strategy centers around two main components: Building a network of lead magnets \- These are influential figures like senior advisors, thought leaders, and strategic partners. Engaging with institutions like IMDA, SUTD, and investors like Plug and Play helps us gain access to the right people and foster warm introductions, which shorten our sales cycle and ensure we’re reaching the right audience. Thought leadership \- We build our brand through customer traction, technology evidence, and regulatory guidelines. This helps us establish credibility in the market and position ourselves as trusted leaders in our field. This holistic approach has enabled us to navigate diverse market conditions in Asia and grow our B2B relationships. By focusing on these areas, we drive business growth and establish strong trust with stakeholders. What's your advice for fundraising? Here are my key takeaways for other founders when it comes to fundraising: Fundraise When You Don’t Need To We closed our seed round in April 2023, a time when we weren't actively raising. Founders should always be in fundraising mode, even when they're not immediately in need of capital. Don’t wait until you have only a few months of runway left. Keep the pipeline open and build relationships. When the timing is right, execution becomes much easier. For us, our investment came through a combination of referrals and inbound interest. Even our lead investor initially rejected us, but after re-engaging, things eventually fell into place. It’s crucial to stay humble, treat everyone with respect, and maintain those relationships for when the time is right. Be Mindful of How You Present Information When fundraising, how you present information matters a lot. We created a comprehensive, easily digestible investment memo, hosted on Notion, which included everything an investor might need—problem, solution, market, team, risks, opportunities, and data. The goal was for investors to be able to get the full picture within 30 minutes without chasing down extra details. We also focused on making our financial model clear and meaningful, even though a 5-year forecast might be overkill at the seed stage. The key was clarity and conciseness, and making it as easy as possible for investors to understand the opportunity. I learned that brevity and simplicity are often the best ways to make a memorable impact. For the pitch itself, keep it simple and focus on 4 things: problem, solution, team, and market. If you can summarize each of these clearly and concisely, you’ll have a compelling pitch. Later on, you can expand into market segments, traction, and other metrics, but for seed-stage, focus on those four areas, and make sure you’re strong in at least three of them. If you do, you'll have a compelling case. How do you run things day-to-day? i.e what's your operational workflow and team structure? Here's an overview of our team structure and process: Internally: Our team is divided into two main areas: backend (internal team) and frontend (market-facing team). There's no formal hierarchy within the backend team. We all operate as equals, defining our goals based on what needs to be developed, assigning tasks, and meeting weekly to share updates and review progress. The focus is on full ownership of tasks and accountability for getting things done. I also contribute to product development, identifying challenges and clearing obstacles to help the team move forward. Backend Team: We approach tasks based on the scope defined by customers, with no blame or hierarchy. It's like a sports team—sometimes someone excels, and other times they struggle, but we support each other and move forward together. Everyone has the creative freedom to work in the way that suits them best, but we establish regular meetings and check-ins to ensure alignment and progress. Frontend Team: For the market-facing side, we implement a hierarchy because the market expects this structure. If I present myself as "CEO," it signals authority and credibility. This distinction affects how we communicate with the market and how we build our brand. The frontend team is split into four main areas: Business Product (Software Engineering) Machine Learning Engineering R&D The C-suite sits at the top, followed by team leads, and then the executors. We distill market expectations into actionable tasks, ensuring that everyone is clear on their role and responsibilities. Process: We start by receiving market expectations and defining tasks based on them. Tasks are assigned to relevant teams, and execution happens with no communication barriers between team members. This ensures seamless collaboration and focused execution. The main goal is always effectiveness—getting things done efficiently while maintaining flexibility in how individuals approach their work. In both teams, there's an emphasis on accountability, collaboration, and clear communication, but the structure varies according to the nature of the work and external expectations.

I Watched My Startup Slowly Dying Over Two Years: Mistakes and Lessons Learned

If you are tired of reading successful stories, you may want to listen to my almost failure story. Last year in April, I went full-time on my startup. Nearly two years later, I’ve seen my product gradually dying. I want to share some of the key mistakes I made and the lessons I’ve taken from them so you don't have to go through them. Some mistakes were very obvious in hindsight; others, I’m still not sure if they were mistakes or just bad luck. I’d love to hear your thoughts and advice as well. Background I built an English-learning app, with both web and mobile versions. The idea came from recognizing how expensive it is to hire an English tutor in most countries, especially for practicing speaking skills. With the rise of AI, I saw an opportunity in the education space. My target market was Japan, though I later added support for multiple languages and picked up some users from Indonesia and some Latin American countries too. Most of my users came from influencer marketing on Twitter. The MVP for the web version launched in Japan and got great feedback. People were reposting it on Twitter, and growth was at its peak in the first few weeks. After verifying the requirement with the MVP, I decided to focus on the mobile app to boost user retention, but for various reasons, the mobile version didn’t launch until December 2023— 8 months after the web version. Most of this year has been spent iterating on the mobile app, but it didn’t make much of an impact in the end. Key Events and Lessons Learned Here are some takeaways: Find co-founders as committed as you are I started with two co-founders—both were tech people and working Part-Time. After the web version launched, one dropped out due to family issues. Unfortunately, we didn’t set clear rules for equity allocation, so even after leaving, they still retained part of the equity. The other co-founder also effectively dropped out this year, contributing only minor fixes here and there. So If you’re starting a company with co-founders, make sure they’re as committed as you are. Otherwise, you might be better off going solo. I ended up teaching myself programming with AI tools, starting with Flutter and eventually handling both front-end and back-end work using Windsurf. With dev tools getting more advanced, being a solo developer is becoming a more viable option. Also, have crystal-clear rules for equity—especially around what happens if someone leaves. Outsourcing Pitfalls Outsourcing development was one of my biggest mistakes. I initially hired a former colleague from India to build the app. He dragged the project on for two months with endless excuses, and the final output was unusable. Then I hired a company, but they didn’t have enough skilled Flutter developers. The company’s owner scrambled to find people, which led to rushed work and poor-quality code which took a lot of time revising myself. Outsourcing is a minefield. If you must do it, break the project into small tasks, set clear milestones, and review progress frequently. Catching issues early can save you time and money. Otherwise, you’re often better off learning the tools yourself—modern dev tools are surprisingly beginner-friendly. Trust, but Verify I have a bad habit of trusting people too easily. I don’t like spending time double-checking things, so I tend to assume people will do what they say they’ll do. This mindset is dangerous in a startup. For example, if I had set up milestones and regularly verified the progress of my first outsourced project, I would’ve realized something was wrong within two weeks instead of two months. That would’ve saved me a lot of time and frustration. Like what I mentioned above, set up systems to verify their work—milestones, deliverables, etc.—to minimize risk. Avoid red ocean if you are small My team was tiny (or non-existent, depending on how you see it), with no technical edge. Yet, I chose to enter Japan’s English-learning market, which is incredibly competitive. It’s a red ocean, dominated by big players who’ve been in the game for years. Initially, my product’s AI-powered speaking practice and automatic grammar correction stood out, but within months, competitors rolled out similar features. Looking back, I should’ve gone all-in on marketing during the initial hype and focused on rapidly launching the mobile app. But hindsight is 20/20. 'Understanding your user' helps but what if it's not what you want? I thought I was pretty good at collecting user feedback. I added feedback buttons everywhere in the app and made changes based on what users said. But most of these changes were incremental improvements—not the kind of big updates that spark excitement. Also, my primary users were from Japan and Indonesia, but I’m neither Japanese nor Indonesian. That made it hard to connect with users on social media in an authentic way. And in my opinion, AI translations can only go so far—they lack the human touch and cultural nuance that builds trust. But honestly I'm not sure if the thought is correct to assume that they will not get touched if they recognize you are a foreigner...... Many of my Japanese users were working professionals preparing for the TOEIC exam. I didn’t design any features specifically for that; instead, I aimed to build a general-purpose English-learning tool since I dream to expand it to other markets someday. While there’s nothing wrong with this idealistic approach, it didn’t give users enough reasons to pay for the app. Should You Go Full-Time? From what I read, a lot of successful indie developers started part-time, building traction before quitting their jobs. But for me, I jumped straight into full-time mode, which worked for my lifestyle but might’ve hurt my productivity. I value work-life balance and refused to sacrifice everything for the startup. The reason I chose to leave the corp is I want to escape the 996 toxic working environment in China's internet companies. So even during my most stressful periods, I made time to watch TV with my partner and take weekends off. Anyways, if you’re also building something or thinking about starting a business, I hope my story helps. If I have other thoughts later, I will add them too. Appreciate any advice.

I have reviewed over 900+ AI Tools for my directory. Here are some of the best ones I have seen for entrepreneurs and startups.

As one of the co-founders at AI Scout, a platform for AI discovery, I've had the privilege (and challenge) of reviewing over 900 AI tools submitted to our directory. I've filtered these down to some of the top AI tools that I believe could bring value to startups and entrepreneurs. It's worth noting that while these tools are great right out the box, the power of AI is truly realized when these tools are used in tandem and strategically aligned with your business needs. The challenge most people face is not about the lack of AI tools available, but the difficulty in finding the right one that fits their specific needs and workflows. Without further ado, here's my top pick of AI tools you should consider looking into if you are an entrepreneur or run a startup. Chatbase - Custom ChatGPT (Trained on Your Own Data) Taking a step up from traditional support bots, Chatbase combines the power of GPT and your own knowledge base. The result is a ChatGPT-like chatbot that is trained on your own websites and documents. You can embed the chatbot into your own website via an iframe or script in the header of your website code. They also have an API you can take advantage of. We use this personally at AI Scout for ScoutBud (AI assistant to find AI tools), which we trained based on our directory site. It would also work great if you have extensive documentation, papers, etc. that you want to quickly reference by simply asking a chatbot for the info you need instead of having to go through dozens of PDFs. Reply - AI-Powered Sales Engagement Platform Great AI tool to manage your entire sales engagement cycle. They have a large database with about a dozen filters to discover optimal B2B leads. From here, you can use their GPT integration to generate cold emails as well as handle responses and meeting scheduling. What I like personally about Reply are the endless integrations available, including Gmail, Outlook, Zoho, and major social platforms such as Twitter and LinkedIn. Instapage - AI Landing Page Generation, Testing, and Personalization This AI tool allows users to generate content variations for landing pages including headlines, paragraphs, and CTAs based on the target audience. You can also conduct A/B testing for more effective and efficient campaigns. Paired with hundreds of professional and cutomizable layouts, Instapage is definitely something I would recommend for entrepreneurs who want to get a high-converting landing page set up quickly and effectively. SaneBox - AI Emails Management If you feel overwhelmed by the sheer volume of emails you receive like myself and many entrepreneurs, this could be something for you. SaneBox’s AI identifies important emails and declutters your inbox, helping you to stay focused on what truly matters. SocialBee - AI Social Media Manager Think of SocialBee as your all-in-one social media command center, powered by AI. You can manage multiple social media accounts from one platform and generate captions with AI as well. SocialBee not only allows you to schedule posts but also helps you analyze growth and engagement with detailed reports. Works well with all social media platforms, including Facebook, Twitter, Instagram, and Linkedin. I believe they also have integrations for TikTok and YouTube, although I haven't tried these personally. MeetGeek - AI Meeting Assistant Lifesaver if you attend a lot of meetings or calls. Great for transcribing, summarizing, and sharing key insights from meetings. The AI also creates meeting highlights, which I've personally fouund quite useful if you ever need to get a very quick and dirty overview of what happened in a call. It also provides analysis (including sentiment evaluation) for meetings. Taskade - AI Productivity Tool for Task Management An all-in-one AI productivity tool. Multiple AI features available, including a chatbot, writing assistant, and workflow creator. It's a great all-around tool for real-time collaboration and efficient task management. Scribe AI (ScribeHow) - AI Documentation Generator Great for any SaaS applications where you need to create resources/documentations/guides for your app. You simply record your process and Scribe generates a written guide for you. Remember, while AI is an excellent assistant, it's also just a tool. The ultimate success of your venture depends on how effectively you leverage these tools. Happy experimenting!

I am starting a startup on AI research automation. Looking for feedback!

Hi everyone, I would like to share a product idea that I'm working on. I studied computer science and have worked for Silicon Valley startups for the last 6 years. I'm currently employed full-time at a startup that sells an AI-powered search engine, so I have gained valuable experience in the AI/information retrieval space. I turned 29 last week, and I think it's time for me to start my own business. I've always wanted to run my own tech company, and I feel like now is the right time to begin with an idea. Are you a researcher in any field? Do you often find yourself learning new, highly complex topics and don't know where to start? Google is a great tool for finding answers to specific questions, but what if you don't know what questions to ask? I am developing a "deep search" engine that, given a topic, produces a multi-page report aggregating information from several properly cited sources. It finds and explains different perspectives and ideas related to the topic of interest. You can use it to automate the research process, but it's much more than that because it can help you uncover hidden perspectives, important questions, and ideas that you might not otherwise find when just googling. I welcome any feedback and ideas! Do you think this product would deliver significant value to your life? Why or why not? Would you be willing to pay to use it? I will post updates about this product in this thread in case you want to follow its development and try the product when it's ready.

This founder was about to shut down his startup and open a restaurant. He pivoted the business and grew it to $45m ARR in 12 months. What else have you seen grow that fast?

I heard that Jasper scaled to $45m ARR in 12 months...with a team of 8. For context, they are one of the fastest-growing companies ever. Grew from $0 to $45m ARR in 12 months (then raised $125m at a $1.5b valuation). As a fellow founder, their story is really inspiring to me (curious about what others think): In December 2020, Dave Rogenmoser and his co-founders were on the brink of shutting down their business. They'd spent 3+ years building a conversion optimization software called Proof...and it was flatlining. A few weeks prior they had to make the painful decision to let go of half their team. Competition and churn had completely eroded growth. Things were painful. 8 years of work left them with a string of startups that never quite made it: 2 failed software businesses (couldn't make money*) A SMB marketing agency (maxed out at $25k/mo*) An online course company (hard to get big*) The Pivot: In January 2021, they had an idea to use Chat GPT-3, the generative AI model released 6 months earlier, to write high-converting Facebook ads. Within 30 days, they launched the business. With the skeleton crew remaining from the last startup, they scaled the business to $45m ARR and 70,000+ customers without hiring a single new person. Soon after, they raised $125m at a $1.5b valuation. Dave Rogenmoser, CEO at Jasper, had some great one-liners in a few podcasts I listened to on the business. Here are some of his learnings: Right Skill, Wrong Vehicle: He spent 8 years building marketing businesses which gave this team the knowledge and confidence to spend $1m/mo on sales and marketing to scale the business to $45m ARR in year 1. Launch Fast & Iterate Quickly: The team agreed that if the business didn't work in 30 days, they'd shut it down. Dave says, "If you have been working on a problem for more than 18 months and haven't found Product market fit (PMF), odds are you won't...Make the hard pivot."* Ride A Big Wave: Generative AI technology is a new technology that is changing the way we work. But it's not just text. It's images, voice, etc. Identify new customer segments (e.g., Municipalities, Banks, Lawyers, etc.), learn their problems, and apply this novel technology to solve them. What other businesses have you seen scale like this? I've never seen a SaaS business grow that fast. I meet interesting founders 2x per week and share the learnings here.

Giving away AI Startup Ideas - but no traction? What am I doing wrong?

Hi All, I think with AI, tech will be democratized. I am fortunate and working on a new AI company with my technical partners but many do not have tech skills and don't know where to start. So I'm working on giving away ideas that can be built easily using no-code or low-code. However, not sure if I’m presenting these ideas wrong or no one really wants to build it. I mean it's no-code, but doesn't mean no-work. Startups take a lot of time and energy, no matter what. So far I've shared the following ideas and wanted to get your thoughts on it. Is it my presentation that's bad or the ideas. LOL. Honest feedback appreciated. You can see the a sample of presentation here on Instagram. https://www.instagram.com/p/C69xWvgvPAT/ and here are the ideas I've shared thus far: AI Coin Maker AI ADHD Holo device (a-la Humane or Rabbit AI but with a focus on ADHD) OnlyAIFans - OnlyFans but only using AI models AI Election Predictor and Poll Maker AI Real-Time Gen Z Translator AI Color Analysis for Fashion Thoughts?

Dev with AI and No-code Experience - Social Startup

Hi fellow startup folks! I am actively seeking an AI-learned, no-code web/app co-founder to support a social startup. Target market is very active on a few different platforms, where they glean a bit of knowledge and support. The problem (opportunity) that I have identified for this group is to build a single platform that will provide them with 100% of the support and experience that they currently crave from multiple, unrelated platforms. My research has shown that this group will easily understand our product offering and should / may be easy to convert. Initial goal is to build and release an MVP and start sharing it with the target market. The MVP will be bulit via a no-code application. Our product will pull APIs from a few trusted data-centric and market-related sources and roll those into a social format that will be fun and interactive. Lots of other cool things, too, but to be discussed later. It will be somewhat similar to the CodeMap . io concept, but with a social/interactive focus. CodeMap is built on Bubble (no-code). A little about me: I live in Denver, Colorado. Married with three dogs. 20+ year Operations and Program Management experience in aerospace (satellites) and renewables (hydropower). I have started a few businesses over the years - some profitable, some not - ranging from e-commerce, affiliate marketing, SaaS, etc. I solely built each of the businesses, but have leaned that I’m better at the Operations and execution side of business, rather than being in the weeds with programming (mainly because I’m not a programmer!). I’m looking forward to (hopefully) interacting with some of you on this project! Cheers!

WE JUST GOT $2,500 in angel investment for our AI Cold Calling Startup! Hooray! Looking for web dev + digital marketing agencies to partner with.

Hey y'all. The AI cold calling startup I've been working on for 3-4 months now just got a $2,500 angel investment, and we have 2 current customers, a credit card processing broker and a hospital equipment rental company based out of Texas. We have around $1,500 revenue so far, but we're having lots of trouble fulfilling the contracts because our tech just isn't "there" yet. I'm the Chief Tech Officer, and I'm also running some operations. The other main person in this is the CEO who has a strong sales background and came up with the idea. I've been working purely remotely, and it's great having some income because I'm stuck at home because I'm disabled, basically... We're using 11labs, openai, google speech to text, and a sh\*tty online dialer right now to run the first MVP which runs locally on our "botrunners" computers, and we're developing a web app with django python + javascript react. Our plan is, after we get the webapp working better, to hire more botrunners for $3 per hour from countries like Phillipines and India, and we're going to try to track all the actions the botrunners take to be able to train the AI to run it fully automated. The biggest problem we're facing right now with the tech is reducing latency, it started at 27 seconds to get a response and I've been able to get it down to 6 seconds, but people are still hanging up. We're trying several ways to mitigate this, including having pre-rendered speech playing something like "Okay" or "As an artificial representative, I'm still learning to be quicker on the pickup. We appreciate your patience." One of the industries we want to target is international web development and digital marketing companies, and we want to use the bot to cold-call businesses to pitch them our services. The goal is to replace $30 an hour cold-callers from the USA with $3 per hour total-cost automation. Apparently the CEO was given a $5 million valuation from the strength of the MVP from a VC. Our investment so far was at a $300k valuation tho. It's exciting. Trying to get Twilio working to be able to make calls programmatically instead of using our hacky workaround. Let me know if you have any questions, or feedback. Looking for digital marketing and web dev agencies to partner with to test the next stage of our business model. Thanks. I just wanted to share this awesome news!

WE JUST GOT $2,500 in angel investment for our AI Cold Calling Startup! Hooray! Looking for web dev + digital marketing agencies to partner with.

Hey y'all. The AI cold calling startup I've been working on for 3-4 months now just got a $2,500 angel investment, and we have 2 current customers, a credit card processing broker and a hospital equipment rental company based out of Texas. We have around $1,500 revenue so far, but we're having lots of trouble fulfilling the contracts because our tech just isn't "there" yet. I'm the Chief Tech Officer, and I'm also running some operations. The other main person in this is the CEO who has a strong sales background and came up with the idea. I've been working purely remotely, and it's great having some income because I'm stuck at home because I'm disabled, basically... We're using 11labs, openai, google speech to text, and a sh\*tty online dialer right now to run the first MVP which runs locally on our "botrunners" computers, and we're developing a web app with django python + javascript react. Our plan is, after we get the webapp working better, to hire more botrunners for $3 per hour from countries like Phillipines and India, and we're going to try to track all the actions the botrunners take to be able to train the AI to run it fully automated. The biggest problem we're facing right now with the tech is reducing latency, it started at 27 seconds to get a response and I've been able to get it down to 6 seconds, but people are still hanging up. We're trying several ways to mitigate this, including having pre-rendered speech playing something like "Okay" or "As an artificial representative, I'm still learning to be quicker on the pickup. We appreciate your patience." One of the industries we want to target is international web development and digital marketing companies, and we want to use the bot to cold-call businesses to pitch them our services. The goal is to replace $30 an hour cold-callers from the USA with $3 per hour total-cost automation. Apparently the CEO was given a $5 million valuation from the strength of the MVP from a VC. Our investment so far was at a $300k valuation tho. It's exciting. Trying to get Twilio working to be able to make calls programmatically instead of using our hacky workaround. Let me know if you have any questions, or feedback. Looking for digital marketing and web dev agencies to partner with to test the next stage of our business model. Thanks. I just wanted to share this awesome news!

The Best AI Tools for Entrepreneurs Startups

Discover the best AI tools for entrepreneurs and startups to boost productivity efficiency and innovation

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.

Technical founders - is "bulling" your way through learning right for a startup? [I will not promote]

Sup, This is a question for technical founders. \--a little backstory-- I am starting a company in AI field that creates something nobody has ever done before. 7 months in. \--- How most software companies are created - you have an improvement idea, then you have a thousand or so problems to solve to make that improvement happen, and for each one that you don't know, you go to Stackoverflow or ChatGPT to look for solutions for that problem. Which involves next-to-no upfront preparation because for vast majority of traditional software you can solve it on-the-go - "traditional" software is very easy compared to, say, mechanical, pharma or AI engineering. However, for more advanced disciplines - can you just "Google" it on-the-go? I'm a solo founder, and 8 months in, creating a foundational model, BECAUSE I did not know things upfront, I've wasted at least 3 months doing something which was mostly technically unviable in the first place. Out of 14000 lines of code that I've done (including tests), I had to scrap 10000 recently. Imagine the scale of it. Obviously I didn't even know how ML works when I've started. Major fuck-up. How do you operate in industries which you've done before? How do you determine that it's time to start creating you big technological leaps instead of continuing to learn? Cheers. Edit: No need to push me on business topics. I know how to create value very well. It's only a tech question, and I'm only asking because - well - to deliver my value, I need to do a lot of novel tech.

For anyone working on LLM / AI startups

My company (which I will not promote) wrote this blog post in compliance with rule #7 :) Introduction to fine-tuning Large Language Models, or LLMs, have become commonplace in the tech world. The number of applications that LLMs are revolutionizing is multiplying by the day — extraction use cases, chatbots, tools for creatives and engineers. In spite of this, at its core, the LLM is a multi-purpose neural network, dozens of layers deep, designed to simply predict one word after the next. It predicts words by performing billions of matrix multiplication steps based on so-called parameter weights, which are discovered during the model training process. Almost all open-source, open-weight models are trained on a massive amount of text from every conceivable genre and topic. How, then, do researchers and engineers create novel specialized applications? The answer is fine-tuning. In this post, we will demystify the process of fine-tuning and discuss the tradeoffs of other approaches to customizing an LLM. The history of fine-tuning In the ancient days of LLMs, by which we mean five years ago, the primary approaches to customizing an LLM was identical to the approaches to customizing any other deep learning model. A machine learning engineer would have two options: Retrain the entire LLM. This would mean discarding the trained weights and instead only using the open source model’s architecture to train it on a specialized dataset. As long as the amount and diversity of the specialized data is comparable to what the original model was trained on, this can be the ideal method of customizing a model. However, of course, this is a massive waste of resources due to the computational power required and the difficulty of collecting such a massive dataset. Even if an organization could provision enough GPUs, the cost of training modern-day models could cost up to $190 million. Retrain the last few layers of the LLM while keeping the rest of the weights frozen. This is a more efficient method in terms of time and computational power required because it significantly cuts down the number of parameters that need to be trained. However, for most tasks, this leads to subpar quality. Of course, almost everyone chooses to retrain the last few layers. And where there is only one option, the research community saw an opportunity to step in. Soon, the LLM space saw an enormous amount of activity in fine-tuning, which leads us to today. Modern approaches to fine-tuning Most fine-tuning approaches today are parameter-efficient. Deep neural networks are composed of matrices and vectors (generally called tensors), which are at their core arrays of floating point numbers. By training a small subset of these tensors, while the rest of the LLM’s weights are kept frozen, practitioners achieve good enough results without having to retrain the entire model. Generally, this method requires at least a hundred or so handcrafted examples of input-output pairs for fine-tuning. This is called supervised learning. The modern fine-tuning landscape involves an unsupervised learning step afterwards. Given a set of inputs, a practitioner gathers the various possible outputs from the LLM and casts votes among them. This preference data is then used to further train the LLM’s weights. Usually, this approach is used for LLM alignment and safety, which defends the application from malicious uses, outputs embarrassing to the organization, and prompt injection attacks. Fine-tuning’s relationship to prompt engineering A natural question arises: why fine-tune instead of crafting a well-considered system prompt? Wouldn’t that be easier and more efficient? The answer is no, it wouldn’t. Here’s why: Advanced techniques make prompt engineering obsolete: \[redacted\]'s product uses soft-prompting and other techniques to train the input layer itself. This obviates the need for prompt engineering entirely, which lets organizations avoid the time-consuming trial-and-error process to get the prompt just right. Prompt engineering has been a stopgap measure in the early days of LLM applications to convey the practitioner’s intent to the LLM. It is not the long-term solution for LLM application development. The system prompt is precious: the limited budget for system prompt length is better used for up-to-date information, e.g., Retrieval-Augmented Generation (RAG). Even as context windows increase in size with each new open-source model, the system prompt is the least efficient place to provide the LLM model with verbose instructions and examples. The longer the prompt, the slower the application: an LLM must attend to the entire system prompt for each token generated. This pain becomes more acute in the chatbot case, where the length of the conversation so far is also counted toward the system context. The longer the conversation, and the longer your beautifully-crafted system prompt, the slower the bot becomes. Even in cases where the model allows for system prompts that are millions of tokens long, doubling the size of the context will quadruple the latency. This means adding a few hundred words to the system prompt may result in several seconds of additional latency in production, making a chatbot impossible to use. Edge case handling: the number of edge cases that the system prompt would need to consider and emphasize to the LLM is too large. The instructions would have to be too nuanced and long to cover them all. However, fine-tuning on a dataset that considers these edge cases would be more straightforward. Do I need to fine-tune the LLM in my production application? Every LLM application in production must be fine-tuned often, not just once at the beginning. Why fine-tune? The world in which the application exists is constantly evolving. New prompt injection attacks are being discovered every day, new ways of embarrassing a chatbot are emerging constantly. This data can be used to further train an LLM model, which protects the application from new failure modes and reputational risk. Like any software, LLM models are constantly improving. Smarter and faster models are open-sourced all the time. For a new model to get deployed to production, it must first be finetuned on the specific dataset of the organization building the application. Fine-tuning does not add latency to LLM applications. Rather than a solution that sits in the middle of the LLM and the rest of the application, fine-tuning leverages the power of the LLM itself to increase the quality of the output. In fact, fine-tuning allows for shorter system prompts, which speeds up the average response generation time.

ChatGPT is down! How is your startup product doing?

So it looks like today one of those rare days when OpenAI ChatGPT downtime. I've noticed something interesting - many of my favorite AI tools that use OpenAI under the hood are still operating. This makes me think that the APIs might still be working, even though some services are down. It got me thinking: What gameplan have you all put in place for your products that heavily rely on OpenAI's APIs? How do you handle situations when the API becomes unavailable? I'm curious to hear about your strategies, especially from those who've dealt with previous outages.

Marketing strategy for early-stage startup

Hi! I'm a first-time founder and now we with my co-founder are working on launching our solution. We are working on a b2c service that visualizes what people feel based on their notes. Our potential users are women 18-29 who are interested in feeling better. I'm non-technical so marketing is in my responsibilities. Now I am trying to create something like a marketing strategy for this early-stage launch, and I would like to know your opinions about it, maybe I missed something that could work for us. In my plan, we start by announcing in personal social networks, like LI, Ig and meeting new people on offline events. We also would try to have some paid or unpaid announcements in groups on Fb about health, well-being, new apps, AI (we use AI in our service), women entrepreneurs supportive communities. We were thinking about Tinder Ads, but I didn't have any reviews of it. We also plan to work with influencers on Ig and YT, maybe TikTok. And also would like to try Google Ads, IG ads, YT ads. ​ Have you tried something of it? How it was? Maybe I missed something that worked for you in similar cases?

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.

Have you ever heard of a successful startup where the founder was way out of their depth?

Hello everyone, I'm an aspiring entrepreneur with a business idea, but I'm currently facing a dilemma and would appreciate some advice from those who have been in similar situations. My business idea involves creating a web app that leverages AI to provide users with insights and personalized recommendations based on their uploaded video. The goal is to help users improve their performance in video. While I believe in the potential of this idea, I’m finding the technical complexity daunting, particularly the development and maintenance of the AI models required to analyze user input effectively. (I haven’t got an experience with ai models, so I would have to use over the counter models & I would rely on chat gpt for insight in how to fix problems with it). I have some technical background, but I’m concerned that I could be outmatched in this space, which has made me question whether this is the right path to pursue. Is it realistic to assume that I could get investors to back me if I gain enough traction even with my lack of technical experience?

I started a Tech Startup, and I feel totally STUCK.

I made "Visual Love," a Computer Vision/AI-driven matchmaking platform. The idea is that although appearance is one of the biggest factors for starting a relationship, current matchmaking services and dating apps do not have the capability to search for people based on appearance. On Visual Love, you can find your ideal match simply by uploading a picture of your "ideal type." Also, you can connect with someone who thinks of you as their ideal type, simply by uploading your own picture. Or, there might be a perfect (mutually ideal) match. I made this CV/AI algorithm to scan faces, retrieve facial features, and make it possible to find the closest match among millions of others in a second. On average, regular dating app users swipe 8000 times over 8 months until they find their love. On Visual Love, users can find one in a million just in a second. You can try the tech demo on the website if you want to (find the link through my LinkedIn at the bottom of the post; I have to follow the "I will not promote" rule.) I thought this app would have the best chance in Asia, as people care a lot more about appearance in Asia (especially Korea and Japan). Also, my nationality is Korean, and I speak both Korean and Japanese as fluently as I speak English. So I came to Korea, and pitched to a number of VC/AC firms in Korea and Japan, and two of them were typically intersted in making investment. However, they both required me to provide market validation: how much it would cost per user acquisition, how much each user would pay on average, and etc, even after I provided them with a 3-years financial projection including market research based on other dating apps. ​ Everything might be going just as expected, or even better than anticipated, but I'm feeling very stuck now. I am not a business expert, and I don't have much idea on how to proceed from here. The problem is, it wouldn't quite work as expected when there are not many users. If I start with a small group of users, it's not any better than any other dating app. Matching users within a small group doesn't quite reflect the values of Visual Love. So I figured a way around: making a game version of Visual Love targeting 100k to 500k users to work as an initial distribution channel. This version will include finding look-alike celebrities, and solving look-alike face puzzles, and etc. But now, the problem is, I cannot continue this project by myself. I have no social/financial support, and I'm running low on cash. Also, although I'm from Korea, I lived in many different countries. I did my undergraduate in New York (Columbia University) and all my friends are in the US. I don't feel very included here. I can't stop feeling frustrated and distressed :( I'm sure Visual Love can reshape the future of the matchmaking market. But, only if I can continue this project by getting the fund I require. I'm open to any advice, and if you're interested in providing any help or working with me, please contact me through LinkedIn. https://www.linkedin.com/in/don-lee-3853b1264/

What Are The AI Tools You Used For Your Startup Business?

I'm starting a new ecommerce brand with Shopify, but I don't have much budget, which is why I decided to go for SEO marketing and Facebook Ads for early stage promotion business. I'm limited to resources, and can't afford too much for copywriters or content creators, I learnt from a few more experienced sellers, and they recommend me to use AI for copywriting and AI Video Ads Creation, Any tools you will recommend? Your help is highly appreciated!

I just had the best idea for a startup! Wait… no… 10 ideas!

LLM wrappers on existing saas tools integrate everything all-in-one AI agent founder/mentorship/indiehacker tools piece of marketing (positioned as SaaS) coding tools for your coding tools AI automation to job hunt faster Sales faster, with … AI track all your things in one app, with Ai build a site in seconds… AI + webflow AI driven templates for your AI workflow Seriously that’s all this sub is bye 👋

What are some B2B sales strategies for an AI startup?

Hey folks, need some advice on how I can book meetings with clients to sell a software that I've built. I'm a Software engineer working at a Fortune 500 company. After grinding on the side for a few months now, I've now built a software product to really automate manual every day tasks using LLMs. Its going to be sold to B2B sales teams, and I think my initial market would be mid-market/small companies. Here's a demo that I recently recorded if you are interested: https://www.youtube.com/watch?v=CPTCHBnItkI Let me know what you guys think of it? I’d really appreciate any advice.

Seeking Feedback & Support: Launching a Nut Mix Startup to Improve Gut Health

This txt is AI summarized but I read it, he just restructured my thoughts accurately. Hey all, I’m Ilia, a Seattle-based entrepreneur working on a product that’s all about making healthy eating easier. I’m creating a premium nut mix with 16+ different nuts (70% organic) aimed at helping people improve their microbiome and overall health. The concept is simple: diverse ingredients lead to better gut health, reduced inflammation, and more energy. No more juggling 20 bags of different foods—my nut mix is a convenient, delicious solution. I’m in the early stages and raising about $7,000 to cover things like regulatory compliance, a commercial kitchen rental, quality ingredients, packaging, and a basic brand presence. I’ve poured my own savings into this and am now turning to the community for support, advice, and maybe even early funding. I made a short (12-min) video walking through the concept, the budget breakdown, and my long-term vision (expanding to seeds, fruit mixes, and maybe even a billion-dollar brand one day!). I’d love your honest feedback, connections, or suggestions. If you’re interested in supporting, even by sharing this post, I really appreciate it. Feel free to ask me anything—transparency is key for me, and I want to build something that genuinely helps people live healthier. https://www.gofundme.com/f/support-my-goal-to-make-healthy-eating-easy-and-convenient

Just recorded a new interview episode where we talk about transitioning from a Fortune 500 to a startup. Let us know what you think!

0:00 Shilpa's Introduction 2:16 A Cosmopolitan Journey 4:46 All Roads Lead to Tech 8:35 From JP Morgan to Intel 12:25 Shilpa's $1 Billion Revenue Pipeline 15:27 Transitioning from IOT to AI 17:42 Flyte Co-Founders (and Marriage Partners!) 22:30 Building an AI Model, for two! 28:04 Flyte's Integration for Workflow Software 30:03 Fundraising Success 31:09 The Bright Flyte Ahead 32:18 Biggest Challenges Building the AI 36:05 Advice to Other AI Founders 38:46 Flyte's Incredible Collaboration Process 44:32 Life Lessons from Shilpa 45:54 Find Flyte! ​ https://preview.redd.it/w4bx22cl10d71.png?width=2042&format=png&auto=webp&s=c14ea0b96a1ffb34165ec84a86ca45b16256c21b Shilpa is a lifelong tech admirer, worldly businesswoman, and now CEO of Flyte -- a Workflow Automation Platform for Complex, Mission-Critical Data and ML Processes at Scale. From her incredible journey from India to her current home in Canada, to the vital role collaboration plays in small teams, Shilpa shares a breadth of knowledge any Entrepreneur would be lucky to hear. Regular link to episode: https://www.youtube.com/watch?v=wJJgAhwSqqo&t=1s

Built an SEO platform for startups (and solo-founders)

Hey! We've built an SEO platform especially for solo-founder that provides ready-to-implement tasks and actionable recommendations to guide busy founders through SEO strategies. It's CTO and me (no-code) working on this project. We've been able to ship the first couple of features for MVP. Currently, the platform gives you: What topic to write (based on the best content marketing practices) Which keywords to include Missing internal linking It's FREE: app.vevy.ai We're looking forward for your feedback. https://preview.redd.it/g5qhuo7hhbsd1.png?width=2862&format=png&auto=webp&s=0b7069b368f117e18abb8ec0258034c65ba49f0b

Most Startup Ideas Die Before They Even Start—Here’s Why (And How I Solved It)

Hey all 👋 Last year, I had an idea I was excited about. I spent weeks brainstorming, validating it, searching for the perfect name, and struggling to create even a decent landing page. By the time I was ready to launch, I’d already lost momentum. Sound familiar? The reality is, many great ideas never see the light of day—not because they’re bad, but because the process of getting started is overwhelming. You get stuck on validation, spend way too much time searching for a name, and designing a logo or building a landing page becomes a huge roadblock. That got me thinking… What if there was a way to make this 10x easier? That’s why I built StarterPilot—an AI-powered tool designed to help entrepreneurs like us go from idea to launch effortlessly. 1-Validates your idea – Get instant feedback on demand, feasibility, and risks. 2-Generates unique names – AI finds names and checks domain/social media availability. 3-Creates logos – Build a custom logo with just a few clicks. 4-Builds landing pages – Generate a professional landing page in minutes. I built this to eliminate the friction that keeps good ideas from ever becoming real businesses. Would love to hear your thoughts, or if you’ve run into any of these challenges, feel free to share! Also, if you’re curious, check out StarterPilot here: link.

The New Fast Growing Framework for any Startups (Non promotion)

Hi community, I'm Julian, Co Founder of 2 AI, Marketing and Automation companies, and Solo Founder of a company that it is starting now, after a long Social-Market research, in which I am looking to other founders to participate under a new Complex Framework without cost. It is difficult to explain on an introduction. If there is someone interested to further information please contact me. Thank you!

AI search startup Perplexity could actually beat Google (disruption strategy lesson)

Everybody's talking about how AI changes everything and all the new business models and products that are now possible. But few talk about how AI legitimizes ideas that we'd previously laugh about. One of them: Disrupting Google. Bing, DuckDuckGo (privacy search), Ecosia (sustainable search), Neeva (subscription search)... none of them made a dent into Google. AI could change this. Most notably: Perplexity. Perplexity is an AI search unicorn founded by Aravind Srinivas. It's got a $20m ARR and $1b+ valuation at about 50 people—all in under 2 years. The product is basically if ChatGPT had a baby with Google: Perplexity aggregate search results for your query and tells you the results (with citations) in a concise answer. You never have to leave their interface to click elsewhere. I think it has a real chance: Its search results for informational queries are (imo) already better than Google's SEO optimized jungle. Plus, millions of people are subscribing (with real money) to a search engine. Of course, Google knows a thing or two about AI. What if Google just copies the product for their own search engine? To some degree, they've started to do this. But Google runs into a problem here: Their core business model is based on ads, which are inserted into search results. So the more search results you can show someone, the more money Google makes. If there's just one result (aka answer), then Google makes less money. This is a clear disincentive for Google to build these AI answers. CEO Aravind Srinivas talks about this in interviews: Google won't build everything Perplexity does because they rely on ads and AI-native search runs counter to their business model. Of course, disrupting Google requires a lot more than to convince a bunch of tech workers excited to try new tools. My mom probably doesn't even know there are other search engines besides Google—and crossing into the mainstream takes a long time. But if I think about how good Perplexity is in 2 years and with 50 people compared to a 26 year-old company with 180k people, I think the AI inflection point gives them a real chance. WDYT? If you want to read my full strategic breakdown, you can read it here: https://www.commandbar.com/blog/perplexity-vs-google/

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 🎁

Looking for a business co-founder for my AI startup in manufacturing.

I spent over a decade as a Senior Software Developer building manufacturing optimization products for global enterprises across the Americas and Europe. It’s a million-dollar industry, and I have strong connections with multinational clients. With AI advancements, there’s a huge opportunity to disrupt the market. My plan is to offer locally hosted AI-powered applications with an option for private cloud deployment. I already have 4–5 high-impact use cases, each of which can be built in a month. I’m looking for a business partner—ideally someone with deep expertise in manufacturing and strong connections in Europe, the USA, China, or India. I’ll handle the tech; you drive the business. Beyond optimization, I have additional solid use cases—some aligned with what YC-backed companies are doing. I found a YC company working on something similar, which I can share in DM.

Looking for a co-founder for a B2B AI startup. I have a development team and funds for at least a year of operations.

Hello, As the title said I'm looking for a co-founder. I built with my team a few ventures that generate revenues but I don't believe that any of them has a future. I have 15 years of experience in Software Engineering and AI. Worked in various industries, but always in data-driven applications. I spent the last 3 years as an entrepreneur and raised successfully money from VCs. ​ A few preconceptions I have: \- B2C is extremely hard. Very quickly you realize that you need to spend all your resources on marketing. \- B2B is extremely hard - but for different reasons. Sales cycles take months. If you want to reach serious buyers and decision-makers, you need to have an amazing network. Even then, companies will prioritize 90% of the time to do things internally rather than paying for anything. \- I hate when people say that "ideas are garbage", and I think that execution is overhyped. Execution is a matter of finding the right people, and paying them (I am confident to say that I can guarantee good execution). Ideas are not garbage, ideas need validation, and garbage "entrepreneurs" are too lazy to validate anything. ​ Your ideal profile: \- You have a great idea, something that has been brewing for some time but you lack resources or technical experience to execute by yourself. \- You have domain expertise, experience, and a network. If we build an MVP in 3 months, you can get 20 interviews with industry people to validate the solution. Once the MVP is built you can put it in front of another 40 people. \- You are a product person. \- You can do efficient sales calls. (Bonus: You are a sales person) If you are an ideal profile, please reach out.

Roast my Startup idea

I've wanted to have an AI chatbot for LinkedIn for myself for a while, so I started dabbling and tinkering behind the scenes. It has contact context (i.e it understand info about who you're talking to), it can book meetings for you, it can capture lead info, it can tell unwanted outreach that you're not interested (politely), it can "pause" conversations for certain contacts and more! I've been talking to multiple entrepreneurs and sellers and would love to know what you think?

AI Startups: Decoding Market Signals, Pivoting & Assessing Risks with Greg Coquillo (What's AI #24)

50 Days of Building an AI Startup in 5 Minutes (Strategy, Tools, & Results)

I made Tinder, but for startups

My Business Mentor Was Always Busy, So I Built an AI Guide For Every Stage of a Startup

AI Business Startups

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How to Build a $1 Billion AI Business Solo

🤝 Let's connect on Linkedin - https://dripl.ink/8sVkw 🚀 Automation Agency - https://dripl.ink/UCCkG 🛠 TOOLS: AI Coding: Lovable: https://dripl.ink/ZkTnR Bolt: https://dripl.ink/3Z5Fy Replit: https://replit.com/ Automation: Make: https://bit.ly/3BQkcjO Zapier: https://zapier.com/ n8n: https://n8n.io/ Agents: Lindy: https://www.lindy.ai/ n8n: https://n8n.io/ Full Repository: https://bit.ly/4bw2uyI 🚨 SUBSCRIBE TO THE CHANNEL https://bit.ly/wearenocodeyt ➡️ VISIT WeAreNoCode Website: https://www.wearenocode.com #nocode #startups #entrepreneur #saas #AI

How To Build a Startup Team of AI Agents (n8n, OpenAI, FeedHive)

Get full access to my software portfolio - for a single one-time purchase. 📦 https://simonl.ink/founderstack (Get $500 OFF in February) Get the full n8n workflows: https://simonl.ink/agent-workflows See the workflow tutorial here: https://simonl.ink/agent-tutorial Tools used for this video: https://n8n.io https://www.feedhive.com https://platform.openai.com 00:00 Intro 02:04 Using ChatGPT directly 04:18 Fine-tuning an AI model 07:07 Creating a brand brief 09:36 Building a team of AI Agents 12:08 n8n workflow: Get Brand Brief 14:18 n8n workflow: Get Content Ideas 15:47 n8n workflow: Get Content Feedback 17:57 n8n AI Agent workflow 20:20 Connecting to FeedHive 21:11 Putting it all together 23:04 FounderStack & workflow files #aiagents #n8n #feedhive

5 Best APP Builders For Beginners In 2025 (No-Code + AI)

❇️ FREE COURSE - Get Access Now: https://bit.ly/4aBjbaU 📱 Build Your App - Coaching: https://dripl.ink/C89tz 🛠 TOOLS: Buzzy: https://bit.ly/4b99lxC Figma - https://psxid.figma.com/gesgvfztofxm Softr - https://bit.ly/3WdJdNU Bolt: https://bolt.new/ Glide - https://glide.grsm.io/2y7fbo8636da Replit: https://replit.com/ Full Repository: https://bit.ly/4bw2uyI 🚨 SUBSCRIBE TO THE CHANNEL https://bit.ly/wearenocodeyt ➡️ VISIT WeAreNoCode Website: https://www.wearenocode.com #nocode #startups #entrepreneur #saas #AI

This AI powered logo generator is used by over 20 million startups

Fast and easy way to create your business logo with this powerful AI tool. Will you try it? -- FREE SHOPIFY TRIAL -- Build your business using a FREE trial of Shopify , one platform with all the ecommerce features you need to start, run, and grow your business. ► https://bit.ly/3C5Zf10 -- FREE SHOPIFY COURSE -- First Day to First Sale https://bit.ly/3MJg7iR #shorts

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!

Anyone finding that they just don't NEED to add more Employees anymore? (I will not promote)

A friend of mine who was looking for work asked me if we were hiring and I responded "You know, it's weird but all of our growth goals don't seem to map back to hiring people anymore." This isn't about the economy or growth goals. It's a really fascinating shift in focus and costs for startups. My gut reaction is that I HATE the idea of not creating more jobs. In my career I've hired thousands of people, and I've always prided myself on job creation. We just sold a company that employed 200 people last year, and I'm proud of the work we were able to create. What's interesting is that I simply don't feel like we NEED to like we used to. As we're looking at all of our growth goals, for the first time I'm not assigning FTEs to them. Nearly everything we're doing is actually reducing the need for more humans, not adding them - and we're not even trying to reduce the need. Obviously the timing of AI has had a major impact. Product - Our team is shipping more code than ever before, and even our designers who have never touched code are shipping final code. If we doubled the size of the team, it would make no difference (this is a big deal considering the historical cost here). Marketing - So many aspects of our marketing are getting automated and streamlined, to the point where even a single FTE can create a massive amount of reach across channels. Support - Our Success team is able to effectively respond to tickets in a fraction of the time, which essentially doubles their capacity without adding any more staff. Management - With less staff we need less managers, which are a big expense, but it also means reporting and decisions are more streamlined, which is a positive. But it also means those positions simply don't get created like they used to. I think this is a big deal for the younger startups because it translates into needing less capital (or none!) which provides for more ownership and agency. Clearly we still need some folks to build out the core team, but that's very different than a massive staffing line item. Anyone else here finding the same trend? Opposite? I don't have a strong opinion either way, but I'd love to hear how other Founders are processing this.

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.

AI No Code

I want to create an artificial intelligence large language model for my startup but I don’t know how to code. Are there any no code tools I can use to accomplish that task or do I need to learn to code? If I need to know how to code what language, tech stacks, technologies etc do I need to know?

What questions to ask to evaluate an offer from start up?

Hello! I am presently working working as a Data Scientist with a medium sized company. Last year my boss left the company to start his own. Very recently his non-solicitation clause expired, and he asked me to join his startup. While I know almost everything about the product idea, and the technical aspect of the startup - I have very less information on more critical points like funding, equity sharing, etc. He has made a verbal unofficial offer, and I have asked for a week to prepare my list of questions for him for me to be able to evaluate his offer. Since I have no knowledge of the startup scene, I would like some help regarding the questions I should put forward to him. Mentioned below are what I know so far and the offer: The company was started by two people, both working full time on it. I would be the third person on the team. The startup aims to introduce AI in a field which has lagged behind in the introduction of technology by at least 2 decades. The big players in this field are conservative, but now they are opening up towards embracing new technology. Personally I have confidence in their idea, and feel this will be a sustainable and profitable company. The offered salary is about 60% of what I make right now. The equity offered is 2%. I do not know the details of the funding they have received so far or the equity split. Any pointers in helping me frame my questions for the evaluation of the offer would be very helpful! Thank you

The Drawing of the Three - Once you look through the veil, nothing is the same again. (I will not promote)

Originally published Nov 5, 2024 In my last post, I talked about assembling a series of filters to use to view the startup landscape, which led me to a few conclusions about what opportunities I should pursue. What did I see through those filters? What I saw through the moire pattern of those two lists overlaid by one another is what I think will be the third great monetization strategy for the internet, matching the pattern of: web1 => Ad monetization web2 => Subscription monetization web3 => For AI, neither of those work anymore, which demands something new. But what? Well that’s the important part, isn’t it? Should I just up and tell you? Yawn. The climax of a movie is at the climax, if they tell you the crux at the beginning, it’s a lot less fun (usually). The standard bearer for web1 and ads was Google (with countless followers), and essentially every website adopted that model for their first pass at content monetization. Google has been… let’s call it fairly successful… so it’s not a bad way to look at things. How many websites live and die by selling advertising? The standard bearers for web2 and subscriptions were Salesforce (for B2B SaaS) and Netflix (for B2C SaaS), with countless followers, to the extent that SaaS has been the dominant startup monetization thesis for the last 15+ years. It’s more old and tired by now than most American politicians, but how many websites live and die by people entering payment details for a monthly or annual subscription? Evidence proves those models for web1 and web2 worked well enough that countless businesses depend on them, and countless fortunes have been made and lost surfing the waves, or crashing against the shorelines, of ads and subs. But it’s also apparent (to me, at least) that now that AI is the dominant startup thesis, neither ads nor subs are going to prevail in an AI-centered world, and for one simple reason: Those monetization strategies are for humans, and AI bots are not humans. Changing Environments Require Changing Strategies Every so often, there’s a fundamental shift that demands everything in the ecosystem adapt to a new habitation strategy to survive. We’ve seen this repeatedly across Earth’s ecology (for instance, introducing free oxygen to the atmosphere, producing respiration while destroying all the life forms that existed before oxygen permeated the atmosphere), and across human society (for example, how nuclear bombs changed war, and how drones are changing it again, for less violent examples, consider the adoption of computers and the subsequent adoption of smartphones). Now the ecosystem of the internet has changed irrevocably, opening up countless new and interesting niches to occupy. Humans may see an ad and buy something stupid (or, occasionally, not-stupid), but an AI won’t unless its programmed to. And subscriptions are designed for humans to consume content at a human rate, not for an AI that can choke down an entire database of content (whatever it may be) at whatever speed the servers can manage. Changing conditions require changing strategies. It was clear to me that: The introduction of AI bots to the internet ecosystem was, is, and will be massively disruptive for a very long time The internet population of bots already exceeds humans and is growing faster than the human population The two dominant monetization strategies are not relevant to bots That disruption of expectations across the ecosystem demands a third strategy, a new strategy to handle a massive change in an existing system. And that strategy needs to accommodate, support, and monetize the new demands from the vast armies of new participants in the internet ecology. Therefore, a method that converts bots from an expense into a revenue source would become a dominant monetization strategy, and therefore whoever owns that strategy will be a dominant player in the internet ecosystem. Set the realization of semi-practical, semi-useful AI against a backdrop of technology cycles that have, in the distant past (in internet terms) produced ads and subs, and more recently produced enormous investment into fintech and crypto, I started to see a path that felt like it would grow over time to become a new monetization strategy that works in the AI ecosystem. Sun Tzu had a couple drinks, saw a couple things… There’s at least, and possibly only, two things I know about fighting: You cannot fight the tide, and it’s much harder to fight an uphill battle. If my whole thesis on this go-around was to go with the flow, and that trickle of insight was leading me from my overlook along a roaring flow of cash coursing through a valley filled with AI startups, where exactly would it lead me? Most rivers lead to the sea eventually, but they can take winding paths, and sometimes the quickest route from the mountain to the sea isn’t to follow the river, but to understand where the river leads and go there instead. Getting a view from on high can save you a lot of time on your journey. But before I get to where the path has led (or is leading) that will explain the objective I’ve identified, and the deliverables I have to produce to reach it, let’s talk about a few of the steps on the path I’ve been taking that highlight the process I followed. I figure if I explain the steps I’m taking, as I’m taking them, it may be easier for people who haven’t trod this route before to follow me and understand how to carve their own course towards their own objectives. And maybe the real treasure will be the friends we make along the way. (I will not promote)

Zero To One [Book Review]

If you don't feel like reading - check out the video here ##Introduction The more I read into Peter Thiel's background, the more ridiculous it seems.. He’s been involved in controversies over: Racism, Sexism, and, [Radical Right wing libertarianism.] (https://www.bloomberg.com/news/articles/2016-07-21/the-strange-politics-of-peter-thiel-trump-s-most-unlikely-supporter) He’s built a tech company that helps the NSA spy on the world. He supported Donald Trumps presidential campaign. He’s funding research on immortality And to top it off, he helped bankrupt online media company and blog network Gawker by funding Hulk Hogan’s sex tape lawsuit - after a report of his rumoured Homosexuality rattled his chain… Zero to One clearly reflects his unique attitude and doesn't pull any punches with a genuinely interesting point of view, that has clearly worked in the past, to the tune of almost 3 billion USD. But at times, his infatuation with the All American attitude is a little much…and, quite frankly, he’s not the kind of guy I could sit and have a pint with…without grinding my teeth anyway. The content is adapted from Blake Masters' lecture notes from Thiel's 2012 Stanford Course. This definitely helped keep the book concise and fast paced, at least compared to other books I’ve reviewed. The type of content is also quite varied, with a good spread from completely abstract theories — like the Technology vs. Globalisation concept, where the book get's it's title — to practical examples such as the analysis of personalities in chapter 14, "The Founders Paradox" covering Elvis Presley, Sean Parker, Lady Gaga and Bill Gates to name a few. ###Pros Monopolies To most people a monopoly is a negative thing. But while perfect competition can drive down costs and benefit the consumer - competition is bad for business. In fact, in Thiel's opinion, every startup should aim to be a monopoly or, as he puts it: Monopoly is the condition of every successful business. I like his honesty about it. While I’m not sure about the morality of encouraging monopolies at a large scale, I can see the benefit of thinking that way when developing a startup. When you're small, you can’t afford to compete. The best way to avoid competition is to build something nobody can compete with. The concept is summed up nicely at the end of chapter 3: Tolstoy opens Anna Karenina by observing: ‘All happy families are alike; each unhappy family is unhappy in its own way.’ Business is the opposite. All happy companies are different: each one earns a monopoly by solving a unique problem. All failed companies are the same: they failed to escape competition. Pareto The Pareto Law, which you might remember as the 80/20 rule in Tim Ferris’ The Four Hour Work Week, is often used synonymously with the power law of distribution, and shows up everywhere. Thiel refers to it in his section on The Power Law of Venture Capital. If Tim Ferris recommends identifying and removing the 20% of things that take 80% of your effort - Thiel recommends finding the 20% of investments that make 80% of your return. Anything else is a waste. Soberingly, he also suggests that the Pareto Law means: ...you should not necessarily start your own company, even if you are extraordinarily talented. But to me this seems more like a venture capitalists problem, than an entrepreneurs problem - Personally, I believe there’s far more benefit in starting up your own company that purely profit. ###Cons Man and machine? Content-wise, there is very little to dislike in this book. As long as you accept that the book is written specifically for startups - where anything short of exponential growth is considered a failure - it’s exceptionally on point. However, there are a couple sections dotted throughout the book where opinion and wild speculation began to creep in. Chapter 12 is a good example of this entitled: Man and Machine. It’s a short chapter, 12 pages in total, and Thiel essentially preaches and speculates about the impact of better technology and strong AI. I like to dog ear pages with interesting or useful content so I can come back later, but this entire chapter remains untouched. America, fuck yeah! It would be really difficult for a personality as pungent as Theil's to go entirely unnoticed in a book like this, and indeed it breaks through every now and then. I only had a feint idea of Thiel's personality before I read the book, but having read up on his background, I’m actually surprised the book achieves such a neutral, if pragmatic, tone. Pretty early on in the book however, we are introduced to Thiel's concept of Economic Optimism and quite frankly the whole of chapter 6 should have been printed on star spangled, red white and blue pages. I’m not necessarily against the egotistic American spirit but when Thiel writes, in relation to European Pessimism: the US treasury prints ‘in god we trust’ on the dollar; the ECB might as well print ‘kick the can down the road’ on the euro I can smell the bacon double cheese burgers, with those tiny little American flags from here. Ooh Rah! ###TL;DR (a.k.a: Conclusion) Overall, however, I really did enjoy this book and I can see myself coming back to it. Peter Thiel IS controversial, but he has also been undeniably successful with a career punctuated by bold business decisions. The ideas in the book reflect this mind set well. Yes, he backed Trump, be he also (sadly) backed the winner.

I studied how 7 Founders found their first 100 customers for their businesses. Summarizing it here!

I am learning marketing, and so I combed through the internet to find specific advice that helped founders reach 100 users and not random Google answers. Here’s what I found: Llama Life by Marie Marie founder of Llama Life, a productivity app ($51.4K+ revenue) got her first 100 users using Snowballing effect. She shared great advice that I want to add here verbatim, “Need to think about what you have that you can leverage based on your current situation. eg..When you have no customers, think about where you can post to get the 1st customer eg Product Hunt. If you do well on PH, say you get #3 product of the day, then you post somewhere else saying ‘I got #3 product of the day’.. to get your next few customers. Maybe that post is on reddit with some learnings that you found. If the reddit post does well, then you might post it on Twitter, saying reddit did well and what learnings you got from that etc. or even if it doesn’t do well you can still post about it.” Another tip she shared is to build related products that get more viral than the product itself. These are small stand-alone sites that would appeal to the same target audience, but by nature, are more shareable. On these sites, you can mention your startup like: ‘brought to you by Llama Life’ and then provide a link to the main website if someone is interested. If one of those gets viral or ranks on Google, you’ll have a passive traffic source. Scraping bee by Pierre Pierre, founder of Scraping Bee, a web scraping tool has now reached $1.5M ARR. Pierre and his cofounder Kevin started with 10 Free Beta Users in 2019, and after 6 months asked them to take a paid subscription if they wanted to continue using the product. That’s how they got their first user within 50 minutes of that email. Then they listed it on dozens of startup directories but their core strategy was writing the best possible content for their target audience — Developers. 3 very successful pieces of content that worked were : A small tutorial on how to scrape single-page application An extensive general guide about web scraping without getting blocked A complete introduction to web scraping with Python They didn’t do content marketing for the sake of content marketing but deep-dived into the value they were providing their customer. One of these got 70K visits, and all this together got them to over 100 users. WePay by Bill Clerico Bill Clerico left his cushy corporate job to build WePay which was then acquired for $400M got his first users by using his app. He got his first users by using his app! The app was for group payments. So he hosted a Poker tournament at his house and collected payments only with his app. Then they hosted a barbecue for fraternity treasurers at San Jose State & helped them do their annual dues collection. Good old word-of-mouth marketing, that however, started with an event where they used what they made! RealWorld by Genevieve Genevieve — Founder and CEO of Realworld stands by the old-school advice of value giving. RealWorld is an app that helps GenZ navigate adulthood. So, before launching their direct-to-consumer platform, they had an educational course that they sold to college career centers and students. They already had a pipeline of adults who turned to Realworld for their adulting challenges. From there, she gained her first 100 followers. Saner dot ai by Austin Austin got 100 users from Reddit for his startup Saner.ai. Reddit hates advertising, and so his tips to market your startup on Reddit is to Write value-driven posts on your niche. Instead of writing posts, find posts where people are looking for solutions DM people facing problems that your SaaS solves. But instead of selling, ask about their problem to see if your product is a good fit Heartfelt posts about why you built it, aren’t gonna cut it To find posts and people, search Reddit with relevant keywords and join all the subreddits A Stock Portfolio Newsletter A financial investor got his first 100 paid newsletter subscribers for his stock portfolio newsletter. His tips : Don’t reinvent the wheel. Work what’s already working. He saw a company making $500M+ from stock picking newsletter, so decided to try that. Find the gaps in “already working” and leverage them. That newsletter did not have portfolios of advisors writing them. That was his USP. He added his own portfolio to his newsletter. Now to 100 users, he partnered with a guy running an investing website and getting good traffic. That guy got a cut of his revenue, in exchange. That one simple step got him to 100 users. Hypefury by Yannick and Samy Yannick and Samy from Hypefury, Twitter and Social Media Automation tool got their first beta testers and users from a paid community. They launched Hypefury there and asked if someone wanted to try it. A couple of people tried it and gave feedback. Samy conducted user interviews and product demos for them, And shared the reviews on Twitter. That alone, along with word-of-mouth marketing on Twitter got them their first 100 users. To conclude: Don’t reinvent the wheel, try what’s working. Find the gaps in what’s working, and leverage that. Instead of thinking about millions of customers, think about the first 10. Then first 100. Leverage what you have. Get the first 10 customers, then talk about this to get the next 100. Use your app. Find ways, events, and opportunities to use your app in front of people. And get them to use it. Write content not only for SEO but also to help people. It won’t work tomorrow, but it will work for years after it picks up. Leverage other sources of traffic by partnering up! Do things that don’t scale. I’m also doing SaaS marketing deep dives over 30 pieces of content. I'm posting here for the first time, so I'm not sure if it will stay or not, sorry if it doesn't. I've helped a SaaS grow from $19K to $100K MRR as a marketer in last 2 years, and now I wanna dive deep. Cheers! (1/30)

So, you want to be a CEO?

I used to post here occasionally with business advice. But it turns out most of you in this sub have a dream, but seemingly no execution. You want to be rich sure, but without understanding what it takes to be a founder, run a startup, create a team around an idea and a strategy, and push them to their limits without burning them out, to win in a market that's never heard of you - not to mention the pressures on your personal life. So, I'm going to post a new game called, "So, You Want to Be A CEO?" The game: Each week I will post a reasonably complex challenge that a startup founder has to overcome, between inception of the company until it goes bust or series A. You respond with your best course of action - that is, what would you do in the situation provided? YOU DON’T HAVE TO DO THE WORK! The rules: One response per person Your upvotes are your score for the week I will track them in the OP Scores are calculated on the Friday of that week You must answer the prompt completely, if you don't you lose half your points earned that week. ChatGPT is allowed, but it may not provide sufficient advice to win the game Prompt 1: "Boomerang" You are an HR executive turned entrepreneur. You have identified a significant issue: professionals over the age of 55 are struggling to re-enter the workforce and you also believe corporations are missing out on a wealth of institutional knowledge in retirement. You believe you can help solve this problem by creating Boomerang, a platform dedicated to empowering these individuals and corporate partners by connecting them with the best candidates aged 55 and older. Objective: Your goal is to validate your concept, develop a Minimum Viable Product (MVP), and balance your personal responsibilities while laying the foundation for Boomerang’s success. This Week's Key Challenges and Decisions: Market Research Challenge 1: You need to validate the market need for Boomerang. This involves understanding the pain points of older job seekers and potential employers. This will take 4 days (non-sequential) How do you get started? Developing an MVP Challenge 2: With limited resources, you need to create an MVP that effectively demonstrates Boomerang’s value. This will take 2 days. Can be combined with other challenges. How do you get started? Dealing with Personal Health Issues Challenge 3: Your doctor mentioned your bloodwork is irregular, but can't pinpoint the cause. They recommend you see a specialist before Friday. This will take 1 day. Give it a shot! There's no right answer, just answer what your plan to do and try to optimize the use of your time to the best of your ability. EDIT: Scoreboard (I realize now the top post generally gets the most upvotes, so I may change the points system): u/conscious_border3019 - 22 u/inBoulderForSummer - 4 u/that_whey-or-the-lee - 3 u/AgencySaas - 3 u/Gold-Ad-8211 - 2 u/93024662 - 2 u/DeusExBam - 2 u/njm19920 - 2 u/SilentEconomist9265 - 2 u/ai_servant - 2 u/Background-Term2759 - 2 u/Insane_squirrel - 2 u/kiss_thechef - 2 u/codeyman2 - 2 u/Xentoxus - 2 u/LongComplex4395 - 2

Practical tips on hiring the best people? Which country? Remote vs. In Person?

Hi Reddit, I run a tech startup that's grown to $20M ARR. While we are relatively big, we are incredibly cash strapped till Q3 due to debt we took on last year and are currently paying back. In Q3, I'll finally have a large budget to sit and focus on building out our team. Now I'm trying to figure out: what are the optimal circumstances? We really screwed it up with our first batch of key hires after our seed round: US Product Manager, US Head of Customer Success - quit; US Head of Sales, US Head of Engineering - fired. We've built a mostly B or C team, and it really annoys me. We are slow, we are not up for big challenges, and people are, on average, not that brilliant. Out of our nearly 150 employees, I think I have ONE A player. However, they are also functioning at 60%. We are building additional "brands" this year, so there might be a way to separate a higher performing culture into our second brand. I have 3 questions, might seem relatively basic, but as we did such a bad job the first time around, I'd love to learn what you all think! I'm trying to build an optimal team with A-players! Q1: Today we are fully remote, should I get an in person office going? In which city? Q2: In general, which city should I hire talent from? I live in San Francisco and sometimes LA, but find the culture here generally too laid back. New York? But to keep a high quality, let's say, marketer, interested long term, they're going to want $200-220k base (and that's not even that competitive). While that is fine, it will slow down my intended plan for hiring. London? Salaries are comparatively much lower, and talent quality is still pretty high, but I am a little unsure of the work culture. In terms of budget, I'd love to aim for $150-180k/key hire and to go as high as $300k if appropriate. Q3: Should I be hiring people with 20 years of relevant experience? 2-3 years with a hunger to prove themselves? Fresh grads we can mould into whatever we need? As for what exactly I'm trying to hire for, lots of key hires: department heads, digital marketers, content people, engineers, AI engineers, operations people, strategy people, and more. I don't know enough about all the working cultures in these places, but I want to find and incentivize people who are willing to own and take responsibility for an area of the business, be trusted to make good decisions, and view it as their responsibility to improve their areas drastically, more than the typical 9-5. I feel today's workforce is not content with base + light equity, and maybe we should consider tying an unlimited-upside incentive to a relevant KPI to incentivize people working harder than just "what is required"? (edit: I know might get some hate for this "work harder than 9-5" mentality, but to clarify, I'm trying to figure out what incentive structures will naturally attract the type of person that wants this type of working life) What do you think? Also, any other practical tips for finding awesome people like this? edit: hooooly! this thread blew up. I'll do my best to reply to everyone, thank you for all your responses!

I studied how 7 Founders found their first 100 customers for their businesses. Summarizing it here!

I am learning marketing, and so I combed through the internet to find specific advice that helped founders reach 100 users and not random Google answers. Here’s what I found: Llama Life by Marie Marie founder of Llama Life, a productivity app ($51.4K+ revenue) got her first 100 users using Snowballing effect. She shared great advice that I want to add here verbatim, “Need to think about what you have that you can leverage based on your current situation. eg..When you have no customers, think about where you can post to get the 1st customer eg Product Hunt. If you do well on PH, say you get #3 product of the day, then you post somewhere else saying ‘I got #3 product of the day’.. to get your next few customers. Maybe that post is on reddit with some learnings that you found. If the reddit post does well, then you might post it on Twitter, saying reddit did well and what learnings you got from that etc. or even if it doesn’t do well you can still post about it.” Another tip she shared is to build related products that get more viral than the product itself. These are small stand-alone sites that would appeal to the same target audience, but by nature, are more shareable. On these sites, you can mention your startup like: ‘brought to you by Llama Life’ and then provide a link to the main website if someone is interested. If one of those gets viral or ranks on Google, you’ll have a passive traffic source. Scraping bee by Pierre Pierre, founder of Scraping Bee, a web scraping tool has now reached $1.5M ARR. Pierre and his cofounder Kevin started with 10 Free Beta Users in 2019, and after 6 months asked them to take a paid subscription if they wanted to continue using the product. That’s how they got their first user within 50 minutes of that email. Then they listed it on dozens of startup directories but their core strategy was writing the best possible content for their target audience — Developers. 3 very successful pieces of content that worked were : A small tutorial on how to scrape single-page application An extensive general guide about web scraping without getting blocked A complete introduction to web scraping with Python They didn’t do content marketing for the sake of content marketing but deep-dived into the value they were providing their customer. One of these got 70K visits, and all this together got them to over 100 users. WePay by Bill Clerico Bill Clerico left his cushy corporate job to build WePay which was then acquired for $400M got his first users by using his app. He got his first users by using his app! The app was for group payments. So he hosted a Poker tournament at his house and collected payments only with his app. Then they hosted a barbecue for fraternity treasurers at San Jose State & helped them do their annual dues collection. Good old word-of-mouth marketing, that however, started with an event where they used what they made! RealWorld by Genevieve Genevieve — Founder and CEO of Realworld stands by the old-school advice of value giving. RealWorld is an app that helps GenZ navigate adulthood. So, before launching their direct-to-consumer platform, they had an educational course that they sold to college career centers and students. They already had a pipeline of adults who turned to Realworld for their adulting challenges. From there, she gained her first 100 followers. Saner dot ai by Austin Austin got 100 users from Reddit for his startup Saner.ai. Reddit hates advertising, and so his tips to market your startup on Reddit is to Write value-driven posts on your niche. Instead of writing posts, find posts where people are looking for solutions DM people facing problems that your SaaS solves. But instead of selling, ask about their problem to see if your product is a good fit Heartfelt posts about why you built it, aren’t gonna cut it To find posts and people, search Reddit with relevant keywords and join all the subreddits A Stock Portfolio Newsletter A financial investor got his first 100 paid newsletter subscribers for his stock portfolio newsletter. His tips : Don’t reinvent the wheel. Work what’s already working. He saw a company making $500M+ from stock picking newsletter, so decided to try that. Find the gaps in “already working” and leverage them. That newsletter did not have portfolios of advisors writing them. That was his USP. He added his own portfolio to his newsletter. Now to 100 users, he partnered with a guy running an investing website and getting good traffic. That guy got a cut of his revenue, in exchange. That one simple step got him to 100 users. Hypefury by Yannick and Samy Yannick and Samy from Hypefury, Twitter and Social Media Automation tool got their first beta testers and users from a paid community. They launched Hypefury there and asked if someone wanted to try it. A couple of people tried it and gave feedback. Samy conducted user interviews and product demos for them, And shared the reviews on Twitter. That alone, along with word-of-mouth marketing on Twitter got them their first 100 users. To conclude: Don’t reinvent the wheel, try what’s working. Find the gaps in what’s working, and leverage that. Instead of thinking about millions of customers, think about the first 10. Then first 100. Leverage what you have. Get the first 10 customers, then talk about this to get the next 100. Use your app. Find ways, events, and opportunities to use your app in front of people. And get them to use it. Write content not only for SEO but also to help people. It won’t work tomorrow, but it will work for years after it picks up. Leverage other sources of traffic by partnering up! Do things that don’t scale. I’m also doing SaaS marketing deep dives over 30 pieces of content. I'm posting here for the first time, so I'm not sure if it will stay or not, sorry if it doesn't. I've helped a SaaS grow from $19K to $100K MRR as a marketer in last 2 years, and now I wanna dive deep. Cheers! (1/30)

I am the founder of a b2c saas - organically scaled to $2M ARR, 1.9M users. Started company when I was 22, moved to Korea, built it here over 9 years. Ask me anything

It's 12:05am on Saturday morning and I just had some coffee so I'd like to take this time to talk with the startup subreddit on my journey building a b2c saas company. Specifically on the topics of growth, product, investment/governance, or strategy. For what we are, we are the most popular AI resume builder these days - the story of the company is a bit unique since I started it as a college graduate, then moved to Korea, where I was able to turn the idea in a scaling startup via government programs and Korean VCs. These days our traction shows that we are doing a few things right. I'm happy to share any knowledge or experiences I've gained over this time! Metrics: ARR - 2M Users - 1.9M Employees - 10

Mentally burnt out

My startup is not working and this is the year I probably need to shut down. I have struggled and tried to make this work for 5 years now but I am down to one person and hired consultants. I have a feeling of utter failure and no significant achievements. The minor alerts I have put out to potential future job prospects look bleak too. I am seen as too broad having been a Founder and not deep. I have an Engineering background but played the Go to Market/CEO role at my startup. Part of my mistake was not getting deep into the Engineering (outsourcing it). I have a rare combination of knowledge in Gen AI, Autonomous Vehicles and Strategy. However, there seem to be no takers - the market is too nascent and my skills have become too broad - a founder skillset rather than deep. What do I do now? I am mentally burnt out to make $$ out of this startup but the job market is not helping either.

AI And Google's Algorithm Updates Push Us To Adopt New Customer Acquisition Strategies

Hello everyone, More and more founders seem to be struggling with SEO and organic traffic… Google has made tons of changes these last 2 years. Their zero-click policy, combined with their own generative AI model now answering questions in the search results, makes it more challenging than ever to attract new customers on Google. I made a post about that and I explore alternative solutions that still work well in 2024: https://kwatch.io/the-impact-of-ai-and-google-algorithm-updates-on-seo-pushes-us-to-adopt-new-strategies As a startup founder continuously exploring new customer acquisition strategies, I would love to hear your opinion/advice. Do you have success with SEO? Would you mind sharing your best customer acquisition strategies? Arthur

Upselling from $8/mo to $2k/mo

I just closed a client for $1947/mo. But 5 months ago he was spending only $8/mo. Most customers have way more purchasing power than you think. Unlock it with the power of stacking. Here's my 3-steps stacking formula: Step 1 - Build trust with a low-ticket product In a world full of scams and deceit, building trust is damn hard. The best way to combat skepticism is through a free or low-ticket product, where you can go above and beyond to demonstrate your credibility. When I first onboarded this client onto my SaaS, an AI to help you with HARO link-building, my product was at a very early stage with many rough edges. He gave me lots of great feedback. I implemented his suggestions the same day and got more feedback from him. After a couple of back-and-forths, I established myself as a trustworthy hustler, instead of just a stranger online. This is easy to do for an agile startup but impossible for big companies, so make good use of opportunities like this to build long-term relationships. Turn your customers into raving fans. Step 2 - Validate a mid-ticket offer Three months into his subscription, he told me he wanted to cancel. When digging into the why, he suggested a performance-based DFY service to remove all the work on his end. Inspired by his suggestion, I took on him and 6 other clients for $237, a one-time package for 1 backlink. It's sold through my newsletter email blast to 300 subscribers, with a total CAC of $0. I wrote about the details of this launch in another long form. At this price range, impulsive purchases can still happen if you have a strong offer and good copywriting. Use this mid-ticket offer to validate your offer and positioning, build out a team, and establish trust. We went beyond the 1 link for almost all our clients, including this one in particular. For $237, we got him on Forbes, HubSpot, 2 DR50+ sites, and a few other smaller media outlets. By doing this, we further built trust into the relationship and established authority in what we do. Step 3 - Create a high-ticket subscription-based offer By now, you'll hopefully have built enough trust to get through the skepticism filter for something high-ticket. Now, it's time to develop an offer that amplifies your previous one. Something that allows you to let your clients achieve their goals to the maximum extent. For me, this is pitching every relevant media query on every platform for this client every day, to leverage HARO link-building to its full extent, all for a fixed price of $1947/mo. This customized offer is based on direct client feedback, isn't publicized on our website, but we're confident it will directly contribute to achieving this client's goal. A subscription-based offer is much superior because it allows you to create a stable source of revenue, especially at the early stage. That's how I created 3 different offers to solve the same problem for one client. By stacking each offer on top of the previous one, I was able to guide clients from one option to the next. This formula isn't some new rocket science I came up with. It's proven over and over again by other agency owners building in public, like Nick from Baked Design who started with a $9 design kit and now sells $9k/mo design subscriptions at $1M ARR. By stacking offers, you position yourself as a committed partner in your client's long-term success. Lastly, I want to address a common objection: "My customers can't afford $2k/month." But consider this: most people are reading your site on their $3000 MacBook or $1000 iPhone. It's not that they lack the funds, it's more likely that your service isn't meeting their expectations. Talk to them to discover the irresistible offer they'll gladly pay for. Update: lots of DM asking about more specifics so I wrote about it here. https://coldstartblueprint.com/p/ai-agent-email-list-building

From “Green” to “Smart” – Tom Gorski’s Word of Advice

Sharing this interview with entrepreneur Tom Gorski. I think it contains a few nice tips for beginner entrepreneurs. What is the problem with the term “Green?” what are the top 3 mistakes entrepreneurs make that can prevent them from enjoying the sweet taste of success? And what should young entrepreneurs always keep in mind? Continuing our expert interview series, we asked entrepreneur Tom Gorski to share some of his secrets to success with us. Gorski is the CEO and Co-Founder at SaaSGenius.com, and an Inbound Marketer & Growth Hacker at InboundWay.com. His career spans over 12 years of developing and implementing online marketing, SEO and conversion optimization campaigns. He defines his biggest accomplishment to date as “achieving 4500% growth for one of my clients over a three­year period.” logo-saasgenius Q: It’s no secret that the SaaS market is saturated, as new companies are having very hard time acquiring, retaining and monetizing users. In your view – what are the top 3 mistakes SaaS companies make? What are some key differentiators you recognize in a successful product? A: Mistake No. 1: Product-market fit is not good enough There are a number of reasons for this, including the fact that inertia, incumbency and bureaucracy are all working against you. For emerging companies, this means finding a way to be exponentially better with fewer resources. As a result, focus is key. Mistake No. 2: Not Specializing Your Sales Roles When you specialize your sales people, you allow them to focus, which creates greater output form your sales team. Mistake No. 3: You Need a Niche To be able to market and sell well, you need to have a niche. The world is noisy and messy, and you’ll struggle if you don’t have a sharp, direct message. When you try to speak to everyone, no one can hear you. Q: Which innovative trends do you recognize in the high tech world nowadays? A: “Green” was a mega trend of the last decade and while it will continue to be very important, there will be a shift towards “smart” solutions, which are intelligent, connected and have the ability to sense, report, and take the right action. Smart solutions will be everywhere around us from smart clothing, phones, to smart homes and smart cities. Q: What is the most significant advice you can give young entrepreneurs? A: Being very successful means learning from those who have already achieved success. Having a mentor is an amazing blessing to an entrepreneur, but not everyone can find one in person. My advice is to work smarter, not harder. This is the most non-intuitive observation I will probably make. If you want to compete in the arena, hard work isn’t enough. And judging yourself on how hard you work, rather than how smart you work can be fatal. Q: We are flooded with buzzwords lately – VR / AI / Bots… where do you think the software world is heading? A: AI and bots are a very hot topic in 2016 and it’s sometimes hard to distinguish the real potential behind the hype. My point of view is that, like with many things, there’s no revolution but evolution. It’s unrealistic to think that AI can become mainstream in SaaS products without proper AI infrastructure. SaaS delivery will significantly outpace traditional software product delivery, growing nearly five times faster than the traditional software market and will become a significant growth driver for all functional software markets. By 2019, the SaaS software model will account for $1 of every $4 spent on software. Q: Let us in on some of your secrets… where do you look for innovation? For inspiration and revolutionary ideas? A: Ideas for new startups often begin with a real problem that needs to be solved. And they don’t come while you’re sitting around sipping coffee and contemplating life. They tend to reveal themselves while you’re at work on something else. Start with brainstorming with problems that you are personally invested in. Building a business is hard and takes the kind of relentless dedication that comes from personal passion. Perhaps the greatest factor that determines whether or not an entrepreneur will be successful isn’t the business idea itself, but rather the entrepreneur’s willingness to try to turn the idea into reality. Great ideas are abundant, but it’s what we decide to do with them that counts. Original post: http://saasaddict.walkme.com/from-green-to-smart-tom-gorskis-words-of-advice/

Building in the open with Founder University - I will not promote

Published Oct 30, 2024 I am on my fifth startup. I ran the last one for a decade, that’s a whole story. A hell of a story. But a different story. I’ll tell it to you when I can, but not right now. The one before that was an e-commerce site that did pretty well but I didn’t love it. Before that were two service businesses. The first one I did for the love of the game, the second one was an attempt to make people stop asking me to fix their computer by charging them outrageous prices, which backfired horribly when they were eager to pay. None are relevant except to say I’ve been around the block and have the scars to prove it. When it was time to get back out there, I wanted to use all I’ve learned to do better. Before I talk about what those lessons produced, I’m going to talk about what those lessons were. Cause before effect, after all. One thing I wanted to do better this time was pattern matching - making the startup look the way that the industry and investors “expect” a startup to look. My last startup was an awesome idea with awesome tech (still is, but like I said, another story), but that one didn’t match patterns. It didn’t match investor patterns, industry buying patterns, patterns of existing, immediate, recognized and admitted needs. Because it didn’t “look” right to anyone, everything about it was way harder than necessary. The “make it look right” approach runs the risk of building a cargo cult, imitating the trappings of something but without understanding the essence of that something, but then again, a thing that looks like a knife is going to make a better knife that a thing that looks like a bowling ball, so sometimes just sharing apparent similarities can get you pretty far, even if it doesn’t get you all the way there. Like how mimicking someone’s accent makes it easier for them to understand you. For this one, I wanted to adopt every tool, method, and pattern that I knew “the industry” wanted to see to minimize the friction from development, go-to-market, scaling, adoption, and that would make investment optional (and, therefore, available if desired) instead of necessary (and, therefore, largely unavailable). That required establishing some expectations for successful patterns I could match against. What patterns am I matching to? Here’s a general sketch of my pattern matching thought process: Software first and software only. It’s the easiest industry to start a business in, lowest startup costs, and easiest customer acquisition. I wanted to build software for an element of the industry that’s actively emerging (and therefore has room to grow) and part of an optimistic investor thesis (and therefore has a cohort of people who are intent on injecting capital into the market to help it grow). It needs to fills a niche that is underexplored (low competition) and highly potent (lots of opportunity), while being aligned to recognized and emerging needs within the industry (readily adopted). I wanted it to have evidence supporting the business thesis that proves the demand exists, but demonstrates that the demand is unanswered (as of yet) by sufficient or adequate supply.* I wanted the lowest number of dominoes to line up and tip for everything to work correctly - the more dominoes in the line, the less likely the last one will fall. I wanted to implement modern toolsets for everything, wherever possible. I wanted to obey the maxim, “When there’s a gold rush, don’t mine the gold, sell the picks and shovels.” Whatever I chose would need to produce cash flow almost immediately with minimal development time or go-to-market delays, because the end of ZIRP killed the “trust me bro” investment thesis predominant over the last 15 years. I wanted to match to YC best practices, not because YC can predict what will definitely work, but because they’ve churned through so many startups in the last 15 years that they have a good sense of what will definitely not work. And I wanted to build client-centric, because if my intent is to to produce cash flow immediately, we need to get clients immediately, and if we need to get clients immediately, we need to focus on what clients need right now. Extra credit: What’s the difference between a customer and a client? Note: Competition is awesome! Competition is validating and not scary, because competition proves a market exists. But competition, especially mature competition against an immature startup, makes it harder to break into a space. A first mover advantage isn’t everything, but seeing demand before it’s sufficiently supplied is a great advantage if you’re capital constrained or otherwise unproven. Think about how much money the first guy to sell fidget spinners or Silly Bandz made versus how much money the last guy to order a pallet of each made. Finding demand that exists already but is as of yet insufficiently satisfied is a great place to start. What opportunity spaces are most relevant? The industries and markets I chose to observe were: AI, because if I’m following a theme & pattern for today, it’s AI. Fintech, because cash is king, and fintech puts your hands on cash flow. Crypto/blockchain, because that’s the “new” fintech (or maybe the “old-new” fintech?), and crypto creates powerful incentives and capital formation strategies, along with a lot of flexibility for transaction systems. Tools, particularly unmet demand in tools, that enable these industries. If you wanted to do some brief and simple homework, you could map each of those bullets to several of the numbered list items preceding them. The reasoning was pretty simplistic - AI is what people want to build and invest in now, while fintech and crypto/blockchain are what people were building and investing in for the last major investment thesis. That means that there’s demand in the market for AI and AI-adjacent startups, while there’s a glut of underutilized and highly developed tools within fintech and crypto/blockchain, with a lot of motivated capital behind the adoption. When someone is thinking “I built this thing and not enough people are using it”, and you then build something that uses it creates a great way to find allies. This rationale harnesses technology that is being built and financed now (which means it needs tools and support methods, and a lot of other “picks and shovels”), while leveraging technology that was recently built and financed and is eager for more widespread adoption of the existing toolkits, which makes it suitable for using to build the AI-adjacent tools that are in demand now. It’s like two harmonics producing constructive interference - it makes two waves into one larger wave, which gives me more momentum to surf against. This was a learning process, and I iterated against my general paradigm repeatedly as I learned more. Neither of us have the patience to go through that in excruciating detail, so I’ll cover the highlights in my next post. Extra credit answer: A customer gets a product, a client gets a service. Challenge: Is software a product or a service?

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.

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.

Is my idea + progress good enough to raise pre-seed round? CRM for construction niches. Non-tech founder.

Is my startup idea and progress good enough to raise a pre-seed round? It’s a CRM with meaningful AI integrations for specific type of B2B construction companies. I only want to continue at my current pace if it’s realistic to start raising within the next 2 weeks. At first, I thought it was fine because simple companies still get on Y-comb such as hammr and Relate CRM , but now I’m not sure. Would love to get the community’s thoughts on this. I’ve been working on this for about a week. &#x200B; Key Highlights (You can skip to longer section below) Product is CRM for B2B construction companies. The previous tech company I worked at used an in-house built CRM for their workflow, and I’m creating that solution and applying it to B2B construction companies that have similar workflows. No competitors I’ve found. I’m uniquely positioned to spearhead: B2B SaaS/tech sales + expertise in construction I’m a non-tech sales founder with experience in UI/UX. Will bring on CTO co-founder once I start raising as that would entice better talent Progress + Traction $400 MRR in pre-sales, can get to \~$800-1000 EOM Validated through customer interviews Created some Figma frames, product overview, user journeys, business plan Made a simple but meaningful AI tool that will be available to those that sign up for waitlist. Did this with GitHub + ChatGPT Landing page website going up this week followed by PPC campaign, email marketing, and outreach. My GF works in enterprise sales and she’ll help me generate more leads. &#x200B; Long Version Background B2B SaaS/Tech sales. I worked at enterprise company as an Account Executive where I worked with funded startups and their development, UI/UX, and Product management teams. I have a general knowledge in all these - my best being UI/UX design as I can work with Figma well Domain expertise: my family has had a construction company since I was young. I have a large network because of this. Problem At my previous company, we had a custom in-house built CRM for our workflow. It worked okay, despite being maintained by multiple engineers costing hundreds of thousands a year. I’m creating a CRM that solves that, and applying it to construction industries that can make use of it. I have a great network here which makes it easy for me get sales quickly. Vision Building this CRM for construction niche will allow us to generate MRR fast. We will be first movers in bringing meaningful AI tools to construction, which is generating significant interest. This gives us the opportunity to build the foundational technology that can be adapted to a wider audience such as my previous company and others - think researchers, consultants, etc. Traction + Current Progress (1 week) Validated idea through user interviews and pre-sales. Currently have $400 MRR in pre-sales. I expect $800-1000 in a month if I continue at my pace. This is from doing typical B2B sales. I’ve set up a CRM for this. Created product overview, user journeys, wireframes and some Figma frames, business plan Created a simple but meaningful AI tool for the niche which will be available to those that sign up for the waitlist. Created with GitHub + ChatGPT Completing landing page website this week. Will start PPC ads (I’m experienced in this) after that to generate sign-ups. I’ll also start email marketing from lists I’ve scraped. Team Solo-founder, will bring on CTO co-founder once I start raising funds. I have promising candidates, but feel that I need to raise funds to really entice a good co-founder. I’m uniquely positioned to head this product; B2B sales having worked with many CRMs + construction expertise and network. That said, I’ve never actually done anything that* impressive besides being an AE at a known enterprise techy company (but not FAANG level). &#x200B; I want to acknowledge that my progress might sound more impressive than it is - it's still just a CRM after all, and I'm non-technical. Should I keep going? Advice? I also have a great offer to lead sales at a profitable startup, but I could always do both if it was worth it. I’m feeling really uncertain for some reason :/ maybe it’s just burnout.

Looking for a technical cofounder with experience in building websites and marketplaces

Are you passionate about revolutionizing traditional processes? Do you have the expertise to build scalable platforms and want to be part of something transformative? I’m a second-year Economics student at the University of Warwick with a deep drive for creating impactful solutions. I’m seeking a technical co-founder to join me in building a startup dedicated to transforming how startups hire entry-level talent. About the Project I’m developing a recruitment marketplace that connects early-stage and growing startups with talented students and graduates. Our goal is to streamline the hiring process, making it hassle-free for startups while creating meaningful career opportunities for the next generation of talent. What I’m Looking For in a Technical Co-Founder I need someone who can complement my non-technical skills and help take this project to the next level. The ideal co-founder will have: A strong background in programming online marketplace platforms. Experience managing large databases efficiently. Knowledge in machine learning and AI, with a vision to integrate these in future features. Skills in scaling online platforms for a larger audience. The ability to work in synergy with me to shape and execute the vision. A passion for the idea—I’m happy to share more details in a meeting! Key responsibilities will include platform development, handling backend work, deploying the MVP, aiding in design, and collaborating on product iterations. About Me I bring experience in business strategy, operations, finance, product/project management, marketing, and sales—essentially, I cover everything except the technical aspects of development. I previously worked on a social communication platform for school students during high school. I also gained valuable experience as a business analyst in another startup. Why Join me? This is an exciting opportunity to build a product from the ground up, make an impact in the startup ecosystem, and grow alongside a venture poised to redefine hiring. We need: A seamless MVP launch. Networking efforts to onboard startups and expand our reach. Together, we can create something transformative, fostering innovation and enabling career growth for students while helping startups find the talent they need to succeed. If you’re excited about the prospect of building something revolutionary and have the technical skills to complement my business acumen, I’d love to connect. Let’s discuss how we can work together to create the next generation of hiring solutions. Please DM if you are interested in getting to know more about this project! Looking forward

Accepted To Y Combinator - I will not promote

UPDATE: Thanks to everyone for giving clear and honest advice. It really helped me to make my decisions and know what I should focus on first. This community is awesome! I'm trying to get accepted to Y Combinator and realistically assessing my profile. I'm a non-technical, solo founder, in my early 40s, and balancing startup ambitions with family responsibilities (two kids). I understand the traditional YC profile often skews younger and technical, and solo non-technical founders might face an uphill climb. I'm specifically interested in hearing from anyone in r/startups who can identify with this background and applied (or was accepted) to YC. Were you accepted to YC despite being a non-technical, solo founder, and/or over 40 with family commitments? If you applied and were not accepted, do you feel these factors played a role, and what was your experience? Any advice for someone in my situation to strengthen my application and approach? Does anyone have recommendations for me as a non-technical founder to effectively attract and onboard a technical co-founder in the AI domain? Thank you for sharing your expertise and experiences.

Non-technical founders with experienced outside vendor — ok?

I’m a non-technical cofounder of early stage startup. (“Non-technical” but I’ve developed multimedia courseware and led teams in the past (LMS, edu content, no code). My question: how crucial is it that my other biz founder and I have a technical co-founder for our data- and AI-driven product rather than use an experienced vendor whose team has been doing machine learning and AI for 10 years? During our manual work as consultants we have identified a problem in a niche market that can be solved via a combo of hard-to-gather data and AI (and other market-specific stuff that that we will train our LLM on). We’ve done market research, designed and validated the solution with potential customers in numerous interviews via click-through prototypes/wireframes, quantified TAM, SAM, SOM, written biz plan, etc. We have deep experience in our market having proven expertise over years. But as we’ve been learning about fundraising (we hope to begin a seed round in early 2025) we continually hear about the importance of technical cofounder. We get it— but our product will only be half-developed by a technical dev team. The other aspect to the product is: gathering hard to find data, and figuring out relationships in the data — that we will do via mapping work with a cohort with unique expertise in our niche market. Also our outside vendor is very reputable with years’ experience in AI and machine learning prior to the latest gen-AI craze — he’s not a newbie and has an established dev team. And our platform is not a consumer product but a more complicated SaaS product. Like, you can’t just code it by yourself. Sure, in the long run we can hire/bring everything in house, but would investors shy away from working with us if our short-term dev effort does not have a “technical” co-founder? Thanks for your thoughts.

Looking for a 3/4 Non-Technical Co-founders and 1 Technical co-founder

I have 3 startups that I would like to others to join. They are already up and running but I don't have the time to run all of them at once. Equity first then hopefully soon salary. Medical Recruitment in the UK - looking for Marketing and/or recruitment experience. You do not have to be in the UK and it can be alongside your current job. I already have 2000 nurses from overseas ready to come over. I have 2 pipelines involving education companies and IELTS training. Branding Company (UK and USA) - looking for marketers to push this company. This is in its infancy but I have a pool of 600 domain names along with a number of branding domains. I also have 2 trademarks pending in the UK. AI Saas - this is something I have been working on for a long time but cannot get off the ground myself. I would love the support of a technical co-founder but if I can get others to help it will be much faster. This software would also help support the other companies. This one is really just in my head and not reality yet but you never know. To clarify this is about people having fun, enjoying what they do and making money through this journey.

Looking for first-time founders who are on their way to get to product-market fit | I will not promote

Hey all! I’m Emil, a product guy and angel investor looking to understand better the needs of aspiring entrepreneurs and early startup founders.  I'm on a mission to make the journey from idea to first customers less stressful. This all started because of personal frustration, and I’d love to hear your experience to make sure we’re creating something that truly helps. If you’re up for it, your insights would mean the world to us and help shape a better future for entrepreneurs worldwide. You can share your thoughts in a quick 10-minute chat with AI-based virtual assistant - drop me a DM to set it up. Thanks a bunch, Emil

So, you want to be a CEO?

I used to post here occasionally with business advice. But it turns out most of you in this sub have a dream, but seemingly no execution. You want to be rich sure, but without understanding what it takes to be a founder, run a startup, create a team around an idea and a strategy, and push them to their limits without burning them out, to win in a market that's never heard of you - not to mention the pressures on your personal life. So, I'm going to post a new game called, "So, You Want to Be A CEO?" The game: Each week I will post a reasonably complex challenge that a startup founder has to overcome, between inception of the company until it goes bust or series A. You respond with your best course of action - that is, what would you do in the situation provided? YOU DON’T HAVE TO DO THE WORK! The rules: One response per person Your upvotes are your score for the week I will track them in the OP Scores are calculated on the Friday of that week You must answer the prompt completely, if you don't you lose half your points earned that week. ChatGPT is allowed, but it may not provide sufficient advice to win the game Prompt 1: "Boomerang" You are an HR executive turned entrepreneur. You have identified a significant issue: professionals over the age of 55 are struggling to re-enter the workforce and you also believe corporations are missing out on a wealth of institutional knowledge in retirement. You believe you can help solve this problem by creating Boomerang, a platform dedicated to empowering these individuals and corporate partners by connecting them with the best candidates aged 55 and older. Objective: Your goal is to validate your concept, develop a Minimum Viable Product (MVP), and balance your personal responsibilities while laying the foundation for Boomerang’s success. This Week's Key Challenges and Decisions: Market Research Challenge 1: You need to validate the market need for Boomerang. This involves understanding the pain points of older job seekers and potential employers. This will take 4 days (non-sequential) How do you get started? Developing an MVP Challenge 2: With limited resources, you need to create an MVP that effectively demonstrates Boomerang’s value. This will take 2 days. Can be combined with other challenges. How do you get started? Dealing with Personal Health Issues Challenge 3: Your doctor mentioned your bloodwork is irregular, but can't pinpoint the cause. They recommend you see a specialist before Friday. This will take 1 day. Give it a shot! There's no right answer, just answer what your plan to do and try to optimize the use of your time to the best of your ability. EDIT: Scoreboard (I realize now the top post generally gets the most upvotes, so I may change the points system): u/conscious_border3019 - 22 u/inBoulderForSummer - 4 u/that_whey-or-the-lee - 3 u/AgencySaas - 3 u/Gold-Ad-8211 - 2 u/93024662 - 2 u/DeusExBam - 2 u/njm19920 - 2 u/SilentEconomist9265 - 2 u/ai_servant - 2 u/Background-Term2759 - 2 u/Insane_squirrel - 2 u/kiss_thechef - 2 u/codeyman2 - 2 u/Xentoxus - 2 u/LongComplex4395 - 2

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😭🙏🏼

What are the latest areas you see tech entrepreneurs focusing on?

I’m interested in the “latest hot area.” For example, I see people developing offerings around bitcoin. Obviously “AI” is huge, but are there subsets of this such as cyber security, or productivity tools, or customer service chat, etc.? AI-powered consumer apps such as fitness or travel recommendations? In other words, what are the recent areas where people see opportunity and are doing startups around it?

Why raise in 2025? - I will not promote

I will not promote Lately, I've been thinking about how AI tools are completely reshaping what it means to bootstrap a startup. It honestly feels like we're living through a golden age for entrepreneurs where you don't necessarily need venture capital to build something big or meaningful. At my company, we're a small team of just four people, bootstrapping our AI-focused startup. Thanks to AI-powered tools, we're able to keep our burn rate ridiculously low, quickly test new ideas, and scale our operations way faster than we ever expected. It’s honestly pretty incredible how accessible advanced technology has become, even compared to just a few years ago. Of course, bootstrapping definitely comes with its own share of headaches. For example, we've noticed that funded startups get significantly better access to cloud credits, advertising budgets, and enterprise-level tools. We do have access to some discounts and free resources, but it rarely compares to what funded startups enjoy. This can feel frustrating, especially when you know you're competing directly with businesses that have those extra advantages. Visibility is another major challenge we've noticed. Without big funding announcements or a well-connected investor backing us, getting attention from media or even early adopters can be tough. It's just harder to make a splash without someone else's endorsement. We've had to accept and work around creatively. That said, there's something genuinely empowering about staying bootstrapped, prioritizing profitability, and maintaining control over our vision. After speaking with several investors, we've become aware of how investors can significantly influence or even redirect the trajectory of a business. We've heard stories where investors gained enough leverage to replace the original founders or have killed perfectly profitable businesses that were not growing "fast enough", which certainly gave us pause. They can definitely be helpful but giving the control over the future of my business to someone else would definitely make me feel anxious. At this time, we simply don't feel raising external capital aligns with our current goals, but we're also aware that this could change in the future. For now, maintaining autonomy and staying close to our original vision remains a priority. I'm curious to hear from others here who've been through this. Have you successfully bootstrapped an AI a tech business? What obstacles did you encounter, and how did you overcome them? EDIT: To give you a bit of perspective, my company is a B2B SaaS in the finance industry based in Europe. We have received VC funding in the past but it was an exceptionally good deal and we don't plan to raise in the near future even-thought it may change if we see the need to help us scale. We have also raised a significant amount in soft funding. Right now, we are growing on our revenues, and we plan to continue this trajectory. Recently, one of our developers left, and although we are a small team, we noticed that it had little to no impact on our productivity.

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.

How Do I Find My First Customers?

Hi r/startups, I've just launched a SAAS platform for ecommerce and digital advertising - automation without full black box campaign management or "AI" led decisions. My question is how do I go about getting people into my platform so I can get feedback/new ideas of things to add? I'm a one man band and am much more comfortable on the technical side than sales etc. I'm happy to offer a couple of months trial - but am lacking the confidence to do stuff like cold messages on LinkedIn (All I see from my connections is moaning about people selling on there) Any tips of how I should reach potential customers? Thanks!

10y of product development, 2 bankruptcies, and 1 Exit — what next? [Extended Story]

10 years of obsessive pursuit from the bottom to impressive product-market fit and exit. Bootstrapping tech products as Software Developer and 3x Startup Founder (2 bankruptcies and 1 exit). Hi everyone, your motivation has inspired me to delve deeper into my story. So, as promised to some of you, I've expanded on it a bit more, along with my brief reflections. There are many founders, product creators, and proactive individuals, I’ve read many of your crazy stories and lessons so I decided to share mine and the lessons I learned from the bottom to impressive product-market fit and exit. I've spent almost the past 10 years building tech products as a Corporate Team Leader, Senior Software Developer, Online Course Creator, Programming Tutor, Head of Development/CTO, and 3x Startup Founder (2 bankruptcies, and 1 exit). And what next? good question... A brief summary of my journey: Chapter 1: Software Developer / Team Leader / Senior Software Developer I’ve always wanted to create products that win over users’ hearts, carry value, and influence users. Ever since my school days, I’ve loved the tech part of building digital products. At the beginning of school, I started hosting servers for games, blogs and internet forums, and other things that did not require much programming knowledge. My classmates and later even over 100 people played on servers that I hosted on my home PC. Later, as the only person in school, I passed the final exam in computer science. During my computer science studies, I started my first job as a software developer. It was crazy, I was spending 200–300 hours a month in the office attending also to daily classes. Yes, I didn’t have a life, but it truly was the fulfillment of my dreams. I was able to earn good money doing what I love, and I devoted fully myself to it. My key to effectively studying IT and growing my knowledge at rocket speed was learning day by day reading guides, building products to the portfolio, watching youtube channels and attending conferences, and even watching them online, even if I didn’t understand everything at the beginning. In one year we’ve been to every possible event within 400km. We were building healthcare products that were actually used in hospitals and medical facilities. It was a beautiful adventure and tons of knowledge I took from this place. That time I built my first product teams, hired many great people, and over the years became a senior developer and team leader. Even I convinced my study mates to apply to this company and we studied together and worked as well. Finally, there were 4 of us, when I left a friend of mine took over my position and still works there. If you’re reading this, I’m sending you a flood of love and appreciation. I joined as the 8th person, and after around 4 years, when I left hungry for change, there were already over 30 of us, now around 100. It was a good time, greetings to everyone. I finished my Master’s and Engineering degrees in Computer Science, and it was time for changes. Chapter 2: 1st time as a Co-founder — Marketplace In the meantime, there was also my first startup (a marketplace) with four of my friends. We all worked on the product, each of us spent thousands of hours, after hours, entire weekends… and I think finally over a year of work. As you might guess, we lacked the most important things: sales, marketing, and product-market fit. We thought users think like us. We all also worked commercially, so the work went very smoothly, but we didn’t know what we should do next with it… Finally, we didn’t have any customers, but you know what, I don’t regret it, a lot of learning things which I used many times later. The first attempts at validating the idea with the market and business activities. In the end, the product was Airbnb-sized. Landing pages, listings, user panels, customer panels, admin site, notifications, caches, queues, load balancing, and much more. We wanted to publish the fully ready product to the market. It was a marketplace, so if you can guess, we had to attract both sides to be valuable. “Marketplace” — You can imagine something like Uber, if you don’t have passengers it was difficult to convince taxi drivers, if you don’t have a large number of taxi drivers you cannot attract passengers. After a year of development, we were overloaded, and without business, marketing, sales knowledge, and budget. Chapter 3: Corp Team Lead / Programming Tutor / Programming Architecture Workshop Leader Working in a corporation, a totally different environment, an international fintech, another learning experience, large products, and workmates who were waiting for 5 pm to finish — it wasn’t for me. Very slow product development, huge hierarchy, being an ant at the bottom, and low impact on the final product. At that time I understood that being a software developer is not anything special and I compared my work to factory worker. Sorry for that. High rates have been pumped only by high demand. Friends of mine from another industry do more difficult things and have a bigger responsibility for lower rates. That’s how the market works. This lower responsibility time allowed for building the first online course after hours, my own course platform, individual teaching newbies programming, and my first huge success — my first B2C customers, and B2B clients for workshops. I pivoted to full focus on sales, marketing, funnels, advertisements, demand, understanding the market, etc. It was 10x easier than startups but allowed me to learn and validate my conceptions and ideas on an easier market and showed me that it’s much easier to locate their problem/need/want and create a service/product that responds to it than to convince people of your innovative ideas. It’s just supply and demand, such a simple and basic statement, in reality, is very deep and difficult to understand without personal experience. If you’re inexperienced and you think you understand, you don’t. To this day, I love to analyze this catchword in relation to various industries / services / products and rediscover it again and again... While writing this sentence, I’m wondering if I’m not obsessed. Chapter 4: Next try — 2nd time as a founder — Edtech Drawing upon my experiences in selling services, offering trainings, and teaching programming, I wanted to broaden my horizons, delve into various fields of knowledge, involve more teachers, and so on. We started with simple services in different fields of knowledge, mainly relying on teaching in the local area (without online lessons). As I had already gathered some knowledge and experience in marketing and sales, things were going well and were moving in the right direction. The number of teachers in various fields was growing, as was the number of students. I don’t remember the exact statistics anymore, but it was another significant achievement that brought me a lot of satisfaction and new experiences. As you know, I’m a technology lover and couldn’t bear to look at manual processes — I wanted to automate everything: lessons, payments, invoices, customer service, etc. That’s when I hired our first developers (if you’re reading this, I’m sending you a flood of love — we spent a lot of time together and I remember it as a very fruitful and great year) and we began the process of tool and automation development. After a year we had really extended tools for students, teachers, franchise owners, etc. We had really big goals, we wanted to climb higher and higher. Maybe I wouldn’t even fully call it Startup, as the client was paying for the lessons, not for the software. But it gave us positive income, bootstrap financing, and tool development for services provided. Scaling this model was not as costless as SaaS because customer satisfaction was mainly on the side of the teacher, not the quality of the product (software). Finally, we grew to nearly 10 people and dozens of teachers, with zero external funding, and almost $50k monthly revenue. We worked very hard, day and night, and by November 2019, we were packed with clients to the brim. And as you know, that’s when the pandemic hit. It turned everything upside down by 180 degrees. Probably no one was ready for it. With a drastic drop in revenues, society started to save. Tired from the previous months, we had to work even harder. We had to reduce the team, change the model, and save what we had built. We stopped the tool’s development and sales, and with the developers, we started supporting other product teams to not fire them in difficult times. The tool worked passively for the next two years, reducing incomes month by month. With a smaller team providing programming services, we had full stability and earned more than relying only on educational services. At the peak of the pandemic, I promised myself that it was the last digital product I built… Never say never… Chapter 5: Time for fintech — Senior Software Developer / Team Lead / Head of Development I worked for small startups and companies. Building products from scratch, having a significant impact on the product, and complete fulfillment. Thousands of hours and sacrifices. This article mainly talks about startups that I built, so I don’t want to list all the companies, products, and applications that I supported as a technology consultant. These were mainly start-ups with a couple of people up to around 100 people on board. Some of the products were just a rescue mission, others were building an entire tech team. I was fully involved in all of them with the hope that we would work together for a long time, but I wasn’t the only one who made mistakes when looking for a product-market fit. One thing I fully understood: You can’t spend 8–15 hours a day writing code, managing a tech team, and still be able to help build an audience. In marketing and sales, you need to be rested and very creative to bring results and achieve further results and goals. If you have too many responsibilities related to technology, it becomes ineffective. I noticed that when I have more free time, more time to think, and more time to bounce the ball against the wall, I come up with really working marketing/sales strategies and solutions. It’s impossible when you are focused on code all day. You must know that this chapter of my life was long and has continued until now. Chapter 6: 3rd time as a founder — sold Never say never… right?\\ It was a time when the crypto market was really high and it was really trending topic. You know that I love technology right? So I cannot miss the blockchain world. I had experience in blockchain topics by learning on my own and from startups where I worked before. I was involved in crypto communities and I noticed a “starving crowd”. People who did things manually and earned money(crypto) on it.I found potential for building a small product that solves a technological problem. I said a few years before that I don’t want to start from scratch. I decided to share my observations and possibilities with my good friend. He said, “If you gonna built it, I’m in”. I couldn’t stop thinking about it. I had thought and planned every aspect of marketing and sales. And you know what. On this huge mindmap “product” was only one block. 90% of the mindmap was focused on marketing and sales. Now, writing this article, I understood what path I went from my first startup to this one. In the first (described earlier) 90% was the product, but in the last one 90% was sales and marketing. Many years later, I did this approach automatically. What has changed in my head over the years and so many mistakes? At that time, the company for which I provided services was acquired. The next day I got a thank you for my hard work and all my accounts were blocked. Life… I was shocked. We were simply replaced by their trusted technology managers. They wanted to get full control. They acted a bit unkindly, but I knew that they had all my knowledge about the product in the documentation, because I’m used to drawing everything so that in the moment of my weakness (illness, whatever) the team could handle it. That’s what solid leaders do, right? After a time, I know that these are normal procedures in financial companies, the point is that under the influence of emotions, do not do anything inappropriate. I quickly forgot about it, that I was brutally fired. All that mattered was to bring my plan to life. And it has been started, 15–20 hours a day every day. You have to believe me, getting back into the game was incredibly satisfying for me. I didn’t even know that I would be so excited. Then we also noticed that someone was starting to think about the same product as me. So the race began a game against time and the market. I assume that if you have reached this point, you are interested in product-market fit, marketing, and sales, so let me explain my assumptions to you: Product: A very very small tool that allowed you to automate proper tracking and creation of on-chain transactions. Literally, the whole app for the user was located on only three subpages. Starving Crowd: We tapped into an underserved market. The crypto market primarily operates via communities on platforms like Discord, Reddit, Twitter, Telegram, and so on. Therefore, our main strategy was directly communicating with users and demonstrating our tool. This was essentially “free marketing” (excluding the time we invested), as we did not need to invest in ads, promotional materials, or convince people about the efficacy of our tool. The community could directly observe on-chain transactions executed by our algorithms, which were processed at an exceptionally fast rate. This was something they couldn’t accomplish manually, so whenever someone conducted transactions using our algorithm, it was immediately noticeable and stirred a curiosity within the community (how did they do that!). Tests: I conducted the initial tests of the application on myself — we had already invested significantly in developing the product, but I preferred risking my own resources over that of the users. I provided the tool access to my wallet, containing 0.3ETH, and went to sleep. Upon waking up, I discovered that the transactions were successful and my wallet had grown to 0.99ETH. My excitement knew no bounds, it felt like a windfall. But, of course, there was a fair chance I could have lost it too. It worked. As we progressed, some users achieved higher results, but it largely hinged on the parameters set by them. As you can surmise, the strategy was simple — buy low, sell high. There was considerable risk involved. Churn: For those versed in marketing, the significance of repeat visitors cannot be overstated. Access to our tool was granted only after email verification and a special technique that I’d prefer to keep confidential. And this was all provided for free. While we had zero followers on social media, we saw an explosion in our email subscriber base and amassed a substantial number of users and advocates. Revenue Generation: Our product quickly gained popularity as we were effectively helping users earn — an undeniable value proposition. Now, it was time to capitalize on our efforts. We introduced a subscription model charging $300 per week or $1,000 per month — seemingly high rates, but the demand was so intense that it wasn’t an issue. Being a subscriber meant you were prioritized in the queue, ensuring you were among the first to reap benefits — thus adding more “value”. Marketing: The quality of our product and its ability to continually engage users contributed to it achieving what can best be described as viral. It was both a source of pride and astonishment to witness users sharing charts and analyses derived from our tool in forum discussions. They weren’t actively promoting our product but rather using screenshots from our application to illustrate certain aspects of the crypto world. By that stage, we had already assembled a team to assist with marketing, and programming, and to provide round-the-clock helpdesk support. Unforgettable Time: Despite the hype, my focus remained steadfast on monitoring our servers, their capacity, and speed. Considering we had only been on the market for a few weeks, we were yet to implement alerts, server scaling, etc. Our active user base spanned from Japan to the West Coast of the United States. Primarily, our application was used daily during the evenings, but considering the variety of time zones, the only time I could afford to sleep was during the evening hours in Far Eastern Europe, where we had the least users. However, someone always needed to be on guard, and as such, my phone was constantly by my side. After all, we couldn’t afford to let our users down. We found ourselves working 20 hours a day, catering to thousands of users, enduring physical fatigue, engaging in talks with VCs, and participating in conferences. Sudden Downturn: Our pinnacle was abruptly interrupted by the war in Ukraine (next macroeconomic shot straight in the face, lucky guy), a precipitous drop in cryptocurrency value, and swiftly emerging competition. By this time, there were 5–8 comparable tools had infiltrated the market. It was a challenging period as we continually stumbled upon new rivals. They immediately embarked on swift fundraising endeavors — a strategy we overlooked, which in retrospect was a mistake. Although our product was superior, the competitors’ rapid advancement and our insufficient funds for expeditious scaling posed significant challenges. Nonetheless, we made a good decision. We sold the product (exit) to competitors. The revenue from “exit” compensated for all the losses, leaving us with enough rest. We were a small team without substantial budgets for rapid development, and the risk of forming new teams without money to survive for more than 1–2 months was irresponsible. You have to believe me that this decision consumed us sleepless nights. Finally, we sold it. They turned off our app but took algorithms and users. Whether you believe it or not, after several months of toiling day and night, experiencing burnout, growing weary of the topic, and gaining an extra 15 kg in weight, we finally found our freedom… The exit wasn’t incredibly profitable, but we knew they had outdone us. The exit covered all our expenses and granted us a well-deserved rest for the subsequent quarter. It was an insane ride. Despite the uncertainty, stress, struggles, and sleepless nights, the story and experience will remain etched in my memory for the rest of my life. Swift Takeaways: Comprehending User Needs: Do you fully understand the product-market fit? Is your offering just an accessory or does it truly satisfy the user’s needs? The Power of Viral Marketing: Take inspiration from giants like Snapchat, ChatGPT, and Clubhouse. While your product might not attain the same scale (but remember, never say never…), the closer your concept is to theirs, the easier your journey will be. If your user is motivated to text a friend saying, “Hey, check out how cool this is” (like sharing ChatGPT), then you’re on the best track. Really. Even if it doesn’t seem immediately evident, there could be a way to incorporate this into your product. Keep looking until you find it. Niche targeting — the more specific and tailored your product is to a certain audience, the easier your journey will be People love buying from people — establishing a personal brand and associating yourself with the product can make things easier. Value: Seek to understand why users engage with your product and keep returning. The more specific and critical the issue you’re aiming to solve, the easier your path will be. Consider your offerings in terms of products and services and focus on sales and marketing, regardless of personal sentiments. These are just a few points, I plan to elaborate on all of them in a separate article. Many products undergo years of development in search of market fit, refining the user experience, and more. And guess what? There’s absolutely nothing wrong with that. Each product and market follows its own rules. Many startups have extensive histories before they finally make their mark (for instance, OpenAI). This entire journey spanned maybe 6–8 months. I grasped and capitalized on the opportunity, but we understood from the start that establishing a startup carried a significant risk, and our crypto product was 10 times riskier. Was it worth it? Given my passion for product development — absolutely. Was it profitable? — No, considering the hours spent — we lose. Did it provide a stable, problem-free life — nope. Did this entire adventure offer a wealth of happiness, joy, and unforgettable experiences — definitely yes. One thing is certain — we’ve amassed substantial experience and it’s not over yet :) So, what lies ahead? Chapter 7: Reverting to the contractor, developing a product for a crypto StartupReturning to the past, we continue our journey… I had invested substantial time and passion into the tech rescue mission product. I came on board as the technical Team Leader of a startup that had garnered over $20M in seed round funding, affiliated with the realm of cryptocurrencies. The investors were individuals with extensive backgrounds in the crypto world. My role was primarily technical, and there was an abundance of work to tackle. I was fully immersed, and genuinely devoted to the role. I was striving for excellence, knowing that if we secured another round of financing, the startup would accelerate rapidly. As for the product and marketing, I was more of an observer. After all, there were marketing professionals with decades of experience on board. These were individuals recruited from large crypto-related firms. I had faith in them, kept an eye on their actions, and focused on my own responsibilities. However, the reality was far from satisfactory. On the last day, the principal investor for the Series A round withdrew. The board made the tough decision to shut down. It was a period of intense observation and gaining experience in product management. This was a very brief summary of the last 10 years. And what next? (Last) Chapter 8: To be announced — Product Owner / Product Consultant / Strategist / CTO After spending countless hours and days deliberating my next steps, one thing is clear: My aspiration is to continue traversing the path of software product development, with the hopeful anticipation that one day, I might ride the crest of the next big wave and ascend to the prestigious status of a unicorn company. I find myself drawn to the process of building products, exploring product-market fit, strategizing, engaging in software development, seeking out new opportunities, networking, attending conferences, and continuously challenging myself by understanding the market and its competitive landscape. Product Owner / Product Consultant / CTO / COO: I’m not entirely sure how to categorize this role, as I anticipate that it will largely depend on the product to which I will commit myself fully. My idea is to find one startup/company that wants to build a product / or already has a product, want to speed up, or simply doesn’t know what’s next. Alternatively, I could be a part of an established company with a rich business history, which intends to invest in digitization and technological advancements. The goal would be to enrich their customer experience by offering complementary digital products Rather than initiating a new venture from ground zero with the same team, I am receptive to new challenges. I am confident that my past experiences will prove highly beneficial for the founders of promising, burgeoning startups that already possess a product, or are in the initial phases of development. ‘Consultant’ — I reckon we interpret this term differently. My aim is to be completely absorbed in a single product, crafting funnels, niches, strategies, and all that is necessary to repeatedly achieve the ‘product-market fit’ and significant revenue. To me, ‘consultant’ resonates more akin to freelancing than being an employee. My current goal is to kickstart as a consultant and aide, dealing with facilitating startups in their journey from point A to B. Here are two theoretical scenarios to illustrate my approach: Scenario 1: (Starting from point A) You have a product but struggle with marketing, adoption, software, strategy, sales, fundraising, or something else. I conduct an analysis and develop a strategy to reach point B. I take on the “dirty work” and implement necessary changes, including potential pivots or shifts (going all-in) to guide the product to point B. The goal is to reach point B, which could involve achieving a higher valuation, expanding the user base, increasing sales, or generating monthly revenue, among other metrics. Scenario 2: (Starting from point A) You have a plan or idea but face challenges with marketing, adoption, strategy, software, sales, fundraising, or something else. I analyze the situation and devise a strategy to reach point B. I tackle the necessary tasks, build the team, and overcome obstacles to propel the product to point B. I have come across the view that finding the elusive product-market fit is the job of the founder, and it’s hard for me to disagree. However, I believe that my support and experiences can help save money, many failures, and most importantly, time. I have spent a great deal of time learning from my mistakes, enduring failure after failure, and even had no one to ask for support or opinion, which is why I offer my help. Saving even a couple of years, realistically speaking, seems like a value I’m eager to provide… I invite you to share your thoughts and insights on these scenarios :) Closing Remarks: I appreciate your time and effort in reaching this point. This has been my journey, and I wouldn’t change it for the world. I had an extraordinary adventure, and now I’m ready for the next exciting battle with the market and new software products. While my entire narrative is centered around startups, especially the ones I personally built, I’m planning to share more insights drawn from all of my experiences, not just those as a co-founder. If you’re currently developing your product or even just considering the idea, I urge you to reach out to me. Perhaps together, we can create something monumental :) Thank you for your time and insights. I eagerly look forward to engaging in discussions and hearing your viewpoints. Please remember to like and subscribe. Nothing motivates to write more than positive feedback :) Matt.

The Cold-Calling AI Project I'm Working On Just Got Some Angel Investment!

Hey y'all. The AI cold calling startup I've been working on for 3-4 months now just got a $2,500 angel investment, and we have 2 current customers, a credit card processing broker and a hospital equipment rental company based out of Texas. We have around $1,500 revenue so far, but we're having lots of trouble fulfilling the contracts because our tech just isn't "there" yet. I'm the Chief Tech Officer, and I'm also running some operations. The other main person in this is the CEO who has a strong sales background and came up with the idea. I've been working purely remotely, and it's great having some income because I'm stuck at home because I'm disabled, basically... &#x200B; We're using 11labs, openai, google speech to text, and a sh\*tty online dialer right now to run the first MVP which runs locally on our "botrunners" computers, and we're developing a web app with django python + javascript react. Our plan is, after we get the webapp working better, to hire more botrunners for $3 per hour from countries like Phillipines and India, and we're going to try to track all the actions the botrunners take to be able to train the AI to run it fully automated. The biggest problem we're facing right now with the tech is reducing latency, it started at 27 seconds to get a response and I've been able to get it down to 6 seconds, but people are still hanging up. We're trying several ways to mitigate this, including having pre-rendered speech playing something like "Okay" or "As an artificial representative, I'm still learning to be quicker on the pickup. We appreciate your patience." One of the industries we want to target is international web development and digital marketing companies, and we want to use the bot to cold-call businesses to pitch them our services. The goal is to replace $30 an hour cold-callers from the USA with $3 per hour total-cost automation. Apparently the CEO was given a $5 million valuation from the strength of the MVP from a VC. Our investment so far was at a $300k valuation tho. It's exciting. Trying to get Twilio working to be able to make calls programmatically instead of using our hacky workaround. Let me know if you have any questions. I just wanted to share this awesome news!

A Structured Approach to Ideation and Validation (I will not promote)

Hi all, I used to work in VC and wanted to share some startup knowledge and insights from startup founders I know. Recently, I interviewed a friend of mine who built an AI Robotics startup ("Hivebotics") that creates automated toilet-cleaning robots. I can't post the full article because of Reddit's word limit, so I'll be posting it in sections here instead. This first section of the transcript goes through his approach to ideation and validation. Enjoy and let me know what you think! (I will not promote) \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\ (1) Ideation and Validation Problem-Market-Solution Framework I like to think of startup ideation and validation using this framework: Problem– What exactly are you solving? Observation– How you identify a problem to work on User Research– How you further understand that problem Market– Is there a large enough market for solving this problem? Size– How many people experience this same problem? Demand– How many of those people are willing to pay for the solution? Solution– Your answer to the problem Desirability– Whether people actually want your solution Feasibility– Whether building the solution is practical and realistic Viability– Whether your solution can generate revenue Problem You always need to start problem-first, which is something that was really drilled into me during my time at Stanford. Too often, founders rush to build solutions first—apps or products they find exciting—without confirming whether there's any real demand for it. The first step is always to identify a specific problem, then further understand its scale, urgency and further details by talking to potential users. Observation– To find problems, observation is key. People may not even realise the inefficiencies in their processes until you point them out. That’s why interviews and field research are so important. There are problems all around us, so it's simply a matter of going out, paying attention and being attuned to them as they occur. User Research– To further understand the problem, conducting user research by interviewing potential customers is essential. Personally, I like to use the "Mom Test" when I conduct interviews to avoid biased and generic feedback. Don’t just ask theoretical questions and avoid being too specific—observe how your potential users work, ask about pain points, and use broad, open-ended questions to ensure you aren't leading them to a specific answer. Market Once you've found an actual problem and talked to enough potential users to really understand its specific pain points, the next step is to determine the market size and demand for a solution. Size– Determining the market size is essential because it determines whether or not it's commercially worthwhile to pursue the problem and develop a solution for it. You need to determine if there are enough potential customers out there experiencing this problem to gauge the market size. There's no secret strategy for this; you have to interview as many potential users as possible to confirm that it's a widespread problem in the industry. Demand– Make sure that you're working on a problem that people will gladly pay to have solved. Even if the problem is large enough, you have to make sure it's painful enough to warrant a paid solution. If many people experience the same problem, but aren't willing to pay for a solution, then you don't have a market and should look for a different problem to validate. Another way of looking at it is that your true market size is the number of potential customers actually willing to pay* for the solution to the problem, not the number of people simply experiencing the same problem. Solution When validating a potential solution to the problem, I would look at the 3 factors of desirability, feasibility and viability. Desirability– the degree to which a solution appeals to people and fulfills their wants and needs. Without strong desirability, even the most technically advanced or economically practical product is unlikely to succeed. The best way to test this is to secure financial commitments early on during the proof-of-concept stage. Most people are polite, so they may simply tell you that your startup's product is good even if it's not. However, if they're actually willing to pay for the solution, this is actual evidence of your product's desirability. Don't just ask people if they would pay for it; actually see whether they will pay for it. Feasibility– whether a product can be built using existing technical capabilities. A lack of feasibility makes it challenging or impossible to develop the product, no matter how appealing it might be to users or how promising its financial prospects are. This is just a matter of conducting initial research and actually trying to build a prototype, which will inform you whether the fully-realised product is truly feasible. Viability– the product's ability to generate sustainable financial returns. Without financial viability, the business supporting the product cannot endure, even if the product is highly appealing to users and technically achievable. Here, you need to look at your unit economics, development costs and other expenses to determine the viability of your solution. \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\ Hope you enjoyed reading this; let me know your honest thoughts in the comments and I'll try to improve how I interview founders based on those!

App ideas. im dry. i will not promote.

Since november ive tried solo creating an app or mini startup but after weeks of work i end up in dead ends. The last one was facebook restricting my whatsapp business for using automated answers, and im not sure when or if its gonna be liberated or not. Another time my computer simply couldn't handle it, and other times the project just got too big and messy for me and the AI to complete. I know you are supposed to copy a working business model and market it, but im not great at marketing. I mean it doesn't seem difficult to market something, but having the account to market with the right number of viral capacity is just very hard to do. I have music, design, data analysis, writing, and other skills, along with minimal coding skills. I need to come up with something that works for at least $1,000 a month. That it takes a month to build. its not too much to ask, is it? how do i find that app or saas? How did you do it? i will not promote

The Cold-Calling AI Project I'm Working On Just Got Some Angel Investment!

Hey y'all. The AI cold calling startup I've been working on for 3-4 months now just got a $2,500 angel investment, and we have 2 current customers, a credit card processing broker and a hospital equipment rental company based out of Texas. We have around $1,500 revenue so far, but we're having lots of trouble fulfilling the contracts because our tech just isn't "there" yet. I'm the Chief Tech Officer, and I'm also running some operations. The other main person in this is the CEO who has a strong sales background and came up with the idea. I've been working purely remotely, and it's great having some income because I'm stuck at home because I'm disabled, basically... &#x200B; We're using 11labs, openai, google speech to text, and a sh\*tty online dialer right now to run the first MVP which runs locally on our "botrunners" computers, and we're developing a web app with django python + javascript react. Our plan is, after we get the webapp working better, to hire more botrunners for $3 per hour from countries like Phillipines and India, and we're going to try to track all the actions the botrunners take to be able to train the AI to run it fully automated. The biggest problem we're facing right now with the tech is reducing latency, it started at 27 seconds to get a response and I've been able to get it down to 6 seconds, but people are still hanging up. We're trying several ways to mitigate this, including having pre-rendered speech playing something like "Okay" or "As an artificial representative, I'm still learning to be quicker on the pickup. We appreciate your patience." One of the industries we want to target is international web development and digital marketing companies, and we want to use the bot to cold-call businesses to pitch them our services. The goal is to replace $30 an hour cold-callers from the USA with $3 per hour total-cost automation. Apparently the CEO was given a $5 million valuation from the strength of the MVP from a VC. Our investment so far was at a $300k valuation tho. It's exciting. Trying to get Twilio working to be able to make calls programmatically instead of using our hacky workaround. Let me know if you have any questions. I just wanted to share this awesome news!

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!

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!

Solo technical founders, has not having a business cofounder even screwed you up? "I will not promote"

I’m creating an AI startup; a reinforcement learning model designed to do a certain thing in a big field. I’ve basically developed code for a prototype (I hope to start training in two weeks) I do believe YC advice for taking a cofounder, but risk of getting a bad one is too high. In my life I also haven't met people with my grit, at least from my countries, so I'll have to "date down", I guess. At least in technical sense. So, has not having a business cofounder actually screwed anyone up badly?  (Looking at people who are doing ventures with $20m+ potential/real ARR,)

We received 25k investment offer, need advice [I will not promote]

We received a $25k for 2.5% on a convertible note offer from a US based investor. The note matures in 18 months with an interest rate of 5%, but the investor said they can extend it further. It’s an AI SaaS in graphic design. We have been bootstrapping till now, and we feel that this money could help us hire better engineers and marketeers, we want to grow it to a good revenue, but don't see it becoming a billion dollar startup as such. Our initial plans were to build it like an indie-hacker, grow it a decent revenue and sell it to someone who can take better care of it. We built it as a side project with full time jobs. We already have decent traction with 10k+ signups and $600+ in revenue per month with <100 dollars spent on marketing. But our AI model costs are high, 0.2 USD per user that we onboard and provide free credits. But we as founders are more interested in another idea that we have been thinking about and see a bigger potential + founder market fit in. The current product is good, and we can foresee that with better hiring and marketing, we can grow our revenue to about 10-20k a month, like a regular online business. What should we do? We don't want to simply let go of the product because it's not that it doesn't work, it's just that we as founders are better fit for something else. We can't sell it yet as the revenue isn't too high and we haven't even incorporated. Is it okay if we think of growing it to 10-20k+ a month and then intend to sell it to someone who can take better care of it? Should we take the investment in such a case, given this investment is definitely gonna help us grow? Process of incorporation will also help us in selling this business later I think?

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.

I feel Ai is mostly marketing - I will not promote

I run into this on X an made me think about it… the post says “AI is effectively ‘useless’ and it’s created a ‘fake it till you make it’ bubble that could end in disaster”. I think this is true. I haven't seen a product that really solves anything specifically except for the gimmicks. It needs a product normally people can use. Also the AI startups have cash burn rates 3x to 10x their revenue. No business model. It's very focused on getting acquired.

Anyone wants to join? Looking for non technical partner

To keep things short, I'm a fullstack dev with 13+ years of exp in few startups (the most successful one is $200m+ valuation). Then launched my own gamedev business which operates well in codev and content production. Now I want to switch back into startups and use 140% of my time and expertise to bring a briliant product live after maybe few pivots. I already have one concept of AI daily newsletter with AI narrator imitating podcasts and/or tv live news but tailored for each user. Sound dumb? - Would love to hear and jump on your ideas baked by usp and market research then. Looking for someone with marketing/UA/fundraising expertise. And remember: It is not success that defines, but dedication.

Just raised and here are the stats (July 2024)

CEO of a startup - bootstrapped for 10 months with a team of 7 - Built a waitlist of $15B AUM (fintech) and here's what it took (with no intended story structure) I didn't want to spam, so I decided to go the old school route and manually write every single email (some copy and paste) In order to feel prepared, I would do my research prior to reaching out, albeit sometimes limited due to time x reward Sent over 350 emails to around 300 funds and we received three yes' (to be honest we received more than three, but they wanted too much equity, or they weren’t a good fit culturally) Pre Seed is different for everyone. Some accept pre revenue others expect 100-300k ARR - this was one of the more frustrating pieces for me, due to the fact that it's extremely subjective to what "Earliest Stage" means to some funds. We're pre-product, we had to remove our CTO in the process due to performance issues, we let go of our front-end, hired a new CTO, hired an AI Engineer, and replaced our front end. It's a numbers game. We received 94% no reply - 5% nos (with meetings) - 1% yes' I used OpenVC as my main resource, highly recommend even their free product. I recommend using discount with a SAFE. Some Angels like it better, some VC’s hate it. You have to be willing to play ball with whoever leads. Mercury for banking, perks (like Carta), and SAFE agreement. All great at Mercury. Raising for a B2B business in an enterprise market is much easier, unless deep tech or science backed. But consumer products right now are not raising pre seed from what I can tell. If it means anything - it's a numbers game. Go get what you deserve, but put in the work because no one will just hand it to you. Love this community, always here to help anyone I can.

Raising sf venture in the south

So I currenlty run a tech startup in Chattanooga TN (between nashville and atlanta) and this city is very few and far between in tech or startup funding. I am planning on moving to SF someday but my girlfriend is still in school here for the next year and we can't quite afford to move to the bay area yet. My startup needs funding and there is little to no vc's in the area and if you can find someone interested in a deal then they want 30% or more of your company. How hard would it be to raise a 350k pre-seed (with 5-10 customers at 250/mo b2b ai powered smb call management) remotely?

Consistency problem? I will not promote

I am struggling with being consistent. I know that as a startup founder, I am supposed to do a few things everyday and that's when compounding effect will kick in. However, everyday, I am missing few important tasks. These tasks are like posting on twitter or reaching out to 5 new people on linkedin. How do you guys manage it? I am not looking for ai tools suggestions to automate these as I won't learn anything in the process if I rely on such tools.

How are you analysing your chatbot/AI interactions to improve customer experience? Struggling to find patterns in conversations... (I will not promote)

Hey startup founders! We’ve been relying heavily on chatbots and AI agents for customer support, but it’s been tough to track whether these interactions are actually driving meaningful outcomes (e.g., retention, upsells). We’re drowning in conversation logs but struggling to: Identify recurring pain points in user queries Spot gaps where the AI gives inconsistent/irrelevant answers Visualize how conversations flow between topics Anyone else dealing with this? How are you structuring your analysis? Do you use any tools to automate insights, or is it all manual digging? (I will not promote)

I studied how 7 Founders found their first 100 customers for their businesses. Summarizing it here!

I am learning marketing, and so I combed through the internet to find specific advice that helped founders reach 100 users and not random Google answers. Here’s what I found: Llama Life by Marie Marie founder of Llama Life, a productivity app ($51.4K+ revenue) got her first 100 users using Snowballing effect. She shared great advice that I want to add here verbatim, “Need to think about what you have that you can leverage based on your current situation. eg..When you have no customers, think about where you can post to get the 1st customer eg Product Hunt. If you do well on PH, say you get #3 product of the day, then you post somewhere else saying ‘I got #3 product of the day’.. to get your next few customers. Maybe that post is on reddit with some learnings that you found. If the reddit post does well, then you might post it on Twitter, saying reddit did well and what learnings you got from that etc. or even if it doesn’t do well you can still post about it.” Another tip she shared is to build related products that get more viral than the product itself. These are small stand-alone sites that would appeal to the same target audience, but by nature, are more shareable. On these sites, you can mention your startup like: ‘brought to you by Llama Life’ and then provide a link to the main website if someone is interested. If one of those gets viral or ranks on Google, you’ll have a passive traffic source. Scraping bee by Pierre Pierre, founder of Scraping Bee, a web scraping tool has now reached $1.5M ARR. Pierre and his cofounder Kevin started with 10 Free Beta Users in 2019, and after 6 months asked them to take a paid subscription if they wanted to continue using the product. That’s how they got their first user within 50 minutes of that email. Then they listed it on dozens of startup directories but their core strategy was writing the best possible content for their target audience — Developers. 3 very successful pieces of content that worked were : A small tutorial on how to scrape single-page application An extensive general guide about web scraping without getting blocked A complete introduction to web scraping with Python They didn’t do content marketing for the sake of content marketing but deep-dived into the value they were providing their customer. One of these got 70K visits, and all this together got them to over 100 users. WePay by Bill Clerico Bill Clerico left his cushy corporate job to build WePay which was then acquired for $400M got his first users by using his app. He got his first users by using his app! The app was for group payments. So he hosted a Poker tournament at his house and collected payments only with his app. Then they hosted a barbecue for fraternity treasurers at San Jose State & helped them do their annual dues collection. Good old word-of-mouth marketing, that however, started with an event where they used what they made! RealWorld by Genevieve Genevieve — Founder and CEO of Realworld stands by the old-school advice of value giving. RealWorld is an app that helps GenZ navigate adulthood. So, before launching their direct-to-consumer platform, they had an educational course that they sold to college career centers and students. They already had a pipeline of adults who turned to Realworld for their adulting challenges. From there, she gained her first 100 followers. Saner dot ai by Austin Austin got 100 users from Reddit for his startup Saner.ai. Reddit hates advertising, and so his tips to market your startup on Reddit is to Write value-driven posts on your niche. Instead of writing posts, find posts where people are looking for solutions DM people facing problems that your SaaS solves. But instead of selling, ask about their problem to see if your product is a good fit Heartfelt posts about why you built it, aren’t gonna cut it To find posts and people, search Reddit with relevant keywords and join all the subreddits A Stock Portfolio Newsletter A financial investor got his first 100 paid newsletter subscribers for his stock portfolio newsletter. His tips : Don’t reinvent the wheel. Work what’s already working. He saw a company making $500M+ from stock picking newsletter, so decided to try that. Find the gaps in “already working” and leverage them. That newsletter did not have portfolios of advisors writing them. That was his USP. He added his own portfolio to his newsletter. Now to 100 users, he partnered with a guy running an investing website and getting good traffic. That guy got a cut of his revenue, in exchange. That one simple step got him to 100 users. Hypefury by Yannick and Samy Yannick and Samy from Hypefury, Twitter and Social Media Automation tool got their first beta testers and users from a paid community. They launched Hypefury there and asked if someone wanted to try it. A couple of people tried it and gave feedback. Samy conducted user interviews and product demos for them, And shared the reviews on Twitter. That alone, along with word-of-mouth marketing on Twitter got them their first 100 users. To conclude: Don’t reinvent the wheel, try what’s working. Find the gaps in what’s working, and leverage that. Instead of thinking about millions of customers, think about the first 10. Then first 100. Leverage what you have. Get the first 10 customers, then talk about this to get the next 100. Use your app. Find ways, events, and opportunities to use your app in front of people. And get them to use it. Write content not only for SEO but also to help people. It won’t work tomorrow, but it will work for years after it picks up. Leverage other sources of traffic by partnering up! Do things that don’t scale. I’m also doing SaaS marketing deep dives over 30 pieces of content. I'm posting here for the first time, so I'm not sure if it will stay or not, sorry if it doesn't. I've helped a SaaS grow from $19K to $100K MRR as a marketer in last 2 years, and now I wanna dive deep. Cheers! (1/30)

AI for sales & marketing...what' the latest? (I will not promote)

I'm part of a B2B SaaS startup and we are about to start pushing sales & marketing. I have been there before 2-3 years ago, and most of what i remember is a "data nightmare". Finding contacts, pushing them from one tool to the other, tracking follow ups, trying to create an effective funnel. I get the chance to do it again, and this time i want to automate everything i can. So what's the latest on AI for sales & marketing? How do you guys autome things? How can we do things with AI that would have been 3 interns three years ago? I will not promote

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!

Anyone building QA automation tools for non-tech users? PM here looking for recommendations!

Is there anyone here building in the QA space? As a PM in a startup, I find myself wearing many hats, including QA! While I’ve tested a few automation tools, many of them tend to be too complex for non-tech users like me or require significant time to build and maintain test cases. I’d love to connect with others working on solutions that simplify QA testing from a non-technical perspective. I see a huge opportunity for AI in this space to make testing more intuitive and efficient. If you’re working on something or have recommendations, please send them!

Should we give up?

I'm currently very demotivated because we're working on our SaaS startup since 1,5 years and we still haven't found active users, let alone a customer. We're building an AI-first tool that automates user research analysis. We've released two MVPs so far and are planning to build a third. People respond well to outreach (5-7% book a demo from those who received a first message) but then they fail to use it. We are talking with users a lot so we are aware of the problems, and we might be able to solve them if we continue building and testing. I find it hard though to solve these problems efficiently, because there are no similar established AI-first products on the market and it feels like we have to create a new UX standard. Some problems might be very hard to be solved, e.g. there are high cost of switching products for many of our potential users. Also, my time is limited, as I recently (5 months ago) became a mother. I can only work 30 hours per week. It's a competitive area we're in and our competitors have gradually developed into the same direction and it's getting harder to position ourselves. Also, GPTs might soon be able to do what we're doing - for free. I feel like AI tools are generally expected by many to be free. The price we're expecting to be able to bill is getting lower and lower and our finance plan is already looking tight. However, there are adjacent audiences which we could target as well, but none of us knows them. Is it normal as a founder to struggle so much at the beginning? I've read that it took established SaaS 2,5 years on average from founding to first revenue. We haven't founded so far so you could say we're not behind \sarcasm\ Shall we keep pushing? My tech co-founder is optimistic and thinks this is where the wheat is separated from the chaff. We're currently supported financially by a government fund so we haven't spent much private money. However, I feel like my career outlook gets worse with each day that I unsuccessfully try to raise this startup.

Good at coding, bad at marketing. Summary

Hello. I posted a question on what to do if you are good at coding but bad at marketing four days ago, and I received so many responses and tips. The original post is here. I was really glad and excited to read comments. To return the favor to the community and add some more value, I’ve summarized all the comments I got on the original post. Here are they, with my personal comments on some of the advice I got. You’ll never believe it, but the most common advice was to learn. Really, the first and only thing you should start with if you’re bad at marketing is learning. Yet learning could be different. I highlighted 5 main areas. Educate yourself on general questions. Learn more about some basics. For example, start by finding out what the 4P’s of marketing are, and afterward, you’ll inevitably run into YouTube videos, seminars, Udemy courses, or any other resource that resonates with you on some ideas/avenues you could pursue. Read books and watch videos. There are tons of books on marketing and sales. People shared in the comments books by Dan Kennedy and “Cashvertising”, written by Drew Eric Whitman. (I’ve never heard of them, but already ordered on Amazon). For sales, the most common idea was to start with YouTube videos. For example, Alex Hormozi videos and Startup school delivered by Ycombinator videos. Check out Indie Hackers and scrutinize it for a piece of good advice from developers in the same situation. Also, there was advice to follow up and read some guy on Twitter. (Don't want to get unfairly banned from here, so won't post it) Educate yourself and hire a professional or find a co-founder to help you: Hire a seasoned marketer in this field to help you out. He will help you achieve cost-efficient scales. But it could be a real problem to find the right person. Marketing agencies are expensive. Try to look on LinkedIn or among your acquaintances. Look for professionals with credentials or extensive experience. Seek marketing referrals from startups of a similar size/industry. If you don't have those, try to bring a trusted/experienced marketer friend into the intro meetings to help assess whether the service provider knows what they are doing. Talented freelancers can often get the job done for less than hiring an entire agency. Look for a co-founder who is savvy in marketing, passionate, and ready to work hard towards mutual success. Educate and DIY Being the face of your business is way better than having faceless communication. The startup checklist is made based on the comments is next: At least have your product defined. Define your target audience. Set up the goals you want to achieve. Make domain expertise and understand the market and the direction of its development. The next stage is answering tricky questions: Have you created a business model? How do you plan to compete? What’s your unique selling point? How much do you plan to budget for marketing? Are you planning to work alone, or will you need other devs? Then you start thinking about clients… You need the exposure to truly understand the customer's pain points and build a product that they love. You need to think about how your clients would think, and you should tailor each step you take for them. Get feedback from your early users if you already have a product. Interview your potential customers to learn how they buy. This will help you narrow your choice of marketing channels. Get your product or service used by several startups and help them achieve their goals. Endorsements are very valuable marketing assets. You need a landing to validate your value proposition and start sending traffic, or you can run meta instant form campaigns... It would depend on the category of your startup. You need a benchmark of the competition's ads both in Meta and Google, blog posts, domain authority, their landing page, and average search volumes. Do affiliate marketing for your product since it's an effective strategy. Educate and use AI tools for dealing with marketing. Build an LLM-based product to automate marketing. (Sounds like an idea for a startup, right?) Learn following ChatGPT advice. In 1–3 months, you will be another updated person. Look at marketowl, an AI marketing department for startups and microbusinesses that have no budget or time to do marketing. It will automate the basic tasks your business needs, but it doesn't require your marketing expertise. Check out AI tools that are delivering very good marketing content (gocharlie, jasper, copyai). Educate yourself and run socials Start a blog or YouTube channel where you can share your expertise in coding or anything else you are good at and how your product simplifies life. Engage with your audience on social media platforms like Instagram and LinkedIn, where you can showcase your industry knowledge. Start a page on Twitter and an account on Reddit. Follow and read subreddits and pages where your potential customers are. Learn the pain from the inside. Do not simply promote, people will lose interest immediately. Start by taking focused time to create informational content, so people will eventually be naturally intrigued by what you do and want to support you when they start to “know” you. Educate your potential users about the value of your product. Create content based on what ideal customers are asking at the various stages of marketing. e.g., if they are at the beginning of the process, they may use basic language; if they are further down the process, maybe they’ll be specific. Try to get on podcasts and build as many social links as you can. In other words, don’t live in a shell! Post regularly, and eventually you’ll find sites or people that are willing to promote for you. I omitted here all personal help offers and newsletters, however you could find them in the original post. Hope that will be helpful!

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.

Seeking Your Feedback: SeedHustle and Your Small Business Journey✨

Hello, everyone, I'm one of the co-founder of SeedHustle, and I wanted to have an authentic discussion with you about our recent developments. SeedHustle is a project dear to us, with the aim of simplifying the often complex process of connecting startups with venture capitalists. 🌟 Why did we embark on this journey? Well, we've been in your shoes, experiencing the frustration of the never-ending search for the right VC partner and the challenges of establishing meaningful connections. This shared experience led to the creation of (https://seedhustle.ai/ ) . So, what's the deal with SeedHustle? It's our effort to streamline the process of finding the ideal VC match. You provide us with your company details, and our AI system goes to work, suggesting potential VCs and explaining why they might be a good fit based on their past investments and backgrounds. We also provide real-time data on their funds. We're currently in the private beta phase and want to extend an invitation to join our Discord community. It's a space where founders can share their stories and possibly make introductions to VCs. As founders who thrive on AI challenges, we believe this could be a game-changer. 👂 I'm here to have an open dialogue. Is there anything you'd like to discuss? Whether it's SeedHustle, our journey, or your own small business experiences, we're all ears. Here are a few conversation starters: \-Does SeedHustle align with your small business journey? \-Do you have any suggestions for how we can improve our platform? \-Is there anything about what we're doing that's unclear or not quite resonating with you? Your feedback is incredibly valuable to us, so please feel free to reach out. Thank you for being a part of this journey, and we hope to see you in our Discord community for a chat! 😊🚀

The Evolution of Financial Technology: How CAs Are Embracing the Digital Age

The Evolution of Financial Technology: How CAs Are Embracing the Digital Age Introduction In an era characterized by rapid technological advancements, the field of finance is undergoing a transformative journey. The emergence of financial technology, or fintech, is reshaping the way businesses manage their finances, and Chartered Accountants (CAs) are at the forefront of this evolution. In this blog post, we'll explore how CAs are embracing fintech and leveraging its potential to enhance financial management, analysis, and advisory services. Fintech's Impact on Financial Services Fintech encompasses a wide range of technologies that leverage data analytics, artificial intelligence, blockchain, and automation to improve financial services. For CAs, this means new tools to streamline processes, enhance decision-making, and offer innovative solutions to clients. Automation of Routine Tasks CAs are increasingly using automation tools to handle repetitive tasks such as data entry, reconciliations, and transaction processing. This not only reduces the risk of human error but also frees up CAs to focus on higher-value tasks like strategic planning and analysis. Advanced Data Analytics Data analytics tools enable CAs to extract meaningful insights from large volumes of financial data. These insights can help businesses identify trends, anticipate risks, and make informed decisions to drive growth. Real-Time Financial Reporting Fintech enables CAs to provide clients with real-time financial reporting, giving businesses immediate access to critical information. This enhances transparency and empowers business owners to respond quickly to changing market conditions. Enhancing Audit Efficiency Fintech tools are revolutionizing the audit process. CAs can use AI-powered algorithms to analyze vast amounts of data, detect anomalies, and identify potential instances of fraud more efficiently. Personalized Financial Planning CAs can leverage fintech to offer personalized financial planning services. With access to detailed financial data, CAs can create tailored strategies that align with a client's unique goals and circumstances. Strengthening Cybersecurity As businesses become more reliant on digital tools, cybersecurity becomes paramount. CAs are playing a critical role in advising clients on cybersecurity measures to protect sensitive financial information. Virtual CFO Services Fintech enables CAs to offer virtual CFO services to startups and small businesses. Through digital platforms, CAs can provide expert financial advice and guidance remotely, making their expertise accessible to a wider range of clients. Embracing Blockchain Technology Blockchain's potential for secure and transparent record-keeping is of interest to CAs. They can explore applications in supply chain finance, smart contracts, and even audit trail verification. Continuous Learning in Fintech CAs recognize the importance of staying updated with fintech trends. Many are investing in continuous learning to master the use of new tools and technologies that can optimize their services. Conclusion The integration of fintech into the realm of finance is reshaping the landscape in profound ways. CAs are embracing these technologies to elevate their roles from traditional number-crunchers to strategic advisors, equipped with tools that enhance efficiency, accuracy, and insight. As fintech continues to evolve, CAs will remain pivotal in guiding businesses through the ever-changing financial landscape, leveraging technology to drive growth, innovation, and success. Find the top verified CA in your City Feel free to let me know if you'd like more blogs on different topics or if you have specific requirements for the content.

Seeking Feedback: Would a No-Code AI Solution Benefit Your Business?

Hi, fellow small business owners. I'm currently working on an AI startup, with the goal of providing small businesses a seamless and intuitive way to integrate AI into operations without the need for any coding or tech expertise. We're designing an auto machine learning application that's user-friendly and tailored to the unique needs of small businesses. Before we scale, I would really appreciate any insights and feedback. Here are a few questions that would be helpful to get answers to: Pain Points: Are there specific tasks or processes in your operations that you think could be automated or enhanced using AI? This could be anything from customer service chatbots, inventory management, sales forecasting, or anything else you might think of. Features: What features would you want in a no-code AI solution? Perhaps easy integration with existing software? Drag-and-drop model training? Pre-built models for common tasks? Training & Support: How important would training and support be for you in implementing and using an AI solution? Would you prefer video tutorials, live-chat support, or hands-on workshops? Pricing: Would you be willing to invest in such a tool? If so, what would be a reasonable price point for you? We're considering a tiered model based on usage, with a potential starting point of $X/month. Does that sound feasible? Trial Period: Would a free trial period be beneficial for you? How long would you need to assess the tool's impact on your business? Data Concerns: How comfortable are you with sharing data with an AI application? What privacy and security measures would make you feel at ease? Your feedback is really useful. We're building this solution with you in mind, and your insights will guide the next steps. In appreciation for your time and input, we're offering a special discount for early adopters from this community once we launch. Just drop a comment below, and I'll make sure to get in touch when we are ready. Many Thanks, Chris Parker

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! 🙌

Anyone seen success with this AI stuff?

Hey r/smallbusiness , I'm an eCommerce manager for a regional electronics retailer (online and physical stores). We focus on higher-end stuff - TVs, audio systems, laptops, appliances, etc. I was recently approached by a startup pitching an AI Sales Assistant for websites. They showed it conversing with customers via voice & chat, and is designed to really guide customers through complex purchases. Think product questions, comparisons, even handling objections etc. I'm curious if anyone has actually implemented AI sales assistants, especially for higher-consideration products like electronics. Have you seen tangible success? And even if you're in a different product category, I'd be interested to hear if you've experimented with this type of tech and what your experience was. Any insights on what worked, what didn't, or general advice would be super helpful as we explore this. Thanks in advance for any thoughts!

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.

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! 🙌

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.

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!

The Evolution of Financial Technology: How CAs Are Embracing the Digital Age

The Evolution of Financial Technology: How CAs Are Embracing the Digital Age Introduction In an era characterized by rapid technological advancements, the field of finance is undergoing a transformative journey. The emergence of financial technology, or fintech, is reshaping the way businesses manage their finances, and Chartered Accountants (CAs) are at the forefront of this evolution. In this blog post, we'll explore how CAs are embracing fintech and leveraging its potential to enhance financial management, analysis, and advisory services. Fintech's Impact on Financial Services Fintech encompasses a wide range of technologies that leverage data analytics, artificial intelligence, blockchain, and automation to improve financial services. For CAs, this means new tools to streamline processes, enhance decision-making, and offer innovative solutions to clients. Automation of Routine Tasks CAs are increasingly using automation tools to handle repetitive tasks such as data entry, reconciliations, and transaction processing. This not only reduces the risk of human error but also frees up CAs to focus on higher-value tasks like strategic planning and analysis. Advanced Data Analytics Data analytics tools enable CAs to extract meaningful insights from large volumes of financial data. These insights can help businesses identify trends, anticipate risks, and make informed decisions to drive growth. Real-Time Financial Reporting Fintech enables CAs to provide clients with real-time financial reporting, giving businesses immediate access to critical information. This enhances transparency and empowers business owners to respond quickly to changing market conditions. Enhancing Audit Efficiency Fintech tools are revolutionizing the audit process. CAs can use AI-powered algorithms to analyze vast amounts of data, detect anomalies, and identify potential instances of fraud more efficiently. Personalized Financial Planning CAs can leverage fintech to offer personalized financial planning services. With access to detailed financial data, CAs can create tailored strategies that align with a client's unique goals and circumstances. Strengthening Cybersecurity As businesses become more reliant on digital tools, cybersecurity becomes paramount. CAs are playing a critical role in advising clients on cybersecurity measures to protect sensitive financial information. Virtual CFO Services Fintech enables CAs to offer virtual CFO services to startups and small businesses. Through digital platforms, CAs can provide expert financial advice and guidance remotely, making their expertise accessible to a wider range of clients. Embracing Blockchain Technology Blockchain's potential for secure and transparent record-keeping is of interest to CAs. They can explore applications in supply chain finance, smart contracts, and even audit trail verification. Continuous Learning in Fintech CAs recognize the importance of staying updated with fintech trends. Many are investing in continuous learning to master the use of new tools and technologies that can optimize their services. Conclusion The integration of fintech into the realm of finance is reshaping the landscape in profound ways. CAs are embracing these technologies to elevate their roles from traditional number-crunchers to strategic advisors, equipped with tools that enhance efficiency, accuracy, and insight. As fintech continues to evolve, CAs will remain pivotal in guiding businesses through the ever-changing financial landscape, leveraging technology to drive growth, innovation, and success. Find the top verified CA in your City Feel free to let me know if you'd like more blogs on different topics or if you have specific requirements for the content.

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?

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.

How is ML/AI implemented in your company?

Hi, I am currently working as a full time SWE. I generally get to work with data/build solutions using data but I don't have any official ML/Data science title or background. Ideally, I'd like to be able to start making more intelligent decisions using that data and that's why I am interested on machine learning. Ideally, I'd like to write production systems that leverage models that I can build and understand end to end but after some research, I am not sure if such jobs exist today or if teams are usually split between modelling team (people that only train and analyze models) and SWE teams (people that take such models and focus on deploying them on production). My current company is using that model of development, so I might need to leave to be able to do what I want. I imagine startups work that way, but I am looking for some stability. Thanks!

Month of August in AI

🔍 Inside this Issue: 🤖 Latest Breakthroughs: This month it’s all about Agents, LangChain RAG, and LLMs evaluation challenges.* 🌐 AI Monthly News: Discover how these stories are revolutionizing industries and impacting everyday life: EU AI Act, California’s Controversial SB1047 AI regulation act, Drama at OpenAI, and possible funding at OpenAI by Nvidia and Apple.* 📚 Editor’s Special: This covers the interesting talks, lectures, and articles we came across recently. Follow me on Twitter and LinkedIn at RealAIGuys and AIGuysEditor to get insight on new AI developments. Please don't forget to subscribe to our Newsletter: https://medium.com/aiguys/newsletter Latest Breakthroughs Are Agents just simple rules? Are Agents just enhanced reasoning? The answer is yes and no. Yes, in the sense that agents have simple rules and can sometimes enhance reasoning capabilities compared to a single prompt. But No in the sense that agents can have a much more diverse functionality like using specific tools, summarizing, or even following a particular style. In this blog, we look into how to set up these agents in a hierarchal manner just like running a small team of Authors, researchers, and supervisors. How To Build Hierarchical Multi-Agent Systems? TextGrad. It is a powerful framework performing automatic “differentiation” via text. It backpropagates textual feedback provided by LLMs to improve individual components of a compound AI system. In this framework, LLMs provide rich, general, natural language suggestions to optimize variables in computation graphs, ranging from code snippets to molecular structures. TextGrad showed effectiveness and generality across various applications, from question-answering and molecule optimization to radiotherapy treatment planning. TextGrad: Improving Prompting Using AutoGrad The addition of RAG to LLMs was an excellent idea. It helped the LLMs to become more specific and individualized. Adding new components to any system leads to more interactions and its own sets of problems. Adding RAG to LLMs leads to several problems such as how to retrieve the best content, what type of prompt to write, and many more. In this blog, we are going to combine the LangChain RAG with DSPy. We deep dive into how to evaluate the RAG pipeline quantitatively using RAGAs and how to create a system where instead of manually tweaking prompts, we let the system figure out the best prompt. How To Build LangChain RAG With DSPy? As the field of natural language processing (NLP) advances, the evaluation of large language models (LLMs) like GPT-4 becomes increasingly important and complex. Traditional metrics such as accuracy are often inadequate for assessing these models’ performance because they fail to capture the nuances of human language. In this article, we will explore why evaluating LLMs is challenging and discuss effective methods like BLEU and ROUGE for a more comprehensive evaluation. The Challenges of Evaluating Large Language Models AI Monthly News AI Act enters into force On 1 August 2024, the European Artificial Intelligence Act (AI Act) enters into force. The Act aims to foster responsible artificial intelligence development and deployment in the EU. The AI Act introduces a uniform framework across all EU countries, based on a forward-looking definition of AI and a risk-based approach: Minimal risk: most AI systems such as spam filters and AI-enabled video games face no obligation under the AI Act, but companies can voluntarily adopt additional codes of conduct. Specific transparency risk: systems like chatbots must clearly inform users that they are interacting with a machine, while certain AI-generated content must be labelled as such. High risk: high-risk AI systems such as AI-based medical software or AI systems used for recruitment must comply with strict requirements, including risk-mitigation systems, high-quality of data sets, clear user information, human oversight, etc. Unacceptable risk: for example, AI systems that allow “social scoring” by governments or companies are considered a clear threat to people’s fundamental rights and are therefore banned. EU announcement: Click here https://preview.redd.it/nwyzfzgm4cmd1.png?width=828&format=png&auto=webp&s=c873db37ca0dadd5b510bea70ac9f633b96aaea4 California AI bill SB-1047 sparks fierce debate, Senator likens it to ‘Jets vs. Sharks’ feud Key Aspects of SB-1047: Regulation Scope: Targets “frontier” AI models, defined by their immense computational training requirements (over 10²⁶ operations) or significant financial investment (>$100 million). Compliance Requirements: Developers must implement safety protocols, including the ability to immediately shut down, cybersecurity measures, and risk assessments, before model deployment. Whistleblower Protections: Encourages reporting of non-compliance or risks by offering protection against retaliation. Safety Incident Reporting: Mandates reporting AI safety incidents within 72 hours to a newly established Frontier Model Division. Certification: Developers need to certify compliance, potentially under penalty of perjury in earlier drafts, though amendments might have altered this. Pros: Safety First: Prioritizes the prevention of catastrophic harms by enforcing rigorous safety standards, potentially safeguarding against AI misuse or malfunction. Incentivizes Responsible Development: By setting high standards for AI model training, the company encourages developers to think critically about the implications of their creations. Public Trust: Enhances public confidence in AI by ensuring transparency and accountability in the development process. Cons: Innovation Stagnation: Critics argue it might stifle innovation, especially in open-source AI, due to the high costs and regulatory burdens of compliance. Ambiguity: Some definitions and requirements might be too specific or broad, leading to legal challenges or unintended consequences. Global Competitiveness: There’s concern that such regulations could push AI development outside California or the U.S., benefiting other nations without similar restrictions. Implementation Challenges: The practicalities of enforcing such regulations, especially the “positive safety determination,” could be complex and contentious. News Article: Click here Open Letter: Click here https://preview.redd.it/ib96d7nk4cmd1.png?width=828&format=png&auto=webp&s=0ed5913b5dae72e203c8592393e469d9130ed689 MORE OpenAI drama OpenAI co-founder John Schulman has left the company to join rival AI startup Anthropic, while OpenAI president and co-founder Greg Brockman is taking an extended leave until the end of the year. Schulman, who played a key role in creating the AI-powered chatbot platform ChatGPT and led OpenAI’s alignment science efforts, stated his move was driven by a desire to focus more on AI alignment and hands-on technical work. Peter Deng, a product manager who joined OpenAI last year, has also left the company. With these departures, only three of OpenAI’s original 11 founders remain: CEO Sam Altman, Brockman, and Wojciech Zaremba, lead of language and code generation. News Article: Click here https://preview.redd.it/0vdjc18j4cmd1.png?width=828&format=png&auto=webp&s=e9de604c26aed3e47b50df3bdf114ef61f967080 Apple and Nvidia may invest in OpenAI Apple, which is planning to integrate ChatGPT into iOS, is in talks to invest. Soon after, Bloomberg also reported that Apple is in talks but added that Nvidia “has discussed” joining the funding round as well. The round is reportedly being led by Thrive Capital and would value OpenAI at more than $100 billion. News Article: Click here https://preview.redd.it/ude6jguh4cmd1.png?width=828&format=png&auto=webp&s=3603cbca0dbb1be3e6d0efcf06c3a698428bbdd6 Editor’s Special The AI Bubble: Will It Burst, and What Comes After?: Click here Eric Schmidt Full Controversial Interview on AI Revolution (Former Google CEO): Click here AI isn’t gonna keep improving Click here General Intelligence: Define it, measure it, build it: Click here

Join the AI4Earth challenge with the European Space Agency to highlight our footprint on Earth using Earth Observation data and Machine Learning

&#x200B; https://preview.redd.it/ww109cba14f71.png?width=2401&format=png&auto=webp&s=8bd3d43e8b63848af85c73478be61e43d9e10189 The primary goal is to get an insight into the human impact on Earth, to drive and guide conservation efforts of this planet we call home. Our approach will be twofold:  Firstly we will work on AI algorithms that can serve as an early detection system of human impact sites. Secondly we will use these detection systems to find satellite images that show the most impactful human-caused changes, which will be used in the creation of a video to launch an awareness campaign. You will be working with ESA to detect things like: Wildfires and Deforestation Marine Litter and Melting Glaciers Air quality detection & Novel animal migration patterns  and much more!  European Space Agency To reach these goals we’ve partnered up with ESA, who are able to use our algorithms to monitor new satellite data and guide conservation efforts. They will provide us with multi-spectral data of their Sentinel-2 satellite pair and with invaluable knowledge and research on the domain of Earth Observation data in participant only masterclasses.  Format The challenge will run throughout September and October, where you will collaborate with a diverse team of over 30 international data specialists and domain experts in subteams, all tackling this problem from different angles. Subtasks like the detection of deforestation, wildfires, marine litter or any other human caused impact. All contributors in the challenge are expected to spend 12 hours or more per week during the entirity of the two month challenge. To learn more subscribe to the info session on the 3rd of August 19:00 CEST HERE! Some important dates: 3rd of August – Info session 1st of September – Challenge Kick-off 29th of September – Midterm presentations 29th of October – Final presentations PARTNERS SUN - https://spacehubs.network The project is spearheaded by SUN whose goal is to increase the commercialization of space enabled solutions and growth of European start-ups and scale-ups in the space downstream and upstream sectors. ESA - https://esa.int ESA will be the main stakeholder and domain knowledge provider in the challenge. Their efforts to aid human’s space endeavours as well as protect the planet we live on will serve us for many years to come.  MLReef - https://mlreef.com MLReef provides an open source platform for collaborative Machine Learning. They provide the computational infrastructure to support the EO4Earth project as part of their AI4GOOD and Open Science initiatives. Brimatech  As a partner in the SUN project, the innovation management and market research expert Brimatech helps out in the overall organisation of the challenge.  Mothership The ‘Mothership’ is a dedicated open innovation program created by Space4Good and World Startup Factory. The Mothershi is leveraging recent advancements in artificial intelligence and satellite technologies in support of the UN Sustainable Development Goals. Space4Good  Space4Good is a geospatial innovation lab supporting impact makers on the ground with earth observation insights from above. Worldstartup  Worldstartup is a collective of international entrepreneurs, experts, mentors and investors, dedicated to help the best impact-driven startups and scaleups.

Sailea Nonprofit Event: 🚀 AI-Powered Innovation: Presentation by Misha Ghosh 🚀

Curious about how AI is transforming industries? Want to learn from a leader who has been at the forefront of data science at Wells Fargo Bank and founded his own innovative AI startup? SAILea is bringing you an exciting opportunity to hear from Misha Ghosh, an expert in AI and data science with real-world experience in driving innovation! 🌟 What you can expect: Insights into how AI is being integrated into creative and practical processes Stories from Mr. Ghosh’s work at Wells Fargo and as the founder of IDiyas Practical advice for launching your own AI-powered startup Engaging Q&A session to get your burning questions answered! 🗓 Event Details: Date: Saturday, October 5th, 2024 Time: 4 PM ET Where: Virtual via Zoom Entry: FREE! Whether you're an AI enthusiast, a student, or an aspiring entrepreneur, this is a unique opportunity to learn from one of the industry's best. 💻 Register now at https://forms.gle/vpnuvK9S5MxffDMd8 secure your spot!

Podcast - LearnOpenCV, OpenCV 20th anniversary celebrations, AI courses

Podcast alert! Recently, I was invited on a podcast run by Ritesh K of Augmented Startups. We discussed my journey as an entrepreneur and educator, why I started the blog - LearnOpenCV, OpenCV 20th anniversary celebrations, the AI courses and what we plan for the future. You can find the full video at https://youtu.be/SbnByehgxps. https://preview.redd.it/0uuchcskork51.png?width=1280&format=png&auto=webp&s=82d232fcf39f46f41788c9898098a1274f9e988a

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.

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!

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.

Is this a good ML learning progression on Coursera?

AI for Everyone Python for Everybody (UMich) Applied Data Science with Python Specialization (UMich) Machine Learning (Andrew Ng) Data Engineering, Big Data, and ML on GCP Specialization Natural Language Processing Specialization TensorFlow Developer Professional Certificate Machine Learning Engineering for Production (MLOps) by DeepLearning.AI Deep Learning Specialization Mathematics for Machine Learning Specialization Cloud Computing Specialization Product Management for AI and ML Products Specialization I’m currently a highschool junior with currently minimal Python knowledge (currently in BC Calc so I do have somewhat of a math background). However, I do own and run a business and in my business space there’s a huge issue I want to solve that affects pretty much every business. But right now obviously I can’t because I don’t have the necessary knowledge. I do intend to turn this into a startup and pursue AI/ML in college. And I aim to at least start building MVP for startup around June-Sept this year. If startup is not successful then I would still want to be an AI engineer. But is this a good learning progression that I should be able to complete before I start college?

New no-code AI copilot creator: Knolli.ai

Introducing Knolli.ai, a new no-code AI chatbot builder that enables founders and entrepreneurs to develop copilots utilizing their own knowledge bases. Once they have built their copilot, we offer a monetization platform to start generating revenue. CPApilot.com, one of our clients, has experienced rapid growth by utilizing our platform. They have landed major clients and went from idea to revenue in just 3 months. We are genuinely thrilled to collaborate with founders to create new AI startups leveraging diverse knowledge bases and obtaining feedback. Mandeep from Knolli.ai

I created leadsnavi that helps small businesses find quality leads without breaking the bank

Hey Redditors, I’m excited to share LeadsNavi, a tool I built specifically to help small businesses and B2B professionals automatically generate leads and reach potential customers in a smarter way. After talking to a lot of small business owners, I realized how tough it is to juggle lead generation with limited resources. So, I decided to create a tool that could simplify the process and make it more accessible to those who don’t have the budget to invest in expensive solutions. What Exactly Is LeadsNavi? LeadsNavi is an intuitive, cost-effective platform that automates the process of lead generation. It's designed to make it easy for small businesses and entrepreneurs to identify quality leads and grow their customer base without the need for manual prospecting. Here’s what makes it stand out: Automatic Lead Tracking: Tracks visitors to your website and matches them with company data, so you get real insights into who’s interested in your business. AI-Powered Lead Recommendations: Based on your website’s traffic, LeadsNavi uses AI to suggest similar companies that could be interested in your product or service, helping you find new leads faster and more accurately. Affordable & Scalable: For only $49/month, you can use a highly effective tool that scales with your business. It’s designed to be affordable even for small businesses. CRM Integration: Connect your CRM to directly import leads and sync your outreach efforts. How Does It Work? LeadsNavi uses advanced algorithms to track website visitors' IP addresses and match them with a comprehensive business database. It provides details like company names, contact information, and helps you identify potential leads for follow-up. The best part? It works automatically, saving you hours of manual work and effort. Lead Identification: Get insights into which companies are visiting your website. AI-Driven Lead Recommendations: The AI analyzes your site’s traffic and suggests other companies in the same industry or with similar needs that might be a great fit for your product or service. Data-Enriched Leads: Gather real-time, actionable data on these leads to make your outreach more targeted. Easy Setup: Simply integrate with your website and CRM to start getting quality leads in minutes. Who’s It For? Small Businesses: You don’t have to be a marketing expert to generate quality leads. B2B Sales Teams: Perfect for anyone looking to target other businesses with a streamlined and automated approach. Entrepreneurs & Startups: Focus on scaling your business without worrying about lead generation overhead. Why Try It? LeadsNavi gives you the power to focus on what really matters—connecting with potential customers and scaling your business. If you’ve been struggling with finding quality leads, or if you’re just getting started, I believe LeadsNavi can help you save time, effort, and money. I’m offering a 14-day free trial, so you can see the tool in action before committing to anything. Give it a try and let me know what you think! I’d love to hear your thoughts, suggestions, and how it works for your business. https://preview.redd.it/fdwil4rssgle1.png?width=1867&format=png&auto=webp&s=eb73b41a2b7665ae1b651fe2a6b7459df6990530

How I got 1000 users on day one.

This might sound like a small number, depending on who you ask, but you know it’s a start. I’ll just share my learnings so far. Introduction: The product is simple: you type what you want to build, like, let's say, a SaaS idea, and it generates the code using a framework of your choice (like NextJS). Currently, it only generates front-end code. The marketing strategy was mainly focused on social media. My social media stats are as follows: I have a whopping 14 followers on Twitter, and 10 of them are bot accounts, and on LinkedIn, it’s about 400 or so. Launching on LinkedIn: LinkedIn is unique in two different ways: The algorithm is friendly to the little guy. Your network (the people) aren’t always friendly to the little guy. Let me elaborate. This is something I learned today, actually. When I posted for the first time and asked about three of my friends to repost it, within the first hour there were about 200 views, and the click-through rate was around 40%. This was really good, given that it was in the morning. I don’t know the exact factors, but I did have a video in my post, and those three reposts probably amplified it. However, people don’t seem to like or comment on it as much as you would think. Most of my connections are CS students because I am a recent grad, so it seems like most people can relate to this product, but none of them would even put a comment or a like. At the same time, I see people liking posts from big brands like OpenAI, Microsoft, etc. I am really confused, to be honest. However, throughout the day, the view count was going up, and people were coming. Launching on Twitter: Twitter didn’t really work for me at all. I think you need a decent audience. But there are tweets like “What startup are you working on?” type questions, and from that, I find you get a couple of views on your profile. Even though Twitter didn’t really help with the views, one guy tweeted, “Keep posting on Twitter and one day this might become something like Notion.” That really made my day, to be honest. Launching on Discord: This worked really well, to be honest, especially given that I was in a lot of Discord servers where there are software devs. If you use the right language that resonates with them, it’s a home run. Not much to say, but don’t use marketing lingo; people don’t like it there. Instagram and TikTok didn’t really work. Mainly, I think my video didn’t really resonate much. Finally, Facebook Launch: The Facebook reels didn’t really do the trick. Then I posted in a bunch of groups, and still, it didn’t really do anything. But then I sent cold DMs on Facebook, and that had a pretty high open rate because I sent them to people who I saw commented on posts related to what my product was solving. Obviously, after a while, Facebook blocks the ability to send DMs. That’s all for now. Thanks I’ll post my promo video in the comment section just so that you know the video and why it might have resonated with some platforms. Also this is the first time I made a video and I’m actually proud of making that more than the product itself. To summarize, for this idea LinkedIn worked really well, because of the algorithm not the ppl commenting and liking which is what I thought should be the way. Followed by Discord groups and Facebook DMs. The video I made seemed to resonate really well with the LinkedIn audience (the engagement was around 60%) despite falling in TikTok and other video sharing platforms.

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

ChatPDF and PDF.ai are making millions using open source tech... here's the code

Why "copy" an existing product? The best SaaS products weren’t the first of their kind - think Slack, Shopify, Zoom, Dropbox, or HubSpot. They didn’t invent team communication, e-commerce, video conferencing, cloud storage, or marketing tools; they just made them better. What is a "Chat with PDF" SaaS? These are AI-powered PDF assistants that let you upload a PDF and ask questions about its content. You can summarize articles, extract key details from a contract, analyze a research paper, and more. To see this in action or dive deeper into the tech behind it, check out this YouTube video. Let's look at the market Made possible by advances in AI like ChatGPT and Retrieval-Augmented Generation (RAG), PDF chat tools started gaining traction in early 2023 and have seen consistent growth in market interest, which is currently at an all-time high (source:google trends) Keywords like "chat PDF" and "PDF AI" get between 1 to 10 million searches every month (source:keyword planner), with a broad target audience that includes researchers, students, and professionals across various industries. Leaders like PDF.ai and ChatPDF have already gained millions of users within a year of launch, driven by the growing market demand, with paid users subscribing at around $20/month. Alright, so how do we build this with open source? The core tech for most PDF AI tools are based on the same architecture. You generate text embeddings (AI-friendly text representations; usually via OpenAI APIs) for the uploaded PDF’s chapters/topics and store them in a vector database (like Pinecone). Now, every time the user asks a question, a similarity search is performed to find the most similar PDF topics from the vector database. The selected topic contents are then sent to an LLM (like ChatGPT) along with the question, which generates a contextual answer! Here are some of the best open source implementations for this process: GPT4 & LangChain Chatbot for large PDF docs by Mayo Oshin MultiPDF Chat App by Alejandro AO PDFToChat by Hassan El Mghari Worried about building signups, user management, payments, etc.? Here are my go-to open-source SaaS boilerplates that include everything you need out of the box: SaaS Boilerplate by Remi Wg Open SaaS by wasp-lang A few ideas to stand out from the noise: Here are a few strategies that could help you differentiate and achieve product market fit (based on the pivot principles from The Lean Startup by Eric Ries): Narrow down your target audience for a personalized UX: For instance, an exam prep assistant for students with study notes and quiz generator; or a document due diligence and analysis tool for lawyers. Add unique features to increase switching cost: You could autogenerate APIs for the uploaded PDFs to enable remote integrations (eg. support chatbot knowledge base); or build in workflow automation features for bulk analyses of PDFs. Offer platform level advantages: You could ship a native mobile/desktop apps for a more integrated UX; or (non-trivial) offer private/offline support by replacing the APIs with local open source deployments (eg. llama for LLM, an embedding model from the MTEB list, and FAISS for vector search). TMI? I’m an ex-AI engineer and product lead, so don’t hesitate to reach out with any questions! P.S. I've started a free weekly newsletter to share open-source/turnkey resources behind popular products (like this one). If you’re a founder looking to launch your next product without reinventing the wheel, please subscribe :)

I got 400+ new customers in first 48 hours after launch!!!!

Yesterday I launched my new software and got 400+ customers in 48 hours. I'm gonna break down the product and my launch strategy. What is it? Remember when Elon was taking over Twitter and he emailed the CEO of Twitter Parag Agrawal saying “What did you get done this week?” Well I turned this idea into a software lol. A couple months ago, I had a realization while talking with some friends: I love asking ChatGPT for business advice, but I never remember to actually do it. Now what if there was a pro-active AI business coach that checked in on me every week? Something to keep me accountable and track my progress building my empire. It could have a database where I could see my progress every single week!!! And what if this AI business coach was a simple email that says “What did you get done this week?” So I built this: Elon Email. A weekly 1-on-1 with Elon Musk Every Sunday night for the last month, I’ve been getting a weekly email from Elon Musk saying “What did you get done this week?” I take a few minutes to write back with everything I got done that week: new revenue metrics, a list of the new features I shipped, new employees onboarded, number of workouts, exciting calls and collaboration opportunities, etc. Then an AI trained on Elon would give me tailored advice all in my email. And here's the best part. Rather than a nice friendly soft-spoken AI, I prompted the AI to be as savage and ruthless as Elon with its business advice. And it actually worked. One user said "it's like a slap in the face". I knew with 2025 New Years resolutions coming, I needed to launch it ASAP so I pushed through an all-nighter on Friday and got it launched today. Launch strategy: \> Focus on X (fka Twitter) as main source. I have 31,000 followers on X from the last few years building startups, so I posted my launch this morning there. X is Elon's social media network now so I didn't waste time on other platforms. I basically didn't look up from my phone for like 12 hours (my wife was pissed at me because we're technically on vacation but yolo) and I commented, engaged, and DMed with everyone I could. It paid off with 50,000+ views on the post and nearly 300 likes so far. \> Purposely exclude people. Yes, I know this sounds weird, but you need to purposely exclude some people to focus on the people who will actually use your product. I know a lot of people hate Elon and will hate me for making this. I don't care. I only care about the people who will actually use it aka my customers. The same thing with making it a "savage AI". I know there will be some people who prefer a nice friendly soft AI, but that's not my customer base. The internet is big enough you can find your customer base but you've gotta be willing to exclude some people to speak to the right people! \> Free tier. The weekly Elon email and AI reply is free. I also have a paid tier for a daily email and database access. I know I'm technically losing money on API fees for the free email and AI requests, but it's a loss leader, the costs are actually quite minimal since it's only 1 API request/week, and some % will convert and already have. Doing free was worth it to give people a chance to try it. I hope this helps with your next launch!!!

Introducing Stratify: Your Ultimate AI Strategy Builder for Business Success

Hello, I’m thrilled to announce the launch of my new startup, Stratify! 🔍 What is Stratify? Stratify is an AI Strategy Builder designed to help businesses of all sizes develop, implement, and optimize their strategic plans using cutting-edge artificial intelligence. Whether you're a startup looking to scale or an established company aiming to innovate, Stratify provides the tools and insights you need to stay ahead in today's competitive landscape. 🌟 Key Features: Automated Strategy Development: Leverage AI to analyze market trends, competitor data, and internal metrics to create comprehensive strategic plans tailored to your business goals. Real-Time Analytics & Insights: Monitor your strategy's performance with real-time data dashboards, enabling you to make informed decisions quickly. Scenario Planning: Use AI-driven simulations to forecast different business scenarios and understand potential outcomes, helping you prepare for uncertainties. Collaborative Tools: Facilitate team collaboration with integrated communication features, ensuring everyone is aligned and contributing to the strategy development process. Customizable Templates: Access a library of industry-specific strategy templates that can be customized to fit your unique business needs. 💡 Why Stratify? In today's fast-paced business environment, creating and adapting effective strategies can be challenging. Many companies struggle with data overload, lack of actionable insights, and inefficient planning processes. Stratify addresses these pain points by harnessing the power of AI to streamline strategy building, making it more efficient, data-driven, and adaptable. 🚀 Our Journey So Far: Founded: August 2024 Milestones Achieved: Developed and tested our MVP with a select group of beta users What's Next: Launching our public beta in Q4 2024 Expanding our feature set based on user feedback Growing our team with experts in AI, business strategy, and customer success 🤝 How You Can Help: We’re eager to connect with early adopters, business strategists, and industry experts who can benefit from or contribute to Stratify. Here’s how you can get involved: Join Our Beta Program: Be among the first to experience Stratify and provide valuable feedback. Share Your Insights: Help us refine our features by sharing your business strategy challenges and needs. Spread the Word: If you know someone who could benefit from an AI-driven strategy builder, please share our mission and be an affiliate to earn rewards! 🌐 Learn More: Visit our website at AI-Powered Brand Strategy & Content Creation | Stratify (brandprovoke.com) and follow us for the latest updates and insights. 🙏 Thank You! A heartfelt thank you to the Reddit community for your support and encouragement. We’re excited to embark on this journey and look forward to your feedback and suggestions! Looking forward to your thoughts and questions!

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.

ChatPDF and PDF.ai are making millions using open source tech... here's the code

Why "copy" an existing product? The best SaaS products weren’t the first of their kind - think Slack, Shopify, Zoom, Dropbox, or HubSpot. They didn’t invent team communication, e-commerce, video conferencing, cloud storage, or marketing tools; they just made them better. What is a "Chat with PDF" SaaS? These are AI-powered PDF assistants that let you upload a PDF and ask questions about its content. You can summarize articles, extract key details from a contract, analyze a research paper, and more. To see this in action or dive deeper into the tech behind it, check out this YouTube video. Let's look at the market Made possible by advances in AI like ChatGPT and Retrieval-Augmented Generation (RAG), PDF chat tools started gaining traction in early 2023 and have seen consistent growth in market interest, which is currently at an all-time high (source:google trends) Keywords like "chat PDF" and "PDF AI" get between 1 to 10 million searches every month (source:keyword planner), with a broad target audience that includes researchers, students, and professionals across various industries. Leaders like PDF.ai and ChatPDF have already gained millions of users within a year of launch, driven by the growing market demand, with paid users subscribing at around $20/month. Alright, so how do we build this with open source? The core tech for most PDF AI tools are based on the same architecture. You generate text embeddings (AI-friendly text representations; usually via OpenAI APIs) for the uploaded PDF’s chapters/topics and store them in a vector database (like Pinecone). Now, every time the user asks a question, a similarity search is performed to find the most similar PDF topics from the vector database. The selected topic contents are then sent to an LLM (like ChatGPT) along with the question, which generates a contextual answer! Here are some of the best open source implementations for this process: GPT4 & LangChain Chatbot for large PDF docs by Mayo Oshin MultiPDF Chat App by Alejandro AO PDFToChat by Hassan El Mghari Worried about building signups, user management, payments, etc.? Here are my go-to open-source SaaS boilerplates that include everything you need out of the box: SaaS Boilerplate by Remi Wg Open SaaS by wasp-lang A few ideas to stand out from the noise: Here are a few strategies that could help you differentiate and achieve product market fit (based on the pivot principles from The Lean Startup by Eric Ries): Narrow down your target audience for a personalized UX: For instance, an exam prep assistant for students with study notes and quiz generator; or a document due diligence and analysis tool for lawyers. Add unique features to increase switching cost: You could autogenerate APIs for the uploaded PDFs to enable remote integrations (eg. support chatbot knowledge base); or build in workflow automation features for bulk analyses of PDFs. Offer platform level advantages: You could ship a native mobile/desktop apps for a more integrated UX; or (non-trivial) offer private/offline support by replacing the APIs with local open source deployments (eg. llama for LLM, an embedding model from the MTEB list, and FAISS for vector search). TMI? I’m an ex-AI engineer and product lead, so don’t hesitate to reach out with any questions! P.S. I've started a free weekly newsletter to share open-source/turnkey resources behind popular products (like this one). If you’re a founder looking to launch your next product without reinventing the wheel, please subscribe :)

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.

Launch your landing page and marketing website faster even if you aren't a designer

As a frontend engineer  who is also good with UI/UX design,  aside from building dashboard and web apps all the time, one other thing I get to build is a landing page or marketing website to sell the product we are building. And just like dashboards, there are lots of repeatable sections in marketing website, just the uniqueness of designs. I find myself always building marketing websites from scratch always, which led me to building astrolandingpage.com , with my years of experience designing and building landing pages, I thought, this is good for me to do. to help me Save time and cut cost Launch early and validate my idea faster Launch beautiful landing page even if you are not a designer With copies that helps you converts, as each templates comes with examples copies and how to write them Although, I’ve been scared to launch this, because I think people might judge me, I probably wont make any sale, all because of how AI is turning out to be. With astrolandingpage, either you are a developer, a non technical person, a freelancer, an agency, a startup founder, you can launch beautiful and modern designs full fledge template in 20 minutes You get to pick from lots of templates and also UI components that you can copy and paste without the need to redesign  I believe this would be of great value to you and also I want to have fun building this. check it our here Astrolandingpage wesbite

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?

How I Built a $6k/mo Business with Cold Email

I scaled my SaaS to a $6k/mo business in under 6 months completely using cold email. However, the biggest takeaway for me is not a business that’s potentially worth 6-figure. It’s having a glance at the power of cold emails in the age of AI. It’s a rapidly evolving yet highly-effective channel, but no one talks about how to do it properly. Below is the what I needed 3 years ago, when I was stuck with 40 free users on my first app. An app I spent 2 years building into the void. Entrepreneurship is lonely. Especially when you are just starting out. Launching a startup feel like shouting into the dark. You pour your heart out. You think you have the next big idea, but no one cares. You write tweets, write blogs, build features, add tests. You talk to some lukewarm leads on Twitter. You do your big launch on Product Hunt. You might even get your first few sales. But after that, crickets... Then, you try every distribution channel out there. SEO Influencers Facebook ads Affiliates Newsletters Social media PPC Tiktok Press releases The reality is, none of them are that effective for early-stage startups. Because, let's face it, when you're just getting started, you have no clue what your customers truly desire. Without understanding their needs, you cannot create a product that resonates with them. It's as simple as that. So what’s the best distribution channel when you are doing a cold start? Cold emails. I know what you're thinking, but give me 10 seconds to change your mind: When I first heard about cold emailing I was like: “Hell no! I’m a developer, ain’t no way I’m talking to strangers.” That all changed on Jan 1st 2024, when I actually started sending cold emails to grow. Over the period of 6 months, I got over 1,700 users to sign up for my SaaS and grew it to a $6k/mo rapidly growing business. All from cold emails. Mastering Cold Emails = Your Superpower I might not recommend cold emails 3 years ago, but in 2024, I'd go all in with it. It used to be an expensive marketing channel bootstrapped startups can’t afford. You need to hire many assistants, build a list, research the leads, find emails, manage the mailboxes, email the leads, reply to emails, do meetings. follow up, get rejected... You had to hire at least 5 people just to get the ball rolling. The problem? Managing people sucks, and it doesn’t scale. That all changed with AI. Today, GPT-4 outperforms most human assistants. You can build an army of intelligent agents to help you complete tasks that’d previously be impossible without human input. Things that’d take a team of 10 assistants a week can now be done in 30 minutes with AI, at far superior quality with less headaches. You can throw 5000 names with website url at this pipeline and you’ll automatically have 5000 personalized emails ready to fire in 30 minutes. How amazing is that? Beyond being extremely accessible to developers who are already proficient in AI, cold email's got 3 superpowers that no other distribution channels can offer. Superpower 1/3 : You start a conversation with every single user. Every. Single. User. Let that sink in. This is incredibly powerful in the early stages, as it helps you establish rapport, bounce ideas off one another, offer 1:1 support, understand their needs, build personal relationships, and ultimately convert users into long-term fans of your product. From talking to 1000 users at the early stage, I had 20 users asking me to get on a call every week. If they are ready to buy, I do a sales call. If they are not sure, I do a user research call. At one point I even had to limit the number of calls I took to avoid burnout. The depth of the understanding of my customers’ needs is unparalleled. Using this insight, I refined the product to precisely cater to their requirements. Superpower 2/3 : You choose exactly who you talk to Unlike other distribution channels where you at best pick what someone's searching for, with cold emails, you have 100% control over who you talk to. Their company Job title Seniority level Number of employees Technology stack Growth rate Funding stage Product offerings Competitive landscape Social activity (Marital status - well, technically you can, but maybe not this one…) You can dial in this targeting to match your ICP exactly. The result is super low CAC and ultra high conversion rate. For example, My competitors are paying $10 per click for the keyword "HARO agency". I pay $0.19 per email sent, and $1.92 per signup At around $500 LTV, you can see how the first means a non-viable business. And the second means a cash-generating engine. Superpower 3/3 : Complete stealth mode Unlike other channels where competitors can easily reverse engineer or even abuse your marketing strategies, cold email operates in complete stealth mode. Every aspect is concealed from end to end: Your target audience Lead generation methods Number of leads targeted Email content Sales funnel This secrecy explains why there isn't much discussion about it online. Everyone is too focused on keeping their strategies close and reaping the rewards. That's precisely why I've chosen to share my insights on leveraging cold email to grow a successful SaaS business. More founders need to harness this channel to its fullest potential. In addition, I've more or less reached every user within my Total Addressable Market (TAM). So, if any competitor is reading this, don't bother trying to replicate it. The majority of potential users for this AI product are already onboard. To recap, the three superpowers of cold emails: You start a conversation with every single user → Accelerate to PMF You choose exactly who you talk to → Super-low CAC Complete stealth mode → Doesn’t attract competition By combining the three superpowers I helped my SaaS reach product-marketing-fit quickly and scale it to $6k per month while staying fully bootstrapped. I don't believe this was a coincidence. It's a replicable strategy for any startup. The blueprint is actually straightforward: Engage with a handful of customers Validate the idea Engage with numerous customers Scale to $5k/mo and beyond More early-stage founders should leverage cold emails for validation, and as their first distribution channel. And what would it do for you? Update: lots of DM asking about more specifics so I wrote about it here. https://coldstartblueprint.com/p/ai-agent-email-list-building

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

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.

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

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

We've built an AI-powered business building platform, and we're looking for entrepreneurs to try out the MVP!

Hey r/sideproject! I'm Felix, co-founder of Buildpad, and we're excited to share our latest project with you. https://reddit.com/link/1eve8n4/video/ahktfda2bgjd1/player Buildpad is an AI-powered (Claude Sonnet 3.5) business-building platform that guides entrepreneurs through every step of creating and growing a business. Here's what makes it unique: Idea validation: Leverage Reddit's API to get real-world data on your ideas through posts, comments and discussions. Structured process: Follow a clear roadmap from idea validation to launch and beyond. Team collaboration: Work with co-founders, all assisted by the same AI. Central context bank: Our AI remembers everything about your project for consistent, informed guidance. We're solving the common problem of entrepreneurs not knowing what to do next, especially during idea generation and validation phases. With Buildpad, you can validate your ideas by searching for relevant keywords across Reddit, helping you understand if people are actually experiencing the problems you're aiming to solve. We're in the MVP stage and looking for early adopters to test the platform and provide feedback. We'd love to hear from you: Does this solution resonate with your entrepreneurial challenges? What features would you find most valuable in a tool like this? Any thoughts or concerns about using AI for startup guidance? If you're interested in trying out Buildpad or have any questions, please comment below or DM me. Thanks for checking it out! buildpad.io

How to get your first 10 customers with cold email

Cold email is an insane channel for growth, especially for bootstrapped startups as it's very low cost but completely scalable. Yet there's a huge difference between blind cold emailing and crafting personalized outreach for select individuals. The latter is a legit channel which makes many businesses scale in short amounts of time (i.e. see Alex Hormozi’s ‘$100 Million Dollar Offer’). My goal here is to help other founders do what I did but quicker. So you can learn faster. And then teach me something new too. These are the step-by-step lessons I've learnt as a bootstrapped founder, showing you how to use cold email to get your first customers: Find your leads Write engaging email copy Personalize your outreach Send emails Scale up Find your leads This is a key step. Once you figure out exactly who you want to target and where to find them, you'll be printing money. There's a few different ways to go about finding valuable leads. The secret? Keep testing different approaches until you strike gold. First, dedicate some time every day to find and organise leads. Then, keep an eye on your numbers and bounce rates. If something's not working, switch it up. Stick with what's bringing in results and ditch what's not. It's all about staying flexible and learning as you go. Apollo.io is a great starting point as an effective lead source. Their tool allows you to specify filters including job titles, location, company size, industry, keywords, technologies, and revenue. Get specific with your searches to find your ideal customers. Once you have some results you can save and export them, you'll get a list of contact information including name, email, company, LinkedIn, ready to be verified and used. LinkedIn Sales Navigator is another good source. You can either do manual searches or use a scraper to automate the process. The scrapers I'd recommend checking out are FindyMail and Evaboot. As with Apollo, it's best to get very specific with your targeting so you know the prospect will be interested in your offer. BuiltWith is more expensive but ideal if you're targeting competitors. With BuiltWith you can build lists based on what technologies companies are using. For example if you're selling a Shopify app, you'd want to know websites or stores using Shopify, and reach out to them. The best lead sources will always be those that haven't been contacted a lot in the past. If you are able to find places where your target audience uniquely hangs out, and you can get their company website domains, they have the potential to be scrapped, and you have a way to personalize like "I spotted your comment on XYZ website". Once you've got your leads, keep them organized. Set up folders for different niches, countries, company sizes, so you can review what works and what doesn't. One more thing – before you start firing off emails, make sure those addresses are verified. Always use an email verifier to clean up your list and avoid bounces that may affect your sending reputation, and land you in the spam folder. I use Neverbounce for this but there are other tools available. Write engaging email copy Writing a good copy that gets replies is difficult, it changes depending on your offer/audience and nobody knows what's going to work. The best approach is to keep testing different targeting and messaging until you find what works. However, there are some key rules to stick to that I've outlined. For the subject line, keep it short and personalized. Try to write something that sparks interest, and mention the recipients name: Thought you’d like this {{first name}} {{firstName}} - quick question For the email body it's best to use a framework of personalization, offer, then call to action. Personalization is an entire subject in its own right, which I've covered below. In short, a personalized email opener is the best way to grab their attention, and let them know the email is relevant to them and to keep reading. Take it from Alex Hormozi and his $100M Offers playbook – your offer is very important to get right. Make sure your offer hits the mark for your target audience, and get as specific as possible. For example: I built a SaaS shopify app for small ecommerce businesses selling apparel that doubles your revenue in 60-days or your money back. We developed a cold email personalization tool for lead generation agencies that saves hundreds of hours, and can 3x your reply rate. Lastly, the CTA. The goal here isn't to get sign-ups directly from your first email. It's better to ask a brief question about whether the prospect would be interested in learning more. Something very low friction, that warrants a response. Some examples might include: Would you be interested in learning more about this? Can we connect a bit more on this? Mind if I send over a loom I recorded for you? Never send any links in the first email. You've reached out to this person because you have good reason to believe they'd find real value in your offer, and you want to verify if that's the case. After you get one reply, this is a great positive signal and from there you can send a link, book a call, provide a free resource, whatever makes sense based on their response. Personalize your outreach Personalization is one of the most important parts of the process to get right. Your recipient probably receives a multitude of emails every day, how can you make yours stand out, letting them know you've done your research, and that your email is relevant to them? Personalizing each email ensures you get more positive replies, and avoid spam filters, as your email is unique and hasn't been copied and pasted a million times over. The goal is to spark the recipient's interest, and let them know that you're contacting them for good reason. You might mention a recent achievement, blog post or product release that led you to reach out to the prospect specifically. For example: Your post on "Doing Nothing" gave me a good chuckle. Savvy marketing on Cadbury's part. Saw that you've been at Google for just under a year now as a new VP of sales. Spotted that you've got over 7 years of experience in the digital marketing space. Ideally you'll mention something specifically about the prospect or their company that relates to your offer. The downside to personalization is that it's hard to get right, and very time consuming at scale, but totally worth it. Full disclosure, me and my partner Igor just launched our new startup ColdClicks which uses AI to generate hyper-personalized email openers at scale. We built the tool as we were sending hundreds of emails a day, and personalizing every individual email took hours out of our day. ColdClicks automates this process, saving you time and getting you 2-3x more replies. Send emails At this stage you've decided on who you're targeting, you've mined some leads, and written copy. Now it's time to get sending. You can do this manually by copy and pasting each message, but one of the reasons cold email is so powerful is that it's scalable. When you build a process that gets customers, you'll want to send as many emails as you can to your target market. To get started quickly, you can use a mail-merge gmail tool, the best I've used is Maileteor. With Maileteor you upload your lead data to Google sheets, set-up an email template and Mailmetor will send out emails every day automatically. In your template you can define variables including name, company, and personalization to ensure your email is unique for each recipient. Alternatively, you may opt for a more comprehensive tool such as Instantly. Instantly includes unlimited email sending and accounts. There's more initial setup involved as you'll need to set-up Google workspace, buy sending domains, and warm up your email accounts, but when you become familiar with the process you can build a powerful lead generation / customer acquisition machine. Some key points to note, it's very important to warm up any new email accounts you set up. Warmup is the process of gradually establishing a positive reputation with email service providers like Gmail or Yahoo. Make sure to set up DKIM and DMARC on those new email accounts too, to maximise your chances of landing in the inbox. Scale up Once you've found a process that works, good things happen, and it becomes a numbers game. As you get replies and start to see new users signing up, you'll want to scale the process and send more emails. It's straightforward to add new sending accounts in a sending tool like Instantly, and you'll want to broaden your targeting when mining to test new markets. Unfortunately, sending more emails usually comes with a drop in reply rate as you have less time to personalize your messaging for each recipient. This is where ColdClicks shines. The tool allows you to upload thousands of leads and generate perfectly relevant email personalizations for every lead in your list, then export to your favorite sending tool. The examples I listed above in the personalization section were all generated by ColdClicks. Wrapping it up Cold email is an amazing way to validate your product and get new customers. The channel gets a bad rap, but there's a huge difference between blind cold emailing and crafting personalized outreach for individuals who will find value in your product. It's perfect for bootstrapped founders due to its affordability and scalability, and it's the driver of growth for many SaaS businesses. Time to get your first 10 customers! As you start sending, make it a habit to regularly check for new leads. Always experiment with market/messaging, track every campaign so you can learn what's working and iterate, and when you do get positive responses, reply as soon as you can!

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.

100 agency business ideas that requires zero investment in 2025

Social Media Manager (Search "smma business for sale" and get started with Sitefy or humanpdesign company) Content Writing Service SEO Specialist (Search "outsourced seo business Sitefy" and get started) Instagram Growth Expert LinkedIn Lead Outreach Cold Email Specialist Ghostwriter Services YouTube Channel Assistant X/Twitter Writing Service UGC Creator Network Virtual Assistant Provider Podcast Manager Influencer Outreach Service Brand Strategy Consultant Resume & Profile Optimizer Media Outreach Services Newsletter Creation Team Lead Gen Specialist Personal Branding Partner Online Reputation Consultant Script Writer Team Funnel Strategy Helper Landing Page Writer Blog Strategy Consultant Growth Tactics Consultant SaaS User Onboarding Service App Store Optimization (ASO) Chatbot Automation Setup Customer Support Automator TikTok Strategy Specialist Short Video Editing Team Long Video Editing Team Digital Product Launch Help Webinar Setup Specialist Affiliate Setup Services Online Community Builder Facebook Group Specialist Pinterest Marketing Service (Search "digital marketing business for sale Sitefy" and get started quickly) Email List Builder CRM Setup Helper eBook Writing Partner YouTube Thumbnail Designer Pinterest Content Designer Proposal Writing Team Press Release Writer Influencer Deal Manager SaaS Growth Advisor Direct-To-Consumer Marketing Help App Review Booster Fiverr Profile Consultant Upwork Profile Expert Gig Profile Optimizer AI Tools Setup Help Freelance Talent Finder Local Search Optimizer Google Profile Optimizer Online Course Setup Notion Workflow Setup Airtable Consultant Trello/ClickUp Helper Automation Strategy Planner Sales Funnel Assistant WhatsApp Campaign Setup Telegram Channel Helper Blog Publishing Help Email-Based Content Creator Startup Deck Consultant Business Name & Tagline Creator Domain Research Help Reddit Growth Assistant Niche Community Builder Free Resource Strategy Website Audit Consultant Brand Guide Creator Business System Organizer Productivity Coach Reputation Fix Specialist Digital Product Reviewer Micro SaaS Idea Tester Ad Copywriter Email Strategy Consultant Influencer Research Team Onboarding Docs Creator Automation Setup Specialist Freelance Team Coordinator AI Marketing Planner Feedback Collection Setup Social Proof Strategist Giveaway Organizer Pricing Strategy Helper Contract Template Consultant Startup Growth Guide AI Prompt Writer Press Kit Creator Podcast Booking Assistant Inbox Performance Checker Customer Journey Planner Trend Report Analyst Testimonial Request Specialist Digital Declutter Coach Which one sounds like your vibe?

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

I built an AI social monitoring that looks for relevant posts, not just keywords

Hey everyone! I've been working on a side project that I'm excited to share with you all—it's called BillyBuzz What is BillyBuzz? BillyBuzz is an AI-powered social monitoring tool that helps businesses spot and analyze relevant conversations on social media platforms, starting with Reddit. It surfaces the most promising leads directly to your Slack channels, email, or Discord, so you don't have to spend hours scrolling through threads. Why I Built It I was spending a ton of time searching for relevant posts in niche subreddits for another product I was working to get off the ground. It was not only time-consuming but also distracting (you know how easy it is to fall into a Reddit rabbit hole). I couldn't find any existing tool that did more than basic keyword searches—which wasn't enough, especially if your brand name has multiple meanings (like "Apple"). So, I decided to build BillyBuzz. It uses AI to understand your business, products, target audience, and value proposition, alongside specific keywords you might want to include. This way, it finds posts where you can genuinely contribute by introducing your product. I used BillyBuzz for a previous product launch and managed to grow it to over $80k/month in volume within about 3 months, purely through Reddit engagement. How It Works Add Information About Your Business: Input details about your business and products. Select Subreddits to Monitor: Choose the subreddits relevant to your niche. Receive Timely Alerts: Get notified via Slack, email, or Discord when relevant posts are identified. Features AI-Powered Relevancy Scoring: Goes beyond keywords by understanding the context to identify truly relevant opportunities. Subreddit Tracking: Monitor specific subreddits with AI-recommended keywords tailored to your company's needs. Real-Time Alerts: Checks for new relevant conversations every 15 minutes, so you can engage at the perfect time. Automated Categorization (Coming Soon): The AI will categorize conversations into topics like competitors, customer complaints, and more. Who It's For BillyBuzz is designed for startup founders, growth marketers, and small business owners who are tech-savvy and focused on scaling their operations. If you're looking to save time and engage more effectively with your target audience on social media, this might be up your alley. Looking for Feedback I'm sharing this here because I'd love to get your thoughts, feedback, or any suggestions you might have. If you're interested in checking it out, you can find more info here: https://billybuzz.com. Feel free to ask me anything or share your experiences with similar challenges!

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

Introducing Stratify: Your Ultimate AI Strategy Builder for Business Success

Hello, I’m thrilled to announce the launch of my new startup, Stratify! 🔍 What is Stratify? Stratify is an AI Strategy Builder designed to help businesses of all sizes develop, implement, and optimize their strategic plans using cutting-edge artificial intelligence. Whether you're a startup looking to scale or an established company aiming to innovate, Stratify provides the tools and insights you need to stay ahead in today's competitive landscape. 🌟 Key Features: Automated Strategy Development: Leverage AI to analyze market trends, competitor data, and internal metrics to create comprehensive strategic plans tailored to your business goals. Real-Time Analytics & Insights: Monitor your strategy's performance with real-time data dashboards, enabling you to make informed decisions quickly. Scenario Planning: Use AI-driven simulations to forecast different business scenarios and understand potential outcomes, helping you prepare for uncertainties. Collaborative Tools: Facilitate team collaboration with integrated communication features, ensuring everyone is aligned and contributing to the strategy development process. Customizable Templates: Access a library of industry-specific strategy templates that can be customized to fit your unique business needs. 💡 Why Stratify? In today's fast-paced business environment, creating and adapting effective strategies can be challenging. Many companies struggle with data overload, lack of actionable insights, and inefficient planning processes. Stratify addresses these pain points by harnessing the power of AI to streamline strategy building, making it more efficient, data-driven, and adaptable. 🚀 Our Journey So Far: Founded: August 2024 Milestones Achieved: Developed and tested our MVP with a select group of beta users What's Next: Launching our public beta in Q4 2024 Expanding our feature set based on user feedback Growing our team with experts in AI, business strategy, and customer success 🤝 How You Can Help: We’re eager to connect with early adopters, business strategists, and industry experts who can benefit from or contribute to Stratify. Here’s how you can get involved: Join Our Beta Program: Be among the first to experience Stratify and provide valuable feedback. Share Your Insights: Help us refine our features by sharing your business strategy challenges and needs. Spread the Word: If you know someone who could benefit from an AI-driven strategy builder, please share our mission and be an affiliate to earn rewards! 🌐 Learn More: Visit our website at AI-Powered Brand Strategy & Content Creation | Stratify (brandprovoke.com) and follow us for the latest updates and insights. 🙏 Thank You! A heartfelt thank you to the Reddit community for your support and encouragement. We’re excited to embark on this journey and look forward to your feedback and suggestions! Looking forward to your thoughts and questions!

Where Do You Save Your Business Ideas? I made an AI-powered notepad that does all the research for your business ideas

Coming up with business ideas is fun… until you realize how much research you need to do for each one—market size, competitors, target audience… it’s a lot. So I built AIdea—a notepad where you write down your idea, and the AI does the research for you. Drop in a rough concept, and it comes back with: Pitch deck: Value Prop, pain points, gain points, unique selling points ICP: Demographic info and how to reach them Branding & names: How to brand the idea and various kinds of names for the idea Execution strategies: How to validate the idea, how to build MVP (recommended tech stack), funding route to take etc Monetization strategies: pros and cons for various ways to make money from this idea Market analysis: TAM, SAM, SOM + Market trends related to the idea (with sources) Competitor analysis: competitors description, strengths, weaknesses (with sources) User sentiments: Summaries of reddit posts, quora questions, articles and key points about what people think about concepts related to your ideas (with sources) Here’s the landing page: https://aidea-app.framer.website/ App store link: https://apps.apple.com/us/app/aidea-idea-to-startup/id6737747359 It’s still early, so I’d love to hear what you think. Would this save you time? What else would you want it to do? Appreciate any feedback! 🚀

Made $3.5k Automating Social Media Posts with AI

"Marketers & creators were spending hours crafting LinkedIn posts & X threads. Built an AI tool that automates the process—here’s how." Backstory A growing startup was struggling to maintain a consistent LinkedIn & X presence. Their team wasted hours every week: Manually drafting posts from raw ideas and reports Figuring out platform-specific formats (hooks, CTAs, structure) Scheduling posts across multiple accounts What I Built in 48 Hours ✅ AI-Powered Post Generator → Open-source LLM (Mistral) formats ideas into optimized LinkedIn/X posts ✅ Engagement Booster → Custom NLP ensures every post follows best practices (hooks, CTA, readability) ✅ Automated Scheduling → FastAPI + React dashboard lets users auto-post across platforms Tech Stack Content Processing: Open-source LLMs (Mistral, Phi-3) + Custom NLP Data Handling: FastAPI backend + PostgreSQL Frontend: React + Tailwind CSS Automation: CRON + Third-party APIs (LinkedIn, X) Results 💡 10x faster content creation (2 hours → 5 minutes per post) 💡 Increased engagement by 3x with AI-optimized copy 💡 $1.5k payout + ongoing $300/month maintenance 💬 "This tool writes better LinkedIn posts than I do—on autopilot!" Biggest Lesson "Most creators don’t lack ideas—they lack execution speed. Simple AI workflows + automation solve 90% of the problem." PSA to Developers Look for boring, repetitive tasks in niche domains like: Personal branding automation Sales outreach personalization E-commerce product descriptions A weekend project could turn into a $5k/month SaaS. What’s the most time-consuming task you’ve automated with AI? 🚀

[Discussion] When ML and Data Science are the death of a good company: A cautionary tale.

TD;LR: At Company A, Team X does advanced analytics using on-prem ERP tools and older programming languages. Their tools work very well and are designed based on very deep business and domain expertise. Team Y is a new and ambitious Data Science team that thinks they can replace Team X's tools with a bunch of R scripts and a custom built ML platform. Their models are simplistic, but more "fashionable" compared to the econometric models used by Team X, and team Y benefits from the ML/DS moniker so leadership is allowing Team Y to start a large scale overhaul of the analytics platform in question. Team Y doesn't have the experience for such a larger scale transformation, and is refusing to collaborate with team X. This project is very likely going to fail, and cause serious harm to the company as a whole financially and from a people perspective. I argue that this is not just because of bad leadership, but also because of various trends and mindsets in the DS community at large. Update (Jump to below the line for the original story): Several people in the comments are pointing out that this just a management failure, not something due to ML/DS, and that you can replace DS with any buzz tech and the story will still be relevant. My response: Of course, any failure at an organization level is ultimately a management failure one way or the other. Moreover, it is also the case that ML/DS when done correctly, will always improve a company's bottom line. There is no scenario where the proper ML solution, delivered at a reasonable cost and in a timely fashion, will somehow hurt the company's bottom line. My point is that in this case management is failing because of certain trends and practices that are specific to the ML/DS community, namely: The idea that DS teams should operate independently of tech and business orgs -- too much autonomy for DS teams The disregard for domain knowledge that seems prevalent nowadays thanks to the ML hype, that DS can be generalists and someone with good enough ML chops can solve any business problem. That wasn't the case when I first left academia for the industry in 2009 (back then nobody would even bother with a phone screen if you didn't have the right domain knowledge). Over reliance on resources who check all the ML hype related boxes (knows Python, R, Tensorflow, Shiny, etc..., has the right Coursera certifications, has blogged on the topic, etc...), but are lacking in depth of experience. DS interviews nowadays all seem to be: Can you tell me what a p-value is? What is elastic net regression? Show me how to fit a model in sklearn? How do you impute NAs in an R dataframe? Any smart person can look those up on Stackoverflow or Cross-Validated,.....Instead teams should be asking stuff like: why does portfolio optimization use QP not LP? How does a forecast influence a customer service level? When should a recommendation engine be content based and when should it use collaborative filtering? etc... (This is a true story, happening to the company I currently work for. Names, domains, algorithms, and roles have been shuffled around to protect my anonymity)  Company A has been around for several decades. It is not the biggest name in its domain, but it is a well respected one. Risk analysis and portfolio optimization have been a core of Company A's business since the 90s. They have a large team of 30 or so analysts who perform those tasks on a daily basis. These analysts use ERP solutions implemented for them by one the big ERP companies (SAP, Teradata, Oracle, JD Edwards,...) or one of the major tech consulting companies (Deloitte, Accenture, PWC, Capgemini, etc...) in collaboration with their own in house engineering team. The tools used are embarrassingly old school: Classic RDBMS running on on-prem servers or maybe even on mainframes, code written in COBOL, Fortran, weird proprietary stuff like ABAP or SPSS.....you get the picture. But the models and analytic functions were pretty sophisticated, and surprisingly cutting edge compared to the published academic literature. Most of all, they fit well with the company's enterprise ecosystem, and were honed based on years of deep domain knowledge.  They have a tech team of several engineers (poached from the aforementioned software and consulting companies) and product managers (who came from the experienced pools of analysts and managers who use the software, or poached from business rivals) maintaining and running this software. Their technology might be old school, but collectively, they know the domain and the company's overall architecture very, very well. They've guided the company through several large scale upgrades and migrations and they have a track record of delivering on time, without too much overhead. The few times they've stumbled, they knew how to pick themselves up very quickly. In fact within their industry niche, they have a reputation for their expertise, and have very good relations with the various vendors they've had to deal with. They were the launching pad of several successful ERP consulting careers.  Interestingly, despite dealing on a daily basis with statistical modeling and optimization algorithms, none of the analysts, engineers, or product managers involved describe themselves as data scientists or machine learning experts. It is mostly a cultural thing: Their expertise predates the Data Science/ML hype that started circa 2010, and they got most of their chops using proprietary enterprise tools instead of the open source tools popular nowadays. A few of them have formal statistical training, but most of them came from engineering or domain backgrounds and learned stats on the fly while doing their job. Call this team "Team X".  Sometime around the mid 2010s, Company A started having some serious anxiety issues: Although still doing very well for a company its size, overall economic and demographic trends were shrinking its customer base, and a couple of so called disruptors came up with a new app and business model that started seriously eating into their revenue. A suitable reaction to appease shareholders and Wall Street was necessary. The company already had a decent website and a pretty snazzy app, what more could be done? Leadership decided that it was high time that AI and ML become a core part of the company's business. An ambitious Manager, with no science or engineering background, but who had very briefly toyed with a recommender system a couple of years back, was chosen to build a data science team, call it team "Y" (he had a bachelor's in history from the local state college and worked for several years in the company's marketing org). Team "Y" consists mostly of internal hires who decided they wanted to be data scientists and completed a Coursera certification or a Galvanize boot camp, before being brought on to the team, along with a few of fresh Ph.D or M.Sc holders who didn't like academia and wanted to try their hand at an industry role. All of them were very bright people, they could write great Medium blog posts and give inspiring TED talks, but collectively they had very little real world industry experience. As is the fashion nowadays, this group was made part of a data science org that reported directly to the CEO and Board, bypassing the CIO and any tech or business VPs, since Company A wanted to claim the monikers "data driven" and "AI powered" in their upcoming shareholder meetings. In 3 or 4 years of existence, team Y produced a few Python and R scripts. Their architectural experience  consisted almost entirely in connecting Flask to S3 buckets or Redshift tables, with a couple of the more resourceful ones learning how to plug their models into Tableau or how to spin up a Kuberneties pod.  But they needn't worry: The aforementioned manager, who was now a director (and was also doing an online Masters to make up for his qualifications gap and bolster his chances of becoming VP soon - at least he now understands what L1 regularization is), was a master at playing corporate politics and self-promotion. No matter how few actionable insights team Y produced or how little code they deployed to production, he always had their back and made sure they had ample funding. In fact he now had grandiose plans for setting up an all-purpose machine learning platform that can be used to solve all of the company's data problems.  A couple of sharp minded members of team Y, upon googling their industry name along with the word "data science", realized that risk analysis was a prime candidate for being solved with Bayesian models, and there was already a nifty R package for doing just that, whose tutorial they went through on R-Bloggers.com. One of them had even submitted a Bayesian classifier Kernel for a competition on Kaggle (he was 203rd on the leaderboard), and was eager to put his new-found expertise to use on a real world problem. They pitched the idea to their director, who saw a perfect use case for his upcoming ML platform. They started work on it immediately, without bothering to check whether anybody at Company A was already doing risk analysis. Since their org was independent, they didn't really need to check with anybody else before they got funding for their initiative. Although it was basically a Naive Bayes classifier, the term ML was added to the project tile, to impress the board.  As they progressed with their work however, tensions started to build. They had asked the data warehousing and CA analytics teams to build pipelines for them, and word eventually got out to team X about their project. Team X was initially thrilled: They offered to collaborate whole heartedly, and would have loved to add an ML based feather to their already impressive cap. The product owners and analysts were totally onboard as well: They saw a chance to get in on the whole Data Science hype that they kept hearing about. But through some weird mix of arrogance and insecurity, team Y refused to collaborate with them or share any of their long term goals with them, even as they went to other parts of the company giving brown bag presentations and tutorials on the new model they created.  Team X got resentful: from what they saw of team Y's model, their approach was hopelessly naive and had little chances of scaling or being sustainable in production, and they knew exactly how to help with that. Deploying the model to production would have taken them a few days, given how comfortable they were with DevOps and continuous delivery (team Y had taken several months to figure out how to deploy a simple R script to production). And despite how old school their own tech was, team X were crafty enough to be able to plug it in to their existing architecture. Moreover, the output of the model was such that it didn't take into account how the business will consume it or how it was going to be fed to downstream systems, and the product owners could have gone a long way in making the model more amenable to adoption by the business stakeholders. But team Y wouldn't listen, and their leads brushed off any attempts at communication, let alone collaboration. The vibe that team Y was giving off was "We are the cutting edge ML team, you guys are the legacy server grunts. We don't need your opinion.", and they seemed to have a complete disregard for domain knowledge, or worse, they thought that all that domain knowledge consisted of was being able to grasp the definitions of a few business metrics.  Team X got frustrated and tried to express their concerns to leadership. But despite owning a vital link in Company A's business process, they were only \~50 people in a large 1000 strong technology and operations org, and they were several layers removed from the C-suite, so it was impossible for them to get their voices heard.  Meanwhile, the unstoppable director was doing what he did best: Playing corporate politics. Despite how little his team had actually delivered, he had convinced the board that all analysis and optimization tasks should now be migrated to his yet to be delivered ML platform. Since most leaders now knew that there was overlap between team Y and team X's objectives, his pitch was no longer that team Y was going to create a new insight, but that they were going to replace (or modernize) the legacy statistics based on-prem tools with more accurate cloud based ML tools. Never mind that there was no support in the academic literature for the idea that Naive Bayes works better than the Econometric approaches used by team X, let alone the additional wacky idea that Bayesian Optimization would definitely outperform the QP solvers that were running in production.  Unbeknownst to team X, the original Bayesian risk analysis project has now grown into a multimillion dollar major overhaul initiative, which included the eventual replacement of all of the tools and functions supported by team X along with the necessary migration to the cloud. The CIO and a couple of business VPs are on now board, and tech leadership is treating it as a done deal. An outside vendor, a startup who nobody had heard of, was contracted to help build the platform, since team Y has no engineering skills. The choice was deliberate, as calling on any of the established consulting or software companies would have eventually led leadership to the conclusion that team X was better suited for a transformation on this scale than team Y.  Team Y has no experience with any major ERP deployments, and no domain knowledge, yet they are being tasked with fundamentally changing the business process that is at the core of Company A's business. Their models actually perform worse than those deployed by team X, and their architecture is hopelessly simplistic, compared to what is necessary for running such a solution in production.  Ironically, using Bayesian thinking and based on all the evidence, the likelihood that team Y succeeds is close to 0%. At best, the project is going to end up being a write off of 50 million dollars or more. Once the !@#$!@hits the fan, a couple of executive heads are going to role, and dozens of people will get laid off. At worst, given how vital risk analysis and portfolio optimization is to Company A's revenue stream, the failure will eventually sink the whole company. It probably won't go bankrupt, but it will lose a significant portion of its business and work force. Failed ERP implementations can and do sink large companies: Just see what happened to National Grid US, SuperValu or Target Canada.  One might argue that this is more about corporate disfunction and bad leadership than about data science and AI. But I disagree. I think the core driver of this debacle is indeed the blind faith in Data Scientists, ML models and the promise of AI, and the overall culture of hype and self promotion that is very common among the ML crowd.  We haven't seen the end of this story: I sincerely hope that this ends well for the sake of my colleagues and all involved. Company A is a good company, and both its customers and its employees deserver better. But the chances of that happening are negligible given all the information available, and this failure will hit my company hard.

[D] The Rants of an experienced engineer who glimpsed into AI Academia (Briefly)

Background I recently graduated with a master's degree and was fortunate/unfortunate to glimpse the whole "Academic" side of ML. I took a thesis track in my degree because as an immigrant it's harder to get into a good research lab without having authorship in a couple of good papers (Or so I delude myself ). I worked as a Full-stack SWE for a startup for 4+ years before coming to the US for a master’s degree focused on ML and AI. I did everything in those years. From project management to building fully polished S/W products to DevOps to even dabbled in ML. I did my Batchelor’s degree from a university whose name is not even worth mentioning. The university for my master’s degree is in the top 20 in the AI space. I didn't know much about ML and the curiosity drove me to university. Come to uni and I focused on learning ML and AI for one 1-1.5 years after which I found advisors for a thesis topic. This is when the fun starts. I had the most amazing advisors but the entire peer review system and the way we assess ML/Science is what ticked me off. This is where the rant begins. Rant 1:Acadmia follows a Gated Institutional Narrative Let's say you are a Ph.D. at the world's top AI institution working under the best prof. You have a way higher likelihood of you getting a good Postdoc at a huge research lab vs someone's from my poor country doing a Ph.D. with a not-so-well-known advisor having published not-so-well-known papers. I come from a developing nation and I see this many times here. In my country academics don't get funding as they do at colleges in the US. One of the reasons for this is that colleges don't have such huge endowments and many academics don't have wealthy research sponsors. Brand names and prestige carry massive weight to help get funding in US academic circles. This prestige/money percolates down to the students and the researchers who work there. Students in top colleges get a huge advantage and the circles of top researchers keep being from the same sets of institutions. I have nothing against top researchers from top institutions but due to the nature of citations and the way the money flows based on them, a vicious cycle is created where the best institutions keep getting better and the rest don't get as much of a notice. Rant 2: Peer Review without Code Review in ML/AI is shady I am a computer scientist and I was appalled when I heard that you don't need to do code reviews for research papers. As a computer scientist and someone who actually did shit tons of actual ML in the past year, I find it absolutely garbage that code reviews are not a part of this system. I am not saying every scientist who reads a paper should review code but at least one person should for any paper's code submission. At least in ML and AI space. This is basic. I don't get why people call themselves computer scientists if they don't want to read the fucking code. If you can't then make a grad student do it. But for the collective of science, we need this. The core problem lies in the fact that peer review is free. : There should be better solutions for this. We ended up creating Git and that changed so many lives. Academic Research needs something similar. Rant 3: My Idea is Novel Until I see Someone Else's Paper The volume of scientific research is growing exponentially. Information is being created faster than we can digest. We can't expect people to know everything and the amount of overlap in the AI/ML fields requires way better search engines than Google Scholar. The side effect of large volumes of research is that every paper is doing something "novel" making it harder to filter what the fuck was novel. I have had so many experiences where I coded up something and came to realize that someone else has done something symbolically similar and my work just seems like a small variant of that. That's what fucks with my head. Is what I did in Novel? What the fuck is Novel? Is stitching up a transformer to any problem with fancy embeddings and tidying it up as a research paper Novel? Is just making a transformer bigger Novel? Is some new RL algorithm tested with 5 seeds and some fancy fucking prior and some esoteric reasoning for its success Novel? Is using an over parameterized model to get 95% accuracy on 200 sample test set Novel? Is apply Self-supervised learning for some new dataset Novel? If I keep on listing questions on novelty, I can probably write a novel asking about what the fuck is "Novel". Rant 4: Citation Based Optimization Promotes Self Growth Over Collective Growth Whatever people may say about collaboration, Academia intrinsically doesn't promote the right incentive structures to harbor collaboration. Let me explain, When you write a paper, the position of your name matters. If you are just a Ph.D. student and a first author to a paper, it's great. If you are an nth author Not so great. Apparently, this is a very touchy thing for academics. And lots of egos can clash around numbering and ordering of names. I distinctly remember once attending some seminar in a lab and approaching a few students on research project ideas. The first thing that came out of the PhD student's mouth was the position in authorship. As an engineer who worked with teams in the past, this was never something I had thought about. Especially because I worked in industry, where it's always the group over the person. Academia is the reverse. Academia applauds the celebration of the individual's achievements. All of this is understandable but it's something I don't like. This makes PhDs stick to their lane. The way citations/research-focus calibrate the "hire-ability" and "completion of Ph.D. thesis" metrics, people are incentivized to think about themselves instead of thinking about collaborations for making something better. Conclusion A Ph.D. in its most idealistic sense for me is the pursuit of hard ideas(I am poetic that way). In a situation like now when you have to publish or perish and words on paper get passed off as science without even seeing the code that runs it, I am extremely discouraged to go down that route. All these rants are not to diss on scientists. I did them because "we" as a community need better ways to addressing some of these problems. P.S. Never expected so many people to express their opinions about this rant. U shouldn’t take this seriously. As many people have stated I am an outsider with tiny experience to give a full picture. I realize that my post as coming out as something which tries to dichotomize academia and industry. I am not trying to do that. I wanted to highlight some problems I saw for which there is no one person to blame. These issues are in my opinion a byproduct of the economics which created this system. Thank you for gold stranger.

[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/

[D] AI Agents: too early, too expensive, too unreliable

Reference: Full blog post There has been a lot of hype about the promise of autonomous agent-based LLM workflows. By now, all major LLMs are capable of interacting with external tools and functions, letting the LLM perform sequences of tasks automatically. But reality is proving more challenging than anticipated. The WebArena leaderboard, which benchmarks LLMs agents against real-world tasks, shows that even the best-performing models have a success rate of only 35.8%. Challenges in Practice After seeing many attempts to AI agents, I believe it's too early, too expensive, too slow, too unreliable. It feels like many AI agent startups are waiting for a model breakthrough that will start the race to productize agents. Reliability: As we all know, LLMs are prone to hallucinations and inconsistencies. Chaining multiple AI steps compounds these issues, especially for tasks requiring exact outputs. Performance and costs: GPT-4o, Gemini-1.5, and Claude Opus are working quite well with tool usage/function calling, but they are still slow and expensive, particularly if you need to do loops and automatic retries. Legal concerns: Companies may be held liable for the mistakes of their agents. A recent example is Air Canada being ordered to pay a customer who was misled by the airline's chatbot. User trust: The "black box" nature of AI agents and stories like the above makes it hard for users to understand and trust their outputs. Gaining user trust for sensitive tasks involving payments or personal information will be hard (paying bills, shopping, etc.). Real-World Attempts Several startups are tackling the AI agent space, but most are still experimental or invite-only: adept.ai - $350M funding, but access is still very limited MultiOn - funding unknown, their API-first approach seems promising HypeWrite - $2.8M funding, started with an AI writing assistant and expanded into the agent space minion.ai - created some initial buzz but has gone quiet now, waitlist only Only MultiOn seems to be pursuing the "give it instructions and watch it go" approach, which is more in line with the promise of AI agents. All others are going down the record-and-replay RPA route, which may be necessary for reliability at this stage. Large players are also bringing AI capabilities to desktops and browsers, and it looks like we'll get native AI integrations on a system level: OpenAI announced their Mac desktop app that can interact with the OS screen. At Google I/O, Google demonstrated Gemini automatically processing a shopping return. Microsoft announced Copilot Studio, which will let developers build AI agent bots. Screenshot Screenshot These tech demos are impressive, but we'll see how well these agent capabilities will work when released publicly and tested against real-world scenarios instead of hand-picked demo cases. The Path Forward AI agents overhyped and it's too early. However, the underlying models continue to advance quickly, and we can expect to see more successful real-world applications. Instead of trying to have one large general purpose agent that is hard to control and test, we can use many smaller agents that basically just pick the right strategy for a specific sub-task in our workflows. These "agents" can be thought of as medium-sized LLM prompts with a) context and b) a set of functions available to call. The most promising path forward likely looks like this: Narrowly scoped, well testable automations that use AI as an augmentation tool rather than pursuing full autonomy Human-in-the-loop approaches that keep humans involved for oversight and handling edge cases Setting realistic expectations about current capabilities and limitations By combining tightly constrained agents, good evaluation data, human-in-the-loop oversight, and traditional engineering methods, we can achieve reliably good results for automating medium-complex tasks. Will AI agents automate tedious repetitive work, such as web scraping, form filling, and data entry? Yes, absolutely. Will AI agents autonomously book your vacation without your intervention? Unlikely, at least in the near future.

[D] What do you think of Mistral.ai's value proposition and open source strategy?

Mistral.ai, a 4 week old startup founded by star researchers from DeepMind and FAIR recently raised a whopping €105m in their seed round just 4 weeks after set up. Their leaked pitch "deck" made several arguments that I find quite interesting and debatable (not that I think they are totally wrong) given recent progress in research and open source. They seem to believe that a moat can be built around better models, or at least the engineering know-how required to pursue such endeavours That closed sourced models served behind an API poses integration challenges and safety issues that will become a bottleneck for adoption That open sourcing the majority of their work, puts them in a position to win over the very finite research talent necessary to keep them ahead of competition Open source models and tools accelerate the development of ecosystems and aid in dominance through standard setting. (personally I think this is their strongest argument). Very similar to what Yann LeCun cited as reasons for FAIR's commitment to open sourcing. Curious what the industry practitioners and academics here think about this? https://sifted.eu/articles/pitch-deck-mistral

[P] Open-source Neural Search framework to implement semantic search & multimedia search. Just released 2.0, seeking your feedback.

I heard your feedback on 1.0 release post on my project Jina, many people were keen to use Jina for multimedia search because that's where use of Neural Networks makes significant difference. So I focused on that part and I was able to transform it from 1.0 to 2.0 within 3 months. Last post on 1.0 release to give you some idea what this project is about Actually, I should say - "'we' made this", because there were more than 155 contributors who did it, not just me. The primary changes we made We saw MachineLearning beginners struggle in using Jina 1.0, so we separated the codebase where Machine Learning expertise is required(jina-hub) and the one which MachineLearning beginners can use(the jina core). Now ML beginners don't need to worry about jina-hub and can use jina hub packages directly to implement ML specific tasks without the need to understand advanced ML concepts. While advanced ML users can create their own jina-hub packages. We cut down a lots of abstractions to make it easy to use for beginners Made python APIs more intuitive to use Improved performance(3.6x faster on startup) Here's Jina 2.0 and here's Jina 1.0. I seek feedback from people who are looking at this project for the first time, as well as people who have tried their hands before but had some challenges in using it. Few questions, I'm seeking answers to Do you feel that we have reduced complexity by a lot of margin? How easy it is to use for a beginner now? What questions are still unanswered?

[N] TheSequence Scope: When it comes to machine learning, size matters: Microsoft's DeepSpeed framework, which can train a model with up to a trillion parameters

Hi there! Offering to your attention the latest edition of a weekly ML-newsletter that focusing on three things: impactful ML research papers, cool ML tech solutions, and ML use cases supported by investors. Please, see it below. Reddit is a new thing for me, and I've been struggling a bit with it, so please don't judge me too harsh for this promotion. This weekly digest is free and I hope you'd find the format convenient for you. Your feedback is very appreciated, and please feel free to sign up if you like it. 📝 Editorial  The recent emergence of pre-trained language models and transformer architectures pushed the creation of larger and larger machine learning models. Google’s BERT presented attention mechanism and transformer architecture possibilities as the “next big thing” in ML, and the numbers seem surreal. OpenAI’s GPT-2 set a record by processing 1.5 billion parameters, followed by Microsoft’s Turing-NLG, which processed 17 billion parameters just to see the new GPT-3 processing an astonishing 175 billion parameters. To not feel complacent, just this week Microsoft announced a new release of its DeepSpeed framework (which powers Turing-NLG), which can train a model with up to a trillion parameters. That sounds insane but it really isn’t.   What we are seeing is a consequence of several factors. First, computation power and parallelization techniques have evolved to a point where it is relatively easy to train machine learning models in large clusters of machines. Second and most importantly, in the current state of machine learning, larger models have regularly outperformed smaller and more specialized models. Knowledge reusability methods like transfer learning are still in very nascent stages. As a result, it’s really hard to build small models that can operate in uncertain environments. Furthermore, as models like GPT-3 and Turing-NLG have shown, there is some unexplainable magic that happens after models go past a certain size. Many of the immediate machine learning problems might be solved by scaling the current generation of neural network architectures. Plain and simple, when it comes to machine learning, size matters.   We would love to hear your opinions about the debate between broader-larger vs. smaller and more specialized models.   Leave a comment Now, to the most important developments in the AI industry this week 🔎 ML Research GPT-3 Falls Short in Machine Comprehension Proposed by researchers from a few major American universities, a 57-task test to measure models’ ability to reason poses challenges even for sophisticated models like GPT-3 ->read more in the original paper Better Text Summarization OpenAI published a paper showing a reinforcement learning with human feedback technique that can surpass supervised models ->read more on OpenAI blog Reinforcement Learning with Offline Datasets Researchers from the Berkeley AI Research (BAIR) Lab published a paper unveiling a method that uses offline datasets to improve reinforcement learning models->read more on BAIR blog 🤖 Cool AI Tech Releases New Version of DeepSpeed Microsoft open-sourced a new version of DeepSpeed, an open-source library for parallelizable training that can scale up to models with 1 trillion parameters->read more on Microsoft Research blog 💸 Money in AI AI-powered customer experience management platform Sprinklr has raised $200 million (kudos to our subscribers from Sprinklr 👏). Sprinklr's “AI listening processing” solution allows companies to get structured and meaningful sentiments and insights from unstructured customer data that comes from public conversations on different websites and social platforms. Xometry, an on-demand industrial parts marketplace, raises $75 million in Series E funding. The company provides a digital way of creating the right combination of buyers and manufacturers. Another example of AI implementation into matching two sides for a deal. Real estate tech company Orchard raises $69 million in its recent funding round. Orchard aims to digitize the whole real estate market, by developing a solution that combines machine learning and rapid human assistance to smooth the search, match the right deal, and simplify buying and selling relationships. Cybersecurity startup Pcysys raised $25 million in its funding round. Pcysys’ platform, which doesn’t require installation or network reconfiguration, uses algorithms to scan and “ethically” attack enterprise networks. Robotics farming company Iron Ox raised $20 million in a funding round. The system of farming robots is still semi-autonomous, the company’s goal is to become fully autonomous.  Insurtech company Descartes Underwriting raised $18.5 million. The company applies AI and machine learning technologies to climate risk predicting and insurance underwriting. Legaltech startup ThoughtRiver raised $10 million in its Series A round. Its AI solution applied to contract pre-screening aims to boost operational efficiency. Medtech startup Skin Analytics raised $5.1 million in Series A funding. Skin Analytics has developed a clinically validated AI system that can identify not only the important skin cancers but also precancerous lesions that can be treated, as well as a range of lesions that are benign. Amazon, along with several government organizations and three other industry partners, helped fund the National Science Foundation, a high-priority AI research initiative. The amount of funding is not disclosed. The content of TheSequence is written by Jesus Rodriguez, one of the most-read contributors to KDNuggets and TDS. You can check his Medium here.

[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.

[D] AI regulation: a review of NTIA's "AI Accountability Policy" doc

How will governments respond to the rapid rise of AI? How can sensible regulation keep pace with AI technology? These questions interest many of us! One early US government response has come from the National Telecommunications and Information Administration (NTIA). Specifically, the NTIA published an "AI Accountability Policy Request for Comment" on April 11, 2023. I read the NTIA document carefully, and I'm sharing my observations here for others interested in AI regulation. You can, of course, read the original materials and form your own opinions. Moreover, you can share those opinions not only on this post, but also with the NTIA itself until June 12, 2023. As background, the NTIA (homepage, Wikipedia) consists of a few hundred people within the Department of Commerce. The official mission of the NTIA is "advising the President on telecommunications and information policy issues". Topics covered by NTIA include broadband internet access, spectrum management, internet health, and now artificial intelligence. I do not know whether the NTIA will ultimately drive thinking around AI regulation in the United States or they are just a spunky lot who got something on paper early. The NTIA document is not a specific policy proposal, but rather a thoughtful discussion of AI regulation, followed by a long list of questions on which the NTIA seeks input. This format seems appropriate right now, as we're all trying to make sense of a fast-changing world. The NTIA document leans heavily on two others: the Blueprint for an AI Bill of Rights from the White House Office of Science and Technology and the AI Risk Management Framework from the National Institute of Standards and Technology (NIST). Without going into these two in depth, even tiny snippets convey their differing audiences and flavors: White House Blueprint: "You should be protected from safe and ineffective systems." NIST Framework: "Risk refers to the composite measure of an event’s probability of occurring and the magnitude or degree of the consequences of the corresponding event." Now, turning back to the NTIA document itself, I'll comment on three aspects (1) scope, (2) problems addressed, and (3) solutions contemplated. Scope is critical to understanding the NTIA document, and is probably worth keeping in mind in all near-term discussion of AI regulation. Over the past several years, at least two different technologies have been called "AI". The document mentions both, but the emphasis is NOT on the one you're probably thinking about. In more detail: A few years ago, regulators began scrutinizing "automated decisions systems", which passed as "AI" in those ancient times. An example would be an ML model used by a bank to decide whether or not you get a loan. That model might take in all sorts of information about you, combine it in mysterious ML ways, and reject your loan request. Then you might wonder, "Did that system effectively use my address and name to deduce that I am black and then reject my loan request on the basis of race?" There is some evidence of that happening, and this seems like an injustice. So perhaps such systems should be audited and certified so people know this won't happen. This is the focus of the document. These days, AI more commonly refers to open-ended systems that can engage on a wide range of topics and approximate human intelligence. The document briefly mentions generative AI models, large language models, ChatGPT, and "foundational models" (sic), but this is not the focus. The passing mentions may obscure this, unfortunately. In my opinion, these two notions of "AI" are radically different, and many of the differences matter from a regulatory perspective. Yet NTIA lumps both under a sweeping definition of an "AI system" as "an engineered or machine-based system that can, for a given set of objectives, generate outputs such as predictions, recommendations, or decisions influencing real or virtual environments." (Hmm, this includes my Magic 8-Ball…) Keep scope in mind as we turn to the next aspect: the problems under discussion. Now, NTIA's goal is to solicit input, so considering a wide range of potential problems associated with AI makes sense. Consistent with that, the document refers to democratic values, civil rights, civil liberties, and privacy. And citing the NIST doc, NTIA vaguely notes "a wide range of potential AI risks". Also, AI systems should be "valid and reliable, safe, secure and resilient, accountable and transparent, explainable and interpretable, privacy-enhanced, and fair with their harmful bias managed". And they should call their mothers \every\ week. (Okay, I made that one up.) A few comments on this formulation of the problem. First, these concerns feel more applicable to older-style AI. This includes automated decisions systems, like for a bank loan or for a prison parole recommendation. Sure, I believe such systems should operate in ways consistent with our consensus societal values, and further regulation may be needed to achieve that. But, hello! There's also another, newer class of AI that poses additional challenges. And I don't see those discussed in the NTIA document. Such challenges might include: People losing jobs because AI takes their work. Ensuring malicious people don't use AI tools to wreak havoc on the world. Sorting out intellectual property issues around AI to ensure both rapid progress in the field and respect for creators' rights. Ensuring laws appropriately assign culpability to humans when AIs cause harm. Planning for an incident analogous to the first internet worm, where an AI goes rogue, wreaks some havoc, and everyone is shocked (before it happens 28,385 more times). Bottom line: when I cntrl-F the doc for "robotic overlords", I get zero hits. ZERO. This is why I now believe scope is so important when considering efforts to regulate AI: are we talking about old-school AI or 2023-era AI or what? Because they are pretty different. The last aspect I'll address is the solutions contemplated. Again, NTIA's goal is to stimulate discussion, not propose something specific. Nevertheless, there is a strong push in one particular direction: unlike, "robotic overlord", the word "audit" appears more than 100 times along with many instances of "assessment" and "certification". On one hand, this approach makes sense. Suppose you want to ensure that a bank loan system is fair, that a social media platform isn't spreading misinformation, that a search engine is returning accurate results, etc. Then someone, somewhere has to assess or audit that system and look for problems. That audit might be done by the creator of the system or a third-party auditing agency. Such audits could be incentivized by mandates, prizes, or shiny gold stars. The government might help by fostering development of auditing tools and data. The NTIA is open to all such possibilities and seeks input on how to proceed. On the other hand, this seems like a tactic best suited to automated decision systems operated by financial institutions, government agencies, and the like. Such formal processes seem a poor fit for the current AI wave. For example: Auditing will take time and money. That's something a bank might pay for a system that will run for years. For something fine-tuned over the weekend at a startup or by some guy living in his mother's basement, that's probably not going to happen. Auditing a straightforward decision system seems far easier than assessing an open-ended AI. Beyond basic practicality, the AI could be taught to lie when it senses an audit. Also, auditing procedures (like the NTIA doc itself) will presumably be online, which means that AIs will read them and could potentially respond. Most current ML models fix parameters after training, but I think we'll soon see some models whose parameters evolve as they engage with the world. Auditing such a system that varies continuously over time seems especially difficult. Auditing a foundation model probably tells you little about derivative models. A sweet-hearted model can surely be made into monster with moderate additional training; you don't need to teach the model new cognitive skills, just repurpose existing ones to new ends. More generally, auditing doesn't address many of my concerns about AI regulation (see list above). For example, auditing sort of assumes a basically responsible actor (bank, government agency, big tech company), but AI could be misused by malicious people who, naturally, will not seek a responsible outside assessment. In any case, for both old-school and modern AI, auditing is only one line of defense, and that's not enough. You can audit until you're blue in the face, stuff will still get through, and AI systems will still cause some harm. So what's the next line of defense? For example, is our legal system ready to sensibly assign culpability to humans for AI-related incidents? In summary, the critical problem with the NTIA document is that it creates a largely false appearance of US government engagement with the new class of AI technology. As a result, people could wrongly believe that the US government is already responding to the rise of AI, and fail to advocate for actual, effective engagement. That said, the NTIA document does address important issues around a prominent technology sometimes (formerly?) called "AI". Even there, however, the proposed approach (auditing) seems like an overly-fragile, single line of defense.

[D]💥 How Underdog AI Companies Will Crush Silicon Valley Giants.

💥 How Underdog AI Companies Will Crush Silicon Valley Giants. Opportunities in AI: Creating Abundant Intelligence. Generative AI like ChatGPT brings complex tasks within reach and is set to transform society. Startups have an opportunity in applying AI to create "abundant intelligence". In the past year, ChatGPT, GitHub Copilot, and Midjourney have rapidly grown to $100M+ revenue. AI startups face competition from tech giants also moving quickly into AI. Startups must pick spots where they have an advantage. Opportunities exist in expanding the application universe into new greenfield opportunities like automating mundane decisions, masking workflow complexity, and reimagining applications. Infrastructure tools make models more powerful by chaining them together and improving accuracy. Opportunity areas include unstructured data management, agent-driven automation, model evaluation, and experimentation. Key players emerging are foundation model providers like OpenAI and Anthropic, companies building domain-specific models, and platforms for autonomous agents. Advantages exist for startups focused on imagination and technical ability to find non-obvious ideas, while large companies retrofit existing businesses.

[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.

[N] Montreal-based Element AI sold for $230-million as founders saw value mostly wiped out

According to Globe and Mail article: Element AI sold for $230-million as founders saw value mostly wiped out, document reveals Montreal startup Element AI Inc. was running out of money and options when it inked a deal last month to sell itself for US$230-milion to Silicon Valley software company ServiceNow Inc., a confidential document obtained by the Globe and Mail reveals. Materials sent to Element AI shareholders Friday reveal that while many of its institutional shareholders will make most if not all of their money back from backing two venture financings, employees will not fare nearly as well. Many have been terminated and had their stock options cancelled. Also losing out are co-founders Jean-François Gagné, the CEO, his wife Anne Martel, the chief administrative officer, chief science officer Nick Chapados and Yoshua Bengio, the University of Montreal professor known as a godfather of “deep learning,” the foundational science behind today’s AI revolution. Between them, they owned 8.8 million common shares, whose value has been wiped out with the takeover, which goes to a shareholder vote Dec 29 with enough investor support already locked up to pass before the takeover goes to a Canadian court to approve a plan of arrangement with ServiceNow. The quartet also owns preferred shares worth less than US$300,000 combined under the terms of the deal. The shareholder document, a management proxy circular, provides a rare look inside efforts by a highly hyped but deeply troubled startup as it struggled to secure financing at the same time as it was failing to live up to its early promises. The circular states the US$230-million purchase price is subject to some adjustments and expenses which could bring the final price down to US$195-million. The sale is a disappointing outcome for a company that burst onto the Canadian tech scene four years ago like few others, promising to deliver AI-powered operational improvements to a range of industries and anchor a thriving domestic AI sector. Element AI became the self-appointed representative of Canada’s AI sector, lobbying politicians and officials and landing numerous photo ops with them, including Prime Minister Justin Trudeau. It also secured $25-million in federal funding – $20-million of which was committed earlier this year and cancelled by the government with the ServiceNow takeover. Element AI invested heavily in hype and and earned international renown, largely due to its association with Dr. Bengio. It raised US$102-million in venture capital in 2017 just nine months after its founding, an unheard of amount for a new Canadian company, from international backers including Microsoft Corp., Intel Corp., Nvidia Corp., Tencent Holdings Ltd., Fidelity Investments, a Singaporean sovereign wealth fund and venture capital firms. Element AI went on a hiring spree to establish what the founders called “supercredibility,” recruiting top AI talent in Canada and abroad. It opened global offices, including a British operation that did pro bono work to deliver “AI for good,” and its ranks swelled to 500 people. But the swift hiring and attention-seeking were at odds with its success in actually building a software business. Element AI took two years to focus on product development after initially pursuing consulting gigs. It came into 2019 with a plan to bring several AI-based products to market, including a cybersecurity offering for financial institutions and a program to help port operators predict waiting times for truck drivers. It was also quietly shopping itself around. In December 2018, the company asked financial adviser Allen & Co LLC to find a potential buyer, in addition to pursuing a private placement, the circular reveals. But Element AI struggled to advance proofs-of-concept work to marketable products. Several client partnerships faltered in 2019 and 2020. Element did manage to reach terms for a US$151.4-million ($200-million) venture financing in September, 2019 led by the Caisse de dépôt et placement du Québec and backed by the Quebec government and consulting giant McKinsey and Co. However, the circular reveals the company only received the first tranche of the financing – roughly half of the amount – at the time, and that it had to meet unspecified conditions to get the rest. A fairness opinion by Deloitte commissioned as part of the sale process estimated Element AI’s enterprises value at just US$76-million around the time of the 2019 financing, shrinking to US$45-million this year. “However, the conditions precedent the closing of the second tranche … were not going to be met in a timely manner,” the circular reads. It states “new terms were proposed” for a round of financing that would give incoming investors ranking ahead of others and a cumulative dividend of 12 per cent on invested capital and impose “other operating and governance constraints and limitations on the company.” Management instead decided to pursue a sale, and Allen contacted prospective buyers in June. As talks narrowed this past summer to exclusive negotiations with ServiceNow, “the company’s liquidity was diminishing as sources of capital on acceptable terms were scarce,” the circular reads. By late November, it was generating revenue at an annualized rate of just $10-million to $12-million, Deloitte said. As part of the deal – which will see ServiceNow keep Element AI’s research scientists and patents and effectively abandon its business – the buyer has agreed to pay US$10-million to key employees and consultants including Mr. Gagne and Dr. Bengio as part of a retention plan. The Caisse and Quebec government will get US$35.45-million and US$11.8-million, respectively, roughly the amount they invested in the first tranche of the 2019 financing.

[Discussion] When ML and Data Science are the death of a good company: A cautionary tale.

TD;LR: At Company A, Team X does advanced analytics using on-prem ERP tools and older programming languages. Their tools work very well and are designed based on very deep business and domain expertise. Team Y is a new and ambitious Data Science team that thinks they can replace Team X's tools with a bunch of R scripts and a custom built ML platform. Their models are simplistic, but more "fashionable" compared to the econometric models used by Team X, and team Y benefits from the ML/DS moniker so leadership is allowing Team Y to start a large scale overhaul of the analytics platform in question. Team Y doesn't have the experience for such a larger scale transformation, and is refusing to collaborate with team X. This project is very likely going to fail, and cause serious harm to the company as a whole financially and from a people perspective. I argue that this is not just because of bad leadership, but also because of various trends and mindsets in the DS community at large. Update (Jump to below the line for the original story): Several people in the comments are pointing out that this just a management failure, not something due to ML/DS, and that you can replace DS with any buzz tech and the story will still be relevant. My response: Of course, any failure at an organization level is ultimately a management failure one way or the other. Moreover, it is also the case that ML/DS when done correctly, will always improve a company's bottom line. There is no scenario where the proper ML solution, delivered at a reasonable cost and in a timely fashion, will somehow hurt the company's bottom line. My point is that in this case management is failing because of certain trends and practices that are specific to the ML/DS community, namely: The idea that DS teams should operate independently of tech and business orgs -- too much autonomy for DS teams The disregard for domain knowledge that seems prevalent nowadays thanks to the ML hype, that DS can be generalists and someone with good enough ML chops can solve any business problem. That wasn't the case when I first left academia for the industry in 2009 (back then nobody would even bother with a phone screen if you didn't have the right domain knowledge). Over reliance on resources who check all the ML hype related boxes (knows Python, R, Tensorflow, Shiny, etc..., has the right Coursera certifications, has blogged on the topic, etc...), but are lacking in depth of experience. DS interviews nowadays all seem to be: Can you tell me what a p-value is? What is elastic net regression? Show me how to fit a model in sklearn? How do you impute NAs in an R dataframe? Any smart person can look those up on Stackoverflow or Cross-Validated,.....Instead teams should be asking stuff like: why does portfolio optimization use QP not LP? How does a forecast influence a customer service level? When should a recommendation engine be content based and when should it use collaborative filtering? etc... (This is a true story, happening to the company I currently work for. Names, domains, algorithms, and roles have been shuffled around to protect my anonymity)  Company A has been around for several decades. It is not the biggest name in its domain, but it is a well respected one. Risk analysis and portfolio optimization have been a core of Company A's business since the 90s. They have a large team of 30 or so analysts who perform those tasks on a daily basis. These analysts use ERP solutions implemented for them by one the big ERP companies (SAP, Teradata, Oracle, JD Edwards,...) or one of the major tech consulting companies (Deloitte, Accenture, PWC, Capgemini, etc...) in collaboration with their own in house engineering team. The tools used are embarrassingly old school: Classic RDBMS running on on-prem servers or maybe even on mainframes, code written in COBOL, Fortran, weird proprietary stuff like ABAP or SPSS.....you get the picture. But the models and analytic functions were pretty sophisticated, and surprisingly cutting edge compared to the published academic literature. Most of all, they fit well with the company's enterprise ecosystem, and were honed based on years of deep domain knowledge.  They have a tech team of several engineers (poached from the aforementioned software and consulting companies) and product managers (who came from the experienced pools of analysts and managers who use the software, or poached from business rivals) maintaining and running this software. Their technology might be old school, but collectively, they know the domain and the company's overall architecture very, very well. They've guided the company through several large scale upgrades and migrations and they have a track record of delivering on time, without too much overhead. The few times they've stumbled, they knew how to pick themselves up very quickly. In fact within their industry niche, they have a reputation for their expertise, and have very good relations with the various vendors they've had to deal with. They were the launching pad of several successful ERP consulting careers.  Interestingly, despite dealing on a daily basis with statistical modeling and optimization algorithms, none of the analysts, engineers, or product managers involved describe themselves as data scientists or machine learning experts. It is mostly a cultural thing: Their expertise predates the Data Science/ML hype that started circa 2010, and they got most of their chops using proprietary enterprise tools instead of the open source tools popular nowadays. A few of them have formal statistical training, but most of them came from engineering or domain backgrounds and learned stats on the fly while doing their job. Call this team "Team X".  Sometime around the mid 2010s, Company A started having some serious anxiety issues: Although still doing very well for a company its size, overall economic and demographic trends were shrinking its customer base, and a couple of so called disruptors came up with a new app and business model that started seriously eating into their revenue. A suitable reaction to appease shareholders and Wall Street was necessary. The company already had a decent website and a pretty snazzy app, what more could be done? Leadership decided that it was high time that AI and ML become a core part of the company's business. An ambitious Manager, with no science or engineering background, but who had very briefly toyed with a recommender system a couple of years back, was chosen to build a data science team, call it team "Y" (he had a bachelor's in history from the local state college and worked for several years in the company's marketing org). Team "Y" consists mostly of internal hires who decided they wanted to be data scientists and completed a Coursera certification or a Galvanize boot camp, before being brought on to the team, along with a few of fresh Ph.D or M.Sc holders who didn't like academia and wanted to try their hand at an industry role. All of them were very bright people, they could write great Medium blog posts and give inspiring TED talks, but collectively they had very little real world industry experience. As is the fashion nowadays, this group was made part of a data science org that reported directly to the CEO and Board, bypassing the CIO and any tech or business VPs, since Company A wanted to claim the monikers "data driven" and "AI powered" in their upcoming shareholder meetings. In 3 or 4 years of existence, team Y produced a few Python and R scripts. Their architectural experience  consisted almost entirely in connecting Flask to S3 buckets or Redshift tables, with a couple of the more resourceful ones learning how to plug their models into Tableau or how to spin up a Kuberneties pod.  But they needn't worry: The aforementioned manager, who was now a director (and was also doing an online Masters to make up for his qualifications gap and bolster his chances of becoming VP soon - at least he now understands what L1 regularization is), was a master at playing corporate politics and self-promotion. No matter how few actionable insights team Y produced or how little code they deployed to production, he always had their back and made sure they had ample funding. In fact he now had grandiose plans for setting up an all-purpose machine learning platform that can be used to solve all of the company's data problems.  A couple of sharp minded members of team Y, upon googling their industry name along with the word "data science", realized that risk analysis was a prime candidate for being solved with Bayesian models, and there was already a nifty R package for doing just that, whose tutorial they went through on R-Bloggers.com. One of them had even submitted a Bayesian classifier Kernel for a competition on Kaggle (he was 203rd on the leaderboard), and was eager to put his new-found expertise to use on a real world problem. They pitched the idea to their director, who saw a perfect use case for his upcoming ML platform. They started work on it immediately, without bothering to check whether anybody at Company A was already doing risk analysis. Since their org was independent, they didn't really need to check with anybody else before they got funding for their initiative. Although it was basically a Naive Bayes classifier, the term ML was added to the project tile, to impress the board.  As they progressed with their work however, tensions started to build. They had asked the data warehousing and CA analytics teams to build pipelines for them, and word eventually got out to team X about their project. Team X was initially thrilled: They offered to collaborate whole heartedly, and would have loved to add an ML based feather to their already impressive cap. The product owners and analysts were totally onboard as well: They saw a chance to get in on the whole Data Science hype that they kept hearing about. But through some weird mix of arrogance and insecurity, team Y refused to collaborate with them or share any of their long term goals with them, even as they went to other parts of the company giving brown bag presentations and tutorials on the new model they created.  Team X got resentful: from what they saw of team Y's model, their approach was hopelessly naive and had little chances of scaling or being sustainable in production, and they knew exactly how to help with that. Deploying the model to production would have taken them a few days, given how comfortable they were with DevOps and continuous delivery (team Y had taken several months to figure out how to deploy a simple R script to production). And despite how old school their own tech was, team X were crafty enough to be able to plug it in to their existing architecture. Moreover, the output of the model was such that it didn't take into account how the business will consume it or how it was going to be fed to downstream systems, and the product owners could have gone a long way in making the model more amenable to adoption by the business stakeholders. But team Y wouldn't listen, and their leads brushed off any attempts at communication, let alone collaboration. The vibe that team Y was giving off was "We are the cutting edge ML team, you guys are the legacy server grunts. We don't need your opinion.", and they seemed to have a complete disregard for domain knowledge, or worse, they thought that all that domain knowledge consisted of was being able to grasp the definitions of a few business metrics.  Team X got frustrated and tried to express their concerns to leadership. But despite owning a vital link in Company A's business process, they were only \~50 people in a large 1000 strong technology and operations org, and they were several layers removed from the C-suite, so it was impossible for them to get their voices heard.  Meanwhile, the unstoppable director was doing what he did best: Playing corporate politics. Despite how little his team had actually delivered, he had convinced the board that all analysis and optimization tasks should now be migrated to his yet to be delivered ML platform. Since most leaders now knew that there was overlap between team Y and team X's objectives, his pitch was no longer that team Y was going to create a new insight, but that they were going to replace (or modernize) the legacy statistics based on-prem tools with more accurate cloud based ML tools. Never mind that there was no support in the academic literature for the idea that Naive Bayes works better than the Econometric approaches used by team X, let alone the additional wacky idea that Bayesian Optimization would definitely outperform the QP solvers that were running in production.  Unbeknownst to team X, the original Bayesian risk analysis project has now grown into a multimillion dollar major overhaul initiative, which included the eventual replacement of all of the tools and functions supported by team X along with the necessary migration to the cloud. The CIO and a couple of business VPs are on now board, and tech leadership is treating it as a done deal. An outside vendor, a startup who nobody had heard of, was contracted to help build the platform, since team Y has no engineering skills. The choice was deliberate, as calling on any of the established consulting or software companies would have eventually led leadership to the conclusion that team X was better suited for a transformation on this scale than team Y.  Team Y has no experience with any major ERP deployments, and no domain knowledge, yet they are being tasked with fundamentally changing the business process that is at the core of Company A's business. Their models actually perform worse than those deployed by team X, and their architecture is hopelessly simplistic, compared to what is necessary for running such a solution in production.  Ironically, using Bayesian thinking and based on all the evidence, the likelihood that team Y succeeds is close to 0%. At best, the project is going to end up being a write off of 50 million dollars or more. Once the !@#$!@hits the fan, a couple of executive heads are going to role, and dozens of people will get laid off. At worst, given how vital risk analysis and portfolio optimization is to Company A's revenue stream, the failure will eventually sink the whole company. It probably won't go bankrupt, but it will lose a significant portion of its business and work force. Failed ERP implementations can and do sink large companies: Just see what happened to National Grid US, SuperValu or Target Canada.  One might argue that this is more about corporate disfunction and bad leadership than about data science and AI. But I disagree. I think the core driver of this debacle is indeed the blind faith in Data Scientists, ML models and the promise of AI, and the overall culture of hype and self promotion that is very common among the ML crowd.  We haven't seen the end of this story: I sincerely hope that this ends well for the sake of my colleagues and all involved. Company A is a good company, and both its customers and its employees deserver better. But the chances of that happening are negligible given all the information available, and this failure will hit my company hard.

[D] AI Agents: too early, too expensive, too unreliable

Reference: Full blog post There has been a lot of hype about the promise of autonomous agent-based LLM workflows. By now, all major LLMs are capable of interacting with external tools and functions, letting the LLM perform sequences of tasks automatically. But reality is proving more challenging than anticipated. The WebArena leaderboard, which benchmarks LLMs agents against real-world tasks, shows that even the best-performing models have a success rate of only 35.8%. Challenges in Practice After seeing many attempts to AI agents, I believe it's too early, too expensive, too slow, too unreliable. It feels like many AI agent startups are waiting for a model breakthrough that will start the race to productize agents. Reliability: As we all know, LLMs are prone to hallucinations and inconsistencies. Chaining multiple AI steps compounds these issues, especially for tasks requiring exact outputs. Performance and costs: GPT-4o, Gemini-1.5, and Claude Opus are working quite well with tool usage/function calling, but they are still slow and expensive, particularly if you need to do loops and automatic retries. Legal concerns: Companies may be held liable for the mistakes of their agents. A recent example is Air Canada being ordered to pay a customer who was misled by the airline's chatbot. User trust: The "black box" nature of AI agents and stories like the above makes it hard for users to understand and trust their outputs. Gaining user trust for sensitive tasks involving payments or personal information will be hard (paying bills, shopping, etc.). Real-World Attempts Several startups are tackling the AI agent space, but most are still experimental or invite-only: adept.ai - $350M funding, but access is still very limited MultiOn - funding unknown, their API-first approach seems promising HypeWrite - $2.8M funding, started with an AI writing assistant and expanded into the agent space minion.ai - created some initial buzz but has gone quiet now, waitlist only Only MultiOn seems to be pursuing the "give it instructions and watch it go" approach, which is more in line with the promise of AI agents. All others are going down the record-and-replay RPA route, which may be necessary for reliability at this stage. Large players are also bringing AI capabilities to desktops and browsers, and it looks like we'll get native AI integrations on a system level: OpenAI announced their Mac desktop app that can interact with the OS screen. At Google I/O, Google demonstrated Gemini automatically processing a shopping return. Microsoft announced Copilot Studio, which will let developers build AI agent bots. Screenshot Screenshot These tech demos are impressive, but we'll see how well these agent capabilities will work when released publicly and tested against real-world scenarios instead of hand-picked demo cases. The Path Forward AI agents overhyped and it's too early. However, the underlying models continue to advance quickly, and we can expect to see more successful real-world applications. Instead of trying to have one large general purpose agent that is hard to control and test, we can use many smaller agents that basically just pick the right strategy for a specific sub-task in our workflows. These "agents" can be thought of as medium-sized LLM prompts with a) context and b) a set of functions available to call. The most promising path forward likely looks like this: Narrowly scoped, well testable automations that use AI as an augmentation tool rather than pursuing full autonomy Human-in-the-loop approaches that keep humans involved for oversight and handling edge cases Setting realistic expectations about current capabilities and limitations By combining tightly constrained agents, good evaluation data, human-in-the-loop oversight, and traditional engineering methods, we can achieve reliably good results for automating medium-complex tasks. Will AI agents automate tedious repetitive work, such as web scraping, form filling, and data entry? Yes, absolutely. Will AI agents autonomously book your vacation without your intervention? Unlikely, at least in the near future.

[P] I made an automation to launch AI Tools with 100+ AI Directories

I've noticed that AI startup founders often face challenges when trying to attract their first 1000 users. It can be quite a hurdle! Here's an idea that might help boost your website's visibility without getting into all the technical SEO stuff – consider AI directories. They're like a hidden gem for improving your online presence. I came across a list of over 100 directories where you can showcase your AI tool. What makes this list valuable is that it includes directories that are: Already popular and have a lot of visitors. On the rise, meaning they're growing quickly. Actively maintained and engaged with by users. So, to make life easier for fellow AI startup builders, I've put together a project that simplifies the submission process using this curated list. It's a straightforward way to get your AI tool in front of potential users without diving deep into complex marketing strategies.

12 months ago, I was unemployed. Last week my side hustle got acquired by a $500m fintech company

I’ve learned so much over the years from this subreddit. I thought I’d return the favour and share some of my own learnings. In November 2020 my best friend and I had an idea. “What if we could find out which stocks the Internet is talking about?” This formed the origins of Ticker Nerd. 9 months later we sold Ticker Nerd to Finder (an Australian fintech company valued at around $500m). In this post, I am going to lay out how we got there. How we came up with the idea First off, like other posts have covered - you don’t NEED a revolutionary or original idea to build a business. There are tonnes of “boring” businesses making over 7 figures a year e.g. law firms, marketing agencies, real estate companies etc. If you’re looking for an exact formula to come up with a great business idea I’m sorry, but it doesn’t exist. Finding new business opportunities is more of an art than a science. Although, there are ways you can make it easier to find inspiration. Below are the same resources I use for inspiration. I rarely ever come up with ideas without first searching one of the resources below for inspiration: Starter Story Twitter Startup Ideas My First Million Trends by the Hustle Trends VC To show how you how messy, random and unpredictable it can be to find an idea - let me explain how my co-founder and I came up with the idea for Ticker Nerd: We discovered a new product on Twitter called Exploding Topics. It was a newsletter that uses a bunch of software and algorithms to find trends that are growing quickly before they hit the mainstream. I had recently listened to a podcast episode from My First Million where they spoke about Motley Fool making hundreds of millions from their investment newsletters. We asked ourselves what if we could build a SaaS platform similar to Exploding Topics but it focused on stocks? We built a quick landing page using Carrd + Gumroad that explained what our new idea will do and included a payment option to get early access for $49. We called it Exploding Stock (lol). We shared it around a bunch of Facebook groups and subreddits. We made $1,000 in pre-sales within a couple days. My co-founder and I can’t code so we had to find a developer to build our idea. We interviewed a bunch of potential candidates. Meanwhile, I was trawling through Wall Street Bets and found a bunch of free tools that did roughly what we wanted to build. Instead of building another SaaS tool that did the same thing as these free tools we decided to pivot from our original idea. Our new idea = a paid newsletter that sends a weekly report that summarises 2 of the best stocks that are growing in interest on the Internet. We emailed everyone who pre-ordered access, telling them about the change and offered a full refund if they wanted. tl;dr: We essentially combined two existing businesses (Exploding Topics and Motley Fool) and made it way better. We validated the idea by finding out if people will actually pay money for it BEFORE we decided to build it. The idea we started out with changed over time. How to work out if your idea will actually make money It’s easy to get hung up on designing the logo or choosing the perfect domain name for your new idea. At this stage none of that matters. The most important thing is working out if people will pay money for it. This is where validation comes in. We usually validate ideas using Carrd. It lets you build a simple one page site without having to code. The Ticker Nerd site was actually built using a Carrd template. Here’s how you can do it yourself (at a high level): Create a Carrd pro account (yes it's a $49 one off payment but you’ll get way more value out of it). Buy a cheap template and send it to your Carrd account. You can build your own template but this will save you a lot of time. Once the template reaches your Carrd account, duplicate it. Leave the original so it can be duplicated for other ideas. Jump onto Canva (free) and create a logo using the free logos provided. Import your logo. Add copy to the page that explains your idea. Use the AIDA formula. Sign up to Gumroad (free) and create a pre-sale campaign. Create a discounted lifetime subscription or version of the product. This will be used pre-sales. Add the copy from the site into the pre-sale campaign on Gumroad. Add a ‘widget’ to Carrd and connect it to Gumroad using the existing easy integration feature. Purchase a domain name. Connect it to Carrd. Test the site works. Share your website Now the site is ready you can start promoting it in various places to see how the market reacts. An easy method is to find relevant subreddits using Anvaka (Github tool) or Subreddit Stats. The Anvaka tool provides a spider map of all the connected subreddits that users are active in. The highlighted ones are most relevant. You can post a thread in these subreddits that offer value or can generate discussion. For example: ‘I’m creating a tool that can write all your copy, would anyone actually use this?’ ‘What does everything think of using AI to get our copy written faster?’ ‘It’s time to scratch my own itch, I’m creating a tool that writes marketing copy using GPT-3. What are the biggest problems you face writing marketing copy? I’ll build a solution for it’ Reddit is pretty brutal these days so make sure the post is genuine and only drop your link in the comments or in the post if it seems natural. If people are interested they’ll ask for the link. Another great place to post is r/entrepreuerridealong and r/business_ideas. These subreddits expect people to share their ideas and you’ll likely make some sales straight off the bat. I also suggest posting in some Facebook groups (related to your idea) as well just for good measure. Assess the results If people are paying you for early access you can assume that it’s worth building your idea. The beauty of posting your idea on Reddit or in Facebook groups is you’ll quickly learn why people love/hate your idea. This can help you decide how to tweak the idea or if you should drop it and move on to the next one. How we got our first 100 customers (for free) By validating Ticker Nerd using subreddits and Facebook groups this gave us our first paying customers. But we knew this wouldn’t be sustainable. We sat down and brainstormed every organic strategy we could use to get traction as quickly as possible. The winner: a Product Hunt launch. A successful Product Hunt launch isn’t easy. You need: Someone that has a solid reputation and audience to “hunt” your product (essentially an endorsement). An aged Product Hunt account - you can’t post any products if your account is less than a week old. To be following relevant Product Hunt members - since they get notified when you launch a new product if they’re following you. Relationships with other builders and makers on Product Hunt that also have a solid reputation and following. Although, if you can pull it off you can get your idea in front of tens of thousands of people actively looking for new products. Over the next few weeks, I worked with my co-founder on connecting with different founders, indie hackers and entrepreneurs mainly via Twitter. We explained to them our plans for the Product Hunt launch and managed to get a small army of people ready to upvote our product on launch day. We were both nervous on the day of the launch. We told ourselves to have zero expectations. The worst that could happen was no one signed up and we were in the same position as we’re in now. Luckily, within a couple of hours Ticker Nerd was on the homepage of Product Hunt and in the top 10. The results were instant. After 24 hours we had around 200 people enter their payment details to sign up for our free trial. These signups were equal to around $5,800 in monthly recurring revenue. \-- I hope this post was useful! Drop any questions you have below and I’ll do my best to respond :)

Is being a solopreneur really that fatal?

Okay, so I need to get something off my chest... People love to say that solopreneurship is a death sentence. That if you can’t find a cofounder, you’ll never build a team, never scale, never succeed. But I wonder about the other side of the coin—something that, browsing here and in other subs, doesn’t seem to get nearly as much attention—how fatal cofounder conflicts can be. I’ve personally seen three startups fail before even getting to an MVP because of cofounder issues. One of them was a company I was briefly a cofounder for. The other two are startups coworkers were previous cofounders for that fell apart before they even got to an MVP. In each case, it wasn’t lack of funding or product-market fit that killed them—it was the people. Yet, somehow, the startup world keeps pushing the idea that finding a cofounder is the most important thing you can do. But here’s the thing: if you can’t find a cofounder, that doesn’t mean you can’t build a business. It doesn’t even mean you can’t build a team. With the tools available today (no-code, AI, fractional hiring), a single person can get an MVP off the ground, validate demand, and take those first steps without needing to rush into a partnership with someone they barely know. And also—I wonder how many people actually succeed with a cofounder they met casually at a networking event or online? People talk about the risks of going solo, but not enough about the risks of tying your company’s future to someone you just met. (If you’re going to have a cofounder, IMO it should be someone you trust deeply, someone whose skills and working style you know complement yours—not just someone you brought on because startup X/YouTube told you to.). At the end of the day, I honestly think it’s about the product. If you can build something valuable and find market fit—whether solo or with a team—you’ll have the leverage to hire, partner, and grow. That’s what actually matters. That said—I know how incredibly hard it is to be a solopreneur—and not to have someone along the journey with you who can take half of the emotional and psychological burden, in addition to the actual work... What do you think? Any thoughts here appreciated.

Thoughts on FasterCapital VC?

TLDR: I pitched to FasterCapital and got an "offer". Trying to figure out if this is a legitimate opportunity or a waste of my time. I'm not familiar with VCs and hadn't considered actually getting an investor on board with my plan. I sent my pitch deck to FasterCapital, honestly not expecting a response. It was my first pitch deck and a complete long shot. I ended up getting a response, they asked me for clarification on a few things. Than I get this email about what they are offering here's the main part We specialize in warm introductions to angel investors, VCs, and HNWIs, ensuring you connect with the right investors through personalized recommendations—not ineffective mass email campaigns. Cold outreach, such as LinkedIn messages, rarely succeeds, as investors receive hundreds of such requests and disregard them. To raise money, you need a strong partner like ourselves who has a wide network and direct connection with those angel investors built throughout 10 years. You can see some of the reviews of the startups we have helped attached and reviews on independent sites. Based on our experience and the matching that we have done already on our own AI system and for raising $55M-$65M in 5 years, a suitable package in your case is $50k - $64k and the chances of raising money is %87 - %93, but you were accepted in the exceptional rising star offer, where you pay half of that amount as an advance which is $25k-$32k and the other half ONLY when we raise you the first $1M. Other startups in our standard offers pays double that amount. First, I don't understand all of it, except for the "where you pay half of that amount as an advance which is $25k-$32k" I am no where near being able to come close to that, mostly because if I had that much, I wouldn't apply to a VC. I responded and politely told her that was not something our company could financially do right now. Than this email Thanks for your kind reply. We are flexible on paying this amount into monthly installments. We offer money back guarantee if we didn't raise the capital in 6 months from signing. This is how much we are confident with our approach of warm introductions. Raising the first amount of money and getting the first investor onboard is the most challenging part. You need time to build trust and network of investors. You need to have a good partner to help you. Please note that the down payment is for raising at least $55M over five years as we are interested in long-term partnership to raise multiple rounds because we make money through the commission. Companies take only commission or success fee are doing cold introductions and mass emails and this approach has low chances of success when it comes to raising capital. It is about the chances of success. You can talk to these companies and ask them about their success rate. Mass emails campaign has zero chances of success.  We have helped more than 742 startups raise more than $2.2B. Our network includes 155,000 angel investors and more than 50K funding institutions (VCs, HNI, family offices..etc). We have been in this business for more than 10 years. We have more than 92% success rate in our program so far. So if you are familiar with VC, Is this an actual opportunity. I have a tendency to jump or dive head first into things. As much as I want to get excited because this would be the jumpstart to most of my goals and ambitions. I'm not familiar with VCs. I have bootstrapped all my ventures so far.

In 2018, I started an AI chatbot company...today, we have over 4000 paying customers and ChatGPT is changing EVERYTHING

Intro: 5 years ago, my co-founders and I ventured into the space of AI chatbots and started our first truly successful company. Never in a million years did I see myself in this business and we truly stumbled upon the opportunity by chance. Prior to that, we ran a successful lead generation business and questioned whether a simple ai chat product would increase our online conversions. Of the 3 co-founders, I was skeptical that it would, but the data was clear that we had something that really worked. We built a really simple MVP version of the product and gave it to some of our top lead buyers who saw even better conversion improvements on their own websites. In just a matter of weeks, a new business opportunity was born and a major pivot away from our lead generation business started. Our growth story: Startup growth is really interesting and in most cases, founders aren't really educated on what a typical growth curve looks like. While we hear about "hockey stick" growth curves, it's really atypical to actually see or experience this. From my experience, growth curves take place in a "stair curve". For example, you can scrap your way to a $100k run rate without much process or tracking. You can even get to $1 million ARR being super disorganized. As you start going beyond $1M ARR, things start to break and growth can flatten out while you put new processes and systems in place. Eventually you'll get to $2M or 3M with your new strategy and then things start breaking again. I've seen the process repeat itself and as you increase your ARR, the processes and systems become more difficult to work through...mainly because more people get involved and the product becomes more complex. When you do end up cracking the code in each step, the growth accelerates faster and faster before things start to break down and flatten out again. Without getting too much into the numbers, here were some of our initial levers for growth: Our first "stair" step was to leverage our existing customer base from our prior lead generation business. Having prior business relationships and a proven track record made it really simple to have conversations with people who already trusted us to try something new that we had to offer. Stair #2 was to build out a partner channel. Since our chat product involved a web developer or agency installing the chat on client sites, we partnered with these developers and agencies to leverage their already existing customer bases. We essentially piggy-backed off of their relationships and gave them a cut of the revenue. We built an internal partner tracking portal which took 6+ months, but it was well worth it. Stair #3 was our most expensive step, biggest headache, but added the most revenue. After COVID, we had and SDR/Account Executive sales team of roughly 30 people. It added revenue fast, but the payback periods were 12+ months so we had to cut back on this strategy after exhausting our universe of clients. Stair #4 involves a variety of paid advertisement strategies with product changes and the introduction of new onboarding features. We're in the middle of this stair and hope it's multiple years before things breakdown again. Don't give up I know it sounds really cliché, but the #1 indicator of success is doing the really boring stuff day in and day out and making incremental improvements. As the weeks, months, and years pass by, you will slowly gain domain expertise and start to see the gaps in the market that can set you apart from your competition. It's so hard for founders to stay focused and not get distracted so I would say it's equally as important to have co-founders who hold each other accountable on what your collective goals are. How GPT is changing everything I could write pages and pages about how GPT is going to change how the world operates, but I'll keep it specific to our business and chatbots. In 2021, we built an industry specific AI model that did a great job of classifying intents which allowed us to train future actions during a chat. It was a great advancement in our customer's industry at the time. With GPT integrated into our system, that training process that would take an employee hours to do, can be done in 5 minutes. The model is also cheaper than our own and more accurate. Because of these training improvements, we have been able to conduct research that is allowing us to leverage GPT models like no one else in the industry. This is both in the realm of chat and also training during onboarding. I really want to refrain from sharing our company, but if you are interested in seeing a model trained for your specific company or website, just PM me your link and I'll send you a free testing link with a model fully trained for your site to play around with. Where we are headed and the dangers of AI The level of advancement in AI is not terribly dangerous in its current state. I'm sure you've heard it before, but those who leverage the technology today will be the ones who get ahead. In the coming years, AI will inevitably replace a large percentage of human labor. This will be great for overall value creation and productivity for the world, but the argument that humans have always adapted and new jobs will be created is sadly not going to be as relevant in this case. As the possibility of AGI becomes a reality in the coming years or decades, productivity through AI will be off the charts. There is a major risk that human innovation and creative thinking will be completely stalled...human potential as we know it will be capped off and there will need to be major economic reform for displaced workers. This may not happen in the next 5 or 10 years, but you would be naïve not to believe the world we live in today will not be completely different in 20 to 30 years. Using AI to create deepfakes, fake voice agents, scam the unsuspecting, or exploit technical vulnerabilities are just a few other examples I could write about, but don't want to go into to much detail for obvious reasons. Concluding If you found the post interesting or you have any questions, please don't hesitate to ask. I'll do my best to answer whatever questions come from this! &#x200B; \*EDIT: Wasn't expecting this sort of response. I posted this right before I went to sleep so I'll get to responding soon.

🚀 Noob Seeking Go To Market experts! What are the most effective strategies to launch an AI Saas in the market and gain early traction with potential clients?

Question: What are some ways you have successfully gone to market for an AI Saas product? Any great (not good, but great) resources? Background: I have little sense of what is the most effective use of my time as I am feeling spread too thin. Creating a startup is not dissimilar to my job (identify problems, design tech based solutions, and roll out to 40,000 employees); however… I’ve never worked on a project where people a) pay for these solutions b) choices in what solutions they go with.

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.

12 months ago, I was unemployed. Last week my side hustle got acquired by a $500m fintech company

I’ve learned so much over the years from this subreddit. I thought I’d return the favour and share some of my own learnings. In November 2020 my best friend and I had an idea. “What if we could find out which stocks the Internet is talking about?” This formed the origins of Ticker Nerd. 9 months later we sold Ticker Nerd to Finder (an Australian fintech company valued at around $500m). In this post, I am going to lay out how we got there. How we came up with the idea First off, like other posts have covered - you don’t NEED a revolutionary or original idea to build a business. There are tonnes of “boring” businesses making over 7 figures a year e.g. law firms, marketing agencies, real estate companies etc. If you’re looking for an exact formula to come up with a great business idea I’m sorry, but it doesn’t exist. Finding new business opportunities is more of an art than a science. Although, there are ways you can make it easier to find inspiration. Below are the same resources I use for inspiration. I rarely ever come up with ideas without first searching one of the resources below for inspiration: Starter Story Twitter Startup Ideas My First Million Trends by the Hustle Trends VC To show how you how messy, random and unpredictable it can be to find an idea - let me explain how my co-founder and I came up with the idea for Ticker Nerd: We discovered a new product on Twitter called Exploding Topics. It was a newsletter that uses a bunch of software and algorithms to find trends that are growing quickly before they hit the mainstream. I had recently listened to a podcast episode from My First Million where they spoke about Motley Fool making hundreds of millions from their investment newsletters. We asked ourselves what if we could build a SaaS platform similar to Exploding Topics but it focused on stocks? We built a quick landing page using Carrd + Gumroad that explained what our new idea will do and included a payment option to get early access for $49. We called it Exploding Stock (lol). We shared it around a bunch of Facebook groups and subreddits. We made $1,000 in pre-sales within a couple days. My co-founder and I can’t code so we had to find a developer to build our idea. We interviewed a bunch of potential candidates. Meanwhile, I was trawling through Wall Street Bets and found a bunch of free tools that did roughly what we wanted to build. Instead of building another SaaS tool that did the same thing as these free tools we decided to pivot from our original idea. Our new idea = a paid newsletter that sends a weekly report that summarises 2 of the best stocks that are growing in interest on the Internet. We emailed everyone who pre-ordered access, telling them about the change and offered a full refund if they wanted. tl;dr: We essentially combined two existing businesses (Exploding Topics and Motley Fool) and made it way better. We validated the idea by finding out if people will actually pay money for it BEFORE we decided to build it. The idea we started out with changed over time. How to work out if your idea will actually make money It’s easy to get hung up on designing the logo or choosing the perfect domain name for your new idea. At this stage none of that matters. The most important thing is working out if people will pay money for it. This is where validation comes in. We usually validate ideas using Carrd. It lets you build a simple one page site without having to code. The Ticker Nerd site was actually built using a Carrd template. Here’s how you can do it yourself (at a high level): Create a Carrd pro account (yes it's a $49 one off payment but you’ll get way more value out of it). Buy a cheap template and send it to your Carrd account. You can build your own template but this will save you a lot of time. Once the template reaches your Carrd account, duplicate it. Leave the original so it can be duplicated for other ideas. Jump onto Canva (free) and create a logo using the free logos provided. Import your logo. Add copy to the page that explains your idea. Use the AIDA formula. Sign up to Gumroad (free) and create a pre-sale campaign. Create a discounted lifetime subscription or version of the product. This will be used pre-sales. Add the copy from the site into the pre-sale campaign on Gumroad. Add a ‘widget’ to Carrd and connect it to Gumroad using the existing easy integration feature. Purchase a domain name. Connect it to Carrd. Test the site works. Share your website Now the site is ready you can start promoting it in various places to see how the market reacts. An easy method is to find relevant subreddits using Anvaka (Github tool) or Subreddit Stats. The Anvaka tool provides a spider map of all the connected subreddits that users are active in. The highlighted ones are most relevant. You can post a thread in these subreddits that offer value or can generate discussion. For example: ‘I’m creating a tool that can write all your copy, would anyone actually use this?’ ‘What does everything think of using AI to get our copy written faster?’ ‘It’s time to scratch my own itch, I’m creating a tool that writes marketing copy using GPT-3. What are the biggest problems you face writing marketing copy? I’ll build a solution for it’ Reddit is pretty brutal these days so make sure the post is genuine and only drop your link in the comments or in the post if it seems natural. If people are interested they’ll ask for the link. Another great place to post is r/entrepreuerridealong and r/business_ideas. These subreddits expect people to share their ideas and you’ll likely make some sales straight off the bat. I also suggest posting in some Facebook groups (related to your idea) as well just for good measure. Assess the results If people are paying you for early access you can assume that it’s worth building your idea. The beauty of posting your idea on Reddit or in Facebook groups is you’ll quickly learn why people love/hate your idea. This can help you decide how to tweak the idea or if you should drop it and move on to the next one. How we got our first 100 customers (for free) By validating Ticker Nerd using subreddits and Facebook groups this gave us our first paying customers. But we knew this wouldn’t be sustainable. We sat down and brainstormed every organic strategy we could use to get traction as quickly as possible. The winner: a Product Hunt launch. A successful Product Hunt launch isn’t easy. You need: Someone that has a solid reputation and audience to “hunt” your product (essentially an endorsement). An aged Product Hunt account - you can’t post any products if your account is less than a week old. To be following relevant Product Hunt members - since they get notified when you launch a new product if they’re following you. Relationships with other builders and makers on Product Hunt that also have a solid reputation and following. Although, if you can pull it off you can get your idea in front of tens of thousands of people actively looking for new products. Over the next few weeks, I worked with my co-founder on connecting with different founders, indie hackers and entrepreneurs mainly via Twitter. We explained to them our plans for the Product Hunt launch and managed to get a small army of people ready to upvote our product on launch day. We were both nervous on the day of the launch. We told ourselves to have zero expectations. The worst that could happen was no one signed up and we were in the same position as we’re in now. Luckily, within a couple of hours Ticker Nerd was on the homepage of Product Hunt and in the top 10. The results were instant. After 24 hours we had around 200 people enter their payment details to sign up for our free trial. These signups were equal to around $5,800 in monthly recurring revenue. \-- I hope this post was useful! Drop any questions you have below and I’ll do my best to respond :)

Secret behind Airbnb's Billion-Dollar Empire? Spamming Craigslist

Silicon Valley wants you to believe that their unicorn startups succeeded doing things legally. But that couldn't be far from truth. For starters, Airbnb used multiple Gmail accounts to spam Craigslist. "They posted unrealistically (fake) cheap rentals of beautiful apartments in places where normal rent should be 10x more. Once people replied, they auto-responded that the unit has been rented, but they should be looking for another unit on AirBnB." The Game of Blackhat is a cat-and-mouse game. You need a lot of guardrails to protect yourself from people using your Social Site by spamming their products. Craigslist is a team of 30 people. There's stuff AI can automate now with such a small team but back then, it wasn't possible. Airbnb used Craigslist as its playground to spam Craigslist visitors to grow their supply-side. In a 2-sided marketplace, growing both supply and demand is very important. And both must grow at the same time for the marketplace to work. A Blackhat Marketer created a new test site to get vacation rental owners to sign-up so that he can test his Airbnb theory. He grabbed their real email-addresses (not Craigslist anonymous addresses) via Craigslist by specifically targeting those who were advertising their vacation rentals on Craigslist. He skipped over the other categories that were directly related to AirBnB's business model because they didn't fit with the test site he built. Once he got 1000+ sign-ups, he then took it upon himself to post it to the advertising section on Craigslist. The email said this: I am emailing you because you have one of the nicest listings on Craigslist in Idaho and I want to recommend you feature it (for free) on one of the largest Idaho housing sites on the web, Airbnb. The site already has 3,000,000 pages views a month. Check it out here to list now: airbnb(dot)com Sarah Surpisingly, all emails were by ladies. He did the same in Week 2 and Week 3 to test if it wasn't a one-time thing. Surely, it wasn't a fluke. After posting 4 ads on Craigslist in 3 weeks, he received 5 identical emails from 2 ladies who were raving fans of AirBnB and spent their days emailing Craigslist advertisers. This is one of the greatest blackhat strategies used in the real world to build a billion-dollar marketplace by growing the supply-side with pure blackhat. These strategies are not mentioned in Press Interviews, Media, or any Founder stories but this is probably the most important piece of the puzzle. Without it, Airbnb probably wouldn't have survived. "Some very famous investors have alluded to the fact that they look for a dangerous streak in the entrepreneurs they invest in…and while those investors will never come out and tell you what they mean, this kind of thing is probably what they mean." It definitely violates CAN-SPAM act. Some comments from Hacker News: "CAN-SPAM, sending from a fake address (illegal headers). CA has a specific law that pre-empts CAN-SPAM that definitely makes this illegal if sent from CA." But I guess it worked in Airbnb's favour lol as they were never caught or fined until after. "It's commercial email 100%. Probably a fake sender name (illegal), against gmail ToS, against CL ToS and no unsubscribe link and no one even subscribed in the first place. 100% against CAN-SPAM." Thanks for reading. If you'd like to learn more blackhat tactics like this, check this site which is a growth hacking newsletter with real-world blackhat examples. PS: Actual emails & screenshots from the Airbnb x Craigslist spam can be found here.

AI Will Make You Extremely Rich or Kill Your Business in 2024

Preface: I'm a solo-founder in the AI space and previously worked as an ML scientist; the new advancements in AI that I'm seeing are going to impact everyone here. It doesn't matter if you're just starting out, or a bootstrapped brick and mortar founder, or even a VC backed hard tech founder. Last year was when the seeds were laid, and this is the year we'll see them bloom. There will be an onslaught of advancements that take place that are borderline inconceivable due to the nature of exponential progress. This will change every single vertical. I'm making this post because I think AI execution strategy will make or break businesses. Dramatically. Over $50B was put into AI startups in 2023 alone. This figure excludes the hundreds of billions poured into AI from enterprises. So, let's follow the money: &#x200B; 1) AI enterprise software. There's a lot to unpack here and this is what I’m currently working on. AI enterprise software will encompass everything from hyper personalized email outbound to AI cold calls to AI that A/B tests ads on synthetic data to vertical specific software. The impact of the former is relatively self explanatory, so I'll focus on the latter. To illustrate vertical specific AI software, I'll use a simple example in the legal space. Lawyers typically have to comb through thousands of pages of documents. Now, using an LLM + a VDB, an AI can instantly answer all of those questions while surfacing the source and highlighting the specific answer in the contract/document. There are dozens of AI startups for this use case alone. This saves lawyers an immense amount of time and allows them to move faster. Firms that adopt this have a fundamental advantage over law firms that don't adopt this. This was 2023 technology. I'm seeing vertical AI software getting built by my friends in areas from construction, to real estate, to even niche areas like chimney manufacturing. This will exist everywhere. Now, this can be extrapolated much further to be applicable to systems that can do reports and even browse the Internet. This brings me to my next point. &#x200B; 2) AI information aggregation and spread. My gut tells me that this will have a crescendo moment in the future with hardware advancements (Rabbit, Tab, etc.). You won't have to google things because it will be surfaced to you. It's predictive in nature. The people who can get information the fastest will grow their business the fastest. This part is semi-speculative, but due to the nature of LLMs being so expensive to train, I have a strong feeling that large institutions will have access to the \fastest\ and \best\ models that can do this quicker than you and I can. This is why it's important to stay on top. &#x200B; 3) AI content generation This is relevant to running advertisements and any digital marketing aspect of your business. If you can rapidly make content faster than your competitors to put in social media, you will outpace your competitors rapidly. I think most folks are familiar with MidJourney, Stable diffusion, etc. but don't know how to use it. You can generate consistent models for a clothing brand or generate images of a product that you would normally need to hire a professional photographer to take. There's also elevenlabs which is relatively easy to use and can be used to make an MP3 clip as a narration for an ad; this is something I've already done. I'm also still shocked by how many people are unfamiliar with tools like Pika which can do video generation. You could imagine companies having fleets of digital influencers that they control or conjuring up the perfect ad for a specific demographic using a combination of all of the aforementioned tools. &#x200B; In summary, if you feel like I'm being hyperbolic or propagating science fiction fantasies, you're likely already behind. I truly recommend that everyone stays up to date on these advancements as much as possible. If your competitor comes across an AI tool that can increase their ROAS by 5x they can crush you. If your competitor uses a tool that increases the rate at which they receive and aggregate information by 200% (modest estimate) they will crush you. If your competitors have a tool that can reduce their employee size, then they will use it. They'll fire their employees to cut costs and reinvest the money back into their business. It will compound to the point where you're outpaced, and this isn't a level of innovation we've seen since the birth of the industrial revolution. Your customers can get stolen overnight, or you can steal your competition’s customers overnight. TL;DR: This is an opportunity for entrepreneurs to scale faster than they could have possibly imagined, but this also comes with the potential for your company to be obliterated. We've never seen advancements that can have this drastic of an impact this quickly. Adoption will happen fast, and first movers will have a disproportionate and compounding advantage. Watch guides, meet with startups, follow the news, and get rich.

Beginner to the 1st sale: my journey building an AI for social media marketers

Hey everyone! Here’s my journey building an AI for social media marketers all the way up until my first pre-launch sale, hope that could help some of you: My background: studied maths at uni before dropping out to have some startup experiences. Always been drawn to building new things so I reckoned I would have some proper SaaS experiences and see how VC-funded startups are doing it before launching my own.  I’ve always leaned towards taking more risks in my life so leaving my FT job to launch my company wasn’t a big deal for me (+ I’m 22 so still have time to fail over and over). When I left my job, I started reading a lot about UI/UX, no-code tools, marketing, sales and every tool a worthwhile entrepreneur needs to learn about. Given the complexity of the project I set out to achieve, I asked a more technical friend to join as a cofounder and that's when AirMedia was born. We now use bubble for landing page as I had to learn it and custom-code stack for our platform.  Here's our goal: streamlining social media marketing using AI. I see this technology has only being at the premises of what it will be able to achieve in the near-future. We want to make the experience dynamic i.e. all happens from a discussion and you see the posts being analysed from there as well as the creation process - all from within the chat. Fast forward a few weeks ago, we finished developing the first version of our tool that early users describe as a "neat piece of tech" - just this comment alone can keep me going for months :) Being bootstrapped until now, I decided to sell lifetime deals for the users in the waitlist that want to get the tool in priority as well as secure their spot for life. We've had the first sale the first day we made that public ! Now what you all are looking for: How ?  Here was my process starting to market the platform: I need a high-converting landing page so I reckoned which companies out there have the most data and knows what convert and what doesn’t: Unbounce. Took their landing page and adapted it to my value proposition and my ICP.  The ICP has been defined from day 1 and although I’m no one to provide any advice, I strongly believe the ICP has to be defined from day 1 (even before deciding the name of the company). It helps a lot when the customer is you and you’ve had this work experience that helps you identify the problems your users encounter. Started activating the network, posting on Instagram and LinkedIn about what we've built (I've worked in many SaaS start-ups in the past so I have to admit that's a bit of a cheat code). Cold outreach from Sales NAV to our ICP, been growing the waitlist in parallel of building the tool for months now so email marketings with drip sequences and sharing dev updates to build the trust along the way (after all we're making that tool for our users - they should be the first aware about what we're building). I also came across some Whatsapp groups with an awesome community that welcomed our platform with excitement.) The landing page funnel is the following: Landing page -> register waitlist -> upsell page -> confirmation. I've made several landing pages e.g. for marketing agencies, for real estate agents, for marketing director in several different industries. The goal now is just testing out the profiles and who does it resonate the most with. Another growth hack that got us 40+ people on the waitlist: I identified some Instagram posts from competitors where their CTA was "comment AI" and I'll send you our tool and they got over 2k people commenting. Needless to say, I messaged every single user to check out our tool and see if it could help them. (Now that i think about it, the 2% conversion rate there is not great - especially considering the manual labour and the time put behind it). We’ve now got over 400 people on the waitlist so I guess we’re doing something right but we’ll keep pushing as the goal is to sell these lifetime deals to have a strong community to get started. (Also prevents us from going to VCs and I can keep my time focussing exclusively on our users - I’m not into boardroom politics, just wanna build something useful for marketers). Now I’m still in the process of testing out different marketing strategies while developing and refining our platform to make it next level on launch day. Amongst those:  LinkedIn Sales Nav outreach (first sale came from there) Product Hunt Highly personalised cold emails (there I’m thinking of doing 20 emails a day with a personalised landing page to each of those highly relevant marketers). Never seen that and I think this could impress prospects but not sure it’s worth it time / conversion wise. Make content to could go viral (at least 75 videos) that I’m posting throughout several social media accounts such as airmedia\\, airmedia\reels, airmedia\ai (you get the hack) always redirecting to the main page both in the profile description and tagging the main account. I have no idea how this will work so will certainly update some of you that would like to know the results. Will do the same across Facebook, TikTok, Youtube Shorts etc… I’m just looking for a high potential of virality there. This strategy is mainly used to grow personal brands but never seen it applied to companies. Good old cold calling Reddit (wanna keep it transparent ;) ) I’m alone to execute all these strategies + working in parallel to refine the product upon user’s feedback I’m not sure I can do more than that for now. Let me know if you have any feedback/ideas/ tasks I could implement.  I could also make another post about the proper product building process as this post was about the marketing. No I certainly haven’t accomplished anything that puts me in a position to provide advices but I reckon I’m on my way to learn more and more. Would be glad if this post could help some of you.  And of course as one of these marketing channels is Reddit I’ll post the link below for the entrepreneurs that want to streamline their social media or support us. Hope I was able to provide enough value in this post for you to consider :) https://airmedia.uk/

I've been building stuff for years, I can build your idea

If anyone is interested at all, I'm a college student whose been massively passionate for entrepreneur and business stuff for years and can build anything from a marketplace to a social network, a booking app, a live streaming app, AI app*, what not. I'm really flexible in general and I'm also very reasonable with pricing. I like this community so I'd like to work on something I enjoy... (indeed... get out of my tabs please). Im a little passionate in general let's say, and I build all the time for fun. But I need money. So I'm here. Viola! Since I'm a college student, and this is my first time ever offering my services as well, chances are I won't expect anything past $500. I might even do it for cheaper if it looks doable for me. If you're generous that's up to you (lol). I'm happy to share my work and live previews you can interact with before we start. And, I'm happy to build things before you pay, so you can see it literally working :) THE "NEGATIVE"/catch: There will be no code from scratch as we'd build using no-code (www.bubble.io) and implement code as needed. This means you will not have the source code ever (even if we wanted to) and if you ever want your own from scratch platform/app you will have to find someone/a team to do it from scratch, as there's no option to export source code out. If you plan to grow past 1 million users, you may consider migrating to something built from scratch at this point scaling wise (and you can't get your source code - so you'll have to pay an entire dev team separately. If you have an app, you'll need a website developed, and Android and iOS developers for the app). For an MVP however, I personally think it's a faster, easier and cheaper way to get things running without investing a lot. THE GOOD: No-code/low-code will be a thousand times more easy, cheap, and fast to maintain. And if you're a startup, chances are you WILL need to tweak things... possibly a lot (100s vs. 1,000s difference in my opinion..). We can still build almost anything and also turn it into a mobile app for iOS / Android, though I'm more comfortable with getting a web app up first and the main posting here. But it depends on the complexity so just ask. Maintenance is likely to end up FAR cheaper and you can even do it free yourself if you decided to learn (which you could totally do in some days, and master in some weeks/months fully). I can build and document everything to be as beginner friendly as possible for you to be able to maintain it yourself if you wanted to learn. Send me a message describing what you need if you're interested. I will give you an estimate on price, time, and can send you some live previews. If we agree, I will start to build before any down payments to a (reasonable) point!:)

5 Habits to go from Founder to CEO

Over the years, I've gathered some knowledge about transitioning from a startup founder to a CEO. I started my company 7 years ago. We are now not super big (65 people), but we have learned a lot. We raised $19M in total and we are now profitable. The transition from Founder to CEO was crucial. Your startup begins to mature and scale and you need to scale with it. It's often a challenging phase, but I've managed to summarize it into five habbits. Say no to important things every day Being able to say "no" to important tasks every day is an essential practice for a growing leader. It's a reality that as the magnitude of your company or ideas expands, so does the influx of good ideas and opportunities. However, to transform from a mere hustler to a true leader, you have to become selective. This means learning to refuse good ideas, which is crucial if you want to consistently execute the outstanding ones. The concept that "Startups don't starve, they drown" resonates deeply because it underlines how challenging it can be to reject opportunities. A key strategy to develop this skill is time-constraining your to-do list. Here's how you can do it: Weekly: Formulate a weekly to-do list, including only those tasks that you're sure to complete within the week. Leave some buffer room for unexpected issues. If there's any doubt about whether you'll have time for a certain task, it should not feature on your weekly list. I use Todoist and Notion for task management. Daily: Apply the same rule while creating your daily to-do list. Only include tasks that you're confident about accomplishing that day. If a task seems too big to fit into one day, break it down into manageable chunks. Journaling Journaling is a powerful strategy that can help an individual transition from a reactive approach to a proactive one. As founders, we often find ourselves caught up in a cycle of endless tasks, akin to chopping trees in a dense forest. However, to ensure sustainable growth, it is crucial to develop an ability to "zoom out", or to view the bigger picture. I use The Morning Pages method, from Julia Cameron. It consists of writing each morning about anything that comes to mind. The act of writing effectively combines linear, focused thinking with the benefits of a thoughtful conversation. If you just want to journal, you can use Day One app (The free version will be enough). If you want to go a bit deeper, you can try a coaching app. I use Wave.ai and I also hired it for the managers in the company because it combines both journaling with habit building. &#x200B; Building Robust Systems and Processes (I know, it is boring and founders hate this) As a founder, you often need to wear multiple hats and juggle various roles. But as a CEO, it's vital to establish strong systems and processes that enable the business to function smoothly, even without your direct involvement. This includes: Implementing project management systems. Establishing clear lines of communication and accountability. Designing efficient workflows and procedures. To many founders, developing these systems might seem monotonous or even tedious. After all, the allure of envisioning the next big idea often proves more exciting. I experienced the same predicament. In response, I brought onboard a competent COO who excelled in systematizing processes. This strategy allowed me to kickstart initiatives and explore them in a flexible, less structured manner. Once an idea showed signs of gaining traction, my COO stepped in to streamline it, crafting a process that turned the fledgling idea into a consistent business operation. &#x200B; Meditating Meditation is about reprogramming unconscious mental processes by repeatedly performing fundamental tasks with a distinct intention. This practice can be even more crucial to leadership than acquiring a business school education. Because meditation provides the most direct route to understanding your mind's workings and thus, forms the most effective basis for transforming it. To transition from a founder to a CEO, a significant shift in your mindset is required. This shift involves moving from a hustle mentality to precision, from acting as a superhero solving problems to consciously stepping back, thereby providing room for your team members to discover their own superpowers. It's about shifting your success indicators - from individual achievements to the triumphs of your team. This transformation might not feel comfortable initially, and your instincts, shaped by your scrappy founder phase, might resist this change. However, with consistent practice, you can align your instincts with the stage of your company, promoting more effective leadership. This is where the value of meditation truly shines. It allows you to identify your distinct thought patterns in real time and, over time, modify them. I use Headspace a lot, and I also encourage the employees to use it. The company pays the subscription as a perk. &#x200B; Balancing the Macro and the Micro As the CEO, your primary focus should be on the big picture – your company's vision and strategy. However, you also need to keep an eye on the details, as these can make or break your execution. It's all about balance: Delegate the details but stay informed. Prioritize strategic planning but be ready to dive into the trenches when needed. Keep your eye on your long-term vision but adapt to short-term realities. The transition from founder to CEO isn't about giving up what made you successful initially but augmenting it with additional skills, perspectives, and practices. It's a personal and professional evolution that can lead to greater success for both you and your business. Every great CEO was once a founder. It's just about taking the next step. I’d love to hear your experiences or any tips you might have for this transition. In which step of your journey are you right now? Do you have employees already? What are your main challenges right now?

My boss taught me how to build a Failed business (15 lessons)

I'm a senior software developer at a three-year-old startup that has been making $0 in revenue. I've been with this startup since its beginning, and it pays me $1200/month. My boss has broken the records of the number of stupid ideas and stupid features that he asked me to implement. He taught me (unintentionally) all the lessons I should NOT do to build a successful business. From bad product ideas, bad business decisions, not listening to your team, not building what target customers want, and falling in love with your bad product. The product we're working on is a desktop program that moves the cursor with your finger using the webcam (gesture recognition). Why in the world would anyone pay money to move the mouse cursor with his finger? No one knows. My boss watched Iron Man (the film) and saw how Tony Starks do gestures in front of his "advanced" computer and thought it was cool so he asked me to build this for him to sell it to enterprises (then pivoted the target customer to schools). Of course, no one bought this software. All the people he meets tell him it is cool but he never hears from them again. No one on the team, except my boss, thinks this software will succeed. He keeps adding irrelevant features to this software just because he "thinks" people will love it. We added 3D object visualizer, ChatGPT integration, and Quizzes. I suggested moving everything to the cloud and focusing only on improving the education industry by providing solutions that help teachers better prepare their lessons and understand where each student lacks by recording lessons, summarizing them for students, generating quizzes using AI, and analyzing the part that each student didn't understand. However, to do that, we need to forget the part of moving the cursor with fingers because it can be done only on Python, not NextJS. He simply replied, "NO, moving the cursor with fingers is COOL". So here are the lessons I learned from my boss to build a failed business: Never listen to your team. Always build what you think is good and never let anyone from your team say it's a bad idea. Fall in love with your business idea. Don't talk to customers. If no one bought your product, it's because they don't understand how cool it is. If a member of your team say it's a bad idea, ignore them, they don't understand how cool your idea is. Always hire interns because they're free labor and give them the most sensitive parts of the work like payments and databases. Make your business dependant on you. Don't let your team do their job the right way, give them orders to do it YOUR way. Hire experts to tell them what to do not to tell you what to do and how to do it. Never do marketing because people will steal your idea. Ask your team "What you think?" but ignore them. If your wife and children think your product is cool then it's cool. Start a business in an industry that you know nothing about but act like you know everything. If no one is buying your product, keep adding irrelevant features that no one asked for. \--- Edit: I didn't mention all the "stupid" ideas I built for him so here you go: Replacing Zoom, Teams, and Meet meetings with meetings in the metaverse. Target customer: Enterprises. An app that lets you scroll through social media without touching your mobile screen (using gesture recognition). We didn't build this because it's technically impossible to continuously use the phone camera outside your own app. He didn't believe me so asked his friend and told him the same thing. A software that controls the computer with gestures (moving cursor, single click, double click, ALT Tab...). Target customers: Enterprises Building a classroom in Decentraland (metaverse) to replace classes through Zoom and Teams He told me to build the startup website but to not make the home page the first page a user lands on when he opens the website. He wants to make the visitor lands on another "almost" empty page and if the user wants to go to the home page he should click on "Home" in the navbar.

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

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 😛

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 😛

New Entrepreneur Looking to Learn

Hi all, long-time lurker, and first-time poster. About six weeks ago, I left my full-time career in tech to dive headfirst into launching an AI-focused startup. It’s my first time as a founder (well, co-founder), and the journey already feels exhilarating and terrifying at the same time! I’ve got a tech team onboard, and we are starting to build out our platform. To make sure I'm building the right thing, it's a top priority for me to connect with our target audience of small business owners for discovery conversations. I’m eager to learn about: How (and if) you’re currently using AI in your business. What kind of value/impact does AI need to deliver for you to be willing to use it in your business. What challenges or blockers do you perceive around implementing AI solutions. I’m open to speaking with US-based business owners with companies ranging from 5-50 employees or so, and am particularly interested if you are non-technical. If you’re willing to share your experience, I’d love to chat for 15-30 minutes. Feel free to comment here or DM me if you’re interested—your insights (and trolling) would mean the world as I navigate this journey. Thanks in advance! P.S. - I know I'm being a little cagey about the details of what my start-up is doing. While I don't think we have the most innovative idea in the world, I'd prefer to hold off on posting details publicly. This isn't a backdoor sales call, I'm just looking to ask questions and learn.

Unmasking Fake Testimonials on a YC backed company

As developers, marketeers and builders, we often rely on trusted platforms to guide us in finding tools that meet our unique needs. Recently, I stumbled upon Overlap, a site marketed as a haven for collaboration tools. Its sleek interface and glowing testimonials initially convinced me I had found a gem. But as I dug deeper, I uncovered a jaw-dropping reality: their testimonials featured stock images, all of which were easily identified through a quick reverse image search. Even more shocking was the realization that Overlap is a Y Combinator-backed company—an organization renowned for nurturing some of the most innovative startups in the world. With significant funding at their disposal, the decision to cut corners with fake testimonials felt like a slap in the face to their user base. They could easily afford a robust testimonial platform, yet chose a path that undermined their credibility. As developers, marketeers and builders, we often rely on trusted platforms to guide us in finding tools that meet our unique needs. Recently, I stumbled upon Overlap, a site marketed as a haven for video AI tools. Its sleek interface and glowing testimonials initially convinced me I had found a gem. But as I dug deeper, I uncovered a jaw-dropping reality: their testimonials featured stock images, all of which were easily identified through a quick reverse image search. Even more shocking was the realization that Overlap is a Y Combinator-backed company—an organization renowned for nurturing some of the most innovative startups in the world. With significant funding at their disposal, the decision to cut corners with fake testimonials felt like a slap in the face to their user base. They could easily afford a robust testimonial platform, yet chose a path that undermined their credibility. A screenshot of Overlap's landing page https://preview.redd.it/zosmdl0v01ce1.png?width=1000&format=png&auto=webp&s=83ced4af92ca284486281f00b020f1f0114b4fcd This discovery was nothing short of a wake-up call. For a developer-focused website—an audience that prizes authenticity and technical precision above all else—faking testimonials with stock photos isn’t just misleading, it’s a catastrophic betrayal of trust. It left me questioning the integrity of their entire operation and serves as a stark reminder for businesses everywhere: your audience notices when you’re not authentic, and they won’t forgive it easily. Position of Fake Testimonials One of the stock images https://preview.redd.it/a7ugasrw01ce1.png?width=341&format=png&auto=webp&s=5261df741f1198a92e537f1e61640e7d6ec60a7f Lessons for Startup Founders and Developers This experience offers several critical lessons for startup founders and developers alike: Authenticity is Non-Negotiable: In a competitive market, trust and transparency can make or break your brand. Fake testimonials might provide a short-term boost, but the long-term damage to credibility far outweighs any temporary gains. Invest in Genuine Solutions: If you have the resources, like a Y Combinator-backed company, prioritize tools and practices that enhance authenticity. Platforms like RapidFeedback allow businesses to dynamically update reviews and manage feedback efficiently. Leverage Real User Feedback: Authentic testimonials not only build trust but also provide actionable insights into your product’s strengths and weaknesses. This feedback loop can be invaluable for refining and growing your business. Understand Your Audience: Developers value precision, integrity, and honesty. Catering to this audience requires a commitment to these principles in every aspect of your business. Let’s ensure that the tools we build and the businesses we run prioritize authenticity. In the long run, a commitment to transparency and user trust will always yield greater rewards than any shortcut could provide. Why Fake Testimonials Are a Problem Fake testimonials damage your brand in more ways than one: Loss of Credibility: Developers are a discerning audience. Trust is everything, and losing it can be catastrophic for your reputation. Hurt User Experience: Knowing a platform misrepresents itself makes users skeptical about its features and promises. Missed Opportunities: Genuine feedback can provide valuable insights for growth and improvement, which fake testimonials completely overlook. A Smarter Way: Authentic Testimonials with RapidFeedback This experience reminded me of why tools like RapidFeedback are invaluable. RapidFeedback helps businesses maintain authenticity by dynamically updating reviews and images in real time. Here’s why it stands out: Real-Time Updates: Reviews are fetched and displayed dynamically, ensuring they’re always up-to-date. Dashboard Management: Businesses can monitor and manage good vs. bad reviews from a centralized dashboard, enabling them to address concerns promptly. Authenticity Guaranteed: Dynamic updates ensure that testimonials reflect real users and their experiences, which builds trust and credibility. Lessons for Developers and Businesses If there’s one takeaway from my Overlap experience, it’s this: authenticity isn’t optional. Whether you’re building tools for developers or selling consumer products, your audience values transparency. Using tools like RapidFeedback ensures your business maintains trust while gaining actionable insights to grow. Let’s commit to prioritizing honesty in our work. Because in the end, authentic relationships with users are what truly drive success.

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!

how I built a $6k/mo business with cold email

I scaled my SaaS to a $6k/mo business in under 6 months completely using cold email. However, the biggest takeaway for me is not a business that’s potentially worth 6-figure. It’s having a glance at the power of cold emails in the age of AI. It’s a rapidly evolving yet highly-effective channel, but no one talks about how to do it properly. Below is the what I needed 3 years ago, when I was stuck with 40 free users on my first app. An app I spent 2 years building into the void. Entrepreneurship is lonely. Especially when you are just starting out. Launching a startup feel like shouting into the dark. You pour your heart out. You think you have the next big idea, but no one cares. You write tweets, write blogs, build features, add tests. You talk to some lukewarm leads on Twitter. You do your big launch on Product Hunt. You might even get your first few sales. But after that, crickets... Then, you try every distribution channel out there. SEO Influencers Facebook ads Affiliates Newsletters Social media PPC Tiktok Press releases The reality is, none of them are that effective for early-stage startups. Because, let's face it, when you're just getting started, you have no clue what your customers truly desire. Without understanding their needs, you cannot create a product that resonates with them. It's as simple as that. So what’s the best distribution channel when you are doing a cold start? Cold emails. I know what you're thinking, but give me 10 seconds to change your mind: When I first heard about cold emailing I was like: “Hell no! I’m a developer, ain’t no way I’m talking to strangers.” That all changed on Jan 1st 2024, when I actually started sending cold emails to grow. Over the period of 6 months, I got over 1,700 users to sign up for my SaaS and grew it to a $6k/mo rapidly growing business. All from cold emails. Mastering Cold Emails = Your Superpower I might not recommend cold emails 3 years ago, but in 2024, I'd go all in with it. It used to be an expensive marketing channel bootstrapped startups can’t afford. You need to hire many assistants, build a list, research the leads, find emails, manage the mailboxes, email the leads, reply to emails, do meetings. follow up, get rejected... You had to hire at least 5 people just to get the ball rolling. The problem? Managing people sucks, and it doesn’t scale. That all changed with AI. Today, GPT-4 outperforms most human assistants. You can build an army of intelligent agents to help you complete tasks that’d previously be impossible without human input. Things that’d take a team of 10 assistants a week can now be done in 30 minutes with AI, at far superior quality with less headaches. You can throw 5000 names with website url at this pipeline and you’ll automatically have 5000 personalized emails ready to fire in 30 minutes. How amazing is that? Beyond being extremely accessible to developers who are already proficient in AI, cold email's got 3 superpowers that no other distribution channels can offer. Superpower 1/3 : You start a conversation with every single user. Every. Single. User. Let that sink in. This is incredibly powerful in the early stages, as it helps you establish rapport, bounce ideas off one another, offer 1:1 support, understand their needs, build personal relationships, and ultimately convert users into long-term fans of your product. From talking to 1000 users at the early stage, I had 20 users asking me to get on a call every week. If they are ready to buy, I do a sales call. If they are not sure, I do a user research call. At one point I even had to limit the number of calls I took to avoid burnout. The depth of the understanding of my customers’ needs is unparalleled. Using this insight, I refined the product to precisely cater to their requirements. Superpower 2/3 : You choose exactly who you talk to Unlike other distribution channels where you at best pick what someone's searching for, with cold emails, you have 100% control over who you talk to. Their company Job title Seniority level Number of employees Technology stack Growth rate Funding stage Product offerings Competitive landscape Social activity (Marital status - well, technically you can, but maybe not this one…) You can dial in this targeting to match your ICP exactly. The result is super low CAC and ultra high conversion rate. For example, My competitors are paying $10 per click for the keyword "HARO agency". I pay $0.19 per email sent, and $1.92 per signup At around $500 LTV, you can see how the first means a non-viable business. And the second means a cash-generating engine. Superpower 3/3 : Complete stealth mode Unlike other channels where competitors can easily reverse engineer or even abuse your marketing strategies, cold email operates in complete stealth mode. Every aspect is concealed from end to end: Your target audience Lead generation methods Number of leads targeted Email content Sales funnel This secrecy explains why there isn't much discussion about it online. Everyone is too focused on keeping their strategies close and reaping the rewards. That's precisely why I've chosen to share my insights on leveraging cold email to grow a successful SaaS business. More founders need to harness this channel to its fullest potential. In addition, I've more or less reached every user within my Total Addressable Market (TAM). So, if any competitor is reading this, don't bother trying to replicate it. The majority of potential users for this AI product are already onboard. To recap, the three superpowers of cold emails: You start a conversation with every single user → Accelerate to PMF You choose exactly who you talk to → Super-low CAC Complete stealth mode → Doesn’t attract competition By combining the three superpowers I helped my SaaS reach product-marketing-fit quickly and scale it to $6k per month while staying fully bootstrapped. I don't believe this was a coincidence. It's a replicable strategy for any startup. The blueprint is actually straightforward: Engage with a handful of customers Validate the idea Engage with numerous customers Scale to $5k/mo and beyond More early-stage founders should leverage cold emails for validation, and as their first distribution channel. And what would it do for you? Update: lots of DM asking about more specifics so I wrote about it here. https://coldstartblueprint.com/p/ai-agent-email-list-building

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.

Secret behind Airbnb's Billion-Dollar Empire? Spamming Craigslist

Silicon Valley wants you to believe that their unicorn startups succeeded doing things legally. But that couldn't be far from truth. For starters, Airbnb used multiple Gmail accounts to spam Craigslist. "They posted unrealistically (fake) cheap rentals of beautiful apartments in places where normal rent should be 10x more. Once people replied, they auto-responded that the unit has been rented, but they should be looking for another unit on AirBnB." The Game of Blackhat is a cat-and-mouse game. You need a lot of guardrails to protect yourself from people using your Social Site by spamming their products. Craigslist is a team of 30 people. There's stuff AI can automate now with such a small team but back then, it wasn't possible. Airbnb used Craigslist as its playground to spam Craigslist visitors to grow their supply-side. In a 2-sided marketplace, growing both supply and demand is very important. And both must grow at the same time for the marketplace to work. A Blackhat Marketer created a new test site to get vacation rental owners to sign-up so that he can test his Airbnb theory. He grabbed their real email-addresses (not Craigslist anonymous addresses) via Craigslist by specifically targeting those who were advertising their vacation rentals on Craigslist. He skipped over the other categories that were directly related to AirBnB's business model because they didn't fit with the test site he built. Once he got 1000+ sign-ups, he then took it upon himself to post it to the advertising section on Craigslist. The email said this: I am emailing you because you have one of the nicest listings on Craigslist in Idaho and I want to recommend you feature it (for free) on one of the largest Idaho housing sites on the web, Airbnb. The site already has 3,000,000 pages views a month. Check it out here to list now: airbnb(dot)com Sarah Surpisingly, all emails were by ladies. He did the same in Week 2 and Week 3 to test if it wasn't a one-time thing. Surely, it wasn't a fluke. After posting 4 ads on Craigslist in 3 weeks, he received 5 identical emails from 2 ladies who were raving fans of AirBnB and spent their days emailing Craigslist advertisers. This is one of the greatest blackhat strategies used in the real world to build a billion-dollar marketplace by growing the supply-side with pure blackhat. These strategies are not mentioned in Press Interviews, Media, or any Founder stories but this is probably the most important piece of the puzzle. Without it, Airbnb probably wouldn't have survived. "Some very famous investors have alluded to the fact that they look for a dangerous streak in the entrepreneurs they invest in…and while those investors will never come out and tell you what they mean, this kind of thing is probably what they mean." It definitely violates CAN-SPAM act. Some comments from Hacker News: "CAN-SPAM, sending from a fake address (illegal headers). CA has a specific law that pre-empts CAN-SPAM that definitely makes this illegal if sent from CA." But I guess it worked in Airbnb's favour lol as they were never caught or fined until after. "It's commercial email 100%. Probably a fake sender name (illegal), against gmail ToS, against CL ToS and no unsubscribe link and no one even subscribed in the first place. 100% against CAN-SPAM." Thanks for reading. If you'd like to learn more blackhat tactics like this, check this site which is a growth hacking newsletter with real-world blackhat examples. PS: Actual emails & screenshots from the Airbnb x Craigslist spam can be found here.

feedback please - working concept of ai sales guide for entrepreneurs & bootstrappers

hey r/Entrepreneur - I've been working tirelessly the past 7 days on developing a new concept called sales sage, featuring an ai guide named aether that evolves with your startup. I'd really appreciate your feedback on this so far. what is sales sage? sales sage is a platform built to help technical founders bridge the gap between innovative products and effective sales strategies. at its core is "aether", an ai sales guide that evolves as your startup grows. how does aether work? aether has four evolutionary stages, each aligned with a startup growth phase: aether, the spark (bootstrapping) aether, the cultivator (pre-seed) aether, the crafter (seed) aether, the luminary (series a & beyond) as your startup progresses, aether evolves, offering more advanced insights and strategies. key features: ai-generated resources tailored to your growth stage outcome-based pricing (you succeed, we succeed) evolving guidance from basic strategies to advanced, ai-driven insights I'd love your feedback on: the concept of an evolving ai guide for startups the naming and stages of aether's evolution how this might be useful (or not) for technical founders any concerns or potential improvements you see thanks in advance for your insights! we're excited to hear what the reddit community thinks about this approach to supporting startup growth.

AI Will Make You Extremely Rich or Kill Your Business in 2024

Preface: I'm a solo-founder in the AI space and previously worked as an ML scientist; the new advancements in AI that I'm seeing are going to impact everyone here. It doesn't matter if you're just starting out, or a bootstrapped brick and mortar founder, or even a VC backed hard tech founder. Last year was when the seeds were laid, and this is the year we'll see them bloom. There will be an onslaught of advancements that take place that are borderline inconceivable due to the nature of exponential progress. This will change every single vertical. I'm making this post because I think AI execution strategy will make or break businesses. Dramatically. Over $50B was put into AI startups in 2023 alone. This figure excludes the hundreds of billions poured into AI from enterprises. So, let's follow the money: &#x200B; 1) AI enterprise software. There's a lot to unpack here and this is what I’m currently working on. AI enterprise software will encompass everything from hyper personalized email outbound to AI cold calls to AI that A/B tests ads on synthetic data to vertical specific software. The impact of the former is relatively self explanatory, so I'll focus on the latter. To illustrate vertical specific AI software, I'll use a simple example in the legal space. Lawyers typically have to comb through thousands of pages of documents. Now, using an LLM + a VDB, an AI can instantly answer all of those questions while surfacing the source and highlighting the specific answer in the contract/document. There are dozens of AI startups for this use case alone. This saves lawyers an immense amount of time and allows them to move faster. Firms that adopt this have a fundamental advantage over law firms that don't adopt this. This was 2023 technology. I'm seeing vertical AI software getting built by my friends in areas from construction, to real estate, to even niche areas like chimney manufacturing. This will exist everywhere. Now, this can be extrapolated much further to be applicable to systems that can do reports and even browse the Internet. This brings me to my next point. &#x200B; 2) AI information aggregation and spread. My gut tells me that this will have a crescendo moment in the future with hardware advancements (Rabbit, Tab, etc.). You won't have to google things because it will be surfaced to you. It's predictive in nature. The people who can get information the fastest will grow their business the fastest. This part is semi-speculative, but due to the nature of LLMs being so expensive to train, I have a strong feeling that large institutions will have access to the \fastest\ and \best\ models that can do this quicker than you and I can. This is why it's important to stay on top. &#x200B; 3) AI content generation This is relevant to running advertisements and any digital marketing aspect of your business. If you can rapidly make content faster than your competitors to put in social media, you will outpace your competitors rapidly. I think most folks are familiar with MidJourney, Stable diffusion, etc. but don't know how to use it. You can generate consistent models for a clothing brand or generate images of a product that you would normally need to hire a professional photographer to take. There's also elevenlabs which is relatively easy to use and can be used to make an MP3 clip as a narration for an ad; this is something I've already done. I'm also still shocked by how many people are unfamiliar with tools like Pika which can do video generation. You could imagine companies having fleets of digital influencers that they control or conjuring up the perfect ad for a specific demographic using a combination of all of the aforementioned tools. &#x200B; In summary, if you feel like I'm being hyperbolic or propagating science fiction fantasies, you're likely already behind. I truly recommend that everyone stays up to date on these advancements as much as possible. If your competitor comes across an AI tool that can increase their ROAS by 5x they can crush you. If your competitor uses a tool that increases the rate at which they receive and aggregate information by 200% (modest estimate) they will crush you. If your competitors have a tool that can reduce their employee size, then they will use it. They'll fire their employees to cut costs and reinvest the money back into their business. It will compound to the point where you're outpaced, and this isn't a level of innovation we've seen since the birth of the industrial revolution. Your customers can get stolen overnight, or you can steal your competition’s customers overnight. TL;DR: This is an opportunity for entrepreneurs to scale faster than they could have possibly imagined, but this also comes with the potential for your company to be obliterated. We've never seen advancements that can have this drastic of an impact this quickly. Adoption will happen fast, and first movers will have a disproportionate and compounding advantage. Watch guides, meet with startups, follow the news, and get rich.

Best use cases of AI for entrepreneurs

Hey guys, I'm an agency owner and I work with entrepreneurs and startups all the time. Here are the 4 best use-cases I've seen for AI in the everyday grind of entrepreneurship: Brainstorming (ChatGPT): When you're trying to figure out how to solve something or you just want someone (something?) to go back and forth with on an idea or problem Legal / document creation (Cimphony): Corporate docs, legal docs, you name it. Cimphony helps reduce legal expenses without sacrificing quality. SEO optimized article creation (Scalenut): There are many different services for this, but I like Scalenut's all-in-one solution. Creation of short-form videos (Opus clips): Make a longer video and automatically create snippets from it to share on socials. This helped several clients 5X their social following. PS. Notice how I didn't say reddit posts? This was written 100% by a human.

AI Content Campaign Got 4M impressions, Thousands of Website Views, Hundreds of Customers for About $100 — This is the future of marketing

Alright. So, a few months ago I tested a marketing strategy for a client that I’ve sense dedicated my life to developing on. The Idea was to take the clients Pillar content (their YouTube videos) and use AI to rewrite the content for all the viable earned media channels (mainly Reddit). The campaign itself was moderately successful. To be specific, after one month it became their 2nd cheapest customer acquisition cost (behind their organic YouTube content). But there is a lot to be done to improve the concept. I will say, having been in growth marketing for a decade, I felt like I had hit something big with the concept. I’m going to detail how I built that AI system, and what worked well and what didn’t here. Hopefully you guys will let me know what you think and whether or not there is something here to keep working on. DEFINING THE GOAL Like any good startup, their marketing budget was minimal. They wanted to see results, fast and cheap. Usually, marketers like me hate to be in this situation because getting results usually either takes time or it takes money. But you can get results fast and cheap if you focus on an earned media strategy - basically getting featured in other people’s publication. The thing is these strategies are pretty hard to scale or grow over time. That was a problem for future me though. I looked through their analytics and saw they were getting referral traffic from Reddit - it was their 5th or 6th largest source of traffic - and they weren’t doing any marketing on the platform. It was all digital word of mouth there. It kind of clicked for me there, that Reddit might be the place to start laying the ground work. So with these considerations in mind the goal became pretty clear: Create content for relevant niche communities on Reddit with the intent of essentially increasing brand awareness. Use an AI system to repurpose their YouTube videos to keep the cost of producing unique content for each subreddit really low. THE HIGH-LEVEL STRATEGY I knew that there are huge amounts of potential customers on Reddit (About 12M people in all the relevant communities combined) AND that most marketers have a really tough time with the platform. I also knew that any earned media strategy, Reddit or not, means Click Through Rates on our content would be extremely low. A lot of people see this as a Reddit specific problem because you can’t self-promote on the platform, but really you have to keep self-promotion to a minimum with any and all earned media. This basically meant we had to get a lot of impressions to make up for it. The thing about Reddit is if your post absolutely crushes it, it can get millions of views. But crushing it is very specific to what the expectations are of that particular subreddit. So we needed to make content that was specifically written for that Subreddit. With that I was able to essentially design how this campaign would work: We would put together a list of channels (specifically subreddits to start) that we wanted to create content for. For each channel, we would write a content guideline that details out how to write great content for this subreddit. These assets would be stored in an AirTable base, along with the transcripts of the YouTube videos that were the base of our content. We would write and optimize different AI Prompts that generated different kinds of posts (discussion starters about a stock, 4-5 paragraph stock analysis, Stock update and what it means, etc…) We would build an automation that took the YouTube transcripts, ran each prompt on it, and then edited each result to match the channel writing guidelines. And then we would find a very contextual way to leave a breadcrumb back to the client. Always as part of the story of the content. At least, this is how I originally thought things would go. CHOOSING THE RIGHT SUBREDDITS Picking the right communities was vital. Here’s the basic rubric we used to pick and prioritize them: • Relevance: We needed communities interested in stock analysis, personal finance, or investing. • Subreddit Size vs. Engagement: Large subreddits offer more potential impressions but can be less focused. Smaller subreddits often have higher engagement rates. • Content Feasibility: We had to ensure we could consistently create high-value posts for each chosen subreddit. We started with about 40 possibilities, then narrowed it down to four or five that consistently delivered upvotes and user signups. CREATING CHANNEL-SPECIFIC GUIDES By the end, creating channel specific writing guidelines looked like a genius decision. Here’s how we approached it and used AI to get it done quickly: Grabbed Top Posts: We filtered the subreddit’s top posts (change filter to “Top” and then “All Time”) of all time to see the kinds of content that performed best Compiled The Relevant Posts: We took the most relevant posts to what we were trying to do and put them all on one document (basically created one document per subreddit that just had the top 10 posts in that subreddit). Had AI Create Writing Guideline Based On Posts: For each channel, we fed the document with the 10 posts with the instructions “Create a writing guideline for this subreddit based on these high performing posts. I had to do some editing on each guideline but this worked pretty well and saved a lot of time. Each subreddit got a custom guideline, and we put these inside the “Channels” table of the AirTable base we were developing with these assets. BUILDING THE AI PROMPTS THAT GENERATED CONTENT Alright this is probably the most important section so I’ll be detailed. Essentially, we took all the assets we developed up until this point, and used them to create unique posts for each channel. This mean each AI prompt was about 2,000 words of context and produced about a 500-word draft. There was a table in our AirTable where we stored the prompts, as I alluded to earlier. And these were basically the instructions for each prompt. More specifically, they detailed out our expectations for the post. In other words, there were different kinds of posts that performed well on each channel. For example, you can write a post that’s a list of resources (5 tools we used to…), or a how to guide (How we built…), etc.. Those weren’t the specific ones we used, but just wanted to really explain what I meant there. That actual automation that generated the content worked as follows: New source content (YouTube video transcript) was added to the Source Content table. This triggered the Automation. The automation grabbed all the prompts in the prompt table. For each prompt in the prompt table, we sent a prompt to OpenAI (gpt-4o) that contained first the prompt and also the source content. Then, for each channel that content prompt could be used on, we sent another prompt to OpenAI that revised the result of the first prompt based on the specific channel guidelines. The output of that prompt was added to the Content table in AirTable. To be clear, our AirTable had 4 tables: Content Channels Prompts Source Content The Source Content, Prompts, and Channel Guidelines were all used in the prompt that generated content. And the output was put in the Content table. Each time the automation ran, the Source Content was turned into about 20 unique posts, each one a specific post type generated for a specific channel. In other words, we were create a ton of content. EDITING & REFINING CONTENT The AI drafts were never perfect. Getting them Reddit-ready took editing and revising The main things I had to go in and edit for were: • Tone Adjustments: We removed excessively cliche language. The AI would say silly things like “Hello fellow redditors!” which sound stupid. • Fact-Checking: Financial data can be tricky. We discovered AI often confused figures, so we fact check all stock related metrics. Probably something like 30-40% error rate here. Because the draft generation was automated, that made the editing and getting publish ready the human bottleneck. In other words, after creating the system I spent basically all my time reviewing the content. There were small things I could do to make this more efficient, but not too much. The bigger the model we used, the less editing the content needed. THE “BREADCRUMB” PROMOTION STRATEGY No where in my prompt to the AI did I mention that we were doing any marketing. I just wanted the AI to focus on creating content that would do well on the channel. So in the editing process I had to find a way to promote the client. I called it a breadcrumb strategy once and that stuck. Basically, the idea was to never overtly promote anything. Instead find a way to leave a breadcrumb that leads back to the client, and let the really interested people follow the trail. Note: this is supposed to be how we do all content marketing. Some examples of how we did this were: Shared Visuals with a Subtle Watermark: Because our client’s product offered stock data, we’d often include a chart or graph showing a company’s financial metric with the client’s branding in the corner. Added Supporting Data from Client’s Website: If we mentioned something like a company’s cash flow statement, we could link to that company’s cash flow statement on the client’s website. It worked only because there was a lot of data on the client’s website that wasn’t gated. These tactics were really specific to the client. Which is should be. For other companies I would rethink what tactics I use here. THE RESULTS I’m pretty happy with the results • Impressions: – Early on posts averaged \~30,000 apiece, but after about a month of optimization, we hit \~70,000 impressions average. Over about two months, we reached 4 million total impressions. • Signups: – In their signups process there was one of those “Where did you find us?” questions and the amount of people who put Reddit jumped into the few hundred a month. Precise tracking of this is impossible. • Cost Efficiency (This is based on what I charged, and not the actual cost of running the campaign which is about $100/mo): – CPM (cost per thousand impressions) was about $0.08, which is far better than most paid channels. – Cost per free user: \~$8-10. After about a 10% conversion rate to a paid plan, our cost per paying user was $80–$100—well below the client’s previous $300–$400. HIGHLIGHTS: WHAT WORKED Subreddit-Specific Content: – Tailoring each post’s format and length to the audience norms boosted engagement. Worked out really well. 1 post got over 1M views alone. We regularly had posts that had hundreds of thousands. Breadcrumbs: – We never had anyone call us out for promoting. And really we weren’t. Our first priority was writing content that would crush on that subreddit. Using the Founder’s Existing Material: – The YouTube transcripts grounded the AI’s content in content we already made. This was really why we were able to produce so much content. CHALLENGES: WHAT DIDN’T WORK AI is still off: – Maybe it’s expecting too much, but still I wish the AI had done a better job. I editing a lot of content. Human oversight was critical. Scheduling all the content was a pain: – Recently I automated this pretty well. But at first I was scheduling everything manually and scheduling a hundred or so posts was a hassle. Getting Data and Analytics: – Not only did we have not very good traffic data, but the data from reddit had to be collected manually. Will probably automate this in the future. COST & TIME INVESTMENT Setup: The setup originally took me a couple weeks. I’ve since figured out how to do much faster (about 1 week). AirTable Setup here was easy and the tools costs $24/mo so not bad. ChatGPT costs were pretty cheap. Less than $75 per month. I’ve sense switched to using o1 which is much more expensive but saves me a lot of editing time Human Editing: Because this is the human part of the process and everything else was automated it mean by default all my time was spent editing content. Still this was a lot better than creating content from scratch probably by a factor of 5 or 10. The main expense was paying an editor (or using your own time) to refine posts. Worth it? Yes even with the editing time I was able to generate way more content that I would have otherwise. LESSONS & ACTIONABLE TAKEAWAYS Reddit as a Growth Channel: – If you genuinely respect each subreddit’s culture, you can achieve massive reach on a tight budget. AI + Human Collaboration: – AI excels at first drafts, but human expertise is non-negotiable for polishing and ensuring factual integrity. Soft Promotion Wins: – The “breadcrumb” approach paid off. It might feel like too light a touch, but is crucial for Reddit communities. Create once, repurpose as many times as possible: – If you have blog posts, videos, podcasts, or transcripts, feed them into AI to keep your message accurate and brand-consistent. CONCLUSION & NEXT STEPS If you try a similar approach: • Begin with smaller tests in a few niches to learn what resonates. • Create a clear “channel guide” for each community. • Carefully fact-check AI-generated posts. • Keep brand mentions low-key until you’ve established credibility.

Secret behind Airbnb's Billion-Dollar Empire? Spamming Craigslist

Silicon Valley wants you to believe that their unicorn startups succeeded doing things legally. But that couldn't be far from truth. For starters, Airbnb used multiple Gmail accounts to spam Craigslist. "They posted unrealistically (fake) cheap rentals of beautiful apartments in places where normal rent should be 10x more. Once people replied, they auto-responded that the unit has been rented, but they should be looking for another unit on AirBnB." The Game of Blackhat is a cat-and-mouse game. You need a lot of guardrails to protect yourself from people using your Social Site by spamming their products. Craigslist is a team of 30 people. There's stuff AI can automate now with such a small team but back then, it wasn't possible. Airbnb used Craigslist as its playground to spam Craigslist visitors to grow their supply-side. In a 2-sided marketplace, growing both supply and demand is very important. And both must grow at the same time for the marketplace to work. A Blackhat Marketer created a new test site to get vacation rental owners to sign-up so that he can test his Airbnb theory. He grabbed their real email-addresses (not Craigslist anonymous addresses) via Craigslist by specifically targeting those who were advertising their vacation rentals on Craigslist. He skipped over the other categories that were directly related to AirBnB's business model because they didn't fit with the test site he built. Once he got 1000+ sign-ups, he then took it upon himself to post it to the advertising section on Craigslist. The email said this: I am emailing you because you have one of the nicest listings on Craigslist in Idaho and I want to recommend you feature it (for free) on one of the largest Idaho housing sites on the web, Airbnb. The site already has 3,000,000 pages views a month. Check it out here to list now: airbnb(dot)com Sarah Surpisingly, all emails were by ladies. He did the same in Week 2 and Week 3 to test if it wasn't a one-time thing. Surely, it wasn't a fluke. After posting 4 ads on Craigslist in 3 weeks, he received 5 identical emails from 2 ladies who were raving fans of AirBnB and spent their days emailing Craigslist advertisers. This is one of the greatest blackhat strategies used in the real world to build a billion-dollar marketplace by growing the supply-side with pure blackhat. These strategies are not mentioned in Press Interviews, Media, or any Founder stories but this is probably the most important piece of the puzzle. Without it, Airbnb probably wouldn't have survived. "Some very famous investors have alluded to the fact that they look for a dangerous streak in the entrepreneurs they invest in…and while those investors will never come out and tell you what they mean, this kind of thing is probably what they mean." It definitely violates CAN-SPAM act. Some comments from Hacker News: "CAN-SPAM, sending from a fake address (illegal headers). CA has a specific law that pre-empts CAN-SPAM that definitely makes this illegal if sent from CA." But I guess it worked in Airbnb's favour lol as they were never caught or fined until after. "It's commercial email 100%. Probably a fake sender name (illegal), against gmail ToS, against CL ToS and no unsubscribe link and no one even subscribed in the first place. 100% against CAN-SPAM." Thanks for reading. If you'd like to learn more blackhat tactics like this, check this site which is a growth hacking newsletter with real-world blackhat examples. PS: Actual emails & screenshots from the Airbnb x Craigslist spam can be found here.

AI Automation Agency, the Future for Solopreneurs?

I want to take a moment to discuss AI automation agencies. If they are any good for new entrepreneurs. Or on the flip side what is wrong with them. &#x200B; Normally when you see something promising to make you thousands of dollars, for very little work, you run the other way. But you see I am not most people, and I love stuff like this. So, when I saw, AI Automation Agencies (AAA) promising to make me thousands of dollars, I ran straight down that rabbit hole. With no hesitation… It was a new term and idea, that I had already played around with. Due to the inherent nature of businesses and AI at the time. It was 100% an opportunity with a potential market down the line. What is an AI Automation Agency? On the surface, an AAA is using AI to automate and augment business processes. With a combination of using no code AI tools, AI LLMs, and simple automation process tools (Zapier). The whole premise of the AAA is to help companies reduce expenses and increase profits. Whether that is through improving business processes or cutting out easy-to-replace jobs. AAAs are all about optimizing your business (The best way to think about it). Run through a quick scenario with me: Say you are a simple e-commerce store, selling your favorite product. I show up, as an AAA, promising to automate your customer service platform. I can build you a fully automated customer service chatbot, and help you answer specific customer questions with AI. With the promises of a faster, more efficient, and more effective customer service platform. Being able to perform 80% of your current team’s work. Would you take the offer? It is a no-brainer, right? That is the premise behind this business model. Make businesses more effective. Which in turn makes them more profitable. A win-win for everyone. Take a look at some of the products an AAA might sell. Robotic Process Automation: Automating repetitive tasks in a business. AI- Power Analytics: Helping businesses understand and act on insights in their data. Sentiment Analysis: Analyzing how customers think and feel about products and markets. Customer Service: AI chatbots for customer questions. Productivity: Help augment processes with AI to cut down on time. Any process in a business that you fully understand you can augment and or automate with AI. And guess what? It is an open market but for good reason… Too Good to be True? The reason that this new business model is wide open is quite funny. No business cares about AI right now. Businesses are too focused to worry about AI and its upsides. Focused on the day-to-day operations, and not worried about AI. Make a few cold calls, and see how many leads you get… At the moment the offer does not resonate with potential clients. Meaning you need to have a massive advertising budget to get any leads. Because no one cares or sees any benefit, they will just brush you off. Which becomes an endless cycle of paid ads, and constant cold calling, just to find any business. So why is this model even popular? The gurus…that’s why. They have the budget for ads and get clients from their videos. Effectively throwing money at the problem. At least until it works. Do not get me wrong, AI automation is going to change businesses. But not right now. The whole growth of this business model is being pushed by influencers and gurus. People that can afford the cost of the startup. Telling others that it is a feasible one-person business. That anyone with no money can do, with a few simple steps. And that is just not the case. This has been a trend for any new profitable and “easy” business model. The gurus get there first, promote the model, show how simple it is, and rope everyone in. Eventually up selling a course on how to do it, or maybe even a community. You’ve seen it with ChatGPT, Facebook ads, SMMA, and so much more. It is a constant cycle that you need to be aware of. The End Result Good news, there is an alternative. It is using a combination of SMMA and AAA. Gathering leads using SMMA. Creating a great offer for your niche. And selling them on the service you can provide through marketing. Then once they are sold, you upsell them on AI automation. Easy to start, low cost, and super effective. Although unproven. It makes complete sense why it would work. It is beginner friendly, with plenty of SMMA tutorials online. With low barriers to entry. Making it a very inciting opportunity. AAA is going to be the future of business. It is a million-dollar opportunity for anyone. But with most startups, it takes skills and capital. With a façade of being easy to operate and start, pushed by gurus. More entrepreneur hopefuls find themselves debating starting an AAA. And guess what, it isn’t a good idea… Do your research to understand the market you want to enter, and how your business is going to operate. And don’t fall for get-rich-quick schemes. Ps. Check out this video if you want to learn more…

Only 2 months of cash in the Bank for my business but was able to save it with the help of AI.

Hi there! I’m excited to share something very personal with you. We needed to book at least 2 appointments per day in the next 60 days, or my business would fail. We were already trying two acquisition channels, LinkedIn and email. The problem with these channels was that the positive response rate was very low in both. So I decided to focus on LinkedIn and get the attention of the lead by sending videos directly to them via LinkedIn messages. (You can send videos to your connections on LinkedIn if you use your cell phone.) This wasn’t new, but I added a small twist to get the lead’s attention. All the covers of the videos had a picture of me holding a sign with the person’s name and an interesting phrase. This showed some okay results, but the rest of the video was not personalized. Only the picture on the cover was. I even developed a Chrome extension for this because I thought this would be the answer and that I would book tons of appointments.  But after more trial and outreach, my leads responded, telling me that because the video itself wasn’t personalized for them, they felt like I didn’t put enough effort in, so they would not book a call with me. So after investing time and effort into my “new bright idea” and getting developers to make the Chrome extension, I was back to square one with no results. A few weeks went by, and after researching online, I found an online course from a guy who promised to teach me how to book 30+ appointments per month, guaranteed (at the time, I was making 2 or 3 appointments per week, maximum). He promised that I would only pay if he actually booked appointments for me and even offered to give me money if his course didn’t work for me. I never paid attention to internet gurus, but the offer was actually not bad, so I looked into this guy’s website. I found out he had hundreds of reviews from people who had taken his course and were talking amazing things about it. The more I read, the more excited I got. I booked a call that day and talked to a salesperson. The call was very short, and he promised I would get at least 2 appointments per day, easily. He seemed a bit cocky and told me that I just needed to trust him and the 100+ reviews from people who had taken the course. He didn’t share details, a proposal, or anything. I asked the price, and he told me it was close to $10k. (Not kidding, this was the price.) Then he told me that I would make the money back in no time with the clients I would get following his course, and that if it didn’t work, he would give me the money back. But I needed to follow everything the course said for at least 6 months. I had never paid $10k for anything in my life; it was extremely expensive for me. Also, my salary from my business was not in dollars but in a currency that was worth much less than the dollar. I continued to research more and more, but no other course was close to the number of reviews and promises that this guy had. I got desperate and told myself that I would bet everything on this course. If it worked for so many others, surely it would work for me. I got a loan from the bank and paid for the course. You might read this and think it was the most stupid thing ever, but the reality is that after 2 months in the course (I did the course as fast as I could), I learned a lot. The course was not bad; it was very extensive—probably more than 200 hours or so—and they taught a lot of things. I don’t think it was worth $10k for me, but I can see how for other people it might be worth that. Now, to the question you’re all thinking: did it get me the 2 appointments I needed per day? The answer is no. Here’s the thing: most of the techniques they taught were innovative and disruptive, but the focus was always on personalization, and they didn’t teach any way to automate the personalization. (I think, at the time they made the course, the tools didn’t exist yet.) So they taught how to do everything manually, and it took a lot—a lot of time and effort. And most annoyingly: an incredible amount of time doing operational things. I did get 2 appointments on some days, but it wasn’t consistent, and I didn’t have the time to spend 14 hours a day doing everything manually or the money to hire someone to do this for me. (I needed to also spend time delivering our service to our current clients; otherwise, they would leave.) I told them this, and they were very reasonable. After some negotiation, they gave me part of the money back. (To be fair, there was a lot of value in the course, so asking for the full $10k back would have been excessive because, in the end, it really taught me a lot of things I didn’t know.) So in the end, I spent $10k and 200+ hours on an online course, spent time and effort developing a Chrome extension, and was still not able to hit the meetings I needed. Money in the business was running out, and I needed to do something fast, or I was doomed. After investing time and effort in tools, research, and spending $10k and over 200 hours on a course that didn’t deliver the consistent results I needed, I was at a crossroads. My businesses were running out of money, and I knew I needed to find a solution quickly, or everything I had worked for would collapse. It was during this time of desperation that I started exploring other options. One night, while scrolling through the internet, I stumbled upon a 2024 article about how AI was being used to revolutionize various industries. It wasn’t directly related to appointment booking, but it sparked an idea in my mind. What if I could use AI to automate the personalization process that I had learned in the course? It seemed like a long shot, but I had nothing to lose. I started researching AI tools and technologies—YouTube videos, podcasts, pretty much everything related to AI—desperate to find something that could help me scale my outreach without investing too much time, while still maintaining the personalization that was so important. After a lot of trial and error, I found a few tools that showed promise. All of these tools were extremely new. Some of them had just launched the versions I needed just weeks ago. I can say I researched and tested more than 50 AI startups, experimenting with them, testing different approaches, checking prices (the problem was that most of them were cheap but became very expensive when applying the volume I needed to get results), and gradually refining my process. It wasn’t an overnight success, but for the first time, I felt like I was onto something that could truly work. The idea of combining AI personalization with volume was something new, and it gave me hope that I could finally book the meetings I needed without burning out. One day, I sent a video of myself talking—completely AI-generated—to my family chat group and waited for their response. None of them noticed it wasn’t actually me. At that moment, I said to myself: “Okay, I am ready to test this in the real world and see if it works.” Like everything in life, focus is key. As I mentioned earlier, we were already trying outbound strategies on LinkedIn and email, but I decided to narrow my focus to LinkedIn and specifically to video outreach. My goal was to stand out from the crowd, where most people were using text or sending generic videos. I knew that if my videos were 100% personalized, it would make a strong impression on my leads. I focused on two key metrics during my tests: Time spent on manual personalized outreach vs. AI-generated personalized outreach. Positive reply rate for non-personalized manual outreach vs. AI-generated personalized outreach. I ran a test using a sample of 50 one-minute videos sent to 50 leads, and here are the results: Time Spent to Make the Videos: Manual Process: It took me up to 10 hours to create and send 50 personalized videos. This included looking good on camera, brushing my hair, choosing appropriate clothing, ensuring proper lighting, not messing up the script, using a camera holder, recharging the phone, pausing to drink water, avoiding external sounds, being in an appropriate room, downloading the videos, deleting the videos that were not good, and sending the final ones. On average, it took me at least 12.5 minutes per one-minute video. AI Process: With AI, it took me just 32 seconds to create the exact same one-minute personalized video—without saying a word or recording a second of footage. In total, I could make and send the same 50 personalized videos in just 27 minutes. Result: The AI process was 24 times faster. Completely crazy! Positive Reply Rate: Non-Personalized Script (Manual): Using a good script without personalization (no name, job title, city, company, etc.) resulted in a positive reply rate of 4-6% on LinkedIn, including follow-ups. Personalized Script (AI): Using the same script but adding personalized details like the lead's name, company, city, and job title resulted in a positive reply rate of 15-20%, including follow-ups. Result: AI personalization led to 3x (three times) more replies. The best part was the responses. Almost everyone who replied thanked me for taking the time to research them, congratulated me on my speech, and appreciated the personalization and eloquence of my message.  These metrics were a complete breakthrough for me. I researched online to see if anyone else had done something similar, but I couldn’t find anything close. After achieving these metrics, booking the two appointments I desperately needed became easy. In fact, in the last 10 weeks, I’ve been able to consistently book 3-4 appointments per day. This success allowed me to train someone in my company to handle the process, freeing me up to focus on other aspects of the business and ultimately saving it. With the AI appointment machine we built, I even have free time now—time that I’ve been using to develop a methodology and tech tools that I now teach to others. I named the methodology Clip2Lead as a reference to the first Chrome extension I developed that didn’t work but ended up being the first step toward everything that followed. I’ve condensed everything I learned and throughout my experiences into a simple and short FREE training where I cover the entire AI appointment booking process. This includes how to find leads, create scripts, set up follow-up sequences, generate AI videos, clone your voice, compare non-AI metrics with AI metrics, and even navigate AI safety controls. I also offer Chrome extensions that helped me automate the process even further, so you can spend your time closing deals or focusing on other acquisition channels, while your AI machine for booking appointments runs with minimal effort from you. If you’re interested please get in touch with me and thank you for taking the time to read my personal story.

What’s a good business idea that isn’t oversaturated in 2025?

I feel like every niche I look into is already flooded with competition. Dropshipping? Oversaturated. Digital marketing agencies? Everyone and their dog has one. AI tools? Big companies are eating up the space. What are some business ideas that still have room for growth in 2025? Ideally, something with low startup costs but high potential.

This founder was about to shut down his business and open a restaurant. He pivoted the business and grew it to $45m ARR in 12 months. What other businesses can scale like this?

I heard that Jasper scaled to $45m ARR in 12 months...with a team of 8. For context, they are one of the fastest-growing companies ever. Grew from $0 to $45m ARR in 12 months (then raised $125m at a $1.5b valuation). As a fellow founder, their story is really inspiring to me (curious about what others think): In December 2020, Dave Rogenmoser and his co-founders were on the brink of shutting down their business. They'd spent 3+ years building a conversion optimization software called Proof...and it was flatlining. A few weeks prior they had to make the painful decision to let go of half their team. Competition and churn had completely eroded growth. Things were painful. 8 years of work left them with a string of startups that never quite made it: 2 failed software businesses (couldn't make money*) A SMB marketing agency (maxed out at $25k/mo*) An online course company (hard to get big*) The Pivot: In January 2021, they had an idea to use Chat GPT-3, the generative AI model released 6 months earlier, to write high-converting Facebook ads. Within 30 days, they launched the business. With the skeleton crew remaining from the last startup, they scaled the business to $45m ARR and 70,000+ customers without hiring a single new person. Soon after, they raised $125m at a $1.5b valuation. Dave Rogenmoser, CEO at Jasper, had some great one-liners in a few podcasts I listened to on the business. Here are some of his learnings: Right Skill, Wrong Vehicle: He spent 8 years building marketing businesses which gave this team the knowledge and confidence to spend $1m/mo on sales and marketing to scale the business to $45m ARR in year 1. Launch Fast & Iterate Quickly: The team agreed that if the business didn't work in 30 days, they'd shut it down. Dave says, "If you have been working on a problem for more than 18 months and haven't found Product market fit (PMF), odds are you won't...Make the hard pivot."* Ride A Big Wave: Generative AI technology is a new technology that is changing the way we work. But it's not just text. It's images, voice, etc. Identify new customer segments (e.g., Municipalities, Banks, Lawyers, etc.), learn their problems, and apply this novel technology to solve them. What other businesses have you seen scale like this? I've never seen a SaaS business grow that fast. I meet interesting founders 2x per week and share the learnings here.

Partnership revenue share uncertainty as test before any equity discussions, please help, urgent

Hi all, It's brand new relationship to collaborate on work and fast moving situation and i want to be fair and informed about revenue share for this work as startup, new agency, unclear still. Sorry for rushed message, its moving fast. Its starting with revenue share to test and see how things go. I contribute some things as a separate entity/consultant/marketing domain expert who designed some AI products and am able to acquire clients reliably with my marketing skills then they do all development and sales assistance. Details below please can you help with advice on contribution and revenue share thats very fair: The "partnership" non ownership (rev share is best correct?) of delivering custom AI software development solutions to smb b2b clients. As a domain expert i designed a product for myself and then others upsells and want to sell it to other biz, there is interest, its been tested as viable with my outreach which I do and now have 5 clients from last night wanting to meet or receive short video explanations before we meet (its my initial offer, a vid demo). I have designed the product or solution completely and have already developed mvp of the first product that i use myself and is immensely valuable to me. I also acquire all the clients as an client outreach/acquisition expert and perform that entire client acquisition function and marketing up until sales call where they provide assistance/ a joint tech and marketing/product domain specialist (me) sales call, still to be discussed. No dedicated sales function but they have experience. Then I partner with a great desirable professional development agency to deploy the solution and everything that entails hoping for a long-term similar arrangement that mutually beneficial and fair. They also assist with the sales process to close deals, we both contribute on the sales calls but client generation and marketing up to the sales call is my contribution. What would the fair revenue share be in a perfectly fair equal situation and what would it be if I wanted to be generous because i really want to work with them moving forward. Also what would the equity split be if a new entity was formed later to formalize partnership and the contribution remained the same. I dont know much about this or what I should be doing in my situation. As I understand searching revenue share online and a summary from perplexity I perform two of the major functions and they one so something like 30-40 them and the rest me? But if i wanted to be generous and show my appreciation for working with me on this as they are high quality and i foresee more opportunity benefits and capabilities in the future due to their expertise and know they would deliver a superb job, would 50/50 be a fair split? Or am I undervaluing/overvaluing myself,, can you not just offer the logic but advice as well based on the info you have, this is brand new and moving super fast, online info seems clear but i want mine to be super fair even generous for them so they are happy, but also not foolish or irresponsible from my side. Its all new to me. Thank you so much!

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!

How Our AI Tool Helped a Small Business Save 15% on Annual Expenses

I’m the founder of a startup that built an AI-powered tool to analyze and optimize business finances, with a special focus on small and medium-sized enterprises (SMEs). After months of development and testing, I’m pumped to share our solution with you and get your feedback. Here’s what we do, how it works, and the results we’ve seen. The Problem We Solve Managing a company’s finances, especially for an SME, is often a nightmare: forgotten subscriptions, poorly negotiated supplier contracts, invoices with errors… We’ve all been there. Our tool uses AI to automate expense analysis, spot issues, and suggest practical ways to cut costs—without you having to spend hours on it. How It Works (A Bit of Tech Talk) We built our tool on a multi-agent architecture using the CREWAI framework. Here are the main AI agents we’ve got running: Expense Analyst: Digs through your invoices and categorizes your spending. Compliance Auditor: Checks for errors, fraud, or compliance hiccups. Financial Reporter: Generates clear reports with actionable recommendations. Supplier Negotiator: Hunts down cheaper supplier options using the Serper API and offers negotiation strategies. To hook up your company’s data, we use NEEDLE, a RAG (Retrieval-Augmented Generation) system that lets our agents tap into your info in real time. Everything’s locked down in an SQLite database with end-to-end encryption. Real Results We tested the tool with 10 companies, and here’s what we found: Average cost reduction of 12% in three months. Fraud detection: For example, we flagged 5 shady invoices at one company, saving them €3,000. Supplier optimization: For an SME, we found an energy supplier 20% cheaper, saving them €8,000 a year. A real-world case: A consulting firm with 50 employees ran our tool on their SaaS subscriptions. Outcome? They ditched 3 unused subscriptions, renegotiated 2 contracts, and saved 15% on their annual expenses. Challenges We Tackled No sugarcoating here—it wasn’t a walk in the park. The biggest hurdle? Data security. We’re handling sensitive stuff, so we went all in: End-to-end encryption for everything we process. GDPR compliance with strict rules. Role-based access controls to limit who sees what. Another tough one was integrating with existing systems. We’ve already got connectors for QuickBooks, Xero, and SAP, and we’re working on more. Why It’s Different Sure, there are tools like Expensify or Ramp out there, but our multi-agent approach digs deeper. We deliver super-detailed analysis and precise recommendations. And our knack for finding cheaper suppliers in real time? That’s a game-changer for quick savings.I’m the founder of a startup that built an AI-powered tool to analyze and optimize business finances, with a special focus on small and medium-sized enterprises (SMEs). After months of development and testing, I’m pumped to share our solution with you and get your feedback. Here’s what we do, how it works, and the results we’ve seen. Ask me your technical questions, share your ideas or critiques we’re here to get better! Thanks you for reading this.

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?

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 ??

AI SaaS: A website to fine-tune LLM model according to your requirements

Hey fellow entrepreneurs and AI enthusiasts! I'm exploring a business idea and would love your thoughts and feedback. The concept is a SaaS platform that allows users to easily fine-tune large language models (LLMs) on their own datasets without needing deep technical expertise. Here's the gist: The Problem: Many businesses and researchers want to leverage LLMs for specific use cases, but fine-tuning these models requires significant technical knowledge and resources. The Solution: A user-friendly web platform where users can: Choose from popular LLM architectures Upload their own dataset or input text Configure fine-tuning parameters through an intuitive interface Automatically fine-tune the model on our GPU infrastructure Download the fine-tuned model or use it via API Key Features: No coding required Scalable cloud infrastructure Support for various fine-tuning techniques (prompt tuning, adapter tuning, full fine-tuning) Job monitoring and results visualization API access for integrated use in applications Target Market: Researchers without extensive ML engineering resources Startups building AI-powered products Enterprises looking to customize LLMs for internal use Monetization: Tiered subscription model based on usage (compute time, model size, etc.) + potential enterprise contracts for high-volume users. I'd really appreciate your thoughts on: Is this solving a real pain point? Would you use a service like this? Why or why not? What features would make this a must-have for you? Any foreseeable obstacles or considerations I'm missing? Suggestions for go-to-market strategy? Thank you!

Seeking Feedback: Would a No-Code AI Solution Benefit Your Business?

I'm currently working on an AI startup, with the goal of providing small-medium businesses a seamless and intuitive way to integrate AI into operations without the need for any coding or tech expertise. We're designing an auto machine learning application that's user-friendly and tailored to the unique needs of small businesses. Before we scale, I would really appreciate any insights and feedback. Here are a few questions that would be helpful to get answers to: Pain Points: Are there specific tasks or processes in your operations that you think could be automated or enhanced using AI? This could be anything from customer service chatbots, inventory management, sales forecasting, or anything else you might think of. Features: What features would you want in a no-code AI solution? Perhaps easy integration with existing software? Drag-and-drop model training? Pre-built models for common tasks? Training & Support: How important would training and support be for you in implementing and using an AI solution? Would you prefer video tutorials, live-chat support, or hands-on workshops? Pricing: Would you be willing to invest in such a tool? If so, what would be a reasonable price point for you? We're considering a tiered model based on usage, with a potential starting point of $X/month. Does that sound feasible? Trial Period: Would a free trial period be beneficial for you? How long would you need to assess the tool's impact on your business? Data Concerns: How comfortable are you with sharing data with an AI application? What privacy and security measures would make you feel at ease? Your feedback is really useful. We're building this solution with you in mind, and your insights will guide the next steps. In appreciation for your time and input, we're offering a special discount for early adopters from this community once we launch. Just drop a comment below, and I'll make sure to get in touch when we are ready. Many Thanks, Chris Parker

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.

Seeking Feedback on Business Idea: AI-Powered Business Partner Matching Platform

Hey everyone, I've been toying with an idea for a new business venture and I'd love to get some feedback and insights from this community. The Idea: I'm considering building a business platform that utilizes AI to match potential business partners. Whether you're a startup looking for a co-founder, a company seeking strategic partnerships, or an investor searching for promising ventures, this platform would help connect you with compatible partners based on your specific needs, goals, and preferences. How It Works: Users would create detailed profiles outlining their business objectives, industry expertise, skills, and what they're looking for in a partner. The AI algorithm would then analyze this data to identify compatible matches, taking into account factors like complementary skill sets, shared values, and mutual goals. The platform would provide users with a curated list of potential partners, along with insights and recommendations to facilitate meaningful connections. Key Features: Comprehensive Profiles: Users can create detailed profiles highlighting their background, experience, and what they bring to the table. AI Matching Algorithm: The platform's AI algorithm would use advanced data analysis techniques to generate accurate partner recommendations. Communication Tools: Built-in messaging and video conferencing tools would enable seamless communication between potential partners. Feedback and Ratings: Users can leave feedback and ratings for their matches, helping to build trust and credibility within the community. Resource Hub: Access to resources, articles, and guides on partnership development, negotiation strategies, and other relevant topics. Why It's Needed: Finding the right business partner can be a daunting task, often relying on personal networks or serendipitous encounters. By harnessing the power of AI, this platform aims to streamline the partner matching process, saving time and increasing the likelihood of finding compatible collaborators. Looking for Feedback: Before diving headfirst into this venture, I wanted to reach out to this community to gather some feedback: Does this idea resonate with you? Why or why not? Are there any existing platforms or services that offer similar functionalities? What features would be essential for you as a user? Any potential challenges or concerns you foresee with this concept? I'm eager to hear your thoughts and suggestions. Thanks in advance for your input!

AI SaaS: A website to fine-tune LLM model according to your requirements

Hey fellow entrepreneurs and AI enthusiasts! I'm exploring a business idea and would love your thoughts and feedback. The concept is a SaaS platform that allows users to easily fine-tune large language models (LLMs) on their own datasets without needing deep technical expertise. Here's the gist: The Problem: Many businesses and researchers want to leverage LLMs for specific use cases, but fine-tuning these models requires significant technical knowledge and resources. The Solution: A user-friendly web platform where users can: Choose from popular LLM architectures Upload their own dataset or input text Configure fine-tuning parameters through an intuitive interface Automatically fine-tune the model on our GPU infrastructure Download the fine-tuned model or use it via API Key Features: No coding required Scalable cloud infrastructure Support for various fine-tuning techniques (prompt tuning, adapter tuning, full fine-tuning) Job monitoring and results visualization API access for integrated use in applications Target Market: Researchers without extensive ML engineering resources Startups building AI-powered products Enterprises looking to customize LLMs for internal use Monetization: Tiered subscription model based on usage (compute time, model size, etc.) + potential enterprise contracts for high-volume users. I'd really appreciate your thoughts on: Is this solving a real pain point? Would you use a service like this? Why or why not? What features would make this a must-have for you? Any foreseeable obstacles or considerations I'm missing? Suggestions for go-to-market strategy? Thank you!

How to get that big idea for your next business? Use trends!

Hello entrepreneurs and aspiring business owners, I am Mikael and I want to share a post about how to spot business ideas. If you're wondering who the owl is, it's Agent O, my sidekick (please bear with him... or me, if you can). Let's get on to it. So, there are basically two ways of getting ideas for your new business: Find a service, product or experience that's already working. Identify and ride a trend. 🦉 : Third, have a rich relative pass you their business and sip margaritas by the sea while scrolling Reddit for the rest of your life! 🕵️ : Refrain yourself, I just got started ffs, I don't want to get banned! So, what are trends? Trends are patterns of adoption of a product, service or experience by people who want to satisfy a common need. Cool, huh? How trends start Trends emerge and evolve as temporary or permanent solutions to human needs. All products, services and experiences are the expression of human needs manifested through a perceived lack, which we humans interpret as problems. Let me make this more clear. Humans have needs: from basic (food, shelter, safety) to advanced (community, knowledge) to evolved (self actualization, spirituality) and everything in between. Don’t see this as a hierarchy, as it’s usually depicted with Maslow’s pyramid. See it as cycles with different degrees of impact on humans that vary in time and intensity. 🦉 : WHAT!?? 🕵️ : Hear me out… How Trends Affect Society Human needs are physical, emotional, intellectual and spiritual. Every day we feel the impact of those needs with different degrees of required fulfillment. You can’t go on without air for more than a few minutes. You can’t live without food and water for more than a few days. So, when it comes to the needs of the body, these have a shorter timeframe in which they need to be addressed. 🦉 : Ahh, I see what you did there… \\🕵️ \\: Thanks! But you can also live with an unfulfilled need for love or friends for a long time. You can live with a decaying health as well. And you also can live your entire life without finding out if there is a God or not. Humans perceive needs as something they lack within, which in turn is expressed as a problem on the outside. I lack food or water, this will create a problem for my survival. So I need to find food and water in my environment. This lack creates a behavior seeking a product, service or experience to fulfill that need. Makes sense? 🦉 : I just went out and got me a “Mice à la Forest” dinner! 🕵️: Bon appétit! See, Agent O fulfilled a bodily need. That’s what animals do, as they’re driven by instinct and are governed by natural laws (survive, reproduce, sleep, repeat). Humans are driven by more complex needs, as our intellect and emotions allow us to override those basic primary instincts. Why Trends Are Important What an entrepreneur does is to shift the perspective: instead of seeing a lack, he/she sees an opportunity by asking the question: how can I fulfill this need? Or, even better put: how can I help people by solving their problem? That’s the first step to solving a problem: asking a question. That is why the best products are actually problems solved by entrepreneurs who work to solve their own need for a product, service or experience. They then provide it to other people for a cost. Easy, right? That’s what entrepreneurship is: solving a problem. The bigger the problem, the bigger the impact. The bigger the impact, the higher the revenue. It’s easier to understand trends now, isn’t it? You can see that trends are nothing more than the initial adoption of a product, service or experience by a group of people who are looking for a solution to their common need. 🦉 : Did you get that from a book? 🕵️ : You snore when you sleep… ¯\\(\ツ)/\¯ 🦉 : $@#&\*! Hooman! Needs are the foundation on which the modern world is built. Once you understand needs, you fundamentally change your perception of problems into opportunities. This mental shift is the entrepreneurial mindset: where others see problems, you see solutions. Where Do Trends Start So, to recap: human needs are translated into problems. Founders understand the root of the problem (the need) and create products, services, experiences as solutions to those needs. They offer the solution to the public through startups and companies, which belong to a specific niche in a particular industry. 🦉 : Aaah, so that’s why it’s called venture capital? 🕵️ : Yeah, because you’re venturing into a new endeavor to let people know about your solution to their (and ideally your) problem. 🦉 : So if you use ads to market your venture, it’s an adventure? 🕵️ : I see what you did there… If the need behind the adoption is strong and real enough, that trend will translate into a niche within an industry. If the adoption isn’t driven by strong fundamental needs, it will turn into a fad and disappear from the perception of the public, no matter how much marketing money is thrown at it. This happens because the solution (product/service/experience) to the need didn’t create the physical, intellectual or emotional response required to create a recurring behavior around it. Remember this: Problem (why) -> Behavior (how) -> Solution (what) Understand this: there are multiple types of trends. There are product or service trends. There are industry driven trends. There are tendency driven trends, like the emergence of a new paradigm that improves a lot of industries (yes, I’m looking at you, AI). Where Do Trends Come From So now you can see that trends are patterns of adoption related to a specific human need that is addressed through one or multiple products or services. This is a bottom up direction coming from evolution. Multiple trends in different industries also emerge from a theme, which is a bigger vision of a human effort to address a high level problem. This is a top down direction, coming from implementation (by governments, different organizations or other interested parties with the power to influence changes at mass level). Conclusion Now you have a better understanding of trends by looking at them through the lens of human needs. Also, you might also understand time better because you realize that human needs have different degrees of impact in time and intensity. So you now see that trends don’t only relate to individuals, but also to groups of people, from the smallest community to countries and even global needs. That is the reason you’ll sometimes hear some say that time is a flat circle: because clothes change, but humans are quite the same. Needs don’t change a lot in time, just the way we address and solve them. Here’s an interesting game for you: take a look at some behaviors in your life. Which of them are driven by a bodily need, which by an intellectual or emotional one? Which ones are completely automated and you had no idea you were doing? How are these behaviors controlling parts of your life that you were unaware of until now? If you made it this far, thank you for taking the time to read this. I hope you enjoyed it, found it useful and entertaining. Ofc, I value your opinion and welcome it in the comments. Thank you!

Launching new type of service agency in this AI world

I don't know whether it is a good idea or not but launching a service agency which can provide AI related services to small businesses and startups who want to build or implement AI in their existing product. Service like: Ai software for your business Automates workflow for your business Fine-tune LLM model based on customer data Personalized chatbot and other products using your own data I'm still working on building a landing page for my agency but I just want to know if it's a good idea or if anybody would be interested in this, please give feedback about this idea.😊

Idea feedback: AI-native self-improvement & wellness

Hello redditors! Thesis: We're all trying to live our best lives and many of us try to leverage technology to become better faster and easier. I’m trying to build a company that builds an AI-native solution for self-improvement. My thesis is that AI is an incredibly powerful tool for solving problems, particularly in programming and generally life - but ChatGPT isn't really designed to be your long-term 'coach'. It's great for handling specific tasks, answering questions, doing research, etc. - but it's memory and UX isn't optimized around things like behavior change, mental health support, and long-term personal life planning I believe my core problems (which I think are shared by many) are: 1) Staying motivated - it's easy to lose motivation when progress isn't immediately apparent, there are setbacks, etc. 2) Self-doubt - it makes me question myself and waste time wondering if I'm the right person to be doing this, if the idea is too broad, etc. Some of this is good - but a lot of it just makes me less effective 3) Staying on Track - I start a thing, but then gradually pivot a million different directions. This may be a touch of ADHD. I find that I'll have a long-term goal (e.g. launching a successful business), but I'll tend to wonder a lot in the process of executing over weeks and months. Staying on track just feels suprisingly difficult. I do create TODO lists and have a Kanban board I’m considering a bunch of features and have built a version focused more specifically towards mental health which implements a few: \----- • Guided Journaling Guided journaling prompts to facilitate deeper reflection • Specialist AI Coaches Personalized, expert AI coaching for your specific area of focus and goals For startup, marketing, life, fashion, whatever you want. • Goal Tracking Define, track, and achieve your goals • Behavior Change & Habit Formation Leverage the science of behavior change to help you make lasting changes in your life • Mood tracking Track and improve your mood leveraging science-backed techniques • Areas for growth Identify and develop your strengths and manage your weaknesses • Insight reports Get personalized insights into your cognitive and behavioral patterns • Inspirational Quotes Stay motivated with curated daily quotes relevant to your journey • Gamification of Growth & Mood Turn your mental health journey into a game and earn rewards for your progress \---- Would love thoughts on the idea, and feedback - and if anyone is interested in being a design partner / early user, I'd love to chat in greater depth 1:1!

AI Implementation Consultancy Business

There are currently an overwhelming number of AI-based products that all boast about how they can help your business. The products range from chatbots to virtual assistants to tax support. AI has taken over as a buzzword and there is an AI-based product for every application under the sun. I would like to start a consulting firm for small business or startups that want to harness the power of AI but don't know where to start. I would work with them to understand their needs and suggest an AI product that would suit their need. I would also work on the implementation for them. If their need wasn't met by an existing product, I can work with custom AI software agencies to make something that would work for them (obviously this would be much more expensive that finds an "off-the-shelf" product that already does what they are looking for). For example: The owner of a neighborhood bakery want to implement some type of predictive analysis to figure out how many pastries to bake during a given season. He does some Googling and is overwhelmed with all the various nuanced options. He hires my consultancy firm to help figure out what is best for his specific need. I provide a variety of options and provide a cost for implementation. What do you think?

My Idea is to generate leads on autopilot.

After 6 years of being a founder of multiple startups, I discovered that 90% of all startups fail not because they don't have a product but because they don't sell. And when talking to my entrepreneur friends, they all seem to struggle with getting (consistent) sales. So far I've built three B2B companies and grown them to multiple $M's ARR. One strategy I use is sending out A LOT of cold emails (500-1000 cold emails per day). Over the years I've created a system that does the following: \- Find targeted potential leads for your industry in bulk (1000+ of leads per day) \- Segment the leads into different funnels \- Reach out to them via email and LinkedIn in a semi-personalized manner using AI-generated icebreakers and generating images with their website or personal content in the images automatically. \- Send them an AI generated video sales letters (which cannot be distinguished from real) \- Book sales calls with them (15 to 30 calls per month) I'm now at a point where I've perfected this system for my own businesses. I want to see if I can apply my strategy to any B2B industry. I want to help up to 5 B2B companies with a $1000+ average deal value. Leave a comment or DM me if you want to participate.

Project to Help Other Business Owners Out There

Researching for a Project to help all (Small) Business Owners out there. Hello Entrepreneurs! I am a student of Computer Science. At the same time, I am a business enthusiast, always looking for ways to form a passive source of income. So I was doing some research in Artificial Intelligence, a subfield of Computer Science, and I came up with a really good idea that can help save time and money for most small business owners and startups out there. But before I indulge into that, I need to do more research on business owners to find if the idea is really feasible or not. I want to stay realistic. Can't be too optimistic, can I. Websites have become life support for most businesses nowdayas. Not having a webiste puts them at a huge disadvantage. So the crux of the idea is integrating AI into web development. My target market includes solo owners/ small business owners and startup because the the type of websites that can be formed with AI suits their needs. AI will be used to understand types and design of all the successful websites out there to form websites that convert So the reason why I am posting over here is that I really really need help from this forum for the research. I have made a Small Google Form. It only consists of small MCQs and 1-2 words answers. If you can fill it up, I will highly appreicate it. It will take 2-4 minutes at most. &#x200B; Thanks in advance!

How to get that big idea for your next business? Use trends!

Hello entrepreneurs and aspiring business owners, I am Mikael and I want to share a post about how to spot business ideas. If you're wondering who the owl is, it's Agent O, my sidekick (please bear with him... or me, if you can). Let's get on to it. So, there are basically two ways of getting ideas for your new business: Find a service, product or experience that's already working. Identify and ride a trend. 🦉 : Third, have a rich relative pass you their business and sip margaritas by the sea while scrolling Reddit for the rest of your life! 🕵️ : Refrain yourself, I just got started ffs, I don't want to get banned! So, what are trends? Trends are patterns of adoption of a product, service or experience by people who want to satisfy a common need. Cool, huh? How trends start Trends emerge and evolve as temporary or permanent solutions to human needs. All products, services and experiences are the expression of human needs manifested through a perceived lack, which we humans interpret as problems. Let me make this more clear. Humans have needs: from basic (food, shelter, safety) to advanced (community, knowledge) to evolved (self actualization, spirituality) and everything in between. Don’t see this as a hierarchy, as it’s usually depicted with Maslow’s pyramid. See it as cycles with different degrees of impact on humans that vary in time and intensity. 🦉 : WHAT!?? 🕵️ : Hear me out… How Trends Affect Society Human needs are physical, emotional, intellectual and spiritual. Every day we feel the impact of those needs with different degrees of required fulfillment. You can’t go on without air for more than a few minutes. You can’t live without food and water for more than a few days. So, when it comes to the needs of the body, these have a shorter timeframe in which they need to be addressed. 🦉 : Ahh, I see what you did there… \\🕵️ \\: Thanks! But you can also live with an unfulfilled need for love or friends for a long time. You can live with a decaying health as well. And you also can live your entire life without finding out if there is a God or not. Humans perceive needs as something they lack within, which in turn is expressed as a problem on the outside. I lack food or water, this will create a problem for my survival. So I need to find food and water in my environment. This lack creates a behavior seeking a product, service or experience to fulfill that need. Makes sense? 🦉 : I just went out and got me a “Mice à la Forest” dinner! 🕵️: Bon appétit! See, Agent O fulfilled a bodily need. That’s what animals do, as they’re driven by instinct and are governed by natural laws (survive, reproduce, sleep, repeat). Humans are driven by more complex needs, as our intellect and emotions allow us to override those basic primary instincts. Why Trends Are Important What an entrepreneur does is to shift the perspective: instead of seeing a lack, he/she sees an opportunity by asking the question: how can I fulfill this need? Or, even better put: how can I help people by solving their problem? That’s the first step to solving a problem: asking a question. That is why the best products are actually problems solved by entrepreneurs who work to solve their own need for a product, service or experience. They then provide it to other people for a cost. Easy, right? That’s what entrepreneurship is: solving a problem. The bigger the problem, the bigger the impact. The bigger the impact, the higher the revenue. It’s easier to understand trends now, isn’t it? You can see that trends are nothing more than the initial adoption of a product, service or experience by a group of people who are looking for a solution to their common need. 🦉 : Did you get that from a book? 🕵️ : You snore when you sleep… ¯\\(\ツ)/\¯ 🦉 : $@#&\*! Hooman! Needs are the foundation on which the modern world is built. Once you understand needs, you fundamentally change your perception of problems into opportunities. This mental shift is the entrepreneurial mindset: where others see problems, you see solutions. Where Do Trends Start So, to recap: human needs are translated into problems. Founders understand the root of the problem (the need) and create products, services, experiences as solutions to those needs. They offer the solution to the public through startups and companies, which belong to a specific niche in a particular industry. 🦉 : Aaah, so that’s why it’s called venture capital? 🕵️ : Yeah, because you’re venturing into a new endeavor to let people know about your solution to their (and ideally your) problem. 🦉 : So if you use ads to market your venture, it’s an adventure? 🕵️ : I see what you did there… If the need behind the adoption is strong and real enough, that trend will translate into a niche within an industry. If the adoption isn’t driven by strong fundamental needs, it will turn into a fad and disappear from the perception of the public, no matter how much marketing money is thrown at it. This happens because the solution (product/service/experience) to the need didn’t create the physical, intellectual or emotional response required to create a recurring behavior around it. Remember this: Problem (why) -> Behavior (how) -> Solution (what) Understand this: there are multiple types of trends. There are product or service trends. There are industry driven trends. There are tendency driven trends, like the emergence of a new paradigm that improves a lot of industries (yes, I’m looking at you, AI). Where Do Trends Come From So now you can see that trends are patterns of adoption related to a specific human need that is addressed through one or multiple products or services. This is a bottom up direction coming from evolution. Multiple trends in different industries also emerge from a theme, which is a bigger vision of a human effort to address a high level problem. This is a top down direction, coming from implementation (by governments, different organizations or other interested parties with the power to influence changes at mass level). Conclusion Now you have a better understanding of trends by looking at them through the lens of human needs. Also, you might also understand time better because you realize that human needs have different degrees of impact in time and intensity. So you now see that trends don’t only relate to individuals, but also to groups of people, from the smallest community to countries and even global needs. That is the reason you’ll sometimes hear some say that time is a flat circle: because clothes change, but humans are quite the same. Needs don’t change a lot in time, just the way we address and solve them. Here’s an interesting game for you: take a look at some behaviors in your life. Which of them are driven by a bodily need, which by an intellectual or emotional one? Which ones are completely automated and you had no idea you were doing? How are these behaviors controlling parts of your life that you were unaware of until now? If you made it this far, thank you for taking the time to read this. I hope you enjoyed it, found it useful and entertaining. Ofc, I value your opinion and welcome it in the comments. Thank you!

AI SaaS: A website to fine-tune LLM model according to your requirements

Hey fellow entrepreneurs and AI enthusiasts! I'm exploring a business idea and would love your thoughts and feedback. The concept is a SaaS platform that allows users to easily fine-tune large language models (LLMs) on their own datasets without needing deep technical expertise. Here's the gist: The Problem: Many businesses and researchers want to leverage LLMs for specific use cases, but fine-tuning these models requires significant technical knowledge and resources. The Solution: A user-friendly web platform where users can: Choose from popular LLM architectures Upload their own dataset or input text Configure fine-tuning parameters through an intuitive interface Automatically fine-tune the model on our GPU infrastructure Download the fine-tuned model or use it via API Key Features: No coding required Scalable cloud infrastructure Support for various fine-tuning techniques (prompt tuning, adapter tuning, full fine-tuning) Job monitoring and results visualization API access for integrated use in applications Target Market: Researchers without extensive ML engineering resources Startups building AI-powered products Enterprises looking to customize LLMs for internal use Monetization: Tiered subscription model based on usage (compute time, model size, etc.) + potential enterprise contracts for high-volume users. I'd really appreciate your thoughts on: Is this solving a real pain point? Would you use a service like this? Why or why not? What features would make this a must-have for you? Any foreseeable obstacles or considerations I'm missing? Suggestions for go-to-market strategy? Thank you!

Launching new type of service agency in this AI world

I don't know whether it is a good idea or not but launching a service agency which can provide AI related services to small businesses and startups who want to build or implement AI in their existing product. Service like: Ai software for your business Automates workflow for your business Fine-tune LLM model based on customer data Personalized chatbot and other products using your own data I'm still working on building a landing page for my agency but I just want to know if it's a good idea or if anybody would be interested in this, please give feedback about this idea.😊

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 ??

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!