Found

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

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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.

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)

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.

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)

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!

Hot take: AI will replace non-technical founders before technical founders [I will not promote]

I think the prevailing theory is AI is going to empower non-technical people to create complex systems just by writing a prompt or prompts. I think a variant of that is getting close which are agentic workflows where something triggers an agent and it does things autonomously. These sort of systems are starting to pop up everywhere (zapier, n8n, others...) however we're a ways off from these being able to respond in near real time. Long story short you'll still need to have code to run and someone to write that code (at least for a few more years) I've been using Deepresearch by OpenAI since Monday. I'm a technical founder without a formal business background. I've had it do product market fit analysis, sam/tam estimates, go to market strategies and it's producing fantastic reports. I could see it handling pretty much 90% of the non-technical business work that doesn't involve action in the physical world. It produced the content of a very well thought out pitch deck for one of my businesses. It's identifying potential customers. It's pretty damn crazy. Like right now I have it building a financial pro-forma for my data room. In fact it finished before I could write this post!

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!

I'm technical looking for non-technical co-founder with a good idea and good background.

Hi all! So things have fell through with my co-founder and I have decided to start looking for new opportunities. I'm a senior software engineer looking to work with someone who already has an idea. I've primarily worked on fintech during my career.  I've build payment systems, fraud prevention mechanisms, and arrear collections infrastructure. The products I've had a key role in developing have helped UK governments and banks save millions of pounds. I've built beautiful front end systems (web, android, and IOS) that are being used by thousands of people and have developed back end infrastructure that is currently servicing millions of requests per day. I'm open to working in most domains if the idea and the founder behind are great. I'm looking for a co-founder who is ideally an expert in there space and has a good idea. I'll try to get back to everyone who pings me. All the best!

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. ​ 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. ​ 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). ​ 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

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

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

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.

Seeking advice from non technical founders

I am building a software platform that will help aspiring non technical AI founders go from 0-1 in less than 24 hours. This is not your typical no-code solution but rather a combination of few bespoke solutions that can be built without platform lock-in. Premise of the idea As a technical founder myself, I find that I and others I have met, lack domain knowledge for an industry other than tech. This makes it harder for us to build vertical SaaS. On the other hand, non technical folks don't have a way to build using AI without knowing the technical details of managing AI and the right architecture to use. Solution A platform where you can pick and choose components for an app. Provide it your own data and then let AI do the heavy lifting. Ask If you are a non technical founder, would you be interested in this? What reservation would you have? What would make this a no brainer for you?

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.

Hot take: AI will replace non-technical founders before technical founders [I will not promote]

I think the prevailing theory is AI is going to empower non-technical people to create complex systems just by writing a prompt or prompts. I think a variant of that is getting close which are agentic workflows where something triggers an agent and it does things autonomously. These sort of systems are starting to pop up everywhere (zapier, n8n, others...) however we're a ways off from these being able to respond in near real time. Long story short you'll still need to have code to run and someone to write that code (at least for a few more years) I've been using Deepresearch by OpenAI since Monday. I'm a technical founder without a formal business background. I've had it do product market fit analysis, sam/tam estimates, go to market strategies and it's producing fantastic reports. I could see it handling pretty much 90% of the non-technical business work that doesn't involve action in the physical world. It produced the content of a very well thought out pitch deck for one of my businesses. It's identifying potential customers. It's pretty damn crazy. Like right now I have it building a financial pro-forma for my data room. In fact it finished before I could write this post!

Looking for a non-technical co-founder (I will not promote)

Hey everyone, I've launched an AI networking agent designed to connect people based on specific prompts. You describe the type of person you're looking for, and it provides a relevant contact. This tool can be used for hiring, procuring services, selling services, and more. I'm currently seeking a highly motivated, high-energy non-technical co-founder to handle sales and marketing, conduct customer interviews to grow our user base. No experience is required, only a strong willingness to succeed. We're completely bootstrapped but plan to start fundraising after securing our first paying users. If you're interested, shoot me a DM. i will not promote

Best AI tools for non-technical founder to demonstrate the vision (I will not promote)

Hey all, I have an MVP nearly ready but when I speak to potential co-founders, customers and investors, I want to be able to show the vision of what it will look like and do in the future. Wondering if people have an opinion on the best tool(s) out there to sell this sort of vision? The output doesn't need to be usable, but I need the people who look at it to understand what the product will be in the future. It could be a bunch of nicely designed screens (something like Uizard or Visily seems to do this) or even a video explaining what it will do (don't think any of the video tools are at this level yet) or it may be piecing together a few tools. Keen to hear what others have done in this case!

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

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!

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.

Looking for an accountability partner as a solo founder. [I will not promote]

Hello! I am a technical founder focused on using AI solutions to drive automation. Recently had a co-founder split after working together for a couple month. We had a very good traction but I made a decision to leave because I believed we didn't have a solid foundational relationship that can be sustained for a long time. Had more of a co-worker style relationship. Took on the short-term pain to set myself up for a long term success. He was the one leading the sales and relation with the businesses, so we decided he will be leading the company moving forward and we split on very good terms. Back in the gulag now and starting from scratch. Took three days to reset and recover. When I tried to get back at things yesterday, my brain wasn't just having it. My stress activation got so high, i did like 4 wim hof breathing sessions and a 10 mile run to relieve the stress buildup. There is something about uncertainty and working without a lack of clear path that is super hard to process especially when you are solo. Currently I am working through my previous idea backlogs that I have built up and re-starting previous conversations. But my brain isn't giving me the dopamine hit from driving toward action as much as I used to. So any work that i do feels like a slogging through mud. I am looking to experiment with having an accountability partner, to make the initial ramp up easier. Thinking of doing the elon musk style "What have you done this week?" report that we can do to drive accountability and give that extra motivation. If you're navigating similar challenges as a solo founder and believe mutual accountability could accelerate our progress and growth, I'd love to connect. Let's help each other build momentum and stay motivated—drop a comment or DM if interested! I will not promote

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,)

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: AI will replace non-technical founders before technical founders [I will not promote]

I think the prevailing theory is AI is going to empower non-technical people to create complex systems just by writing a prompt or prompts. I think a variant of that is getting close which are agentic workflows where something triggers an agent and it does things autonomously. These sort of systems are starting to pop up everywhere (zapier, n8n, others...) however we're a ways off from these being able to respond in near real time. Long story short you'll still need to have code to run and someone to write that code (at least for a few more years) I've been using Deepresearch by OpenAI since Monday. I'm a technical founder without a formal business background. I've had it do product market fit analysis, sam/tam estimates, go to market strategies and it's producing fantastic reports. I could see it handling pretty much 90% of the non-technical business work that doesn't involve action in the physical world. It produced the content of a very well thought out pitch deck for one of my businesses. It's identifying potential customers. It's pretty damn crazy. Like right now I have it building a financial pro-forma for my data room. In fact it finished before I could write this post!

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!

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 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)

Seeking co-founder to build LinkedIn’s biggest rival(curated version)

How do you connect with likeminded people? You see the polished wins everywhere, but what about the messy drafts , the awkward pitches and the moments you’re not sure you’ve got it right? Problem: The whole idea of founding and starting a business can be super intimidating for some people, specially those who don’t know any founders personally, those who don’t have a large network, those who don’t have rich parents with large networks, those not inserted in an entrepreneurial culture like in the US for example (which is my case). Sometimes all you need is the right support network, and too see others do what you want, to know that it’s possible! Everyone has an “ultimate guide” to make 7 figures or build a business on YouTube but NO ONE shoes you the HOW, just the results… I’ve tried joining founder communities, LinkedIn ,Reddit … you name it. Most of these founder communities are inaccessible for regular people and often ask for you to have an already existing business with a min ARR… or their simply geography based and if you’re not in a certain area you can’t really participate… As of LinkedIn… full of empty AI generated posts about how some random dude raised $10m in 7 days. Okay Jonathan, but what about the HOW?? How did you write your first pitch? How many rejection calls did you get? What is an MVP? There simply isn’t a platform out there to document your founding journey and find inspiration within a community of people who are doing the same as you. What better way to feel motivated then to see someone actually document their process? Solution: I’m working on building a social media platform for aspiring/founders to connect through the RAW, UNFILTERED process of turning ideas into reality in REAL time. It’s all gonna be around the “building in public “ concept and content creation. Picture an instagram/tiktok profile where instead of seeing someone’s dog you see them documenting their founding process—from the moment they had the idea, to the moment they launched, you’re going to see the successes, the fails, the rejected calls, all documented through actual content and not some AI generated LinkedIn post. Imagine if you wanted to learn about how Steve Jobs started Apple , you could simply go through his profile on this app—exactly. To make sure all interactions are meaningful people would have to apply. It’s a truly curated community, with REAL people, building REAL things in REAL time, and not just tell us the story of how they did it… Audience: I’m targeting people who have a burning desire of building a business and early stage founders starting their founder journeys, that don’t have a support network and simply don’t know where to start. People who are tired of watching 30 min “ultimate guides “ on how to make it on YouTube from “business gurus” selling courses. People who haven’t reached the min ARR required to join an “exclusive “ founder a community. People who can’t simply just move to the US to get into the “exclusive” YC combinator. People who want to connect with real people building real things and not anonymous people on Reddit, or LinkedIn influencers again trying to promote their services. I believe in the idea because I’m also part of my audience. Have always wanted to start my own thing just never knew how to and where to find a community of likeminded people . I don’t know any founders myself, I come from a non-entrepreneurial society and I’d pay good money to access a community of REAL passionate founders building REAL things, in REAL time. This would be my first ever business, and I want to share my journey building it and hopefully inspire others to just start so I’ve created a mailing list to keep anyone interested in the project updated on my fails , learnings and successes. I’m not worried about “making it” but just “starting” and hopefully reach the right audience and inspire anyone to start whatever they have marinating in their thoughts. If you’re a founder struggling with staying consistent or an aspiring founder with an insane desire of starting and don’t know how to start, I’d love to get your feedback on what’s stopping you, your challenges starting out and what you’d find useful in such platform. And finally would this be something that interests you?? PS: casually looking for a technical co-founder

Essential Tools/System/Automations for Solo Founders

As a technical founder managing all aspects of my business on my own rn, I'm finding it increasingly overwhelming. I’m looking for recommendations on systems, automations, and AI tools that can help improve productivity and streamline operations across various functions. What have you found particularly useful in areas like software development, sales/ marketing, customer support etc? Any insights or tools you swear by would be greatly appreciated!

Question from a non-technical founder

I'm having an automotive photography app built. I want to build a simple AI model that will resize, rotate and reposition automotive photos so they're all consistent. There's about 30 standard photos to build a model for. I've got hundreds of before and after photos to train it with. I'd class myself as a non-technical founder. I can build wordpress sites, basic HTML layouts. Been in digital marketing for 20 years. Is this a simple project that I might be able to learn some AI basics then just feed the model? Or will it require coding knowledge that'll be beyond me? I don't have time to deep dive into the code side. Thanks for any feedback.

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.

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

Does anyone else struggle to communicate with Devs as a non tecnical founder/manager?

Hey everyone, I'm an dev agency owner and, honestly, sometimes I feel like I'm speaking a different language when trying to communicate with my development team. It's not just about understanding the technical jargon—they're great at explaining things—but more about converting my ideas and requirements into something actionable and clear for them. I've been thinking a lot about this challenge and wondering if others are in the same boat. Do you ever find yourself struggling to get your thoughts across or worrying that important details might be lost in translation? To tackle this issue, I've been toying with the idea of creating a tool—kind of like Jira, but specifically designed for non-technical folks. The idea is to help bridge the communication gap by making it easier to convert our thoughts into dev-friendly action items using chatGPT. It could offer templates, guided prompts, and maybe even some AI assistance to ensure nothing gets lost in translation. Before I dive too deep into this, I wanted to get a sense from this community: Do you face similar communication challenges with your dev teams? What tools or methods have you found helpful in bridging this gap? Would a tool like the one I'm envisioning be something you'd find valuable? Looking forward to hearing your thoughts and experiences! Thanks,

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!

Looking for a technical co-founder to build LinkedIn’s rival

How do you connect with likeminded people? You see the polished wins everywhere, but what about the messy drafts , the awkward pitches and the moments you’re not sure you’ve got it right? Problem: The whole idea of founding and starting a business can be super intimidating for some people, specially those who don’t know any founders personally, those who don’t have a large network, those who don’t have rich parents with large networks, those not inserted in an entrepreneurial culture like in the US for example (which is my case). Sometimes all you need is the right support network, and too see others do what you want, to know that it’s possible! Everyone has an “ultimate guide” to make 7 figures or build a business on YouTube but NO ONE shoes you the HOW, just the results… I’ve tried joining founder communities, LinkedIn ,Reddit … you name it. Most of these founder communities are inaccessible for regular people and often ask for you to have an already existing business with a min ARR… or their simply geography based and if you’re not in a certain area you can’t really participate… As of LinkedIn… full of empty AI generated posts about how some random dude raised $10m in 7 days. Okay Jonathan, but what about the HOW?? How did you write your first pitch? How many rejection calls did you get? What is an MVP? There simply isn’t a platform out there to document your founding journey and find inspiration within a community of people who are doing the same as you. What better way to feel motivated then to see someone actually document their process? Solution: I’m working on building a social media platform for aspiring/founders to connect through the RAW, UNFILTERED process of turning ideas into reality in REAL time. It’s all gonna be around the “building in public “ concept and content creation. Picture an instagram/tiktok profile where instead of seeing someone’s dog you see them documenting their founding process—from the moment they had the idea, to the moment they launched, you’re going to see the successes, the fails, the rejected calls, all documented through actual content and not some AI generated LinkedIn post. Imagine if you wanted to learn about how Steve Jobs started Apple , you could simply go through his profile on this app—exactly. To make sure all interactions are meaningful people would have to apply. It’s a truly curated community, with REAL people, building REAL things in REAL time, and not just tell us the story of how they did it… Audience: I’m targeting people who have a burning desire of building a business and early stage founders starting their founder journeys, that don’t have a support network and simply don’t know where to start. People who are tired of watching 30 min “ultimate guides “ on how to make it on YouTube from “business gurus” selling courses. People who haven’t reached the min ARR required to join an “exclusive “ founder a community. People who can’t simply just move to the US to get into the “exclusive” YC combinator. People who want to connect with real people building real things and not anonymous people on Reddit, or LinkedIn influencers again trying to promote their services. I believe in the idea because I’m also part of my audience. Have always wanted to start my own thing just never knew how to and where to find a community of likeminded people . I don’t know any founders myself, I come from a non-entrepreneurial society and I’d pay good money to access a community of REAL passionate founders building REAL things, in REAL time. This would be my first ever business, and I want to share my journey building it and hopefully inspire others to just start so I’ve created a mailing list to keep anyone interested in the project updated on my fails , learnings and successes. I’m not worried about “making it” but just “starting” and hopefully reach the right audience and inspire anyone to start whatever they have marinating in their thoughts. If you’re a founder struggling with staying consistent or an aspiring founder with an insane desire of starting and don’t know how to start, I’d love to get your feedback on what’s stopping you, your challenges starting out and what you’d find useful in such platform. And finally would this be something that interests you?? Feel free to subscribe to get new updates 🫶🏼 : https://mailchi.mp/037c56b89994/d-founder PS: casually looking for a technical co-founder

[P] How I found & fixed 4 bugs in Microsoft's Phi-4 model

Hey r/MachineLearning! Last week, Microsoft released Phi-4, a 14B open-source model that rivals OpenAI's GPT-4-o-mini. I managed to find & fix 4 bugs impacting its output quality. You might remember me previously from fixing 8 bugs in Google's Gemma model! :) I'm going to walk you through how I found & fixed the bugs. Phi-4's benchmarks were amazing, however many users reported weird or just wrong outputs. Since I maintain the open-source project called 'Unsloth' (fine-tuning LLMs 2x faster with 70% less VRAM) with my brother, I firstly tested Phi-4 for inference and found many errors. Our GitHub repo: https://github.com/unslothai/unsloth This time, the model had no implementation issues (unlike Gemma 2) but did have problems in the model card. For my first inference run, I randomly found an extra token which is obviously incorrect (2 eos tokens is never a good idea). Also during more runs, I found there was an extra assistant prompt which is once again incorrect. And, lastly, from past experience with Unsloth's bug fixes, I already knew fine-tuning was wrong when I read the code. These bugs caused Phi-4 to have some drop in accuracy and also broke fine-tuning runs. Our fixes are now under review by Microsoft to be officially added to Hugging Face. We uploaded the fixed versions to https://huggingface.co/unsloth/phi-4-GGUF Here’s a breakdown of the bugs and their fixes: Tokenizer bug fixes The Phi-4 tokenizer interestingly uses as the BOS (beginning of sentence), EOS (end of sentence) and PAD (padding) tokens. The main issue is the EOS token is wrong - it should be . Otherwise, you will get in generations. Fine-tuning bug fixes The padding token should be a designated pad token like in Llama () or we can use an untrained token - for example we use , fixing infinite generations and outputs. Chat template issues The Phi-4 tokenizer always adds an assistant prompt - it should only do this if prompted by add\generation\prompt. Most LLM serving libraries expect non auto assistant additions, and this might cause issues during serving. We dive deeper into the bugs in our blog: https://unsloth.ai/blog/phi4 Do our Fixes Work? Yes! Our fixed Phi-4 uploads show clear performance gains, with even better scores than Microsoft's original uploads on the Open LLM Leaderboard. https://preview.redd.it/d8hew26e06ce1.png?width=2366&format=png&auto=webp&s=173c23feacc625566271470839fe7a5e25eb860e Some redditors even tested our fixes to show greatly improved results in: Example 1: Multiple-choice tasks https://preview.redd.it/qx50pkq706ce1.png?width=1579&format=png&auto=webp&s=437da2cabdbf98ef5a8b8cbdc5592907a20e2316 Example 2: ASCII art generation https://preview.redd.it/sw1o3a3yt4de1.png?width=2326&format=png&auto=webp&s=fc6bfc45d14134d45f332ba58bbd1de049f5776b We also made a Colab notebook fine-tune Phi-4 completely for free using Google's free Tesla T4 (16GB) GPUs: https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi\4-Conversational.ipynb Thank you for reading this long post and hope you all found this insightful! If you have any questions, please feel free to ask! :) How I found the bugs: I first downloaded the original Phi-4 from https://huggingface.co/microsoft/phi-4, and tested inference out. Weirdly I found assistant to be appended at the even with addgenerationprompt = False in Hugging Face, so I theorized there was a chat template problem. Adding assistant prompts by default can break serving libraries. And yes, https://huggingface.co/microsoft/phi-4/blob/f957856cd926f9d681b14153374d755dd97e45ed/tokenizer\config.json#L774 had by default added the assistant prompt - I first fixed this! I then found ` to be used for the BOS, EOS and PAD tokens, which is a common issue amongst models - I ignored the BOS, since Phi-4 did not have one anyways, but changed the PAD token to `. You can select any of the tokens since they're empty and not trained. This counteracts issues of infinite generations during finetuning. For Llama-fication, I used torch.allclose to confirm all tensors are in fact equivalent. I also used some fake random data to check all activations are also mostly similar bitwise. I also uploaded the model to the HF Open LLM Leaderboard to confirm if the original Phi-4 arch and the new Llama-fied models are equivalent. Finally I verified all finetuning runs with Unsloth in a Colab Notebook to confirm all runs were correct.

[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.”

[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.

[R] TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs - Yaobo Liang et al Microsoft 2023

Paper: https://arxiv.org/abs/2303.16434 Abstract: Artificial Intelligence (AI) has made incredible progress recently. On the one hand, advanced foundation models like ChatGPT can offer powerful conversation, in-context learning and code generation abilities on a broad range of open-domain tasks. They can also generate high-level solution outlines for domain-specific tasks based on the common sense knowledge they have acquired. However, they still face difficulties with some specialized tasks because they lack enough domain specific data during pre-training or they often have errors in their neural network computations on those tasks that need accurate executions. On the other hand, there are also many existing models and systems (symbolic-based or neural-based) that can do some domain specific tasks very well. However, due to the different implementation or working mechanisms, they are not easily accessible or compatible with foundation models. Therefore, there is a clear and pressing need for a mechanism that can leverage foundation models to propose task solution outlines and then automatically match some of the sub tasks in the outlines to the off-the-shelf models and systems with special functionalities to complete them. Inspired by this, we introduce TaskMatrix.AI as a new AI ecosystem that connects foundation models with millions of APIs for task completion. Unlike most previous work that aimed to improve a single AI model, TaskMatrix.AI focuses more on using existing foundation models (as a brain-like central system) and APIs of other AI models and systems (as sub-task solvers) to achieve diversified tasks in both digital and physical domains. As a position paper, we will present our vision of how to build such an ecosystem, explain each key component, and use study cases to illustrate both the feasibility of this vision and the main challenges we need to address next. https://preview.redd.it/0guexiznhxqa1.jpg?width=979&format=pjpg&auto=webp&s=e5d818ae789cfc493cfb82fdf8b002a8dfe11939

[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] 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.

[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.”

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.

[Ultimate List] A list of Marketing Tools That I’ve tested over the years and found helpful to do better marketing with less work. More than 50 Tools To Help you with Marketing, Copywriting & Sales!

Starting to focus on marketing for your business, You will come across the same tools mentioned over and over by marketers. I would like to mention here tools that you might haven’t seen going viral in the community but actually will help you grow faster and efficiently. Starting off with My favourite Marketing Channel! #Email Marketing For SMBs Convertkit / Mailerlite / Mailchimp - These 3 Platforms are the best options for SMBs and entrepreneurs just starting out with email marketing. All 3 have free plans up to 1,000 subscribers. Scribe - Email Signature Tool, Create Great Email signatures for your emails. Liramail - Most Email marketing platforms don’t offer great email templates. This tool will help you build great email templates with drag and drop. Quick mail Auto-Warmer - Most Businesses at the beginning don’t know what to do when open rate drops. You need to use an email warmer like this to keep it up. #Email Marketing For Big Businesses SendGrid - Overall Email Marketing Tools, this tool is best for brands that have huge email lists and email marketing is the key marketing channel. Braze - This tool is leading in email marketing for large Email senders. When I was working for agencies, this was one of the best email marketing tools I had used. NeoCertified - Protect your emails for spammers and threats. To keep your email list healthy, this is a must have! Sparkloop - Referral Marketing For Email Campaigns. Email can generate great huge amount of referrals for you and Sparkloop makes it easier. #Cold Emails & Lead Generation Hunter - A Great Tool to scrape emails from domain names. The tool comes with a green free plan but Pro plan is worth the amount of features it provides. Icyleads - It’s better than Hunter as it’s heavily focused on the sales and prospecting to help you derive great results from your campaigns. Mailshake - Beginner Friend Cold Email Tool with Great features like email list warming. #Communication Tools Twilio - One do the best customer engagement platform used by Companies like Stripe and mine too. Chatlio - Use Live chat feature on your website with slack integration. My favourite easier to catch up on conversations through slack integration. Intercom - Used by Most Marketers, Industry Leading customer communication platform. Great for beginners! Chatwoot - Another Amazing Communication Tool but the best part is they have a great free plan useful for new businesses. Loom - Communicate with your audience through Videos. Loom is great for SaaS and to show human interaction to close new visitors effectively. #CRM Outseta - This tool provides great CRM and their billing system is better than other tools out their which makes it stands out! Hubspot - I don’t think this tool needs an introduction because Hubspot’s CRM is the best in industry. Salesflare - This CRM is a great alternative to hubspot as it’s beginner friendly and helpful for SMBs. #SEO Tools Ahrefs - One of the best SEO tool in the industry. They also just launched a bunch of free tools to help SEO beginners. Screaming frog - The only website crawler I have used since I bought my first domain. It’s the best! Ubersuggest- The Tool by Neil Patel is the best SEO tool for you. (I’m Joking, it’s the worst) Contentking - This tool is good at Real-time SEO Auditing, they do a lot of Marketing work through Newsletters. If you are subscribed to any SEO newsletter. You may have seen this tool. SEOquake & Semrush - SEOquake is a great tool to conduct on-page analysis, SERP, and much more. Great tool but it’s owned by Semrush. You should go for Semrush because that tool will cover all SEO aspects for you. #Content Marketing Buzzsumo - This tool is great for content research and but you may find the regular emails pretty annoying sometimes. Contentrow - Analyse Your Content and find it’s strength. Highly recommended who are weak at content structuring like me. Grammarly - If you are not a native English speaker like me, you might think you need it or not. You need it for sure for grammar corrections. #Graphic Design Tools Visme - At agencies, Infographics can be more effective than usual postscript. Visme is a graphic design tool focused on infographics and designs related to B2B and B2C. It’s great for agencies! Glorify - A Graphic Design Tool focused on E-commerce, filled with Designs useful for E-commerce store owners. Canva - All-in-one Industry leading Graphic Design Tool that everyone knows and every template is overused now. Adobe Creative Cloud ( previously Sparkpost) - It’s a great alternative to Canva filled with Amazing Stock images to use in your visuals but the only backlash is the exports in this tool are not high quality. Snaps - A Canva Alternative that might not have overused templates for your Social Accounts. #Advertising Tools Plai - It’s a great PPC tool to create Ads for Instagram and Tiktok. Wordstream - It’s an industry leading PPC Tool, great for Ad Grading and auditing. AdEspresso - This Is a tool by Hootsuite. They have a lot of Data sourced at the backend, which helps in Ad optimisation through this tool. That’s the reason I recommend this tool. #Video Editing Tools Veed Studio - I have been using Veed from last year. It’s one of the best Video Marketing Tool Optimized for Instagram & Tiktok. Synthesia - It’s a new AI video generation platform. From last few months, if you have seen marketing agencies including Videos in Emails. The chances are that’s not a Agency member taking but AI generated Human. Motionbox - It’s also a great video editing tool focused on video editing for Digital Marketers. Jitter Video - It’s a great motion design tool. Comes with great templates, the only place where other tools I mentioned lacks. It’s great and beginner friendly. #Copywriting Jasper AI - Google’s John Mueller says AI generated content is banned on Search but I think with Jasper AI you can generate SEO optimised Content but you have to put in some efforts like at least give 30 minutes for editing the Copy by yourself. Copy AI - Another AI tool to help you write better copy. This one is more focused on helping you write copy suitable for Ads and Social media campaigns. Hemingway App - To help you write more clearly and Bold. This tool is better than Grammarly if you look for writing perspective and it’s free. #Social Media Management App I’ve used a Lot of SMM Tools and that’s why going to mention all of them with a short review. Sprout social - The Best with deep insights coverage. Hootsuite - Great Scheduling tool just under sprout social. Later - Heavily Focused on Instagram from beginning and Now Tiktok too. SkedSocial - It’s like a Later alternative with great addition features like link-in-bio. Facebook’s Business Manager- Great but sometimes bugs can make a huge issue for you and customer support is like dead. Tweet Hunter & Hypefury- Both are Twitter Scheduling tools growing very fast on platform and are great for growth. Buffer - It’s a great tool but I haven’t seen any new updates to help with management. Zoho Social - It’s a great SMM tool and if you use other marketing solutions from Zoho. It’s a must have! #Market Research Tool • SparkToro - That’s the only one I have ever used. It’s great for audience research and comes with great customer service. Founded by Rand Fishkin, it’s one of the best research tool. #Influencer Marketing & UGC InfluenceGrid - A free search engine To find Tiktok & Instagram Influencers for your campaigns. Tiktok Creative Center- TikTok’s in-built tool called “Creative Center” is the best to find content trends, audience demographics and much more. Archive - Find Instagram Stories and Posts mentioning Your brands and use them as Ads for your business Marketing. #Landing Page Builders Leadpages - Its a great landing page builder because the integration and drag-and-drop features makes it easier to work with! Cardd co - A Great Landing page builder with easy step up but it lacks the copywriting and tracking features. Instapage - It’s one of the best out and I think the overall product is effective enough to help you stand out with your landing page. Unbounce - It’s a great alternative to Instapage due its well polished landing page templates that might be helpful for you. #Community Building Mighty Networks - A Great Community building platform, and you can also sell courses within the platform. Circle so - A great alternative to Mighty networks focused on Communities specifically. We are currently using for small community Of ours. #Sales Tools Drift - You can get much more out of Drift than just sales tools but The Sales solutions provided in Drift are one of the best. Salesforce - It’s the industry Sales solution provider. A go-to and have various pricing plans making it suitable for majority of SMBs. #Social Proof Tools People don’t have enough time to search across internet to decide to trust you after seeing your Ad first time. That’s what you might be facing too. Here are two tools I absolutely love for social proof! Use Proof - Show Recent Activities occurring on your website and build the trust of your visitors. Testimonial to - Gather Testimonials across Social Media platforms related to your business with this tool. Capture tweets and comments mentioning your brands and mention them. #Analytics Tools Plausible Analytics- A privacy friendly Analytics alternative to Google Analytics if you hate Analytics 4 like me. Mixpanel - Product Analytics and funnel reports better than Google Analytics. #Reddit Marketing Gummysearch- This tool will help To find your target audience on Reddit and interact with them with its help and close your new customers. Howitzer- It’s another pretty similar tool to Gummysearch focused on Reddit cold outreach to get clients and new customers. Both are great but Gummysearch provides better customer support while Howtizer is helpful on a large scale Reddit Marketing. #Text Marketing Klaviyo - It’s an email + SMS marketing tool, it’s taking up space in marketing industry very quickly as an industry leader due to its great integrations but you need to learn the platform usage to maximise the outcome. Cartloop - This tool provides great text marketing solutions with integration with Spotify and other e-commerce marketing tools. Attentive Mobile - This is my favourite Text marketing tool due to the interactive dashboard + they have a library of Text marketing examples to help you out with your campaigns. #Other Tools I have used throughout my journey! Triple Whale - It’s a great E-commerce marketing tools with Triple pixel to help you track your campaigns more efficiently. Fastory - To create well optimized Instagram & Tiktok Stories for your business. Jotform - Online Form Builder with integrations with leading marketing tools. Gated - As an entrepreneur and marketer, you may receive a bunch of unwanted emails. Use Gated to get rid of them and receive useful mails only! ClickUp- The main Tool for Project Management, one of the best and highly recommended. Riverside - Forget Zoom or Google Meet, For your Podcast Interviews and Marketing conferences. You need riverside with great video quality and recording features. Manychat- Automate your Instagram DMs and interact with your followers more efficiently + sell out your products/ services when you are offline. Calendy - To schedule meetings with your ideal clients. ServiceProviderPro - It’s a client portal for SEO & Growing Agencies, very helpful in scaling agencies. SendCheckit - Compare your Email Subject Lines with 100,000+ others in the database for free. Otter AI - Using AI track your meetings more effectively, you can easily edit, annotate and share notes from the meetings. Ryte - Optimise your website User experience with this tool focused on UX aspects + SEO too. PhantomBuster - Scrape LinkedIn Profile and Data from Facebook/LinkedIn groups. I clearly love this tool! #Honourable Mentions Zapier - The Only tool you need to integrate your favourite tool with a new effective tool. Elementor - That’s what I use for web design and it’s great! Marketer Hire - To hire world class marketers to work with you. InShot & Capcut - I create Instagram Reels and TikTok’s and life without these tools isn’t possible. Nira - It’s a great tool to Manage your workspace and this tool has launched many marketing templates in-built helpful for marketers and also entrepreneurs. X - The tool you love that wasn’t mentioned here is valuable and I honour that tool and share that if you would like to! I mean thanks for reading what I have curated all over my life as a marketer. I share 5 Marketing Tools, 5 Marketing Resources and 1 Free Resourceevery week in my newsletter, you can subscribe here to receive that for free. Also, You can read an expanded list of email marketing tools in this Reddit post!

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. ​ 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. ​ 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. ​ 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?

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.

First time founder, looking for guidance

Hello I am non technical founder based in the UK building a CRM and Order Management System. I have a POC built in Figma that showcases new features that current market options don’t have and improvements on existing features. I lack the technical skill to built a functioning MVP but I do have some technical knowledge. I have enough to understand the complexity and size of what I want to build. My current plan is the following: Raise preseed funding from angel investors or preseed VCs. I have a solid business plan and pitch deck in their final drafts. Find/hire a technical cofounder/development head to build and develop MVP (platform is complex and big enough it will require more then one developer to finish it in a reasonable timeframe) Once MVP is complete, begin sales to ICPs. I have strong connections in the industry already making this step easier. Once the above is done plan is to continue growing, develop main product and create supporting software How would you recommend going forward from the point I’m at? Should I build a functional prototype using a no code webapp builder? Will this be needed when I have a POC in Figma? If so any recommendations? Currently there is no plan for integration of AI but should I add some to drum up more hype when pitching to investors? Adding AI will further improve my planned features but will massively increase complexity. It may be worth noting i have already developed a product internally for my current job that they’re intending to release for internal use down the line. This wasn’t a viable solo business as it was impossible to defend and easy to replicate. Cheers for reading

Seeking co-founder to build LinkedIn’s biggest rival(curated version)

How do you connect with likeminded people? You see the polished wins everywhere, but what about the messy drafts , the awkward pitches and the moments you’re not sure you’ve got it right? Problem: The whole idea of founding and starting a business can be super intimidating for some people, specially those who don’t know any founders personally, those who don’t have a large network, those who don’t have rich parents with large networks, those not inserted in an entrepreneurial culture like in the US for example (which is my case). Sometimes all you need is the right support network, and too see others do what you want, to know that it’s possible! Everyone has an “ultimate guide” to make 7 figures or build a business on YouTube but NO ONE shoes you the HOW, just the results… I’ve tried joining founder communities, LinkedIn ,Reddit … you name it. Most of these founder communities are inaccessible for regular people and often ask for you to have an already existing business with a min ARR… or their simply geography based and if you’re not in a certain area you can’t really participate… As of LinkedIn… full of empty AI generated posts about how some random dude raised $10m in 7 days. Okay Jonathan, but what about the HOW?? How did you write your first pitch? How many rejection calls did you get? What is an MVP? There simply isn’t a platform out there to document your founding journey and find inspiration within a community of people who are doing the same as you. What better way to feel motivated then to see someone actually document their process? Solution: I’m working on building a social media platform for aspiring/founders to connect through the RAW, UNFILTERED process of turning ideas into reality in REAL time. It’s all gonna be around the “building in public “ concept and content creation. Picture an instagram/tiktok profile where instead of seeing someone’s dog you see them documenting their founding process—from the moment they had the idea, to the moment they launched, you’re going to see the successes, the fails, the rejected calls, all documented through actual content and not some AI generated LinkedIn post. Imagine if you wanted to learn about how Steve Jobs started Apple , you could simply go through his profile on this app—exactly. To make sure all interactions are meaningful people would have to apply. It’s a truly curated community, with REAL people, building REAL things in REAL time, and not just tell us the story of how they did it… Audience: I’m targeting people who have a burning desire of building a business and early stage founders starting their founder journeys, that don’t have a support network and simply don’t know where to start. People who are tired of watching 30 min “ultimate guides “ on how to make it on YouTube from “business gurus” selling courses. People who haven’t reached the min ARR required to join an “exclusive “ founder a community. People who can’t simply just move to the US to get into the “exclusive” YC combinator. People who want to connect with real people building real things and not anonymous people on Reddit, or LinkedIn influencers again trying to promote their services. I believe in the idea because I’m also part of my audience. Have always wanted to start my own thing just never knew how to and where to find a community of likeminded people . I don’t know any founders myself, I come from a non-entrepreneurial society and I’d pay good money to access a community of REAL passionate founders building REAL things, in REAL time. This would be my first ever business, and I want to share my journey building it and hopefully inspire others to just start so I’ve created a mailing list to keep anyone interested in the project updated on my fails , learnings and successes. I’m not worried about “making it” but just “starting” and hopefully reach the right audience and inspire anyone to start whatever they have marinating in their thoughts. If you’re a founder struggling with staying consistent or an aspiring founder with an insane desire of starting and don’t know how to start, I’d love to get your feedback on what’s stopping you, your challenges starting out and what you’d find useful in such platform. And finally would this be something that interests you?? PS: casually looking for a technical co-founder

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!

[Ultimate List] A list of Marketing Tools That I’ve tested over the years and found helpful to do better marketing with less work. More than 50 Tools To Help you with Marketing, Copywriting & Sales!

Starting to focus on marketing for your business, You will come across the same tools mentioned over and over by marketers. I would like to mention here tools that you might haven’t seen going viral in the community but actually will help you grow faster and efficiently. Starting off with My favourite Marketing Channel! #Email Marketing For SMBs Convertkit / Mailerlite / Mailchimp - These 3 Platforms are the best options for SMBs and entrepreneurs just starting out with email marketing. All 3 have free plans up to 1,000 subscribers. Scribe - Email Signature Tool, Create Great Email signatures for your emails. Liramail - Most Email marketing platforms don’t offer great email templates. This tool will help you build great email templates with drag and drop. Quick mail Auto-Warmer - Most Businesses at the beginning don’t know what to do when open rate drops. You need to use an email warmer like this to keep it up. #Email Marketing For Big Businesses SendGrid - Overall Email Marketing Tools, this tool is best for brands that have huge email lists and email marketing is the key marketing channel. Braze - This tool is leading in email marketing for large Email senders. When I was working for agencies, this was one of the best email marketing tools I had used. NeoCertified - Protect your emails for spammers and threats. To keep your email list healthy, this is a must have! Sparkloop - Referral Marketing For Email Campaigns. Email can generate great huge amount of referrals for you and Sparkloop makes it easier. #Cold Emails & Lead Generation Hunter - A Great Tool to scrape emails from domain names. The tool comes with a green free plan but Pro plan is worth the amount of features it provides. Icyleads - It’s better than Hunter as it’s heavily focused on the sales and prospecting to help you derive great results from your campaigns. Mailshake - Beginner Friend Cold Email Tool with Great features like email list warming. #Communication Tools Twilio - One do the best customer engagement platform used by Companies like Stripe and mine too. Chatlio - Use Live chat feature on your website with slack integration. My favourite easier to catch up on conversations through slack integration. Intercom - Used by Most Marketers, Industry Leading customer communication platform. Great for beginners! Chatwoot - Another Amazing Communication Tool but the best part is they have a great free plan useful for new businesses. Loom - Communicate with your audience through Videos. Loom is great for SaaS and to show human interaction to close new visitors effectively. #CRM Outseta - This tool provides great CRM and their billing system is better than other tools out their which makes it stands out! Hubspot - I don’t think this tool needs an introduction because Hubspot’s CRM is the best in industry. Salesflare - This CRM is a great alternative to hubspot as it’s beginner friendly and helpful for SMBs. #SEO Tools Ahrefs - One of the best SEO tool in the industry. They also just launched a bunch of free tools to help SEO beginners. Screaming frog - The only website crawler I have used since I bought my first domain. It’s the best! Ubersuggest- The Tool by Neil Patel is the best SEO tool for you. (I’m Joking, it’s the worst) Contentking - This tool is good at Real-time SEO Auditing, they do a lot of Marketing work through Newsletters. If you are subscribed to any SEO newsletter. You may have seen this tool. SEOquake & Semrush - SEOquake is a great tool to conduct on-page analysis, SERP, and much more. Great tool but it’s owned by Semrush. You should go for Semrush because that tool will cover all SEO aspects for you. #Content Marketing Buzzsumo - This tool is great for content research and but you may find the regular emails pretty annoying sometimes. Contentrow - Analyse Your Content and find it’s strength. Highly recommended who are weak at content structuring like me. Grammarly - If you are not a native English speaker like me, you might think you need it or not. You need it for sure for grammar corrections. #Graphic Design Tools Visme - At agencies, Infographics can be more effective than usual postscript. Visme is a graphic design tool focused on infographics and designs related to B2B and B2C. It’s great for agencies! Glorify - A Graphic Design Tool focused on E-commerce, filled with Designs useful for E-commerce store owners. Canva - All-in-one Industry leading Graphic Design Tool that everyone knows and every template is overused now. Adobe Creative Cloud ( previously Sparkpost) - It’s a great alternative to Canva filled with Amazing Stock images to use in your visuals but the only backlash is the exports in this tool are not high quality. Snaps - A Canva Alternative that might not have overused templates for your Social Accounts. #Advertising Tools Plai - It’s a great PPC tool to create Ads for Instagram and Tiktok. Wordstream - It’s an industry leading PPC Tool, great for Ad Grading and auditing. AdEspresso - This Is a tool by Hootsuite. They have a lot of Data sourced at the backend, which helps in Ad optimisation through this tool. That’s the reason I recommend this tool. #Video Editing Tools Veed Studio - I have been using Veed from last year. It’s one of the best Video Marketing Tool Optimized for Instagram & Tiktok. Synthesia - It’s a new AI video generation platform. From last few months, if you have seen marketing agencies including Videos in Emails. The chances are that’s not a Agency member taking but AI generated Human. Motionbox - It’s also a great video editing tool focused on video editing for Digital Marketers. Jitter Video - It’s a great motion design tool. Comes with great templates, the only place where other tools I mentioned lacks. It’s great and beginner friendly. #Copywriting Jasper AI - Google’s John Mueller says AI generated content is banned on Search but I think with Jasper AI you can generate SEO optimised Content but you have to put in some efforts like at least give 30 minutes for editing the Copy by yourself. Copy AI - Another AI tool to help you write better copy. This one is more focused on helping you write copy suitable for Ads and Social media campaigns. Hemingway App - To help you write more clearly and Bold. This tool is better than Grammarly if you look for writing perspective and it’s free. #Social Media Management App I’ve used a Lot of SMM Tools and that’s why going to mention all of them with a short review. Sprout social - The Best with deep insights coverage. Hootsuite - Great Scheduling tool just under sprout social. Later - Heavily Focused on Instagram from beginning and Now Tiktok too. SkedSocial - It’s like a Later alternative with great addition features like link-in-bio. Facebook’s Business Manager- Great but sometimes bugs can make a huge issue for you and customer support is like dead. Tweet Hunter & Hypefury- Both are Twitter Scheduling tools growing very fast on platform and are great for growth. Buffer - It’s a great tool but I haven’t seen any new updates to help with management. Zoho Social - It’s a great SMM tool and if you use other marketing solutions from Zoho. It’s a must have! #Market Research Tool • SparkToro - That’s the only one I have ever used. It’s great for audience research and comes with great customer service. Founded by Rand Fishkin, it’s one of the best research tool. #Influencer Marketing & UGC InfluenceGrid - A free search engine To find Tiktok & Instagram Influencers for your campaigns. Tiktok Creative Center- TikTok’s in-built tool called “Creative Center” is the best to find content trends, audience demographics and much more. Archive - Find Instagram Stories and Posts mentioning Your brands and use them as Ads for your business Marketing. #Landing Page Builders Leadpages - Its a great landing page builder because the integration and drag-and-drop features makes it easier to work with! Cardd co - A Great Landing page builder with easy step up but it lacks the copywriting and tracking features. Instapage - It’s one of the best out and I think the overall product is effective enough to help you stand out with your landing page. Unbounce - It’s a great alternative to Instapage due its well polished landing page templates that might be helpful for you. #Community Building Mighty Networks - A Great Community building platform, and you can also sell courses within the platform. Circle so - A great alternative to Mighty networks focused on Communities specifically. We are currently using for small community Of ours. #Sales Tools Drift - You can get much more out of Drift than just sales tools but The Sales solutions provided in Drift are one of the best. Salesforce - It’s the industry Sales solution provider. A go-to and have various pricing plans making it suitable for majority of SMBs. #Social Proof Tools People don’t have enough time to search across internet to decide to trust you after seeing your Ad first time. That’s what you might be facing too. Here are two tools I absolutely love for social proof! Use Proof - Show Recent Activities occurring on your website and build the trust of your visitors. Testimonial to - Gather Testimonials across Social Media platforms related to your business with this tool. Capture tweets and comments mentioning your brands and mention them. #Analytics Tools Plausible Analytics- A privacy friendly Analytics alternative to Google Analytics if you hate Analytics 4 like me. Mixpanel - Product Analytics and funnel reports better than Google Analytics. #Reddit Marketing Gummysearch- This tool will help To find your target audience on Reddit and interact with them with its help and close your new customers. Howitzer- It’s another pretty similar tool to Gummysearch focused on Reddit cold outreach to get clients and new customers. Both are great but Gummysearch provides better customer support while Howtizer is helpful on a large scale Reddit Marketing. #Text Marketing Klaviyo - It’s an email + SMS marketing tool, it’s taking up space in marketing industry very quickly as an industry leader due to its great integrations but you need to learn the platform usage to maximise the outcome. Cartloop - This tool provides great text marketing solutions with integration with Spotify and other e-commerce marketing tools. Attentive Mobile - This is my favourite Text marketing tool due to the interactive dashboard + they have a library of Text marketing examples to help you out with your campaigns. #Other Tools I have used throughout my journey! Triple Whale - It’s a great E-commerce marketing tools with Triple pixel to help you track your campaigns more efficiently. Fastory - To create well optimized Instagram & Tiktok Stories for your business. Jotform - Online Form Builder with integrations with leading marketing tools. Gated - As an entrepreneur and marketer, you may receive a bunch of unwanted emails. Use Gated to get rid of them and receive useful mails only! ClickUp- The main Tool for Project Management, one of the best and highly recommended. Riverside - Forget Zoom or Google Meet, For your Podcast Interviews and Marketing conferences. You need riverside with great video quality and recording features. Manychat- Automate your Instagram DMs and interact with your followers more efficiently + sell out your products/ services when you are offline. Calendy - To schedule meetings with your ideal clients. ServiceProviderPro - It’s a client portal for SEO & Growing Agencies, very helpful in scaling agencies. SendCheckit - Compare your Email Subject Lines with 100,000+ others in the database for free. Otter AI - Using AI track your meetings more effectively, you can easily edit, annotate and share notes from the meetings. Ryte - Optimise your website User experience with this tool focused on UX aspects + SEO too. PhantomBuster - Scrape LinkedIn Profile and Data from Facebook/LinkedIn groups. I clearly love this tool! #Honourable Mentions Zapier - The Only tool you need to integrate your favourite tool with a new effective tool. Elementor - That’s what I use for web design and it’s great! Marketer Hire - To hire world class marketers to work with you. InShot & Capcut - I create Instagram Reels and TikTok’s and life without these tools isn’t possible. Nira - It’s a great tool to Manage your workspace and this tool has launched many marketing templates in-built helpful for marketers and also entrepreneurs. X - The tool you love that wasn’t mentioned here is valuable and I honour that tool and share that if you would like to! I mean thanks for reading what I have curated all over my life as a marketer. I share 5 Marketing Tools, 5 Marketing Resources and 1 Free Resourceevery week in my newsletter, you can subscribe here to receive that for free. Also, You can read an expanded list of email marketing tools in this Reddit post!

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.

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.

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.

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!

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.

Founder Pitch: AI Agent for Simplifying Public Cloud Management

Video to understand : https://youtu.be/9ocUjlUrU\w?si=S0ETDbKSdJqlVDyg Are You Ready to Redefine Cloud Management with AI? Imagine an intelligent AI agent that transforms the complexity of managing public cloud infrastructure into simple, natural language commands. No more navigating through endless configurations or deciphering technical documentation—our AI agent is here to revolutionize the way organizations interact with cloud platforms. About the Project We’re building an AI-powered agent designed to handle public cloud management tasks seamlessly. Whether you’re setting up your organization’s cloud foundation or deploying complex workloads, this AI agent makes it as easy as having a conversation. What Can the AI Agent Do? Cloud Foundation Setup: Example: “Please set up a cloud foundation blueprint for my organization on Google Cloud.”* The AI agent will ask key questions (e.g., organization ID) and guide you through authentication. Once authorized, it sets up the foundation using GCP APIs. Workload Deployment: Example: “Spin up a GKE cluster for me.”* The agent will ask for necessary details (e.g., number of nodes, VPC info), authenticate, and deploy the cluster in minutes. Security and Compliance Validation: Example: “Validate my organization’s cloud setup and check for security vulnerabilities.”* The agent audits your setup, identifies potential risks, and provides actionable insights. Current Progress We’ve developed a working prototype that integrates with major cloud providers like Google Cloud. The AI agent can already: Authenticate with cloud APIs Execute foundational tasks such as setting up organizations and spinning up clusters Perform initial security validations Who I’m Looking For I’m searching for a co-founder with enterprise sales experience and a strategic vision to grow our user base. You will be instrumental in helping us: Build relationships with companies willing to pilot our product Develop go-to-market strategies for enterprise adoption Identify opportunities for partnerships with cloud service providers Your Role As a co-founder, you’ll lead efforts to: Secure Pilot Programs: Identify and onboard enterprises for product trials to gather feedback and refine the solution. Drive Growth: Develop scalable strategies to grow our user base across industries. Market Positioning: Work with me to define our unique value proposition and establish thought leadership in the cloud management space. My Background I bring over a decade of experience in tech, with a strong focus on software engineering and infrastructure. My contributions so far include: Developing the core AI engine and cloud integrations Designing workflows that simplify complex cloud tasks Why Join This Project? Revolutionize Cloud Management: Be part of a project that will redefine how organizations interact with public clouds. Tackle Challenging Problems: Work at the cutting edge of AI and cloud computing. High Growth Potential: Join an industry projected to grow exponentially as enterprises embrace AI-driven automation. Build a Company from Scratch: Shape the product, team, and culture as we grow together. What’s Next? Our immediate priorities include: Expanding the AI agent’s capabilities to support multi-cloud setups. Conducting pilot programs with enterprise clients. Iterating on the product based on real-world feedback. What We Need to Succeed Expertise in enterprise sales and partnerships A deep understanding of enterprise challenges and cloud adoption trends A shared passion for leveraging AI to solve complex problems Let’s work together to build the future of cloud management. If you’re excited about this vision and bring the expertise we need, I’d love to connect and discuss how we can take this project to the next level.

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.

Founder Pitch: AI Agent for Simplifying Public Cloud Management

Video to understand : https://youtu.be/9ocUjlUrU\w?si=S0ETDbKSdJqlVDyg Are You Ready to Redefine Cloud Management with AI? Imagine an intelligent AI agent that transforms the complexity of managing public cloud infrastructure into simple, natural language commands. No more navigating through endless configurations or deciphering technical documentation—our AI agent is here to revolutionize the way organizations interact with cloud platforms. About the Project We’re building an AI-powered agent designed to handle public cloud management tasks seamlessly. Whether you’re setting up your organization’s cloud foundation or deploying complex workloads, this AI agent makes it as easy as having a conversation. What Can the AI Agent Do? Cloud Foundation Setup: Example: “Please set up a cloud foundation blueprint for my organization on Google Cloud.”* The AI agent will ask key questions (e.g., organization ID) and guide you through authentication. Once authorized, it sets up the foundation using GCP APIs. Workload Deployment: Example: “Spin up a GKE cluster for me.”* The agent will ask for necessary details (e.g., number of nodes, VPC info), authenticate, and deploy the cluster in minutes. Security and Compliance Validation: Example: “Validate my organization’s cloud setup and check for security vulnerabilities.”* The agent audits your setup, identifies potential risks, and provides actionable insights. Current Progress We’ve developed a working prototype that integrates with major cloud providers like Google Cloud. The AI agent can already: Authenticate with cloud APIs Execute foundational tasks such as setting up organizations and spinning up clusters Perform initial security validations Who I’m Looking For I’m searching for a co-founder with enterprise sales experience and a strategic vision to grow our user base. You will be instrumental in helping us: Build relationships with companies willing to pilot our product Develop go-to-market strategies for enterprise adoption Identify opportunities for partnerships with cloud service providers Your Role As a co-founder, you’ll lead efforts to: Secure Pilot Programs: Identify and onboard enterprises for product trials to gather feedback and refine the solution. Drive Growth: Develop scalable strategies to grow our user base across industries. Market Positioning: Work with me to define our unique value proposition and establish thought leadership in the cloud management space. My Background I bring over a decade of experience in tech, with a strong focus on software engineering and infrastructure. My contributions so far include: Developing the core AI engine and cloud integrations Designing workflows that simplify complex cloud tasks Why Join This Project? Revolutionize Cloud Management: Be part of a project that will redefine how organizations interact with public clouds. Tackle Challenging Problems: Work at the cutting edge of AI and cloud computing. High Growth Potential: Join an industry projected to grow exponentially as enterprises embrace AI-driven automation. Build a Company from Scratch: Shape the product, team, and culture as we grow together. What’s Next? Our immediate priorities include: Expanding the AI agent’s capabilities to support multi-cloud setups. Conducting pilot programs with enterprise clients. Iterating on the product based on real-world feedback. What We Need to Succeed Expertise in enterprise sales and partnerships A deep understanding of enterprise challenges and cloud adoption trends A shared passion for leveraging AI to solve complex problems Let’s work together to build the future of cloud management. If you’re excited about this vision and bring the expertise we need, I’d love to connect and discuss how we can take this project to the next level.

Building No-Code Apps with AppSheet: Foundations

In this course you will learn the fundamentals of no-code app development and recognize use cases for no-code apps. The course provides an overview of the AppSheet no-code app development platform and its capabilities. You learn how to create an app with data from spreadsheets, create the app’s user experience using AppSheet views and publish the app to end users.

OMG - I'm a tech founder trying to promote my SAAS product and I'm like a deer in headlights!

I've been building a tool for agile project management for the past year or two and I think it is looking pretty good. I've got good feedback from agile leads and such and have adjusted the product accordingly and I think now it is solving the problem it is focused on. It isn't perfect technically and as I am a tech head and I love all the challenges of solving and building tricky software solutions it is very tempting to program and program and program. But I have to bite the bullet and get out of my comfort zone and get some customers! But I have no budget for marketing, and no acumen for it and I don't know where to start. I have researched forums, blogs, AI, etc. and there is a lot of advice, too much in fact. Every intro email or announcement text I write, I review it and rewrite it over and over as I am constantly wondering if it is too wordy, or doesn't focus on benefits, or whatever advice I read last. How do people just get on with it without doubting whether this wording is the most compelling, or the website design doesn't look clunky, or I'm not getting to the right people? The sales and marketing world is so much harder than coding, and at the same time much more squishy and ill defined :-) Can anyone out there relate, offer some advice, help?

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

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?

Looking for a tech cofounder. Revoltionary (yes really!) gig economy app. I will not promote.

Hey everyone! I’m building a new gig-work app that cuts out the hassles of interviews, applications, and sky-high fees. We’re aiming to make it easy for businesses to hire qualified freelancers for short shifts or one-off tasks—and for freelancers to set their own rates and get paid quickly. Why This App? Time-Saving Model: Instead of posting jobs and conducting multiple interviews, employers can instantly book from a list of KYC-verified freelancers who showcase their skills via 30-second video bios. Cost Leadership: We plan to charge only 5%, far below the 15–50% common in other gig platforms. This keeps more money in the pockets of both freelancers and businesses. Proven Demand: A beta test in 2018 drew nearly 600 active users, validating that there’s appetite for a simpler, fairer way to fill short shifts. About Me 20+ years’ experience in payroll, workforce management, and operations for Fortune 500 companies. Led cross-functional teams, implemented large-scale solutions, and believe in building with a user-first mindset. Offering meaningful equity—I want a true partner, not a hired gun. Who I’m Looking For Full-Stack Developer (comfortable with Node.js, React, Python, or similar and ML/Ai) who can manage everything from front-end to database integration (ideally Postgres/MySQL) and build a same day payments system. Passion for creating solutions that genuinely help gig workers and small businesses. Excitement to collaborate on the product roadmap, from the booking interface to same-day payment features. The Opportunity Major Market: The gig economy is huge and still growing. If we nail speed, cost-effectiveness, and ease of use, we can capture a significant share of it. Remote-Friendly: We can work together from anywhere, though I’m planning to relaunch in London where the initial beta gained momentum. If this sounds like your kind of challenge, drop a comment or DM me. Let’s chat about how we can merge our strengths—my operations background and your technical expertise—to build a platform that truly transforms the gig-work experience. Thanks for reading, and I look forward to creating something impactful together!

Hiring technical team software engineer/full stack as a non technical founder tips to assess competency

Hey guys I run a non profit enterprise where we help legacy enterprises and also brick and mortar non profits and charities transform digitally through Ai driven solutions and also building right infrastructure for them government and companies pays us to deliver this service so we don’t charge our clients. We are growing the team and wanted to know more about how you would go around hiring someone if your non technical.

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.

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?

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.

Looking for a tech cofounder. Revoltionary (yes really!) gig economy app. I will not promote.

Hey everyone! I’m building a new gig-work app that cuts out the hassles of interviews, applications, and sky-high fees. We’re aiming to make it easy for businesses to hire qualified freelancers for short shifts or one-off tasks—and for freelancers to set their own rates and get paid quickly. Why This App? Time-Saving Model: Instead of posting jobs and conducting multiple interviews, employers can instantly book from a list of KYC-verified freelancers who showcase their skills via 30-second video bios. Cost Leadership: We plan to charge only 5%, far below the 15–50% common in other gig platforms. This keeps more money in the pockets of both freelancers and businesses. Proven Demand: A beta test in 2018 drew nearly 600 active users, validating that there’s appetite for a simpler, fairer way to fill short shifts. About Me 20+ years’ experience in payroll, workforce management, and operations for Fortune 500 companies. Led cross-functional teams, implemented large-scale solutions, and believe in building with a user-first mindset. Offering meaningful equity—I want a true partner, not a hired gun. Who I’m Looking For Full-Stack Developer (comfortable with Node.js, React, Python, or similar and ML/Ai) who can manage everything from front-end to database integration (ideally Postgres/MySQL) and build a same day payments system. Passion for creating solutions that genuinely help gig workers and small businesses. Excitement to collaborate on the product roadmap, from the booking interface to same-day payment features. The Opportunity Major Market: The gig economy is huge and still growing. If we nail speed, cost-effectiveness, and ease of use, we can capture a significant share of it. Remote-Friendly: We can work together from anywhere, though I’m planning to relaunch in London where the initial beta gained momentum. If this sounds like your kind of challenge, drop a comment or DM me. Let’s chat about how we can merge our strengths—my operations background and your technical expertise—to build a platform that truly transforms the gig-work experience. Thanks for reading, and I look forward to creating something impactful together!

Hire No-Code Developers: 7 Reasons Smart SAAS Founders Lean on AI

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!

Founder I’m working with is helping companies implement AI agents

We build it and for you

I searched for unexplored AI business opportunities for 2024 and found 8 promising ideas

https://solansync.beehiiv.com/p/8-innovative-ai-business-opportunities-2024-evaluation-resources Entering 2024, the AI landscape presents numerous uncharted business opportunities. Solan Sync, on February 06, 2024, shared an insightful exploration into nine innovative AI business prospects that stand out for their potential market impact and implementation feasibility. Here's a brief overview of each: No-Code AI Chatbot Development Platforms: These platforms enable businesses to create efficient chatbots without coding knowledge, catering to a variety of communication needs and boasting a significant market potential projected at $19.8 billion by 2027. AI-Powered Document Management Systems: Offering a solution to automate data extraction and management, this opportunity targets sectors overwhelmed by paperwork, with a market growth expected to reach $4.4 billion by 2026. Automated AI Customer Support Platforms: AI-driven platforms are transforming customer support by handling inquiries with advanced conversational agents, aiming for a part of the $15.3 billion market by 2027. AI-Driven Content Generation Platforms: Utilizing advanced language models for content creation, this area addresses the high demand for engaging content across digital platforms, with the market projected to hit $12 billion by 2025. AI-Powered Recommendation System APIs: Tailored product recommendations can significantly enhance user experience, tapping into a market anticipated to grow to $6.3 billion by 2027. AI-Enhanced Digital Media Buying Solutions: These platforms optimize advertising strategies using AI, targeting the native advertising market expected to reach $59 billion by 2025. Enterprise-grade Voice-activated AI Assistants: Improving workplace efficiency with voice commands, this segment has a potential market of $1.1 billion by 2026. AI-Enhanced Supply Chain Management Solutions: By applying AI for real-time optimization, this opportunity aims at improving efficiency within the vast data-rich environments of modern supply chains. Each idea is detailed with its overview, target customer segments, key AI functionalities, profitability evaluations, and examples of current pioneers. This exploration not only highlights the vast potential within AI-driven business models but also encourages entrepreneurs and corporations to delve into these promising sectors. The rapid advancement of AI technology and its practical applications suggest these ideas represent just the beginning of what the future holds. Now is the time to leverage AI's capabilities to innovate and enhance products, services, and operations across industries.

Tech founders -- you're being lied to

I've been meaning to post this for a while. I saw a video recently that put me over the edge. You guys need to know what's up. Venture capitalists, angels, and accelerators all want you to build fast and fail faster. They want to you get your mvp buult in as little as a couple weeks. I'm a software dev and I own SaaS company. I'm here to tell you that you're being lied to. It's 2023. Unless some customer is about to drown because of their problem, they are not going to respect, or consider your trashy looking mvp. People these days expect a certain level of polish and professionalism when it comes to software before they give it more than 3s of their time. If your software took 80 hours to build, good chance that even customers from your target market will disregard it unless you're solving some insanely painful problem. And if you're using you're mvp for market research, people aren't going to talk to you if they believe that they spent more time getting dressed that morning than you put into your product. Build things that you can be proud of. Time boxing your first dev cycle into a few days or even weeks limits the scope of what you can build. I've spent more time than this figuring out a single api. Its this time boxing that leads 1000s of people to build the same shit. It's low quality work and exists in a super saturated market. And given the small scope of the product, the amount you'll be able to charge means the LTV of a customer will be lower than you CAC. Meaning your company will always lose money. The negative reception from your pre alpha product will have you think that people don't like you or your work. It's simply not the case. Few on this planet could produce something captivating in 100 hours. VCs tell you to ship your garbage MVP asap because of the following reason. They view every product that ships as a lotto ticket. If they like the look of it, they'll buy a ticket. And the more products there are and the shittier they are, it means a) they have more ticket numbers to select from and b) the cost of the ticket is a lot cheaper than it would otherwise be if the product was nice. VCs are not your friends and often, don't know how to build or market products. They are in it for the money and any advice they give to you or the community will be self serving. The indie community needs to wake up and realize that quality software built by a small team that people will pay for in this saturated market often takes months if not years to build. The idea of building a product and putting it in front of customers in 2 weeks is dumb. I've used some of these products and they are so limited in scope, broken and poorly designed that I don't give them anymore than a minute or two of my time. Note: validate your ideas before writing code. I'm not advocating spending a year writing software for an unproven market or problem. Yes, there are exceptions and stories of people shipping in no time and getting traction, but these are not the norm. Lastly, this philosophy is why you have and will continue to see a million products centered around AI. For those of you who aren't devs, Open AI made chatgpt accessible to developers and it's like 3 lines of code to ask it a question, get a response and save that response within your program. It's super low effort to integrate and that's why everyone will be building the same types of products with it. Tl;dr: Investors and gurus have agendas. Be logical about the level of effort required to build a software company and put forth only work that you're proud of. Being able to code doesn't give you a magical ability to create massive value with only a few weeks of work. You have to grind like pretty much every other successful business owner. I'll likely be banned for this, but fuck it. Ive got a sub where I'll share more insight and ban bullshit and idiotic posts with zero warning. It's not for everyone and I'll usually let you know pretty quick if our relationship isn't going to work. 6000 people and growing. r/cutthebull I'll write a post on that sub in the next few mins on how to guarentee accountability from top level management at your company.

If you are looking for free courses about AI, LLMs, CV, or NLP, I created the repository with links to resources that I found super high quality and helpful. The link is in the comment.

Airtable builds with Amazon Bedrock to transform workflows with generative AI | Amazon Web Services

Airtable, a cloud based low-code platform, enables non-programmers to build next-gen business applications. To democratize AI adoption for non-technical users across organizations, Airtable launched Airtable AI, powered by Amazon Bedrock. Through this partnership, Airtable AI seamlessly incorporates powerful foundation models like Anthropic's Claude and Amazon's Titan on Amazon Bedrock, allowing customers to choose models that best suits their use cases and workflows. Key benefits include a unified API for integrating AWS services, secure hosting of foundation models and data, access to cutting-edge technologies, fostering bottoms-up AI adoption among non-technical teams, and generative AI use cases including content generation, automation actions, and intelligent data Q&A. All this is unified within Airtable's intuitive low-code environment. Learn more at: https://go.aws/3Ta68X4 Subscribe: More AWS videos: https://go.aws/3m5yEMW More AWS events videos: https://go.aws/3ZHq4BK Do you have technical AWS questions? Ask the community of experts on AWS re:Post: https://go.aws/3lPaoPb ABOUT AWS Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform, offering over 200 fully featured services from data centers globally. Millions of customers — including the fastest-growing startups, largest enterprises, and leading government agencies — are using AWS to lower costs, become more agile, and innovate faster. #AmazonBedrock #FoundationModels #generativeAI #AnthropicClaude #AmazonTitan #Airtable #AWS #AmazonWebServices #CloudComputing

Using APIs for Network Automation

This course will provide a solid foundation for understanding how APIs are utilized in network automation by discussing important topics such as data encoding formats, REST APIs, and the Python Requests library. Upon completion of the course, you will be equipped with the necessary skills to utilize APIs in your network automation solution and be able to describe the usefulness of APIs in this context.

Introduction to Generative AI in Healthcare

This course "Introduction to Generative AI in Healthcare" offers a comprehensive introduction to Generative AI in Healthcare, designed specifically for individuals who are curious about the intersection of cutting-edge technology and the medical field. Whether you are a healthcare professional, a tech enthusiast, or someone eager to understand how artificial intelligence (AI) is revolutionizing the healthcare industry, this course will provide you with foundational knowledge and actionable insights to navigate this exciting space.

Introduction to AI for Cybersecurity

In "Introduction to AI for Cybersecurity," you'll gain foundational knowledge of how artificial intelligence (AI) is transforming the field of cybersecurity. This course covers key AI techniques and how they can be applied to enhance security measures, detect threats, and secure digital systems. Learners will explore hands-on implementations of AI models using tools like Jupyter Notebooks, allowing them to detect spam, phishing emails, and secure user authentication using biometric solutions.

Introduction to AI and Machine Learning on Google Cloud

This course introduces the artificial intelligence (AI) and machine learning (ML) offerings on Google Cloud that support the data-to-AI lifecycle through AI foundations, AI development, and AI solutions. It explores the technologies, products, and tools available to build an ML model, an ML pipeline, and a generative AI project based on the different goals of users, including data scientists, AI developers, and ML engineers.

Generative AI Cybersecurity & Privacy for Leaders: A Primer

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Fundamentals of Machine Learning and Artificial Intelligence

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AI Python for Beginners

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AI for Business: Generation & Prediction

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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.

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 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.

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.

How I made a high tech salary in my first selling month

For over 7 years I worked as a full-stack developer, helping other companies bring their ideas to life. But one day, I thought “Why not try making my own dream come true?”. That’s when I decided to quit my job and start my own journey to becoming an entrepreneur. At first, it wasn’t easy. I didn’t make any money for months and had no idea where to start. I felt lost. Then, I decided to focus on something popular and trending. AI was everywhere, and ChatGPT was the most used AI platform. So I looked into it and I found the OpenAI community forum where people had been asking for features that weren’t being added. That gave me an idea. Why not build those features myself? I created a Chrome extension and I worked on some of the most requested features, like: Downloading the advanced voice mode and messages as MP3 Adding folders to organize chats Saving and reusing prompts Pinning important chats Exporting chats to TXT/JSON files Deleting or archiving multiple chats at once Making chat history searches faster and better It took me about a week to build the first version, and when I published it, the response was incredible. People loved it! Some even said things like, “You’re a lifesaver!” That’s when I realized I had something that could not only help people but also turn into a real business. I kept the first version free to see how people would respond. Many users have been downloading my extension, which prompted Chrome to review it to determine if it qualified for the featured badge. I received the badge, and it has significantly boosted traffic to my extension ever since. After all the positive feedback, I launched a paid version one month ago. A few minutes after publishing it, I made my first sale! That moment was so exciting, and it motivated me to keep going. I already have over 4,000 users and have made more than $4,500 in my first selling month. I’ve decided to release 1-2 new features every month to keep improving the extension based on what users ask for. I also created the same extension for Firefox and Edge users because many people have been asking for it! I also started a Reddit community, where I share updates, sales, discount codes, and ideas for new features. It’s been awesome to connect with users directly and get their feedback. Additionally, I’ve started working on another extension for Claude, which I’m hoping will be as successful as this one. My message to you is this: never give up on your dreams. It might feel impossible at first, but with patience, hard work, and some creativity, you can make it happen. I hope this inspires you to go after what you want. Good luck to all of us!

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

Best no-code tools for prototyping AI

building a b2b aI powered saas which will tap into an API and then use RAG. I’m searching for a co founder but an accelerator is interested in my project and I’d like to have a small prototype to show them. I’d also like to develop a small free version that can run on my site for customers to use. I’ve been looking at bubble but would appreciate any suggestions (especially free) that’s good for a non-techie (who actually enjoys building things and learning too hehe)

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?

How good is a No Code/AI application to validate your idea

Hi, I'm a Non-tech founder and I'm working on building a simple board type video game. The product is heavily reliant on design and user perception. I do intend to find a tech co-founder to build and scale the product, but before that I want to do some testing to validate the idea and go for a pre-seed round and find a co-founder through VCs or incubators. How good is AI or a no-code tool to build a prototype that will help me test out the idea, gameplay and help me with some validation metrics before pitching to a Venture Capitalist. How good are the one's currently in market and what are the suggestions from your experience

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.

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

Created the Shopify Alternative in a 3rd world country “I will not promote”

Built a Shopify alternative I’ve been a long-time follower of this subreddit and have always valued the insights shared here. Today, I’m reaching out to share our story and seek advice or guidance on potential next steps for our business. Four years ago, we set out to build a local e-commerce platform tailored to the unique challenges of operating in a third-world country where global solutions like Shopify fall short. Shopify, while a fantastic platform, doesn’t provide localized support or integrations here, and the costs of running a Shopify store are prohibitively high due to: The need for multiple apps to replicate basic functionality Expensive international support calls or long chat queues Higher payment gateway fees (no Shopify Pay) USD-only subscription payments, which incur additional bank conversion fees And more We built a solution that addresses these pain points, and today, we’re proud to have over 4,000 merchants on our platform, with 1,600+ paying customers. We’re processing over $1 million per month across 50,000+ orders, which translates to a significant impact in our local economy. As experienced founders, we’ve managed our financials meticulously, allowing us to thrive while many local competitors have shut down. However, scaling in our current economic climate has been challenging, and raising capital has proven to be incredibly tough. We’re exploring strategic options, including potential partnerships, acquisitions, or investments. For example, we believe our platform could be an attractive opportunity for a player like Shopify or another company looking to expand into emerging markets. I’m reaching out to this community to ask: Are there doors we haven’t knocked on? Are there opportunities or strategies we might be overlooking? Any advice, introductions, or insights would be immensely appreciated. Thank you for taking the time to read this, and I look forward to any feedback or ideas you might have! [post refined by AI]

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

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.

No revenue for 6 months, then signed $10k MRR in 2 weeks with a new strategy. Here’s what I changed.

This is my first company so I made A LOT of mistakes when starting out. I'll explain everything I did that worked so you don't have to waste your time either. For context, I built a SaaS tool that helps companies scale their new client outreach 10x (at human quality with AI) so they can secure more sales meetings. Pricing I started out pricing it way too low (1/10 as much as competitors) so that it'd be easier to get customers in the beginning. This is a HUGE mistake and wasted me a bunch of time. First, this low pricing meant that I was unable to pay for the tools I needed to make sure my product could be great. I was forced to use low-quality databases, AI models, sending infrastructure -- you name it. Second, my customers were less invested in the product, and I received less input from them to make the product better. None ended up converting from my free trial because my product sucked, and I couldn't even get good feedback from them. I decided to price my product much higher, which allowed me to use best-in class tools to make my product actually work well. Outreach Approach The only issue is that it's a lot harder to get people to pay $500/month than $50/month. I watched every single video on the internet about cold email for getting B2B clients and built up an outbound MACHINE for sending thousands of emails a day. I tried all the top recommended sales email formats and tricks (intro, painpoint, testimonial, CTA, etc). Nothing. I could send 1k emails and get a few out of office responses and a handful of 'F off' responses. I felt bad and decided I couldn't just spam the entire world and expect to make any progress. I decided I needed to take a step back and learn from people who'd succeeded before in sales. I started manually emailing CEOs/founders that fit my customer profile with personal messages asking for feedback on my product -- not even trying to sell them anything. Suddenly I was getting 4-6 meetings a day and just trying to learn from them (turns out people love helping others). And without even prompting, many of them said 'hey, I actually could use this for my own sales' and asked how they could start trying it out. That week I signed 5 clients between $500-$4k/month (depending how many contacts they want to reach). I then taught my product to do outreach the same way I did that worked (include company signals, make sure the person is a great match with web research, and DONT TALK SALESY). Now, 6 of my first 10 clients (still figuring out who it works for, lol) have converted from the free trial and successfully used it to book sales meetings. I'm definitely still learning, but this one change in my sales approach changed everything for me, so I wanted to share. If anyone has any other tips/advice that changed their business's sales, would love to hear!

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

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/

100 AI-Driven Businesses Later… Here’s the Truth No One Tells You! (I will not promote)

I analyzed 100 businesses across industries that implemented AI, and here’s what I found! Most businesses use AI for: Marketing automation (15%) – email copy, ad creatives, SEO research (ChatGPT, Jasper, Copy.ai) Customer support (12%) – AI chatbots handling up to 70% of inquiries before escalation (Intercom, Drift) Data analysis & insights (10%) – AI summarizing reports, trends in seconds (Tableau AI, ChatGPT for spreadsheets) Sales prospecting (10%) – lead scoring, automated outreach (Apollo.io, HubSpot AI) Workflow automation (8%) – connecting tools and automating repetitive tasks (Zapier, Make.com) Biggest AI adoption challenges: 20% couldn’t decide which AI solution would give the highest ROI 15% faced technical difficulties like integration with existing systems 10% struggled to choose the right tools from too many options Honestly, a lot of businesses waste time on AI solutions that don’t align with their needs. I made a quick questionnaire to help find the right ones tailored to your needs. Let me know if you want to check it out.

There's a handful of frequent discussion/post types tanking this sub's overall quality

We've all seen them, here's a few that I've routinely noticed: Painfully obvious ChatGPT'd dos-and-donts list gurus Poorly veiled posts attempting to disguise crowdsourcing market research as random curiosity (e.g, what are your pain points with todo apps and what would it take for you to pay a million dollars for mine?) Delusions of grandeur - recruitment posts for taking down FAANG companies with no existing traction, moat, funding, team, or product Idea guys asking how get full time engineers to build massive products for free Seeking a tech founder with no details about the business and people in the comments begging to join despite knowing nothing about it Building an app like but with amateuraly implemented, needless AI that could easily do better in a week to sink us Asking for product validation, expecting a standing ovation and losing their cool when it's decidedly no good Did I miss anything?

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.

Equity vesting schedule as CTO - I will not promote

Hi everyone, in short I recently got an offer as CTO for a start-up with 17% equity vesting in 3 years. The product is like 6 months away from MVP but the co-founders had the core idea proved and figured out (it's an AI company), so my duty is to implement and deploy it. My question is that the first cliff is at the 13th month for 3%, which is definitely after the product is built. What if before the 13th month they just kick me off the bus? Should I ask for a 3% or sth at Day 0? Plus is that vesting schedule common (13th month, 25th month and 37th month) for tech start-up? Oh btw I'm getting paid with a very low almost 0 level until seed investment. Much appreciated for your input! I will not promote.

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.

Has anyone successfully implemented AI for customer support?

I'm spending some time dealing with the same discord messages over and over, most of them could just be answered with some sort of Retrieval-Augmented Generation on my FAQ and documentation. Unfortunately, I haven't found anything to actually pull this off yet, and the last thing I want to do right now is build another internal tool.

Validate before building is no longer relevant - (I will not promote)

Change my mind: So many accelerators and guidance shouts about the mom test and validation, but I believe it's out of date. It all harks back to a time before you could build easily build in a week at no cost. Now, as a semi-technical but non coding founder, at no cost I can quickly test a thesis, using AI and a lightweight tech stack. Sure I'm not getting a blind interview about a problem, but the mom test really was about reducing the risk in an idea before committing to build, because building carries cost. But why waste the time? Sales & prospecting is so difficult, people aren't interested in taking interviews without a bit of meat, and instead of farting around asking questions, I think we're now in the era of testing thesis with a quick build, rather than extensive mom testing. After all: User feedback > Interview feedback P.s I will not promote

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.

Technical entrepreneur looking for next team

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

Looking for a B2C Platform Tech Stack (Repo/Stack/Boilerplate)

Hi everyone, I'm a non-technical founder and am looking for a complete tech stack/repo for a B2C platform, where user participation (e.g. upvotes) is key. I’m familiar with solutions like ShipFast, but it is super tuned in for SaaS and I need something more similar to a ProductHunt-style platform. Ideally, I’d love to find a repo that also includes a boilerplate to simplify setup and customization. I use an AI co-pilot for many of my tasks, so having a pre-configured stack would be incredibly useful to streamline my work. If you know of any existing repos that I can clone, or if you think it might be a good idea to hire someone from a platform like Upwork to create one for me, I'd love to hear your thoughts. Any recommendations or help would be greatly appreciated!

Need advise on equity

Hey all, I am in the late stages of launching an app (Cloud native, API and AI based) with 3rd party data integration. We don’t have any user/revenue at this time but the base platform is almost ready with some user feedback. So far I have been the only one tackling the technical aspects (with some external help) and other non-technical aspects. Now I am teaming up with another partner who is going to be my technical co-founder who ideally will help in taking my platform forward and tweak the product depending on further user feedback. I am wondering what should be the equity offering to the co-founder in this situation considering that the product is in slightly late stage but also considering the skills required for this and also potential pivots we might have to do in future.

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. &#x200B; Have you tried something of it? How it was? Maybe I missed something that worked for you in similar cases?

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?

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.

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).

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😭🙏🏼

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

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.

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.

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.

We're building a tool to help you find grants. Would you use it?

Hi there! In Europe alone, we spend almost € 400 billion annually on subsidies to support businesses. We estimate there are around 18,000 different grants available to entrepreneurs. Via the EU of course, but also via cities, national governments and literally countless public bodies all offering different kinds of support. I've been working for the government for about 10 years now, and I've noticed how difficult it can be for smaller companies to identify the right subsidy opportunities. Because they don't know they exist (and thus don't know where to find them), or because the eligibility criteria are just overwhelming, for example. Together with my co-founder we're building a tool to make it easier to find the grants that best align with your goals (fingers crossed!). Basically we scan all subsidies in Europe, use AI to analyze the goals and criteria and match companies with those subsidies that work best for them. So my question for you guys: do you look for subsidies or grants at all? And would such a tool be helpful for you? And if so, what kind of features would be most helpful for you? Really appreciate any thoughts from your side! Also if you think the idea is just rubbish :) Jesse

How I made a high tech salary in my first selling month

For over 7 years I worked as a full-stack developer, helping other companies bring their ideas to life. But one day, I thought “Why not try making my own dream come true?”. That’s when I decided to quit my job and start my own journey to becoming an entrepreneur. At first, it wasn’t easy. I didn’t make any money for months and had no idea where to start. I felt lost. Then, I decided to focus on something popular and trending. AI was everywhere, and ChatGPT was the most used AI platform. So I looked into it and I found the OpenAI community forum where people had been asking for features that weren’t being added. That gave me an idea. Why not build those features myself? I created a Chrome extension and I worked on some of the most requested features, like: Downloading the advanced voice mode and messages as MP3 Adding folders to organize chats Saving and reusing prompts Pinning important chats Exporting chats to TXT/JSON files Deleting or archiving multiple chats at once Making chat history searches faster and better It took me about a week to build the first version, and when I published it, the response was incredible. People loved it! Some even said things like, “You’re a lifesaver!” That’s when I realized I had something that could not only help people but also turn into a real business. I kept the first version free to see how people would respond. Many users have been downloading my extension, which prompted Chrome to review it to determine if it qualified for the featured badge. I received the badge, and it has significantly boosted traffic to my extension ever since. After all the positive feedback, I launched a paid version one month ago. A few minutes after publishing it, I made my first sale! That moment was so exciting, and it motivated me to keep going. I already have over 4,000 users and have made more than $4,500 in my first selling month. I’ve decided to release 1-2 new features every month to keep improving the extension based on what users ask for. I also created the same extension for Firefox and Edge users because many people have been asking for it! I also started a Reddit community, where I share updates, sales, discount codes, and ideas for new features. It’s been awesome to connect with users directly and get their feedback. Additionally, I’ve started working on another extension for Claude, which I’m hoping will be as successful as this one. My message to you is this: never give up on your dreams. It might feel impossible at first, but with patience, hard work, and some creativity, you can make it happen. I hope this inspires you to go after what you want. Good luck to all of us!

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

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.

West coast vs east coast investor attitude towards digital health and AI start-up

I am a neurologist and co-founder of a start-up company automating the detection of stroke in the home. We have been fundraising for the last few months and have had the experience of west coast investors telling us to ask for way more and east coast investors suggesting we ask for less. Many west coast investors seem excited by the AI component while many east coast investors see this aspect as a technical risk/a negative. Is this consistent with other people's experience and where on the east coast is the best place to try to fund-raise if you happen to be a start-up in the digital health space (unfortunately we are located in Virginia)? 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.

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!

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.

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!

Serious B2B businesses will not try to create a solution using AI - This is why. [i will not promote]

After architecting and developing multiple B2B SaaS platforms and resolving countless challenges, here's why I don't think a proper B2B solution can be developed using AI. You must have senior tech-folks in your teams - even if you choose to leverage AI for expediting some code generation. This isn't theory - this is battle-tested reality. You can use this as a template if you're building one. Core Considerations: Multi-Tenancy Foundation (B2B) Proper tenant isolation at every layer (data, compute, networking) Flexible deployment models (pooled vs. silo) based on customer tier Tenant-aware everything (logging, metrics, tracing) Identity & Security (B2B/Standalone) Enterprise-grade authentication, often with SSO support Role-based access control (RBAC) at tenant level (may need dynamic policy generation for resource access) Audit trails for all system actions (specially if you're in a regulated domain) Client/Tenant Management (B2B) Self-service onboarding with admin approval workflows Automated tenant provisioning/deprovisioning Tenant-specific configurations and customizations Cross-tenant analytics and administration Operational Excellence (B2B/Standalone) Zero-downtime deployments (helps with canary releases) Tenant-isolated debugging capabilities Resource quotas and throttling by tenant tier Automated backup and disaster recovery per tenant Scalability Architecture (B2B) Independent scaling of tenant workloads Resource isolation for "noisy neighbor" prevention Tier-based performance guarantees (SLAs) Dynamic resource allocation Each of these topics can be as complicated as you can think of - depends on the solution you're building. I have seen many seasoned architects and developers struggle also because of their "single-tenant" mindset. Here are some common pitfalls to avoid (B2B/Standalone): Standalone - mindset in database design Hard-coded configurations Lack of context in logging/monitoring Insufficient tenant isolation in shared services (B2B) Missing tenant-aware cost allocation (B2B) You need people great with infrastructure as well. They need to consider: Tenant-aware routing (API Gateway or whatever you're using) Code with isolation when/if required Data storage with proper partitioning Shared services vs. dedicated services strategy There are a number of common problems I have seen people often make. Often it's because of a pressure from high above. But every architectural decision must considered in terms of the solution you're building. In many cases, security cannot be bolted on later, observability must be tenant-aware from day one, operations must scale. This is just the foundation. Your actual business logic sits ON TOP of all this. Now, would you think these can be done by AI? I'll be waiting for that day. :-)

AI Might Be the Best Industry for Ambitious Lazy People i will not promote

Not here to promote, just talk about my experience with starting a new business. I'm not new to the game, so I was able to speed-run my new AI automation agency. I currently run a software company with a co-founder, and I recently decided to start a new AI and automation agency. I just had a private launch this week, and I already have a $2K client lined up, ready to buy, along with some other interested parties, as I have sales calls with them coming up. I found all of my leads after choosing a niche to focus on and using this tool I built that automatically scrapes IG data. My tool is “unique” because it automagically qualifies the leads with AI. It even has a custom frontend, and I used this tool as a case study to secure the client I have now, and I accomplished all of this in the span of about a week. Granted, I have a background in engineering, so picking up AI was easier for me, but anyone can do it. You can even use AI to learn AI. If you haven’t used AI in your business yet, I would strongly suggest getting into AI and automation. If you've been considering starting a business but don't know what business to start, take this as a sign to turn AI into a business. i will not promote

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

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!

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?

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 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.

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?

How I made a high tech salary in my first selling month

For over 7 years I worked as a full-stack developer, helping other companies bring their ideas to life. But one day, I thought “Why not try making my own dream come true?”. That’s when I decided to quit my job and start my own journey to becoming an entrepreneur. At first, it wasn’t easy. I didn’t make any money for months and had no idea where to start. I felt lost. Then, I decided to focus on something popular and trending. AI was everywhere, and ChatGPT was the most used AI platform. So I looked into it and I found the OpenAI community forum where people had been asking for features that weren’t being added. That gave me an idea. Why not build those features myself? I created a Chrome extension and I worked on some of the most requested features, like: Downloading the advanced voice mode and messages as MP3 Adding folders to organize chats Saving and reusing prompts Pinning important chats Exporting chats to TXT/JSON files Deleting or archiving multiple chats at once Making chat history searches faster and better It took me about a week to build the first version, and when I published it, the response was incredible. People loved it! Some even said things like, “You’re a lifesaver!” That’s when I realized I had something that could not only help people but also turn into a real business. I kept the first version free to see how people would respond. Many users have been downloading my extension, which prompted Chrome to review it to determine if it qualified for the featured badge. I received the badge, and it has significantly boosted traffic to my extension ever since. After all the positive feedback, I launched a paid version one month ago. A few minutes after publishing it, I made my first sale! That moment was so exciting, and it motivated me to keep going. I already have over 4,000 users and have made more than $4,500 in my first selling month. I’ve decided to release 1-2 new features every month to keep improving the extension based on what users ask for. I also created the same extension for Firefox and Edge users because many people have been asking for it! I also started a Reddit community, where I share updates, sales, discount codes, and ideas for new features. It’s been awesome to connect with users directly and get their feedback. Additionally, I’ve started working on another extension for Claude, which I’m hoping will be as successful as this one. My message to you is this: never give up on your dreams. It might feel impossible at first, but with patience, hard work, and some creativity, you can make it happen. I hope this inspires you to go after what you want. Good luck to all of us!

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

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?

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!

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)

AI Might Be the Best Industry for Ambitious Lazy People i will not promote

Not here to promote, just talk about my experience with starting a new business. I'm not new to the game, so I was able to speed-run my new AI automation agency. I currently run a software company with a co-founder, and I recently decided to start a new AI and automation agency. I just had a private launch this week, and I already have a $2K client lined up, ready to buy, along with some other interested parties, as I have sales calls with them coming up. I found all of my leads after choosing a niche to focus on and using this tool I built that automatically scrapes IG data. My tool is “unique” because it automagically qualifies the leads with AI. It even has a custom frontend, and I used this tool as a case study to secure the client I have now, and I accomplished all of this in the span of about a week. Granted, I have a background in engineering, so picking up AI was easier for me, but anyone can do it. You can even use AI to learn AI. If you haven’t used AI in your business yet, I would strongly suggest getting into AI and automation. If you've been considering starting a business but don't know what business to start, take this as a sign to turn AI into a business. 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!

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

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

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.

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 👋

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!

Nuts and bolts AI implementation for small business

How can small businesses use AI to increase sales or decrease expenses without massive disruption? One way for us is using AI to process our email history to identify patterns and write personalized messages based on past correspondence. According to legal advice in which I have confidence, email that is personalized for each recipient (and meets other standards) does not need to be opt in. If you disagree - understood - but spam morality is not the topic here. Bottom line - obviously a game changer. Knowing phrases people have used before becoming clients - and all of the possible permutations of those phrases, and detecting where those phrases show up will make our sales and marketing many times more effective for a fraction of the cost. There's a reason big corps. record calls, and now small business can leverage the same technology. We are setting up a process that yields accurate, up to date, comprehensive data for our own business operations. Our clients - who are they and how has their demographic changed over time? To answer this question and for email personalization, we also need access to external data sources e.g. like accurate up to date company demographics. IMO - the leader in company data in the US? THEY SUCK. We found there is no magic fairy who is going to make good data appear for our AI. The process of applying our own proprietary knowledge to code and categorize the data is just as important, and obviously highly sensitive. How do we leverage the AI technologies of companies like Google and Microsoft (or anyone else) without being their bitch? Below is a list of some of the sources of my business's data: Data sources: PST/OST/Other Email data files Microsoft data from Windows/O365 Windows/Linux/Android/IOS application logs and other data Web server logs for the company website. SEO/Analytics Data Google data export Google voice/VOIP logs OneDrive/G drive Other Phone system/cell service logs Other SAAS and in-house application data. Facebook/social media data for company pages. QuickBooks/other accounting systems/business bank account logs POS/Credit card processing systems/PayPal, etc. OSINT to fill in the blanks

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.

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!

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?

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.

The Advantages of a Custom CRM Solution

The growth in the global CRM market continues to accelerate. According to techspective, the global CRM market is now worth \~ $40B USD and is expected to surpass $80B USD by 2025. Despite this phenomenal growth, the CRM market is still dominated by off-the-shelf solutions that are “cookie-cutter” in design and that provide little to no options for customization. These non-customized CRM solutions can significantly inhibit an enterprise’s ability to maximize the advantages of CRM adoption and to realize a robust ROI. As a result, companies are increasingly opting for digital CRM solutions that are customized to meet the unique needs of the enterprise. What is driving the increased demand for custom CRM solutions? What are some of the inherent advantages of a custom CRM solution when compared to a typical off-the-shelf product? Off-the-Shelf CRM Solutions – the Limitations Static CRM solutions are inflexible and self-limiting. Enterprises saddled with these cookie-cutter solutions increasingly report a consistent listing of issues that limit business growth.  These include…. A lack of real-time visibility into shifting customer trends and demands Delayed reaction to coordination of internal resources to meet changing business conditions Lost business opportunities due to lack of flexible, and real-time, opportunity lifecycle management Reporting and dashboarding capabilities that are slow, static, and disconnected Poor quote-to-cash performance that degrades financial performance A CRM investment that delivers poor ROI and that cannot grow with the enterprise All of the above can combine to limit the enterprise’s ability to fully capitalize on its hard-won business opportunities and, over time, limit its ability to create new opportunities. A Customized CRM – What is it? What is a “customized CRM”? Simply put, it is a holistic CRM solution that has been specifically tailored for the individual enterprise. The provider of a truly customized CRM solution will deliver a solution that has been designed to meet the specific—and unique—demands and objectives of the enterprise. A tailored CRM solution will address the enterprise’s sales and operational requirements as well as its customer experience objectives. Unlike standard off-the-shelf CRM providers, a provider of enterprise-grade custom CRM solutions will employ a comprehensive project discovery and requirements gathering process. This is an integral process that provides the foundation for the development of a custom solution that will provide the enterprise with long term flexibility and scalability. A customized digital CRM solution can provide distinct competitive advantages; including: Dynamic, Flexible, Powerful, Real-Time Management and Engagement A customized, technology\-fueled, CRM solution provides the enterprise with the means with which to dynamically engage with customers in ways that build customer loyalty, generate market growth, and drive strong financial performance. Distinct advantages include: Real-time sales opportunity tracking. Helps eliminate lost opportunities due to slow or inadequate reaction. Customized, AI and IoT-fueled, data analytics. A customized CRM solution can be designed to deliver real-time insights. Allows the enterprise to anticipate, and then satisfy, the needs of the customer. Customizable Dashboards and Reports. Widget-based, customized, dashboards and reports that provide real-time data and actionable insights. Sales process automation. Intelligent Workflow-based automation and control of critical sales processes. Increases overall operational efficiency. Outstanding ROI. A custom CRM solution typically delivers superior ROI when compared to off-the-shelf CRM products. Enterprises today spend considerable time, money, and effort in the development of customer relationships. For many enterprises, the continued use of CRM solutions that are rigid and outdated can prove to be impediments to business growth. When considering investment in a new CRM solution any enterprise will be well served by full consideration of a CRM solution that can be fully customized to meet its long-range requirements.

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! 😊🚀

thinking about starting a small business for creating personalised podcasts - what do you think?

I have an idea for a consumer app and I'd love to get peoples thoughts... Your favorite podcasters have all the answers you need - but they're buried in hundreds of episodes, scattered across different shows. Let me paint you a picture that every founder and creator knows too well… You're looking for specific advice, like growth tactics for your social sports app. You find a great episode from Greg Isenberg on growth hacking, but while he talks about internet directories, you need strategies for converting online matches into real-world meetups. While it’s interesting, it’s not really what you need. So you close the tab and keep searching... But what if it didn't have to be this way? With PodCraft, AI crafts personalized podcast content just for you. Simply ask for exactly what you need, and get a podcast in seconds in your favorite creator's voice and style, perfectly tailored to your specific needs. No more: Hours wasted searching for the right content Listening to multiple podcasts and articles to get one answer Generic advice that doesn't fit your context Instead move to: Instant access to exactly the content you need One personalized podcast instead of many Content that truly understands you and your business What do you think? Would you use a personalised podcast app like this?

Virtual Assistants -- Service vs. Freelance

Hi all! I run a small marketing agency (by small, I mean tiny, we only have three full-time employees). We do a lot of strategic SEO work and really embed with our client teams to help them build holistic content/search/AI-optimization strategies. Anyway, I've been working with a virtual assistant I found through a reference, and she was absolutely fantastic, but she's moving on to a full-time role. She was managing our calendars, updating templated audit presentations, and handling a lot of our CRM processes. I need to hire someone to replace her part-time. I'm not sure how to proceed, so I thought I'd ask here. I need to decide if I should hire someone contract or it might be be better to hire a managed VA service. My concerns are: Having to retrain a VA whenever they leave Confidentiality/security concerns Continuity of support I've been looking at services like Prialto and Belay and the managed element is compelling, but I know a contractor is cheaper. People who've tried both or either, what did you prefer?

Struggling with Cold Start for Our AI PowerPoint Tool - Seeking Platform and Strategy Suggestions!

Hello everyone, I'm one of the co-founders of a new AI-generated PowerPoint company, and I handle the marketing side of things. Our product is currently in the cold start phase, and we’re facing some challenges in gaining traction. We've already tried some influencer marketing, but the results have been underwhelming. We're looking for advice on the best platforms and strategies to effectively launch our product and reach our target audience. Here’s a bit more about our product: AI-Powered: Our tool leverages AI to help users quickly create professional PowerPoint presentations by simply entering their desired topic. User-Friendly: The process is streamlined to save users time and effort, making it ideal for professionals, educators, and students. Given our current situation, we would greatly appreciate any suggestions on: Platforms: Which platforms have you found most effective for cold starts, especially for tech or AI products? Strategies: What marketing strategies or tactics have worked for you in the early stages? Any tips on refining our influencer marketing approach or alternative methods to consider? Partnerships: Are there any specific types of partnerships or collaborations that you’ve found beneficial for similar products? Thank you in advance for your insights and advice. We're eager to learn from this community and hopefully turn things around for our launch. Best, Maggie

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

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

Optimizing your business with AI

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

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.

AI Myths Debunked

After speaking with a couple dozen SMB founders, I found that three-quarters believed AI was too expensive to implement in their businesses. When I asked them what processes they considered using AI for, they didn't have an exact process, they would say sales or operations. Is AI something that is not ready for the SMB, or is the SMB not prepared for AI?

Nuts and bolts AI implementation for small business

How can small businesses use AI to increase sales or decrease expenses without massive disruption? One way for us is using AI to process our email history to identify patterns and write personalized messages based on past correspondence. According to legal advice in which I have confidence, email that is personalized for each recipient (and meets other standards) does not need to be opt in. If you disagree - understood - but spam morality is not the topic here. Bottom line - obviously a game changer. Knowing phrases people have used before becoming clients - and all of the possible permutations of those phrases, and detecting where those phrases show up will make our sales and marketing many times more effective for a fraction of the cost. There's a reason big corps. record calls, and now small business can leverage the same technology. We are setting up a process that yields accurate, up to date, comprehensive data for our own business operations. Our clients - who are they and how has their demographic changed over time? To answer this question and for email personalization, we also need access to external data sources e.g. like accurate up to date company demographics. IMO - the leader in company data in the US? THEY SUCK. We found there is no magic fairy who is going to make good data appear for our AI. The process of applying our own proprietary knowledge to code and categorize the data is just as important, and obviously highly sensitive. How do we leverage the AI technologies of companies like Google and Microsoft (or anyone else) without being their bitch? Below is a list of some of the sources of my business's data: Data sources: PST/OST/Other Email data files Microsoft data from Windows/O365 Windows/Linux/Android/IOS application logs and other data Web server logs for the company website. SEO/Analytics Data Google data export Google voice/VOIP logs OneDrive/G drive Other Phone system/cell service logs Other SAAS and in-house application data. Facebook/social media data for company pages. QuickBooks/other accounting systems/business bank account logs POS/Credit card processing systems/PayPal, etc. OSINT to fill in the blanks

(Social Media) Marketing Advice

I'm a founder, currently with just my 2 other co-founders in the team. We have gotten through the ideation phase, with the help of an accelerator and have been given a grant of $5000. We're two technical founders and one non-technical founder, so do not need to hire any devs for now and are close to releasing our MVP. We're mainly using our money at this point for subscriptions (notion, chatGPT, etc.). Now we want to start spending on ads and social media marketing, but we all suck at social media, so we found a tool to do it for us: https://www.kendal.ai/. Do you think it's a good idea for us to spend $99 on a subscription to this AI tool so it can handle our content for us? If not, how can we do it better/cheaper?

I’ve Tested All the Image Generation Tools for My Small Business

Personally I hate paying for subscriptions unless it was absolutely necessary. Given that I don't have the budget to hire a graphic designer I started playing with all the new Generative AI tools and these are the ones I've narrowed it down to that have made the most impact. I posted this breakdown on r/AIforBusinessFounders but will share it here as well. Hope this compilation helps a fellow entrepreneur. If you’ve been exploring AI tools for generating images, you’ve probably come across big names like DALL·E, Adobe Photoshop, and MidJourney (finally moved off the dreadful Discord prompting thankfully!)  While they each have their strengths, they also have their quirks. Here’s the breakdown: DALL·E by OpenAI Pros: It’s integrated directly into ChatGPT, so if you’re already on a paid plan, you’re good to go—no extra fees. It's also embedded in Canva which is convenient if you’re designing social media posts or quick mockups. Cons: The image quality isn’t amazing. It often looks a bit flat or off, but I think where I struggle is you only get one output per generation, so there’s not much variety. Adobe Photoshop Pros: If you’re already using Photoshop, this is a nice addition. It lets you partially generate images within your edits, which can be handy for things like background replacements. When it comes to generating full images though, I find this tool really struggles. Cons: The image quality still has room for improvement—hands and fingers, in particular, are a consistent issue. Plus, you need an Adobe Creative Cloud subscription to access it. MidJourney Pros: Hands down, this tool produces the best-quality images. You get multiple outputs per prompt, and what really sets it apart is the ability to refine your favorite image. You can subtly tweak or drastically change it, depending on your needs. It previously only operated on Discord but it now has migrated to it's own platform so that's been a huge pro for me. Cons: It’s not cheap—MidJourney requires its own paid membership and comes with limited tokens, so you’ll need to budget your usage. The biggest con for me in the past was that you had to prompt in a Discord channel but now that it has own platform, it's no longer an issue. After putting all three to the test, my personal favorite is MidJourney. If image quality and creative control are your priorities, it’s hard to beat. That said, DALL·E and Adobe are solid options if you’re already using their platforms and want to save money. Are there any hidden gems I might have missed? If so let me know, I'd love to give them a try.

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.

The Advantages of a Custom CRM Solution

The growth in the global CRM market continues to accelerate. According to techspective, the global CRM market is now worth \~ $40B USD and is expected to surpass $80B USD by 2025. Despite this phenomenal growth, the CRM market is still dominated by off-the-shelf solutions that are “cookie-cutter” in design and that provide little to no options for customization. These non-customized CRM solutions can significantly inhibit an enterprise’s ability to maximize the advantages of CRM adoption and to realize a robust ROI. As a result, companies are increasingly opting for digital CRM solutions that are customized to meet the unique needs of the enterprise. What is driving the increased demand for custom CRM solutions? What are some of the inherent advantages of a custom CRM solution when compared to a typical off-the-shelf product? Off-the-Shelf CRM Solutions – the Limitations Static CRM solutions are inflexible and self-limiting. Enterprises saddled with these cookie-cutter solutions increasingly report a consistent listing of issues that limit business growth.  These include…. A lack of real-time visibility into shifting customer trends and demands Delayed reaction to coordination of internal resources to meet changing business conditions Lost business opportunities due to lack of flexible, and real-time, opportunity lifecycle management Reporting and dashboarding capabilities that are slow, static, and disconnected Poor quote-to-cash performance that degrades financial performance A CRM investment that delivers poor ROI and that cannot grow with the enterprise All of the above can combine to limit the enterprise’s ability to fully capitalize on its hard-won business opportunities and, over time, limit its ability to create new opportunities. A Customized CRM – What is it? What is a “customized CRM”? Simply put, it is a holistic CRM solution that has been specifically tailored for the individual enterprise. The provider of a truly customized CRM solution will deliver a solution that has been designed to meet the specific—and unique—demands and objectives of the enterprise. A tailored CRM solution will address the enterprise’s sales and operational requirements as well as its customer experience objectives. Unlike standard off-the-shelf CRM providers, a provider of enterprise-grade custom CRM solutions will employ a comprehensive project discovery and requirements gathering process. This is an integral process that provides the foundation for the development of a custom solution that will provide the enterprise with long term flexibility and scalability. A customized digital CRM solution can provide distinct competitive advantages; including: Dynamic, Flexible, Powerful, Real-Time Management and Engagement A customized, technology\-fueled, CRM solution provides the enterprise with the means with which to dynamically engage with customers in ways that build customer loyalty, generate market growth, and drive strong financial performance. Distinct advantages include: Real-time sales opportunity tracking. Helps eliminate lost opportunities due to slow or inadequate reaction. Customized, AI and IoT-fueled, data analytics. A customized CRM solution can be designed to deliver real-time insights. Allows the enterprise to anticipate, and then satisfy, the needs of the customer. Customizable Dashboards and Reports. Widget-based, customized, dashboards and reports that provide real-time data and actionable insights. Sales process automation. Intelligent Workflow-based automation and control of critical sales processes. Increases overall operational efficiency. Outstanding ROI. A custom CRM solution typically delivers superior ROI when compared to off-the-shelf CRM products. Enterprises today spend considerable time, money, and effort in the development of customer relationships. For many enterprises, the continued use of CRM solutions that are rigid and outdated can prove to be impediments to business growth. When considering investment in a new CRM solution any enterprise will be well served by full consideration of a CRM solution that can be fully customized to meet its long-range requirements.

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.

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

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

Seeking Feedback: Offering Free Customer Support Automation Setup for Small Business Owners

Hello Redditors, I run a small online digital business solo and found myself spending a lot of time handling repetitive customer support tasks. To reduce my time spent on these tasks, improve response times, and increase customer satisfaction, I built a small customer support automation tool that connects my Gmail account with OpenAI agents. This tool knows my business well, can reply to questions about my products, and provides common-sense responses. It also integrates with Stripe and my order processing system, allowing it to fetch order details and process refunds automatically. Impressively, my setup has automated 95% of customer inquiries, providing immediate resolutions and consistently impressing my customers. I'm now considering turning this automation into a SaaS product, but first, I want to understand if there is a demand for it and identify the most common needs of small business owners. To gather insights, I would like to offer a free service to set up this customer support automation for a few small business owners. If you're interested, please post a comment or send me a DM with a summary of your business, your existing customer support system, and your customer support volume. Your feedback and experience will be invaluable in shaping this product. Looking forward to hearing from you!

Seeking Feedback: Offering Free Customer Support Automation Setup for Small Business Owners

Hello Redditors, I run a small online digital business solo and found myself spending a lot of time handling repetitive customer support tasks. To reduce my time spent on these tasks, improve response times, and increase customer satisfaction, I built a small customer support automation tool that connects my Gmail account with OpenAI agents. This tool knows my business well, can reply to questions about my products, and provides common-sense responses. It also integrates with Stripe and my order processing system, allowing it to fetch order details and process refunds automatically. Impressively, my setup has automated 95% of customer inquiries, providing immediate resolutions and consistently impressing my customers. I'm now considering turning this automation into a SaaS product, but first, I want to understand if there is a demand for it and identify the most common needs of small business owners. To gather insights, I would like to offer a free service to set up this customer support automation for a few small business owners. If you're interested, please post a comment or send me a DM with a summary of your business, your existing customer support system, and your customer support volume. Your feedback and experience will be invaluable in shaping this product. Looking forward to hearing from you!

AI for small business

Hello fellow small business owners, I'm curious to learn how you are incorporating artificial intelligence (AI) into your business to improve efficiency and productivity. With the rapid advancements in AI technology, I believe there are numerous opportunities for small businesses to leverage these tools to streamline operations and gain a competitive edge. I would love to hear your experiences and insights on the following: What specific AI tools or platforms are you currently using in your business? How have these AI solutions helped you streamline your workflows, reduce costs, or improve customer experience? Are there any particular areas of your business where you have found AI to be especially beneficial (e.g., marketing, customer service, inventory management)? Did you face any challenges or obstacles when implementing AI in your business, and how did you overcome them? Based on your experience, what advice would you give to other small business owners who are considering integrating AI into their operations? Thank you in advance for sharing your valuable insights. I believe this discussion will be beneficial for all small business owners looking to harness the power of AI to grow and succeed in today's competitive landscape.

AI Myths Debunked

After speaking with a couple dozen SMB founders, I found that three-quarters believed AI was too expensive to implement in their businesses. When I asked them what processes they considered using AI for, they didn't have an exact process, they would say sales or operations. Is AI something that is not ready for the SMB, or is the SMB not prepared for AI?

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

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

I’m sharing my learnings on how to use ChatGPT and other AI tools to generate leads, automate and grow my business.

Ok! I've been diving deep into how I can streamline and optimize my lead gen processes, and the things I've discovered have been mind-blowing. Recently came across a term called "EmotionAI". Wondered if anyone here has jumped on this and started implementing for lead gen? According to Exploding Topics, searches for "emotion AI" have increased by 525% over the past five years. Simply put, the tech can be used to provide more personalized experiences. And we all know that more personalized outreach means more likely conversions. As the AI market continues to grow, this will become increasingly mainstream. So, I thought why not share my learnings and real-life examples of how to use some these AI coupled with good ol’ chatGPT to grow. The first newsletter issue went out today. I plan to send every other Saturday but I might send every Saturday, still undecided. Anyway please check it out and subscribe if you want. Also let me know if you found it insightful.

I Accidentally Made My Mom Redundant at Her Office Job

At my tech job, AI assistants have boosted productivity so much we’ve almost stopped hiring. It made me wonder: why isn’t AI used more in small businesses? My mom works in the office of a small local manufacturing company, handling repetitive tasks like inputting orders into their ERP system. After shadowing her for a half day, I built an AI program that automates most of her work. Now she has so much free time, she’s watching Netflix at the office lol I’m thinking of quitting my job and starting my own small local business, offering this as a service to other small businesses in the era, but before I jump on it and quit my current job, I’d love to know if this is a more common issue. My main question are: What’s the most repetitive/time-consuming (computer) tasks you wish you could automate? If possible, could you break it down step by step with a lot of boring details ? 😅 Any hacks or tools you've found to make things easier so far? And maybe what else I should think on that I am missing before quitting my job and starting my own business?

𝐁𝐮𝐢𝐥𝐝 𝐋𝐋𝐌𝐬 𝐟𝐫𝐨𝐦 𝐬𝐜𝐫𝐚𝐭𝐜𝐡

“ChatGPT” is everywhere—it’s a tool we use daily to boost productivity, streamline tasks, and spark creativity. But have you ever wondered how it knows so much and performs across such diverse fields? Like many, I've been curious about how it really works and if I could create a similar tool to fit specific needs. 🤔 To dive deeper, I found a fantastic resource: “Build a Large Language Model (From Scratch)” by Sebastian Raschka, which is explained with an insightful YouTube series “Building LLM from Scratch” by Dr. Raj Dandekar (MIT PhD). This combination offers a structured, approachable way to understand the mechanics behind LLMs—and even to try building one ourselves! https://preview.redd.it/35sdlxdb2m0e1.jpg?width=1037&format=pjpg&auto=webp&s=dd228136fbf7cbdeeae253118ee7a46b04948c24 While AI and generative language models architecture shown in the figure can seem difficult to understand, I believe that by taking it step-by-step, it’s achievable—even for those without a tech background. 🚀 Learning one concept at a time can open the doors to this transformative field, and we at Vizuara.ai are excited to take you through the journey where each step is explained in detail for creating an LLM. For anyone interested, I highly recommend going through the following videos:  Lecture 1: Building LLMs from scratch: Series introduction https://youtu.be/Xpr8D6LeAtw?si=vPCmTzfUY4oMCuVl  Lecture 2: Large Language Models (LLM) Basics https://youtu.be/3dWzNZXA8DY?si=FdsoxgSRn9PmXTTz  Lecture 3: Pretraining LLMs vs Finetuning LLMs https://youtu.be/-bsa3fCNGg4?si=j49O1OX2MT2k68pl  Lecture 4: What are transformers? https://youtu.be/NLn4eetGmf8?si=GVBrKVjGa5Y7ivVY  Lecture 5: How does GPT-3 really work? https://youtu.be/xbaYCf2FHSY?si=owbZqQTJQYm5VzDx  Lecture 6: Stages of building an LLM from Scratch https://youtu.be/z9fgKz1Drlc?si=dzAqz-iLKaxUH-lZ  Lecture 7: Code an LLM Tokenizer from Scratch in Python https://youtu.be/rsy5Ragmso8?si=MJr-miJKm7AHwhu9  Lecture 8: The GPT Tokenizer: Byte Pair Encoding https://youtu.be/fKd8s29e-l4?si=aZzzV4qT\nbQ1lzk  Lecture 9: Creating Input-Target data pairs using Python DataLoader https://youtu.be/iQZFH8dr2yI?si=lH6sdboTXzOzZXP9  Lecture 10: What are token embeddings? https://youtu.be/ghCSGRgVB\o?si=PM2FLDl91ENNPJbd  Lecture 11: The importance of Positional Embeddings https://youtu.be/ufrPLpKnapU?si=cstZgif13kyYo0Rc  Lecture 12: The entire Data Preprocessing Pipeline of Large Language Models (LLMs) https://youtu.be/mk-6cFebjis?si=G4Wqn64OszI9ID0b  Lecture 13: Introduction to the Attention Mechanism in Large Language Models (LLMs) https://youtu.be/XN7sevVxyUM?si=aJy7Nplz69jAzDnC  Lecture 14: Simplified Attention Mechanism - Coded from scratch in Python | No trainable weights https://youtu.be/eSRhpYLerw4?si=1eiOOXa3V5LY-H8c  Lecture 15: Coding the self attention mechanism with key, query and value matrices https://youtu.be/UjdRN80c6p8?si=LlJkFvrC4i3J0ERj  Lecture 16: Causal Self Attention Mechanism | Coded from scratch in Python https://youtu.be/h94TQOK7NRA?si=14DzdgSx9XkAJ9Pp  Lecture 17: Multi Head Attention Part 1 - Basics and Python code https://youtu.be/cPaBCoNdCtE?si=eF3GW7lTqGPdsS6y  Lecture 18: Multi Head Attention Part 2 - Entire mathematics explained https://youtu.be/K5u9eEaoxFg?si=JkUATWM9Ah4IBRy2  Lecture 19: Birds Eye View of the LLM Architecture https://youtu.be/4i23dYoXp-A?si=GjoIoJWlMloLDedg  Lecture 20: Layer Normalization in the LLM Architecture https://youtu.be/G3W-LT79LSI?si=ezsIvNcW4dTVa29i  Lecture 21: GELU Activation Function in the LLM Architecture https://youtu.be/d\PiwZe8UF4?si=IOMD06wo1MzElY9J  Lecture 22: Shortcut connections in the LLM Architecture https://youtu.be/2r0QahNdwMw?si=i4KX0nmBTDiPmNcJ  Lecture 23: Coding the entire LLM Transformer Block https://youtu.be/dvH6lFGhFrs?si=e90uX0TfyVRasvel  Lecture 24: Coding the 124 million parameter GPT-2 model https://youtu.be/G3-JgHckzjw?si=peLE6thVj6bds4M0  Lecture 25: Coding GPT-2 to predict the next token https://youtu.be/F1Sm7z2R96w?si=TAN33aOXAeXJm5Ro  Lecture 26: Measuring the LLM loss function https://youtu.be/7TKCrt--bWI?si=rvjeapyoD6c-SQm3  Lecture 27: Evaluating LLM performance on real dataset | Hands on project | Book data https://youtu.be/zuj\NJNouAA?si=Y\vuf-KzY3Dt1d1r  Lecture 28: Coding the entire LLM Pre-training Loop https://youtu.be/Zxf-34voZss?si=AxYVGwQwBubZ3-Y9  Lecture 29: Temperature Scaling in Large Language Models (LLMs) https://youtu.be/oG1FPVnY0pI?si=S4N0wSoy4KYV5hbv  Lecture 30: Top-k sampling in Large Language Models https://youtu.be/EhU32O7DkA4?si=GKHqUCPqG-XvCMFG

How I Started Learning Machine Learning

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

How I Started Learning Machine Learning

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

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

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

Creating a strategy game AI without writing a board evaluation function?

I've recreated a favorite obscure two player board game of mine, where the board is divided into cells and pieces move across these cells (like chess, checkers, etc...) I want to try and create an AI that will be able to play the game - but I want to avoid writing the board evaluation function myself (for a minmax algorithm). Are there any general good ways to achieve that? From the little research I've done, I found out about genetic algorithms - and they seem like they would fit but I can't fathom how would one create a dna schema that describes a board evaluation function

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

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

Teaching an AI to Play Mario: A Learning Journey

TLDR: I've always wanted to learn reinforcement learning, but the notation and concepts often seemed overwhelming (and scary). So, \~3 months ago, I set myself a challenge: Train an AI to Speedrun Mario Watch the progression here: https://youtu.be/OQitI066aI0 &#x200B; Full Story: Three months ago, I stared at the dense forest of Reinforcement Learning (RL) papers and felt like Mario facing Bowser for the first time: unequipped and overwhelmingly outmatched. The notation seemed like hieroglyphics, and terms like "policy gradients" felt like they belonged in a sci-fi novel, not a beginner's project. But RL always seemed so cool, and I was really determined to achieve my goal. So, I started with the Sutton & Barto RL textbook, learning things like the Multi-Armed Bandit problem and MDPs working my way up to Actor-Critic methods. That book is literal gold & I highly recommend you work through it (even though it can be tough at times). I tried everything from random courses online to books on amazon & this textbook has been by far the most clear and effective way to learn RL. The biggest issue with the textbook is you learn a lot of theory, but don't learn implementation. So, I would go through a chapter a week & set aside Friday + the weekend to actually implement what I learned (usually by watching youtube tutorials & looking at Github Repos). Eventually, while searching for practical resources for implementing PPO, I stumbled upon a GitHub repository that literally trained an AI to play Mario. Rather than just cloning and running the code, I took a deeper approach. I aimed to understand the repository thoroughly, ensuring each line of code made sense in the context of what I had studied. But of course, this wasn't easy. One of the biggest issues was my hardware limitation. I was working on an old Mac. So, I started using Google Collab, but that had its own problems (session timeouts & limited GPU access). Ultimately, I found AWS Sagemaker to be pretty good. &#x200B; After rewriting the code, I felt confident it would work because I understood every aspect of it. So, I trained the AI to play Mario across a variety of different levels (took a long time and a lot of trial and error with the learning rate). It feels amazing seeing your theoretical knowledge translate into tangible results & this project gave me a big confidence boost. &#x200B; Anyways I made a video showing off the results (Note that I simplified the technical parts for it to reach a wider audience): https://youtu.be/OQitI066aI0 &#x200B; Feel free to drop any questions or feedback, I'm more than happy to help or chat about my experiences. I hope my journey can inspire some of you who might be feeling overwhelmed with the idea of diving into reinforcement learning or any other area of AI. Remember, the hardest part is often taking the first step. Once you start, the momentum will carry you forward. Thank you for reading my super long post and sharing in my little success story! 🚀🕹️🎮

𝗠𝗮𝘀𝘁𝗲𝗿 𝗟𝗶𝗻𝗲𝗮𝗿 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻: 𝗔 𝗠𝘂𝘀𝘁-𝗞𝗻𝗼𝘄 𝗳𝗼𝗿 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿𝘀

Scikit-learn vs Statsmodel Linear regression is often the first model introduced to those stepping into the world of data science and machine learning. A deep understanding of this fundamental concept is crucial for building a solid foundation. In this post, I explore two widely used approaches to linear regression, each with distinct purposes: 1️⃣ 𝗦𝗰𝗶𝗸𝗶𝘁-𝗟𝗲𝗮𝗿𝗻’𝘀 𝗟𝗶𝗻𝗲𝗮𝗿 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻: Optimized for machine learning applications and large datasets, this model focuses on efficiency and scalability. 2️⃣ 𝗦𝘁𝗮𝘁𝘀𝗺𝗼𝗱𝗲𝗹𝘀’ 𝗢𝗿𝗱𝗶𝗻𝗮𝗿𝘆 𝗟𝗲𝗮𝘀𝘁 𝗦𝗾𝘂𝗮𝗿𝗲𝘀 (𝗢𝗟𝗦): Known for its comprehensive statistical insights, this approach provides a detailed report ideal for understanding relationships and diagnosing issues like multicollinearity. It’s essential to gain hands-on experience with both libraries to appreciate their unique strengths. To make this learning process more accessible, I’ve created detailed videos and example code to guide you through practical applications: 🎥 𝗛𝗮𝗻𝗱𝘀-𝗢𝗻 𝗧𝘂𝘁𝗼𝗿𝗶𝗮𝗹𝘀: 📌 Learn Linear Regression in Python with LLM Prompt Chaining : https://www.youtube.com/watch?v=KOEG4rs1SUU 📌 In-Depth Linear Regression: Statsmodels OLS, Multicollinearity, and VIF : https://www.youtube.com/watch?v=QQWKY30XzNA 💻 𝗖𝗼𝗱𝗲 𝗳𝗼𝗿 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲: 🔗 Scikit-Learn Implementation: https://github.com/pritkudale/ML-for-Teachers/blob/main/Linear%20Regression/Linear\Regression.ipynb 🔗 Statsmodels Implementation: https://github.com/pritkudale/ML-for-Teachers/blob/main/Linear%20Regression/Linear\regression\using\stats\model.ipynb What makes these tutorials unique? I’ve incorporated LLM prompt chaining, enabling beginners to confidently write code without requiring extensive Python expertise. 📩 𝘚𝘵𝘢𝘺 𝘢𝘩𝘦𝘢𝘥 𝘪𝘯 𝘈𝘐 𝘢𝘥𝘷𝘢𝘯𝘤𝘦𝘮𝘦𝘯𝘵𝘴! 𝘚𝘶𝘣𝘴𝘤𝘳𝘪𝘣𝘦 𝘵𝘰 𝘰𝘶𝘳 𝘯𝘦𝘸𝘴𝘭𝘦𝘵𝘵𝘦𝘳 𝘧𝘰𝘳 𝘦𝘹𝘱𝘦𝘳𝘵 𝘪𝘯𝘴𝘪𝘨𝘩𝘵𝘴: 𝘝𝘪𝘻𝘶𝘢𝘳𝘢 𝘈𝘐 𝘕𝘦𝘸𝘴𝘭𝘦𝘵𝘵𝘦𝘳: https://vizuara.ai/email-newsletter/

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.

Is it worth doing a Msc Conversion course

I’m a philosophy student currently, graduating with a 2.1 from a russell group uni. I’m looking for a career change into ML with the ultimate goal being research, whether it be comp neuro, biology, or using it in sports. I’ve found a Msc conversion course (1 year with no CS background required) where, as the name suggests, they teach you AI and ML techniques and how to implement them in a a real world context. It also includes a 3 month dissertation project with the possibility of working with industry. I was speaking to a friend today who suggested I learn on my own and save the money. I couldn’t help but think a qualification is useful alongside a portfolio, rather than just a portfolio (which he suggested). Given my current situation, a beginner programmer looking for a way in, what do you think I do?

Starting with Deep Learning in 2025 - Suggestion

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

Learning Resources + Side Project Ideas

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

Neural Networks you can try to implement from scratch (for beginners)

I was reading a tweet talking about how useful it is to implement neural networks from scratch. How it allowed for a greater understanding of the topic. The author said he found it more useful than other people explaining the concept to him. While I disagree with the author’s opinion that it stops the need for explanations. It certainly does help the understanding of one’s model. I recommend giving it a go. In the blog post, I will suggest which models you should try to implement from scratch using NumPy or your favourite library. Also, I will link to some accompanying resources. Simple Feedforward Network This is the most famous example because it’s so simple. But allows you to learn so much. I heard about this idea from Andrew Trask. It also helped me think about implementing networks from scratch in general. In the Feedforward network, you will be using NumPy. As you won't need Pytorch or TensorFlow. To do the heavy-lifting for complex calculations. You can simply create a Numpy Array for training and testing data. You can also create a nonlinear function using Numpy. Then work out the error rate between the layer’s guess and real data. Resource for this task: https://iamtrask.github.io/2015/07/12/basic-python-network/ Follow this tutorial. It does a much better job of explaining how to do this in NumPy. With code examples to follow. Feedforward Network with Gradient Descent This is an extension of the network above. In this network, we allow the model to optimise its weights. This can also be done in NumPy. Resource for this task: https://iamtrask.github.io/2015/07/27/python-network-part2/ A follow-on from the previous article. Pytorch version of Perceptrons and Multi-layered Perceptrons. Here will go up a level by using a library. Examples I'm using will be done in Pytorch. But you can use whatever library you prefer. When implementing these networks, you learn how much a library does the work for you. Recourses for the task: https://medium.com/@tomgrek/building-your-first-neural-net-from-scratch-with-pytorch-56b0e9c84d54 https://becominghuman.ai/pytorch-from-first-principles-part-ii-d37529c57a62 K Means Clustering Yes, this does not count as a neural network. But a traditional machine learning algorithm is still very useful. As this is non deep learning algorithm it should be easier to understand. This can be done just using NumPy or Pandas depending on the implementation. Recourse for this task: https://www.machinelearningplus.com/predictive-modeling/k-means-clustering/ http://madhugnadig.com/articles/machine-learning/2017/03/04/implementing-k-means-clustering-from-scratch-in-python.html https://gdcoder.com/implementation-of-k-means-from-scratch-in-python-9-lines/ There are quite a few choices to choose from. So pick whatever implementation helps you understand the concepts better. These networks or models should be simple enough that you won't get lost trying to implement them. But still, help learn a few stuff along the way. \- If you found this post useful, then check out my mailing list where I write more stuff like this.

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

GPT Weekly - 19the June Edition - OpenAI's function calling, Meta's free LLM, EU Regulation and more.

This is a recap covering the major news from last week. 🔥Top 3 news - OpenAI’s updates, Meta’s upcoming free LLM and EU Regulation 🗞️Interesting reads include PSA about protecting your keys, The GPT ouroboros, Reddit - OpenAI’s moat, and more.. 🧑‍🎓Learning includes a Step-by-step guide from a non-technical founder who launched his MVP, Chatbot for your Gdrive and more 🔥Top 3 AI news in the past week OpenAI: New Pricing, Models, & Functions OpenAI has been on a roll. Last week we saw the release of OpenAI best practice on using GPT. This week we saw some amazing updates. Three major buckets were: First, the price decreases for both embeddings and GPT-3.5 tokens. Second, new models for gpt-4 and gpt-3.5. A new longer context model for gpt-3.5. Third, a new function calling capability. Why is it important? Previously, the output from OpenAI was all text. So, calling an external API from GPT was quite difficult. You had to parse the text data and things were often incorrect. Langchain created the Agents and Tools feature to tackle this problem. It was still unreliable and prone to issues. Now you get native support to generate a fixed format output. You can use the output to generate functional calls and also pass functions which need to be called. For example, if your app has multiple API endpoints then you can use GPT to generate the API calls with parameters. You can also pass the endpoints as function calls to ensure the correct function is executed. This functionality can further be used to generate structured data (JSON) out of GPT. So, you can generate data from GPT and load it into your backend. What’s next? This functionality allows turning natural language responses into structured data. This can be used to create “intelligent” backends using LLMs. We might see implementations in no-code tools to allow more robust and natural-language tools for non-technical folks. The structured data process goes both ways. You can also feed structured data into GPT for better responses. This feature also has its share of issues. Function calling suffers from the same prompt injection issues. Malicious actors can pass malicious code in function or the responses. For example, creation of queries using functions might contain malicious code to delete data. Without proper user validation this code will be executed automatically and delete data. So, using LLM as the back-end layer needs proper security implementation. Meta's LLM: Commercial Use Ahead Llama has been a boon for the open source community. Many of the open source models rely on Llama. The issue is that Llama is research-only and cannot be used commercially. So, no one can use it to build any product. Meta is now working on the next version of the model. This model will be available for commercial use. This is in stark contrast to both OpenAI and Google. Both safe-guarde their models and make it available through API. Why is it important? Certain industries cannot use LLM APIs because of strict restrictions on data privacy. These companies would want to run their own instance of a foundational model. A commercially available foundational model is also going to help people who want to keep their “API call” costs next to 0. A commercially available free-for-all model will also help push the open source community further. Just like Llama. What’s next? Sam Altman has said OpenAI didn’t release GPT-3 as open-source because they didn’t think people would be able to run it. Now OpenAI is working on an open-source model. This is going to be weaker than GPT-4. Let the battle of LLMs begin. EU's Proposed Legislation and Its Impact on AI Usage The EU parliament voted to move ahead with the E.U. AI Act. This act aims to ensure consumer protection against the dangers of AI. Why is it important? OpenAI and Sam Altman want regulations for models. They have proposed a IAEA-type of agency to stop the proliferation of LLM models. As per OpenAI, all models should be regulated and monitored. The suggestion of a license based regulation has led to significant backlash. Many people have called it “regulatory capture” - with the aim of shutting down competing LLMs. Licensing based regulations might not really be effective. The EU is approaching regulation from a different angle. It doesn’t focus on how models are developed. Rather focuses on how AI will/can be used. They have broken down use cases into 4 categories - unacceptable (prohibited), high, medium and low risk. For example, Building a Pre-Crime software,on%20crimes%20not%20yet%20committed.) to predict crimes? Building a Social credit system? Unacceptable. Using tools to influence elections or recommendation algorithms? High (Highly regulated). Using generative AI tools to create text or images on news sites? Medium (Add label that the content is AI generated) AI providers also need to disclose their training source. To me this sounds like good legislation. What do you guys think? But, OpenAI has warned that EU regulations might force them to pull out completely. What’s next? The disclosure requirements might help various publishing companies. AI and media companies are in talks to pay for training data. Google has been leading the charge. Additionally, OpenAI and Deepmind will open their models for safety and research purposes to the UK government. 🗞️10 AI news highlights and interesting reads PSA: If you are using Repl to write code, you might want to check your OpenAI API keys. If you have left them embedded then people can pirate and steal the keys. LLMs rely on human annotation or human feedback to learn. And one way to generate human annotation is crowdsourcing. But what if the crowdsource human annotators use LLMs? Research shows 33-46% workers used LLMs. So, basically we go from Human -> AI -> Human -> AI. The AI ouroboros. Researchers also say generated data to train models might cause serious issue. All the talks about moats \- Reddit might be OpenAI’s \future\ moat. Given the amount of complaints about how Google search experience has deteriorated during the blackout, this might be true? Doctors are using ChatGPT but not to diagnose.Rather to be more empathetic. We discussed this just a month ago. And guess where the data for this study came from? Reddit AskDocs. Moat FTW?! Beatles to make a comeback…using Generative AI. SnapFusion - Text to Image diffusion on mobile phones. Large context lengths are important for better GPT experience. The secret sauce for 100k context length. There is a lot of bad AI research out there. Some border on snake oil. Most AI “research” should be double checked and challenged. A new research on huggingface said that GPT-4 can ace MIT curriculum. Now someone is replicating the results and say that GPT-4 can’t beat MIT. Are we seeing peak AI? Especially when people from Deepmind and Meta are involved? Mistral AI raised $113 million in seed round with no product. Some might say this funding is for the team and the team is really solid. The issue though is whether the valuation is justified when OpenAI and Google already have a head start. The AI Hype Wall of Shame. \- Collection of articles which mislead people about AI in various aspects. 🧑‍🎓3 Learning Resources Building and Launching a company using GPT-4 with prompts. (The author didn’t know how to code but created and launched the MVP in a month). Chatbot for your Gdrive - https://www.haihai.ai/gpt-gdrive/ Building ChatGPT plugin using Supabase - https://supabase.com/blog/building-chatgpt-plugins-template That’s it folks. Thank you for reading and have a great week ahead. If you are interested in a focused weekly recap delivered to your inbox on Mondays you can subscribe here. It is FREE!

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! &#x200B; 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

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!

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!

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

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

AI For Entrepreneurs Episode 2 : Kwabena Agyeman

In 2015, they did a Kickstarter campaign for a smart camera based on a microcontroller. The campaign was a huge success and they raised more than $100k.  And then came a disaster they were not prepared for. One of the components in the camera failed leaving 80% of the cameras unusable. But they fought back hard and delivered the cameras against all odds.  In this episode, we will learn about OpenMV, microcontrollers, why they matter, and the challenges of porting computer vision algorithms to microcontrollers.  But above all, we will learn about the amazing journey of a resilient entrepreneur Kwabena Aygeman who is one of the co-founders of OpenMV.  https://youtu.be/EBiI627K8A8

MMML | Deploy HuggingFace training model rapidly based on MetaSpore

A few days ago, HuggingFace announced a $100 million Series C funding round, which was big news in open source machine learning and could be a sign of where the industry is headed. Two days before the HuggingFace funding announcement, open-source machine learning platform MetaSpore released a demo based on the HuggingFace Rapid deployment pre-training model. As deep learning technology makes innovative breakthroughs in computer vision, natural language processing, speech understanding, and other fields, more and more unstructured data are perceived, understood, and processed by machines. These advances are mainly due to the powerful learning ability of deep learning. Through pre-training of deep models on massive data, the models can capture the internal data patterns, thus helping many downstream tasks. With the industry and academia investing more and more energy in the research of pre-training technology, the distribution warehouses of pre-training models such as HuggingFace and Timm have emerged one after another. The open-source community release pre-training significant model dividends at an unprecedented speed. In recent years, the data form of machine modeling and understanding has gradually evolved from single-mode to multi-mode, and the semantic gap between different modes is being eliminated, making it possible to retrieve data across modes. Take CLIP, OpenAI’s open-source work, as an example, to pre-train the twin towers of images and texts on a dataset of 400 million pictures and texts and connect the semantics between pictures and texts. Many researchers in the academic world have been solving multimodal problems such as image generation and retrieval based on this technology. Although the frontier technology through the semantic gap between modal data, there is still a heavy and complicated model tuning, offline data processing, high performance online reasoning architecture design, heterogeneous computing, and online algorithm be born multiple processes and challenges, hindering the frontier multimodal retrieval technologies fall to the ground and pratt &whitney. DMetaSoul aims at the above technical pain points, abstracting and uniting many links such as model training optimization, online reasoning, and algorithm experiment, forming a set of solutions that can quickly apply offline pre-training model to online. This paper will introduce how to use the HuggingFace community pre-training model to conduct online reasoning and algorithm experiments based on MetaSpore technology ecology so that the benefits of the pre-training model can be fully released to the specific business or industry and small and medium-sized enterprises. And we will give the text search text and text search graph two multimodal retrieval demonstration examples for your reference. Multimodal semantic retrieval The sample architecture of multimodal retrieval is as follows: Our multimodal retrieval system supports both text search and text search application scenarios, including offline processing, model reasoning, online services, and other core modules: https://preview.redd.it/mdyyv1qmdz291.png?width=1834&format=png&auto=webp&s=e9e10710794c78c64cc05adb75db385aa53aba40 Offline processing, including offline data processing processes for different application scenarios of text search and text search, including model tuning, model export, data index database construction, data push, etc. Model inference. After the offline model training, we deployed our NLP and CV large models based on the MetaSpore Serving framework. MetaSpore Serving helps us conveniently perform online inference, elastic scheduling, load balancing, and resource scheduling in heterogeneous environments. Online services. Based on MetaSpore’s online algorithm application framework, MetaSpore has a complete set of reusable online search services, including Front-end retrieval UI, multimodal data preprocessing, vector recall and sorting algorithm, AB experimental framework, etc. MetaSpore also supports text search by text and image scene search by text and can be migrated to other application scenarios at a low cost. The HuggingFace open source community has provided several excellent baseline models for similar multimodal retrieval problems, which are often the starting point for actual optimization in the industry. MetaSpore also uses the pre-training model of the HuggingFace community in its online services of searching words by words and images by words. Searching words by words is based on the semantic similarity model of the question and answer field optimized by MetaSpore, and searching images by words is based on the community pre-training model. These community open source pre-training models are exported to the general ONNX format and loaded into MetaSpore Serving for online reasoning. The following sections will provide a detailed description of the model export and online retrieval algorithm services. The reasoning part of the model is standardized SAAS services with low coupling with the business. Interested readers can refer to my previous post: The design concept of MetaSpore, a new generation of the one-stop machine learning platform. 1.1 Offline Processing Offline processing mainly involves the export and loading of online models and index building and pushing of the document library. You can follow the step-by-step instructions below to complete the offline processing of text search and image search and see how the offline pre-training model achieves reasoning at MetaSpore. 1.1.1 Search text by text Traditional text retrieval systems are based on literal matching algorithms such as BM25. Due to users’ diverse query words, a semantic gap between query words and documents is often encountered. For example, users misspell “iPhone” as “Phone,” and search terms are incredibly long, such as “1 \~ 3 months old baby autumn small size bag pants”. Traditional text retrieval systems will use spelling correction, synonym expansion, search terms rewriting, and other means to alleviate the semantic gap but fundamentally fail to solve this problem. Only when the retrieval system fully understands users’ query terms and documents can it meet users’ retrieval demands at the semantic level. With the continuous progress of pre-training and representational learning technology, some commercial search engines continue to integrate semantic vector retrieval methods based on symbolic learning into the retrieval ecology. Semantic retrieval model This paper introduces a set of semantic vector retrieval applications. MetaSpore built a set of semantic retrieval systems based on encyclopedia question and answer data. MetaSpore adopted the Sentence-Bert model as the semantic vector representation model, which fine-tunes the twin tower BERT in supervised or unsupervised ways to make the model more suitable for retrieval tasks. The model structure is as follows: The query-Doc symmetric two-tower model is used in text search and question and answer retrieval. The vector representation of online Query and offline DOC share the same vector representation model, so it is necessary to ensure the consistency of the offline DOC library building model and online Query inference model. The case uses MetaSpore’s text representation model Sbert-Chinese-QMC-domain-V1, optimized in the open-source semantically similar data set. This model will express the question and answer data as a vector in offline database construction. The user query will be expressed as a vector by this model in online retrieval, ensuring that query-doc in the same semantic space, users’ semantic retrieval demands can be guaranteed by vector similarity metric calculation. Since the text presentation model does vector encoding for Query online, we need to export the model for use by the online service. Go to the q&A data library code directory and export the model concerning the documentation. In the script, Pytorch Tracing is used to export the model. The models are exported to the “./export “directory. The exported models are mainly ONNX models used for wired reasoning, Tokenizer, and related configuration files. The exported models are loaded into MetaSpore Serving by the online Serving system described below for model reasoning. Since the exported model will be copied to the cloud storage, you need to configure related variables in env.sh. \Build library based on text search \ The retrieval database is built on the million-level encyclopedia question and answer data set. According to the description document, you need to download the data and complete the database construction. The question and answer data will be coded as a vector by the offline model, and then the database construction data will be pushed to the service component. The whole process of database construction is described as follows: Preprocessing, converting the original data into a more general JSonline format for database construction; Build index, use the same model as online “sbert-Chinese-qmc-domain-v1” to index documents (one document object per line); Push inverted (vector) and forward (document field) data to each component server. The following is an example of the database data format. After offline database construction is completed, various data are pushed to corresponding service components, such as Milvus storing vector representation of documents and MongoDB storing summary information of documents. Online retrieval algorithm services will use these service components to obtain relevant data. 1.1.2 Search by text Text and images are easy for humans to relate semantically but difficult for machines. First of all, from the perspective of data form, the text is the discrete ID type of one-dimensional data based on words and words. At the same time, images are continuous two-dimensional or three-dimensional data. Secondly, the text is a subjective creation of human beings, and its expressive ability is vibrant, including various turning points, metaphors, and other expressions, while images are machine representations of the objective world. In short, bridging the semantic gap between text and image data is much more complex than searching text by text. The traditional text search image retrieval technology generally relies on the external text description data of the image or the nearest neighbor retrieval technology and carries out the retrieval through the image associated text, which in essence degrades the problem to text search. However, it will also face many issues, such as obtaining the associated text of pictures and whether the accuracy of text search by text is high enough. The depth model has gradually evolved from single-mode to multi-mode in recent years. Taking the open-source project of OpenAI, CLIP, as an example, train the model through the massive image and text data of the Internet and map the text and image data into the same semantic space, making it possible to implement the text and image search technology based on semantic vector. CLIP graphic model The text search pictures introduced in this paper are implemented based on semantic vector retrieval, and the CLIP pre-training model is used as the two-tower retrieval architecture. Because the CLIP model has trained the semantic alignment of the twin towers’ text and image side models on the massive graphic and text data, it is particularly suitable for the text search graph scene. Due to the different image and text data forms, the Query-Doc asymmetric twin towers model is used for text search image retrieval. The image-side model of the twin towers is used for offline database construction, and the text-side model is used for the online return. In the final online retrieval, the database data of the image side model will be searched after the text side model encodes Query, and the CLIP pre-training model guarantees the semantic correlation between images and texts. The model can draw the graphic pairs closer in vector space by pre-training on a large amount of visual data. Here we need to export the text-side model for online MetaSpore Serving inference. Since the retrieval scene is based on Chinese, the CLIP model supporting Chinese understanding is selected. The exported content includes the ONNX model used for online reasoning and Tokenizer, similar to the text search. MetaSpore Serving can load model reasoning through the exported content. Build library on Image search You need to download the Unsplash Lite library data and complete the construction according to the instructions. The whole process of database construction is described as follows: Preprocessing, specify the image directory, and then generate a more general JSOnline file for library construction; Build index, use OpenAI/Clip-Vit-BASE-Patch32 pre-training model to index the gallery, and output one document object for each line of index data; Push inverted (vector) and forward (document field) data to each component server. Like text search, after offline database construction, relevant data will be pushed to service components, called by online retrieval algorithm services to obtain relevant data. 1.2 Online Services The overall online service architecture diagram is as follows: &#x200B; https://preview.redd.it/nz8zrbbpdz291.png?width=1280&format=png&auto=webp&s=28dae7e031621bc8819519667ed03d8d085d8ace Multi-mode search online service system supports application scenarios such as text search and text search. The whole online service consists of the following parts: Query preprocessing service: encapsulate preprocessing logic (including text/image, etc.) of pre-training model, and provide services through gRPC interface; Retrieval algorithm service: the whole algorithm processing link includes AB experiment tangent flow configuration, MetaSpore Serving call, vector recall, sorting, document summary, etc.; User entry service: provides a Web UI interface for users to debug and track down problems in the retrieval service. From a user request perspective, these services form invocation dependencies from back to front, so to build up a multimodal sample, you need to run each service from front to back first. Before doing this, remember to export the offline model, put it online and build the library first. This article will introduce the various parts of the online service system and make the whole service system step by step according to the following guidance. See the ReadME at the end of this article for more details. 1.2.1 Query preprocessing service Deep learning models tend to be based on tensors, but NLP/CV models often have a preprocessing part that translates raw text and images into tensors that deep learning models can accept. For example, NLP class models often have a pre-tokenizer to transform text data of string type into discrete tensor data. CV class models also have similar processing logic to complete the cropping, scaling, transformation, and other processing of input images through preprocessing. On the one hand, considering that this part of preprocessing logic is decoupled from tensor reasoning of the depth model, on the other hand, the reason of the depth model has an independent technical system based on ONNX, so MetaSpore disassembled this part of preprocessing logic. NLP pretreatment Tokenizer has been integrated into the Query pretreatment service. MetaSpore dismantlement with a relatively general convention. Users only need to provide preprocessing logic files to realize the loading and prediction interface and export the necessary data and configuration files loaded into the preprocessing service. Subsequent CV preprocessing logic will also be integrated in this manner. The preprocessing service currently provides the gRPC interface invocation externally and is dependent on the Query preprocessing (QP) module in the retrieval algorithm service. After the user request reaches the retrieval algorithm service, it will be forwarded to the service to complete the data preprocessing and continue the subsequent processing. The ReadMe provides details on how the preprocessing service is started, how the preprocessing model exported offline to cloud storage enters the service, and how to debug the service. To further improve the efficiency and stability of model reasoning, MetaSpore Serving implements a Python preprocessing submodule. So MetaSpore can provide gRPC services through user-specified preprocessor.py, complete Tokenizer or CV-related preprocessing in NLP, and translate requests into a Tensor that deep models can handle. Finally, the model inference is carried out by MetaSpore, Serving subsequent sub-modules. Presented here on the lot code: https://github.com/meta-soul/MetaSpore/compare/add\python\preprocessor 1.2.2 Retrieval algorithm services Retrieval algorithm service is the core of the whole online service system, which is responsible for the triage of experiments, the assembly of algorithm chains such as preprocessing, recall, sorting, and the invocation of dependent component services. The whole retrieval algorithm service is developed based on the Java Spring framework and supports multi-mode retrieval scenarios of text search and text search graph. Due to good internal abstraction and modular design, it has high flexibility and can be migrated to similar application scenarios at a low cost. Here’s a quick guide to configuring the environment to set up the retrieval algorithm service. See ReadME for more details: Install dependent components. Use Maven to install the online-Serving component Search for service configurations. Copy the template configuration file and replace the MongoDB, Milvus, and other configurations based on the development/production environment. Install and configure Consul. Consul allows you to synchronize the search service configuration in real-time, including cutting the flow of experiments, recall parameters, and sorting parameters. The project’s configuration file shows the current configuration parameters of text search and text search. The parameter modelName in the stage of pretreatment and recall is the corresponding model exported in offline processing. Start the service. Once the above configuration is complete, the retrieval service can be started from the entry script. Once the service is started, you can test it! For example, for a user with userId=10 who wants to query “How to renew ID card,” access the text search service. 1.2.3 User Entry Service Considering that the retrieval algorithm service is in the form of the API interface, it is difficult to locate and trace the problem, especially for the text search image scene can intuitively display the retrieval results to facilitate the iterative optimization of the retrieval algorithm. This paper provides a lightweight Web UI interface for text search and image search, a search input box, and results in a display page for users. Developed by Flask, the service can be easily integrated with other retrieval applications. The service calls the retrieval algorithm service and displays the returned results on the page. It’s also easy to install and start the service. Once you’re done, go to http://127.0.0.1:8090 to see if the search UI service is working correctly. See the ReadME at the end of this article for details. Multimodal system demonstration The multimodal retrieval service can be started when offline processing and online service environment configuration have been completed following the above instructions. Examples of textual searches are shown below. Enter the entry of the text search map application, enter “cat” first, and you can see that the first three digits of the returned result are cats: https://preview.redd.it/d7syq47rdz291.png?width=1280&format=png&auto=webp&s=b43df9abd380b7d9a52e3045dd787f4feeb69635 If you add a color constraint to “cat” to retrieve “black cat,” you can see that it does return a black cat: &#x200B; https://preview.redd.it/aa7pxx8tdz291.png?width=1280&format=png&auto=webp&s=e3727c29d1bde6eea2e1cccf6c46d3cae3f4750e Further, strengthen the constraint on the search term, change it to “black cat on the bed,” and return results containing pictures of a black cat climbing on the bed: &#x200B; https://preview.redd.it/2mw4qpjudz291.png?width=1280&format=png&auto=webp&s=1cf1db667892b9b3a40451993680fbd6980b5520 The cat can still be found through the text search system after the color and scene modification in the above example. Conclusion The cutting-edge pre-training technology can bridge the semantic gap between different modes, and the HuggingFace community can greatly reduce the cost for developers to use the pre-training model. Combined with the technological ecology of MetaSpore online reasoning and online microservices provided by DMetaSpore, the pre-training model is no longer mere offline dabbling. Instead, it can truly achieve end-to-end implementation from cutting-edge technology to industrial scenarios, fully releasing the dividends of the pre-training large model. In the future, DMetaSoul will continue to improve and optimize the MetaSpore technology ecosystem: More automated and wider access to HuggingFace community ecology. MetaSpore will soon release a common model rollout mechanism to make HuggingFace ecologically accessible and will later integrate preprocessing services into online services. Multi-mode retrieval offline algorithm optimization. For multimodal retrieval scenarios, MetaSpore will continuously iteratively optimize offline algorithm components, including text recall/sort model, graphic recall/sort model, etc., to improve the accuracy and efficiency of the retrieval algorithm. For related code and reference documentation in this article, please visit: https://github.com/meta-soul/MetaSpore/tree/main/demo/multimodal/online Some images source: https://github.com/openai/CLIP/raw/main/CLIP.png https://www.sbert.net/examples/training/sts/README.html

Seeking Guidance to Transition from SRE to Quant Developer/HFT

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

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

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

I started with 0 AI knowledge on the 2nd of Jan 2024 and blogged and studied it for 365. Here is a summary.

FULL BLOG POST AND MORE INFO IN THE FIRST COMMENT :) Edit in title: 365 days\* (and spelling) Coming from a background in accounting and data analysis, my familiarity with AI was minimal. Prior to this, my understanding was limited to linear regression, R-squared, the power rule in differential calculus, and working experience using Python and SQL for data manipulation. I studied free online lectures, courses, read books. \Time Spent on Theory vs Practice\ At the end it turns out I spent almost the same amount of time on theory and practice. While reviewing my year, I found that after learning something from a course/lecture in one of the next days I immediately applied it - either through exercises, making a Kaggle notebook or by working on a project. \2024 Learning Journey Topic Breakdown\ One thing I learned is that \fundamentals\ matter. I discovered that anyone can make a model, but it's important to make models that add business value. In addition, in order to properly understand the inner-workings of models I wanted to do a proper coverage of stats & probability, and the math behind AI. I also delved into 'traditional' ML (linear models, trees), and also deep learning (NLP, CV, Speech, Graphs) which was great. It's important to note that I didn't start with stats & math, I was guiding myself and I started with traditional and some GenAI but soon after I started to ask a lot of 'why's as to why things work and this led me to study more about stats&math. Soon I also realised \Data is King\ so I delved into data engineering and all the practices and ideas it covers. In addition to Data Eng, I got interested in MLOps. I wanted to know what happens with models after we evaluate them on a test set - well it turns out there is a whole field behind it, and I was immediately hooked. Making a model is not just taking data from Kaggle and doing train/test eval, we need to start with a business case, present a proper case to add business value and then it is a whole lifecycle of development, testing, maintenance and monitoring. \Wordcloud\ After removing some of the generically repeated words, I created this work cloud from the most used works in my 365 blog posts. The top words being:- model and data - not surprising as they go hand in hand- value - as models need to deliver value- feature (engineering) - a crucial step in model development- system - this is mostly because of my interest in data engineering and MLOps I hope you find my summary and blog interesting. https://preview.redd.it/pxohznpy4dae1.png?width=2134&format=png&auto=webp&s=03c16bb3535d75d1f009b44ee5164cc3e6483ac4 https://preview.redd.it/0y47rrpy4dae1.png?width=1040&format=png&auto=webp&s=f1fdf7764c7151ff0a05ae92777c5bb7d52f4359 https://preview.redd.it/e59inppy4dae1.png?width=1566&format=png&auto=webp&s=2566033777a90410277350947617d3ce8406be15

Backend dev wants to learn ML

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

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

Built an interactive guide using a no-code AI tool. 700 visitors already and growing!

Hey Community, As a Founding Product & Growth Manager, I wanted to level up my ability to ship products end-to-end, so a few months ago, I got a Replit subscription and started building myself. One of the things I created is a Slack Etiquette Guide to help teams use Slack more effectively with best practices. It took me just 4-5 hours to build, no tech team needed! Here’s what I built: Slack Etiquette Guide Would love to get your feedback on what can be improved or added.

I am building a free app to fight propaganda online. Need your help

So long story short I was recently bombarded with an insane amounts of right-wing propaganda online and Russian bots so I decided I can somehow simplify the validation of the facts that are posted by people online. I have made a research of tools that are currently available for that and I've found that ChatGpt is quite biased with some topics and it lacks data about latest issues even when search mode is enabled. At least it takes some time for it to update it's memory or something Then I've checked a Google Fact Check tools which were even worse. Bad UI... I had no idea how it validates things because usually proof links that Google provides are quite random. So I decided to build a tool that simplifies the research. The idea was simple - you have a claim - you paste it in app and it goes on web collects articles on topic then processes them with ai and makes a decision based on those articles. The app is running right now. It is completely free and no ads. Also it generates a small article where it tells about how it made a conclusion. Also I need your help - How can I make this app better? Also feel free to test your claims so that I can have more real data to tune this app on. I believe that this is something that is necessary for all of us especially in those crazy times! Here is the link - truthorfake.com https://preview.redd.it/vp5fbzpyfkne1.png?width=854&format=png&auto=webp&s=84df7913382634614814866c8df743d8378d28dd

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 :)

My Marketing App made $10,000 in 2024. Here is how I target to make $100,000 in 2025:

You totally get me, I think. It’s a bizarre feeling when you build something, and people appreciate it and are even ready to pay! Pleasant though) In early 2024 my mate and I created a marketing tool that generates ads, content and strategy blocks with a click – Aiter.io. Users can just insert a URL, hit the button and everything is ready. TBH, I built this tool because I’m too lazy to chat with ChatGPT) https://preview.redd.it/ew2kud7ceyde1.png?width=1140&format=png&auto=webp&s=f3fe5b67075858cea3d52278e8063113efa3b97e In 2024 we made $10,000, here is what worked for us: AI directories. Still is the best channel of traffic and clients for us. We listed on TAAFT and other directories scrape TAAFT, so, eventually, we became listed on all major ones. I wrote a Reddit post earlier that explained this process in detail. Email marketing. Gosh, I thought it was dead – I have never been so wrong! We set up automatic emails that share marketing insights and they have a \~25% open rate + consistently convert people. It works great. Product marketing. Having a free version really helps with word-of-mouth and leads, which can be converted via email. Also, we consistently worked on product improvement. I’d say, that our free updates give people a feeling that the devs care about their stuff that’s why they are more confident investing in it. Google Ads. TBH, we had a shitty landing page all the time because were busy with the product. So, Google Ads didn’t work well for us. But we’ve launched the 2.0 version which has a better landing page, and will try it again. Influencers. Worked well for us, but we didn’t pay a dime for this. They just found our tool on directories and created videos about Aiter, so it was a sporadic marketing channel for us. We hope to change it in 2025. We see that our product works and attracts the audience, so we want to deliver and get more in 2025. Here is the plan: Product: add ad banners and video generation. So far, we generate only text data and it’s not so valuable in the time of ChatGPT and Claude. But to generate a high-quality ad banner is still challenging, so we put this on our roadmap. Another feature – one-click market analysis to get marketing insights. Become a TOP50 tool on TAAFT. We’ve become a top tool in our category (content generation) but will need to promote our profile on the profile far more aggressively to get into TOP50 Email marketing. We are fools because we almost didn’t have product emails that explain how it works. Will fix it. Also, we are considering participating more in paid newsletters, like collaborating with Substack influencers. Youtube marketing. Search for low-tail marketing keywords on YouTube and create videos on them, placing my product in them. Blog. Our new platform is Webflow which gives a lot of flexibility in terms of blogging. So, we will repeat the YouTube strategy with blogging. Paid marketing. With an updated landing page, we hope that paid campaigns will work better. We plan to launch campaigns that target different jobs to be done and customer objections to find the right message. Product Management. For 2025, our two key product metrics are retention and product activation rate. For this, we plan to simplify onboarding and make it simpler as well as conduct a lot of in-depth interviews to understand how we can retain users better. Funding. All of this exciting stuff requires money, so we are in the process of securing funding (fingers crossed). Having an indie project is exciting and invigorating. With all these activities, I hope we will achieve the goal of $100,000 in 2025. And what are your goals and marketing steps for 2025? Or maybe you could share some exciting marketing ideas I overlooked?

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 made a website to generate craft ideas from existing materials

Idea: When browsing crafts groups, I noticed that members searched for craft ideas, either randomly, using available materials, or recycling existing things: First attempt: This attempt was a failure. After studying the idea well, I started trying to implement it with the help of Gpt. Because I am a beginner in programming, I only have a little experience in. html and css. I used gpt for php and js. The goal of the site was for the user to enter the materials he owns and the degree of difficulty of implementation to get a title for the idea. And how to implement it. This was all writing generated through the open ai api. After a few struggles, it became clear to me that writing could not describe the idea as it is. I changed the idea. Ideas are generated using images. The results were amazing. Here I faced a budget problem, as the subscriptions to the API for images were very high in my opinion. Because I plan to make the tool free.So I left the project. Second attempt. A year later while I was browsing I discovered stability.ai. From stablediffision, I found their prices to be reasonable, so I returned to the project and completed it. Website link: Craftivi

An Algorithm for Making Truly Stand-Out Advertising Content (+ something more | Part 1)

Hi everyone. my friend and I are software engineers and new to marketing. A few months ago we decided to leverage our software skills for a colleague in ecommerce. It started by implementing a Flux.1 model, then began using texture-based recreations with a canny mask, and then found that we could optimize on both with an added layer of inpainting...and the list goes on. This is the first of a series of posts here about it and I look forward to learning from your feedback. I realized that the most difficult parts of the marketing process when I started out (and most likely for other beginners too) are: Customer Acquisition Costs / Brand Differentiation: Competition is intensifying and it is getting more difficult to stand out in crowded markets and target ad spend more effectively. Maintaining Authenticity at Scale / Data Overload: Balancing growth with authenticity and leveraging available data to successfully engage with customers is a big ask. Creative Fatigue: Maintaining multiple marketing channels in hard, and it becomes harder when you're constantly demanding more and more creative content for campaigns. For 1) I tried using AI to help me summarize, systematize, and gain insights from the information available for a given brand or product (from a page link, prompt, input image, etc.). I know AI is everywhere now, many people are using it unnecessarily and many people are skeptical about it. However, I know from experience, that it is quite helpful in gaining insights/summarizing large amounts of data, and helping people make sense of the creative content, strategy, campaign, etc., that should be created. For 2) By leveraging reviews, forums, and other relevant brand information, AI is able to maintain the story that your brand currently tells, and enhance it based on how your customer base. For 3) Faster results means less creative fatigue- this translates to an easier time managing omnichannel marketing efforts and scaling advertising. If you're interested, please have a look at the result at madsimpleads.com You’ll need to log in to access the solution, and I'll add credits to your account to try it out! (we want to prevent from random people or bots using it because I'm paying to multiple providers for model access). DM me here or drop me a line at austin@madsimpleads.com if you need more. Thank you so much, I'll be happy to get your thoughts I hope the website will help with your advertising, please reach out if you like what I do and want to support the project! Disclaimers: the website looks a bit rough in terms of UI/UX, but we tried focusing on the functionality first available on mobile, works better on desktop I hope this doesn't come across as trying to advertise for my business or breaking any of the community rules. genuinely looking for feedback. Thank you

Just stop paying for social media scheduling tools!

If you wanna pay for something, just pay for features but not to schedule posts. In Dec 2024 I just decided to launch a social media scheduler that is free and unlimited. Because I still don't understand why many people are paying $50+ to schedule posts on multi platforms, I want to make it accessible since it does not represent a huge amount of money to maintain. My challenge is rather to convert free users and provide to them a bunch of tools (meme creators, AI content creations, advanced analytics,...) that could streamline their social media management. So far, I have received tons of really good feedback and got around 300 people signed up. In terms of social media, it supports: Youtube, Tiktok, Instagram, Facebook, X, Bluesky (I'll implement Thread and Linkedin very soon). If you wanna give it a try, that's 100% free and unlimited: https://qayle.ai Surprisingly, I haven't found any abuses so far, but I'm monitoring the usage! Stay safe everyone and I wish you a wonderful day :)

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

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

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. 😅

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.

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

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

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 :)

A sample library that implements Generative AI-Augmented Program Execution (GAPE)

Recently I had a crazy idea. What if you would mix formal program logic with "common sense reasoning" done by an LLM? Like so: statement = "Planet Earth is a flat disk." istrue = applycommonsensereasoningbyan_llm(statement) if is_true: print("Welcome to flat earth society!") else: print("Welcome to the scientific community") This idea can be generalized to many more things. Need to filter a list of concepts (strings) by a certain criterion that is accessible through common sense reasoning? Need to sort a list of concepts by a certain criterion applying common sense reasoning? I call this approach Generative AI-Augmented Program Execution (GAPE). I created a small Python sample library called PyGAPE here: https://github.com/fkostadinov/pygape/ I would love to get some feedback. Some people I showed the idea to were fascinated, other bewildered, some found it completely useless, others immediately got what I was after.

I create customized report on implementing AI in your company

I create personalized reports on how to implement cutting-edge AI technology, specifically GPT-4, in your company to optimize everyday tasks, workflows and boost employee efficiency. Here's the deal: 🎯 I'm looking for businesses with 10-200 employees. 🎯 Your company receives customized reports on implementing AI to stay ahead of the competition. 🎯 In exchange, I'm looking for testimonials from founders like you! Why should you care? ✅ Improve productivity and efficiency within your organization. ✅ No risk on your side If you're interested in receiving your personalized report, comment below or send me a message!

How I Implemented OpenAI's API In My First SaaS! DebateTrend!

About Me I'm a 17 y/o aspiring solopreneur from Bulgaria! I have a passion for 3D printing, coding, video creation, and space! My Project https://debatetrend.com is a website where you can debate with AI on different topics! You can have multiple debates with different debate styles for different lengths of time! The special part is that at the end of each debate, you can have another AI look through the debate! It will give you and the AI you debated with a score from a system I call "debate score" It will also give you recommendations on where you can improve your debating skills! Quick showcase How I implemented AI - ChatGPT 4-o mini It's my first time implementing AI in a project but I think I got the hang of it relatively quickly! Here's a diagram explaining what I did Diagram of the process of debate creation. I used the assistants API from the OpenAI API. Using the threads functionality, I created a different thread operated by an assistant I had already made. When a user creates a debate my app creates a thread with custom information that was decided by the user. I save parts of the thread object in my MongoDB database which is hosted on Atlas. After that, I redirect the user to a generated page in which you can chat with the AI. I maintain the connection using Pusher which is what I use for web sockets. When a user refreshes the page the previous messages are displayed by making a call to the API with the thread id. After that, I retrieve the user messages while still allowing the user to continue the debate. This is part of what I do on the back end. Of course, I have some security measures in check but I don't know if they are enough. I chose the 4-o mini because it's still very intelligent, with a good response time, and a lot cheaper than 4-o. My Tech Stack Language: Javascript Database: MongoDB hosted on Atlas Hosting: Vercel Framework: Express Auth: PassportJs Emails: Resend Payments: Stripe Front End: TailWindCSS, some regular CSS, and DaisyUI WebSockets: Pusher, because Vercel = no sockets integrated into your web app which I found the painful way AI: OpenAI’s API ChatGPT 4-o mini Do you think I could have done anything better? I would love to hear your opinions!

How are you validating ideas?

Hey guys, I’m a non technical founder who built a prototype of my product using ai. It doesn’t work but it has a minimal front end with an idea of how it would work. My strategy is to create an organic audience while simultaneously trying to find atechnical confounder. But before even any of that, how are you validating ideas? My thoughts would be to message people on Reddit / x / instagram on viral posts related to my topic, send them a quick product demo video and a feedback form to see what they think and if they would even use it But curious to hear what others have done or are currently doing.

I grew my mobile app to 1.4 million downloads

I started developing the app in early 2017, well before the AI era, when mobile apps were at their peak popularity. My idea was to create an app for emotional and psychological support in the form of helpful articles and various quizzes, such as personality assessments and life satisfaction tests. I named the app "Emotional Intelligence" because this keyword showed good ASO potential for positioning at the top of mobile stores. This proved to be accurate, and the app quickly gained traction in terms of downloads. A major problem I faced then was monetization. Unfortunately, in my country, it wasn't possible to sell through Google Play then, so I could only display ads. I started with Google AdMob, earning $2000 monthly after just a few months. The app then got about 1500 organic downloads daily and quickly surpassed 500,000. Three years after launching the app, I decided it was time for branding to build recognition. By combining the words "sentiment" and "intelligence," I came up with "Sintelly." I then pushed the app toward a social network, which differed from the right move. Adding features like discussion forums for problems, likes, and comments would result in even more growth, but the opposite happened. The app started declining, and I began investing in advertising campaigns. I managed to maintain a balance between income and expenses but without any profit. Then COVID-19 hit, and everything went downhill. I had to give up development and find a job as a developer to ensure my livelihood. Two years passed since I gave up, and that's when ChatGPT started gaining popularity. This immediately showed me how to steer the app towards active support for well-being questions. As I'm not an expert in psychology, I found several external psychotherapists who helped me put together CBT therapy, which I then implemented through a chatbot. This is how the new Sintelly app was born, with its main feature being a chatbot system composed of 17 AI agents that adapt to the user and guide them through a five-phase CBT therapy (I'll write a post about the technology). In addition to the agents, I added various exercises and tests to provide better personalization for the user. Initially, I made all of this free, which was also a mistake. I followed the principle of first showing what the app can do and gathering enough new users before starting to charge. I started selling subscriptions at the beginning of July, and since then, the app has had stable growth. If you want a check app, here is the link. Lessons learned: If things are working, don't touch them Start selling immediately upon app release; there's no need to wait Regularly test prices and types of subscriptions Onboarding is the most essential part of the app because most users buy subscriptions during onboarding It's essential to listen to user feedback. From day one, have a website and work on content to generate organic visits and redirect users from the web to the mobile app Stats: Over 1.4 million downloads 4.4 rating Only 40,000 active users (I had a massive loss during the period when I gave up) 280 active subscribers $3000 monthly revenue Next steps: Work on improving the Agent AI approach Setting up email campaigns and transactional emails Introducing in-app and push notifications Introducing gamification Potential for B2B I hope you can extract useful information from my example and avoid repeating my mistakes. I'm interested in your thoughts and if you have any recommendations for the next steps. I'm always looking to learn and improve.

I just built footyGPT.com , an AI assistant for football stats, data and data-backed predictions

Hey Amigos! I am based in London and I build lots of fun tools and websites in the sports data space to help sports bettors make better data-driven decisions. One of my recently launched ones is footyGPT.com I have tried to launch this earlier in 2023 but my team were not able to achieve the end goal (I am a non-technical founder) Funny story, I bought the domain footygpt(dot)com back in 2023 and let it expire because i felt like since we weren't able to acheieve the end goal, there was no need to keep the domain. But 2 months ago I started working on it one more time with a different team, and since AI has become "more friendly" than it was early in 2023, it was a much smoother ride and i was so scared someone else had already bought the domain, only to find out it was still available to buy! I was so scared I lost such a clean domain. Below is an example of how footyGPT works: Ask FootyGPT for Predictions for a Given Fixture how footyGPT works \(case study\) Ask FootyGPT to Scrape and Summarize Data from a Stats Website using footyGPT to scrape football stats websites Ask FootyGPT for Sentiments and Online Opinion About a Given Match or Events. ask footygpt for up to date sentiments and opinions about football matches Ask FootyGPT to Find Teams with Certain Streaks, Trends, or Stats https://reddit.com/link/1frdk04/video/9oyaj5ldqjrd1/player If you are into football (soccer) please give it a spin and let me know your thoughts! Cheers Amigos

a product code-to-pitch deck in 60 seconds. could use some beta testers for this.

following up to my recent post about ai sales guide for new founders! so, in about 14 days, I'm launching a (hopeful) game-changer for dev/technical founders. you simply input your short (or long, your choice) product description (text, code, GitHub README snippet) and receive a tailored, professional-grade downloadable deck in under a minute. really good when you need a quick explainer (for potential customers, investors, etc.) I'm building this b/c trying to explain your product to non-devs in clear and compelling way is very hard, and I really want to make this easier for new founders. If you're interested in joining my upcoming beta test group for this, feel free to DM me.

Hit a major milestone with my app that helps people find jobs and optimize their resume

I've been grinding for ~1.5 years now on my sideproject, Jobs-Scout, which helps people find jobs and optimize their resume for any specific role. I've been in industry a number of years as a senior manager, so I have done lots of interviewing and hiring and I think that helps my tool do a better job than others in knowing how to position a resume. It's the non-technical stuff, like branding, SEO etc. thats the hardest for me, but my buddy is now helping and we're starting to make in-roads and traffic is up a lot! 🚀 We hit our 200th paid customer today which is a major milestone for us, and just wanted to share! The app features: resume-to-job analysis using AI (of course!) a free job board updated daily matching and emailing new jobs to users (ie. 'scouting' for jobs hence the name!) resume builder and editor with inline AI optimization free tier (3 free analyses a month) and paid tiers If you're curious, everything's running on AWS, and I code and maintain it myself in the evenings after work, and my colleague is now on marketing and business development. Biggest lesson was finding a trustworthy co-founder. I went through a couple of iterations, but you need a certain personality that's able to keep up with the hustle. Would love feedback or tips on how to keep growing 🚀 . Edit: link: https://www.jobs-scout.com

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.

Introducing Vest: Your AI-Powered Due Diligence Partner - Looking for feedback!

TLDR; We are introducing Vest, an AI powered due-diligence and stock recommendation platform. We have bootstrapped ourselves so far and are wanting to get as much feedback from Reddit as we can to see where we can improve, but also what we are doing right. So please have a look around, give us feedback and if you like it, feel free to use it. Hi Reddit, My name is Drian and I'm one of the founders of Vest. We believe we are crafting something special at Vest and we want to get the word out and gather as much feedback as possible! Our major goal at Vest is to help new retail investors make sense of the investment landscape and get AI powered assistance, or even help experienced investors get confirmation of their potential moves. Overall, we want people to start their journey to financial freedom and not be daunted by the complexity of it. So how do we do this? Vest is a user-friendly service that harnesses fundamental metrics, social and news sentiment, and technical analysis, that we feed into some advanced AI models to generate clear buy, sell, or hold signals for US-based (for now!) stocks, offering our users transparent due-diligence for confident investing. The service is currently free with no ads - however, at some point we do plan on adding a paid tier. What's included: &#x200B; Financial Metrics. Our financial metrics take all the potentially complex mathematical equations and present the fundamentals of a company to users in a simple 1 pager, with a score displaying if the metric is positive for a stock. We also provide publicly available analyst ratings from investment banks as well as price targets they have set. News Sentiment. We take publications about a specific stock from new articles, journals and socials and give these all a rating to determine if social sentiment is positive around a stock or not. Each article and its rating is visible to our users through through our dashboard. AI assisted Stock Signals. We have developed an algorithm to take all the metrics, sentiment and technical analysis we collate and analyze this with historic performance data for every stock to attempt to figure out if a stock is undervalued (great time to buy) or overvalued (great time to sell). 155 US stock tickers and counting. We currently have trained our models for around 155 US based stocks on the NASDAQ and NYSE exchanges. As we get more funding/runway we do plan on adding more, with the eventual goal to expand to more exchanges, countries and securities. Knowledge base and community. Our knowledge base & community contains explanations and articles for all metrics and the other good stuff behind Vest. We don’t want to just tell users what to do, but to also assist in their financial education. We hope our knowledge base can also become a thriving community where users can interact with us and each, ask questions around investing and keep gaining knowledge. Is it 100% accurate? Absolutely not. While we do a pretty great job at tracking and surfacing signals, we are not presenting a fool-proof, silver bullet with a guarantee here - rather a starting point for users to make more informed decisions, find potential new investment opportunities and hopefully learn about investing as they do so. We encourage our users to do their own research and due-diligence and not just take our signals as gospel - we know each and every person has a different risk appetite and goals, and we encourage you to use Vest in a way that fits with your own financial goals and risk appetite. We also display our win rates, average returns, and comparisons with buy and hold for each stock - and we are transparent about it when we’ve fallen short. Next steps: &#x200B; Hope over to vestapp.ai and sign-up From the dashboard, play around, inspect our stock information and add some stocks to your watchlist. If you like what you see, and you’ve done your homework - use your favourite brokerage account to make an investment and watch Vest for changes in a stocks signals. If you don’t have one, we have a pop-up when you click buy/sell on any given stock with some non-affiliated brokerage options for the US, Australia and New Zealand - we don’t get a kickback from these brokerages, they are just what we’ve personally been using. FEEDBACK - We’re just getting started and we know the value of a fresh pair of eyes - our current mission is to get as much feedback as possible - anything you think of please send it through here or on the dedicated feedback form on our website in the sidebar on the left. Features we’re working on We're quietly thrilled about the direction Vest is headed, and we want to give you a sneak peek of what's in store for the next couple of quarters. Some of these may roll out as premium features, but we're diligently fine-tuning the details. Here's what you can expect: &#x200B; Insider Trading Insights: Get daily reports on major stock moves by whales and company insiders. Institutional Holders: We're adding daily reports on institutional holders, keeping you informed about their moves. Lobbying Activity: We're actively working on daily updates about lobbying activities, so you can stay informed. Government Contracts Data: We'll provide a quarterly snapshot of government contract values for the companies you're tracking. US Congress Stock Activity: Keep an eye on daily trading actions of House and Senate members. Daily Summaries & Signal Alerts: We're currently hard at work on this feature. Soon, receive daily email summaries covering signals, watchlist updates, and key news. Personalized Risk Management: Tailor signals to match your unique risk management strategy. Your investments, your way. AI Assistant: Our LLM integration is almost ready, allowing you to ask it straightforward questions about particular securities in plain English. It will provide you with real-time context on fundamentals, news, and all the metrics and data points we monitor.

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?

What are Boilerplates?

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

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

I built an AI Sales Copilot for Reps & Sales Engineers

I had this problem as a founder that it was really hard to transfer everything I knew about my product and industry to the reps & Sales Engineers I was hiring. This problem also exists for non-founders, say the SE manager or VP Sales ramping new teammates I solved this problem by building HeySam, the AI Sales Copilot that shadows your top reps and SEs on calls, learning the “tribal knowledge” around positioning, objection handling, and technical questions. Customize Sam with your documentation, Slack, and sales collateral to create your team's perfect sales copilot for: Sales calls with live help on objections and questions Call Intelligence to summarize meetings, identify competitive pressures, and update the CRM Meeting Prep with prospect briefs combining account research, past interactions, and competitive insights Contact Tracking to follow key relationships as people change roles. RFPs completed in minutes Slack responses to sales teams’ technical questions, freeing up SE and engineering time Docs and community for self-serve customer education You can start with any of the seven powerful use cases. One AI Agent. That’s Sam.

I Launched a Side Project That People Love, But Scaling It Is Brutal

I Built a Side Project with Great Engagement—But It’s Still Not Making Money Six months ago, I started a side project in the consumer AI space: opencharacter.org. It’s been a grind, but I’ve built something people actually love—high retention, strong engagement, and users spending a ton of time on the platform. By all product metrics, it’s a success. But financially? It’s not quite there yet. The biggest challenge hasn’t been technical, managing infrastructure, or even dealing with a community. It’s distribution. Getting people to actually find and use your side project at scale is insanely hard. What’s Worked Reddit – Thoughtful, non-spammy comments in relevant threads drove early users. Instagram – Short-form videos brought in surprising traction. Paid ads – Somewhat effective, but tough to balance customer acquisition costs and revenue. What Hasn’t (Yet) TikTok – Dozens of videos later, still struggling to make it a reliable growth channel. Discord – Great for engagement, but not a strong acquisition channel. Recently, I brought on a co-founder who has done over 100 million views on Instagram Reels in under two years, so I’m hopeful we can crack the growth formula. Because without a scalable system for getting users, even a great side project won’t reach its potential. If I could start over, I’d think much more about distribution before building. Would love to hear from others—how do you drive growth for your side project?

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

Just reached 300 users in 3 months!!!

Just reached 300 users after 3 months live!!! My co-founder has been posting a bit here and always got some strong support and he suggested I share my side of things so here it is: How it started I co-founded AirMedia almost a year ago and we both didn’t know much about design/marketing/coding (just studied programming during my 6-month exchange period. The quickest way to get started seemed to get a no-code product that we could put in front of users and get feedback. My co-founder then started learning about bubble and we put together a basic platform to show users. I was working on a custom-code database in the meantime and decided after month 2 that we wanted to get something better I.e. AI would be interacting with the UI and had to do everything custom-code for it. We’re now month 3 and started from scratch again. While I was working on the code, we started talking to some potential users and selling lifetime deals to validate the idea (this is where I would start if I had to do it over again). Well I progressively found out it was more complicated than expected and we only released our first beta product last August (6 months later) Some challenges pre-launch: Getting the Meta/LinkedIn permissions for scheduling took around 1 month As the whole process took more time than expected, the waitlist of 300 that we managed to put together only converted by 10% (into free users). Please don’t make our mistakes and always keep your waitlist updated on what’s going on. Some challenges post-launch: Getting the right feedback and how to prioritise Getting users Monetising (yes - we’re bootstrapped) To get the best feedback we implemented some tracking (according to GDPR of course) on the platform and implemented Microsoft Clarity. The latter is a game-changer, if you have a SaaS and don’t use it you’re missing out. I wasn’t really into getting users as my co-founder handled that but it’s mainly manual and personalised LinkedIn outreach at the beginning and Reddit sharing about the progress, answering questions and getting some feedback at the same time. To monetise we realised we’re too common and there are 100+ other nice schedulers around so we’re now focusing on cracking the content creation side of AI (to be released next week 👀) as there’s much less competitors and it seems like that’s our users want. In the meantime of growing the company, we had to find a way to pay the bills as it’s two of us living together. So my co-founder started using the bubble skills gained and doing some freelance. He did around 7 platforms the last 6 months and we’re now just launching a bubble agency as a part of the main company to get your idea of a SaaS done in 30 days. That’s QuickMVP. It seemed like the right move to help other people (I met many non-technical founder looking for someone to bring their idea to life that didn’t cost $10k and was reliable) and include the AirMedia subscription in the package so let’s see how this next step plays out. Thanks for reading until here :)

I created an AI that turns your business ideas into full plans in 30 Minutes

Hey folks, I made AIFounder because a friend was stuck trying to turn his business idea into a solid plan, and I knew I could create a tool that does it faster and better than any consultant. How does it work? You give me the key details of your business idea—just the essentials. My AI gets to work, using industry-specific prompts created by experts, and in 30 minutes you’ve got a personalized business plan in PDF format, ready to go. What do you get? A 100-page plan, tailored just for you. Clear, actionable steps to turn your vision into reality. Market research, brand identity, marketing strategies, sales plans, and more—all customized to your idea. Why AIFounder? It’s like having a professional consultant by your side, but faster and without breaking the bank. Whether you’re just starting out or looking to grow, my AI-generated plans have already helped entrepreneurs secure funding and move forward with confidence. I’d love to hear what you think, any feedback or questions are welcome. Thanks in advance! 🚀

How I went from $27 to $3K as a solopreneur still in a 9-5

My journey started back in November 2023. I was scrolling through Twitter and YouTube and saw a word that I had never come across before. Solopreneur. The word caught my eye. Mainly because I was pretty sure I knew what it meant even though it's not a word you'll find in the dictionary. I liked what it was describing. A solo entrepreneur. A one man business. It completely resonated with me. As a software engineer by trade I'm used to working alone, especially since the pandemic hit and we were forced to work remotely. See, I always wanted to ditch the 9-5 thing but thought that was too big and too scary for a single person to do. Surely you would need a lot of money to get started, right? Surely you would need investors? The whole concept seemed impossible to me. That was until I found all the success stories. I became obsessed with the concept of solopreneurship. As I went further down the rabbit hole I found people like Justin Welsh, Kieran Drew and Marc Louvion to name a few. All of whom have one person businesses making huge money every year. So I thought, if they can do it, why can't I? People like this have cleared the pathway for those looking to escape the 9-5 grind. I decided 2024 would be the year I try this out. My main goal for the year? Build a one man business, earn my first $ online and learn a sh\*t ton along the way. My main goal in general? Build my business to $100K per year, quit my 9-5 and live with freedom. From December 2023 to February 2024 I began brainstorming ideas. I was like a lost puppy looking for his ball. How on earth did people find good ideas? I began writing everything and anything that came to mind down in my notes app on my phone. By February I would have approximately 70 ideas. Each as weird and whacky as the other. I was skeptical though. If I went through all the trouble of building a product for one of these ideas how would I know if anyone would even be interested in using it? I got scared and took a break for a week. All these ideas seemed too big and the chance that they would take off into the atmosphere was slim (in my mind anyways). I was learning more and more about solopreneurship as the weeks went on so I decided to build a product centered around everything I was learning about. The idea was simple. Enter a business idea and use AI to give the user details about how to market it, who their target customers were, what to write on their landing page, etc. All for a measly $27 per use. I quickly built it and launched on March 3rd 2024. I posted about it on Indie Hackers, Reddit and Hacker News. I was so excited about the prospect of earning my first internet $! Surely everyone wanted to use my product! Nope...all I got was crickets. I was quickly brought back down to earth. That was until 5 days later. I looked at my phone and had a new Stripe notification! Cha-ching! My first internet $. What a feeling! That was goal number 1 complete. It would be another 6 days before I would get my second sale...and then another 15 days to get my third. It was an emotional rollercoaster. I went from feeling like quitting the 9-5 was actually possible to thinking that maybe the ups and downs aren't worth it. On one hand I had made my first internet dollar so I should my ecstatic, and don't get me wrong, I was but I wanted more. More validation that I could do this long term. By May I was starting to give up on the product. I had learned so much in the past few months about marketing, SEO, building an audience, etc. and I wanted to build something that I thought could have more success so I focused on one critical thing that I had learned about. What was it? Building a product that had SEO potential. A product that I knew hundreds of people were looking for. See this was my thinking - If I could find a keyword that people were searching for on Google hundreds/thousands of times every month and it was easy to rank high on search engines then I would go all in (in SEO land this equates to a Keyword that has a Keyword Difficulty of = 500). I began researching and found that the keyword "micro saas ideas" was being searched for around 600 times each month. Micro Saas was something that really interested me. It was perfect for solopreneurs. Small software products that 1 person could build. What's not to like if you're in the game of software and solopreneurship? Researching keywords like this became like a game for me. I was hooked. I was doing it every day, finding gems that were being searched for hundreds and thousands of times every month that still had potential. That's when I came up with my next product idea. I decided to create a database of Micro Saas Ideas all with this sort of SEO potential. See if you can build a product that you know people are looking for then that's all the validation you need. So I put this theory to the test. I created a database of Micro Saas Ideas with SEO Potential and launched it in June 2024. This time it was different. I made $700 in the first week of launching. A large contrast to my previous failed attempt at becoming the worlds greatest solopreneur. Since launch I have grown the product to $3K and I couldn't be happier. I know what you're saying, $3K isn't a lot. But it's validation. It's validation that I can earn $ online. Validation that I can grow a business and it gives me hope that one day I'll be able to quit that 9-5 grind. My plan is to keep growing the business. I expect there to be a few challenges up ahead but I'll tackle them as I go and learn from the failures and successes. I have a newsletter where I share Micro Saas Ideas with SEO potential every week which I'll leave below in the first comment. Feel free to come along for the ride. If not I hope this post brings you some value If you're thinking about starting as a solopreneur, stop thinking and start doing, you won't regret it.

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

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!

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

How I Automated Amazon Affiliate Marketing: A Developer's Journey

From Manual Labor to 1000x Efficiency As a developer who ventured into affiliate marketing, I discovered a significant gap between technical possibilities and current practices. This revelation led me to create AutoPin, a tool that's now helping hundreds of affiliate marketers reclaim their time. The Problem: A Time-Consuming Reality Every affiliate marketer knows this scenario: you spend hours copying and pasting links, checking prices, and updating product information. I found myself dedicating 4-6 hours daily to these repetitive tasks. As a programmer, this felt fundamentally wrong. The typical affiliate marketing workflow looked like this: Find promising products Generate affiliate links one by one Monitor price changes manually Check product availability regularly Update content when things change Repeat this process daily This manual process had several critical issues: Time Waste: 20-30 hours weekly on repetitive tasks Missed Opportunities: Unable to scale beyond 100 products Human Error: Inevitable mistakes in manual updates Delayed Updates: Lost commissions due to outdated information The Solution: Building AutoPin After three months of development and six months of testing, I created a system that could: Generate hundreds of affiliate links in minutes Monitor price changes automatically Update product availability in real-time Export data in multiple formats Scale infinitely without additional effort Real Results, Real Impact The impact was immediate and significant: 📊 Efficiency Metrics: Link generation: From 2 minutes per link to 0.1 seconds Monitoring capacity: From 50 to 5000+ products Update frequency: From daily to real-time Error rate: Reduced by 99.9% 💡 User Success Stories: "Increased my product portfolio by 10x without adding work hours" "Revenue grew 300% in the first month" "Finally able to focus on content creation instead of link management" Technical Insights The system architecture focuses on three core components: Data Extraction Engine Efficient web scraping Rate limiting and proxy management Data validation and cleaning Real-time Monitoring System Websocket connections for instant updates Queue management for large-scale monitoring Smart scheduling based on price volatility Export Framework Multiple format support (CSV, HTML, Markdown) Custom templating engine Batch processing capabilities The Future of Affiliate Marketing Automation We're currently developing AI capabilities to: Generate product descriptions automatically Optimize link placement for conversion Predict price trends and best promotion times Create content variations for different platforms Key Learnings Automation is Essential The future of affiliate marketing lies in automation. Manual processes simply can't compete with automated systems in terms of efficiency and accuracy. Focus on Value Creation When marketers spend less time on repetitive tasks, they can focus on strategy and content quality. Scale Matters With automation, the difference between managing 10 products and 1000 products becomes minimal. Getting Started If you're an affiliate marketer spending hours on manual tasks, it's time to automate. Here's what you can do: Analyze your current workflow Identify repetitive tasks Start with basic automation Scale gradually Monitor and optimize Conclusion The transformation from manual to automated affiliate marketing isn't just about saving time—it's about unlocking potential. When you remove the tedious aspects of affiliate marketing, you create space for creativity, strategy, and growth. Want to experience the difference? Visit AutoPin at autopin.pro and join the automation revolution. Remember: The best time to automate was yesterday. The second best time is now. About the Author: A developer turned affiliate marketer who believes in the power of automation to transform digital marketing. #AffiliateMarketing #Automation #Programming #DigitalMarketing #SaaS #ProductivityTools

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

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

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

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.

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

My humble analysis on how @levelsio grew PhotoAI to 155K/m

In 2023,@levelsiol aunched PhotoAi and nailed perfect timing with the AI-generated photo trend He saw the rise of AI Image Generators as early as Jan 2023—when search volume was just 246K. Fast forward to now, that’s exploded to 1M+ searches/month! @levelsio didn’t guess—he tracked this exponential growth, built fast, and launched a product that people needed before they even realized it themselves. That’s how you go from idea to$155K/month in revenue. But why does this work? Demand Explosion: The need for AI tools like PhotoAI surged as more creators, marketers, and businesses searched for automated ways to generate images (graph 1 shows this insane growth curve). Timing is everything: When trends move this fast, you have to act quicker. He launched at the right time, capturing the early adopters and setting the foundation for sustainable revenue (graph 2 showcases the clear spike). Build based on data, not assumptions: The growth of AI Character Generators also hit major traction (graph 3). Both trends signaled an opportunity, and he jumped in before the market was saturated. Lesson: Don’t guess what people want—watch the trends and build fast. Trends show you what’s working, before the mainstream even knows it. This is how you launch products that solve real problems. Check out the graphs for context: AI Image Generator Growth \(graph 1\) Search volume spiking \(graph 2\) AI Character Generators \(graph 3\) Use data, not just intuition. Track the trends and execute. Simple but powerful. I hope you liked this thread.

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!

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.

I recreated an AI phone calling agent that increased booked calls by 30% for a plumbing business in 30 days

AI has always intrigued me, especially when it comes to automating repetitive tasks and streamlining business operations. Recently, I found a compelling case study about a voice agent that significantly enhanced customer service and lead capture for a plumbing company. Motivated by the potential of this technology, I decided to build a similar system to see how it could be adapted for other industries. I’ve added the case study below along with a number to the demo voice agent I created to see if this is something people would really be interested in. AI technology is advancing rapidly, and I’m excited to dive deeper into this space. Case Study A family-owned plumbing business was facing challenges managing a high volume of customer calls. They were missing potential leads, particularly during after-hours and weekends, which meant lost revenue opportunities. Hiring a dedicated call support team was considered but deemed too expensive and hard to scale. Solution To solve these issues, the company deployed an AI-powered voice agent capable of handling calls autonomously. The system collected essential customer information, identified service needs, and sent real-time alerts to service technicians via SMS. It also had the ability to transfer calls to human agents if necessary, ensuring a seamless experience for customers. Impact The AI voice agent quickly proved its worth by streamlining call management and improving response times. With the AI handling routine inquiries and initial call filtering, the plumbing business saw a noticeable improvement in how quickly they could respond to customer needs. Details The AI-powered voice agent included several advanced features designed to optimize customer service: Answer Calls Anytime: Ensured every call received a friendly and professional response, regardless of the time of day. Spot Emergencies Fast: Quickly identified high-priority issues that required urgent attention. Collect Important Info: Accurately recorded critical customer details to facilitate seamless follow-ups and service scheduling. Send Alerts Right Away: Immediately notified service technicians about emergencies, enabling faster response times. Live Transfers: Live call transfer options when human assistance was needed. Results The AI-powered voice agent delivered measurable improvements across key performance metrics: 100% Call Answer Rate: No missed calls ensured that every customer inquiry was addressed promptly. 5-minute Emergency Response Time: The average response time for urgent calls was reduced significantly. 30% Increase in Lead Capture: The business saw more qualified leads, improving their chances of conversion. 25% Improvement in Resource Efficiency: Better allocation of resources allowed the team to focus on high-priority tasks. By implementing the AI-powered voice agent, the plumbing business enhanced its ability to capture more leads and provide better service to its customers. The improved call handling efficiency helped reduce missed opportunities and boosted overall customer satisfaction. Here’s the number to the demo agent I created: +1 (210) 405-0982 I’d love to hear some honest thoughts on it and which industries you think could benefit the most from this technology.

Acquired our first 10 customer for Trustty Reporter - an AI first Business Intelligence Platform.

Hi All, My co founder and I have built Trustty Reporter (www.trusttyreporter.com).  We spent the last couple of months working on launch our AI powered BI platform and gain our first 10 users. We wanted to reach out the community to get your feedback on the platform and how we can take it to the next level. Below is a brief introduction of the platform: Trustty Reporter – your AI-first business intelligence partner that transforms data into actionable insights in minutes! Imagine turning complex data and documents into easy-to-understand reports with clear recommendations, all at the click of a button. No more BI complexities—Trustty Reporter makes business insights accessible to everyone, from business owners to CXOs. Here’s Why You’ll Love Trustty Reporter: Instant Insight Generation – Convert raw data into insights in just 5-15 minutes. No expertise needed! Easy Reporting Access – Persistent reports that let you track, compare, and build strategies over time. Tailored Solutions for Business Problems – Just describe your challenge, and Trustty Reporter delivers custom insights. Interactive Reports – Dive deeper with a chat interface that offers further clarification and recommendations. By now you would have realized that this aces any traditional BI tools. That aside, it’s better than the likes of ChatGPT and Claude since you don’t have to supply multiple prompts to get context specific insights catering to your business! File Requirements: For Excel files with multiple sheets/tabs: Please save each sheet as a separate file Upload them as individual files for processing File Format: The first row must contain your column headers Remove any empty rows above the headers https://preview.redd.it/olmk6lfmwuzd1.png?width=3024&format=png&auto=webp&s=aa2bbc8edb4a299dbeee67b692cd4acf1704c2be

I recreated an AI phone calling agent that increased booked calls by 30% for a plumbing business in 30 days

AI has always intrigued me, especially when it comes to automating repetitive tasks and streamlining business operations. Recently, I found a compelling case study about a voice agent that significantly enhanced customer service and lead capture for a plumbing company. Motivated by the potential of this technology, I decided to build a similar system to see how it could be adapted for other industries. I’ve added the case study below along with a number to the demo voice agent I created to see if this is something people would really be interested in. AI technology is advancing rapidly, and I’m excited to dive deeper into this space. Case Study A family-owned plumbing business was facing challenges managing a high volume of customer calls. They were missing potential leads, particularly during after-hours and weekends, which meant lost revenue opportunities. Hiring a dedicated call support team was considered but deemed too expensive and hard to scale. Solution To solve these issues, the company deployed an AI-powered voice agent capable of handling calls autonomously. The system collected essential customer information, identified service needs, and sent real-time alerts to service technicians via SMS. It also had the ability to transfer calls to human agents if necessary, ensuring a seamless experience for customers. Impact The AI voice agent quickly proved its worth by streamlining call management and improving response times. With the AI handling routine inquiries and initial call filtering, the plumbing business saw a noticeable improvement in how quickly they could respond to customer needs. Details The AI-powered voice agent included several advanced features designed to optimize customer service: Answer Calls Anytime: Ensured every call received a friendly and professional response, regardless of the time of day. Spot Emergencies Fast: Quickly identified high-priority issues that required urgent attention. Collect Important Info: Accurately recorded critical customer details to facilitate seamless follow-ups and service scheduling. Send Alerts Right Away: Immediately notified service technicians about emergencies, enabling faster response times. Live Transfers: Live call transfer options when human assistance was needed. Results The AI-powered voice agent delivered measurable improvements across key performance metrics: 100% Call Answer Rate: No missed calls ensured that every customer inquiry was addressed promptly. 5-minute Emergency Response Time: The average response time for urgent calls was reduced significantly. 30% Increase in Lead Capture: The business saw more qualified leads, improving their chances of conversion. 25% Improvement in Resource Efficiency: Better allocation of resources allowed the team to focus on high-priority tasks. By implementing the AI-powered voice agent, the plumbing business enhanced its ability to capture more leads and provide better service to its customers. The improved call handling efficiency helped reduce missed opportunities and boosted overall customer satisfaction. Here’s the number to the demo agent I created: +1 (210) 405-0982 I’d love to hear some honest thoughts on it and which industries you think could benefit the most from this technology.

I acquired a SaaS for ~5 figures to solve my content problem

In 2023 I bought a SaaS called Cuppa AI. I actually found the product on twitter, run by a very talented engineer in the UK.  I’ve spent tens of thousands of dollars on content for various media companies. In one consumer health company, it cost us around $200-$500 for each SEO optimized article. This adds up pretty quickly. Not forgetting the 20 hours of edits! This isn’t just an isolated problem for a single company. It’s industry wide and affects small business + agency owners alike. I spent over a decade in media, and have seen many agency founders complain about long lead times and high costs for low output.  This is an issue. Large swathes of would-be customers that prefer to consume content before buying are being ignored - either because it takes too long or costs too much for founders to scale this channel.   I eventually became tired of the media content game in 2022 and looked into using SaaS to solve my previous life’s challenges. I started building, acquiring and scaling a portfolio of products that I found useful in my day to day. But the content issue was still there.  So I started to look for ways to reduce the time + cost content burden for my own portfolio.   I initially discovered Cuppa using it for my own personal pains of content research, editing, publishing, and scaling. But then I saw potential. I wanted to turn it into an end to end solution for the content gap that myself and other business owners weren’t taking advantage of because of time, cost, or other priorities.  I sent a DM. Then a few calls later, I acquired it in June 2023.  I chose cuppa vs other competing products for a few reasons:  The founder gave excellent support during and post acquisition  It already had a large, loyal existing user base I’d personally used it and solved a pain with it. I saw the potential to solve many others for more people like me  The founder has put a ton of quality and care into it. There wasn’t a risk of picking up a patchy product, plus it already had great social distribution  It naturally fits my expertise from the ‘other side’. I was the original customer of it, so I knew I could evolve it with features that could create content at scale without losing the human touch  Since then we’ve added a lot of new stuff: Chat with articles Image generation for articles API keys to reduce cost Brand / persona voice custom prompts  Month on month iterative content improvement  Full stack content team that blends AI and human editors for agencies I’m still in full build mode with the team. I want to take it to a place where agencies and SMB owners can trust the AI + human content model enough to see this product as a no-brainer for their biz. I don’t believe in AI slop - there’s enough of that out there - I DO believe in using AI to do the grunt work, but to always have that human element a machine can’t quite mimic.  We have a lot more to get through, but I’m very excited about it. View of the done for you content workflow

My side project is starting getting attentions. We create AI virtual customer to train you and your sales.

Our team and I has years of SAAS sales engineering and sales trainer experience, brining the top Sales strategy, training & coaching experience to founders and SMB sales leaders. Peak Mojo transforms your sales training with our AI roleplay tool. Here’s what we offer: AI Customer Roleplay: Virtual AI personas for realistic sales practice. Chrome Plugin: Gathers info from LinkedIn for authentic scenarios. Mock Sessions: Managers review mock calls to evaluate new reps. Looking for early adopters and help shape our product! Reply to book a demo meeting. I will sit down with you and discuss what can we shape the product to fit your need. https://www.peakmojo.ai/

My experience trying to scrape google maps with no code

A few months back I was working on a project to help founders that sell to SMBs get better quality leads (Current solutions like Zoominfo and Apollo don’t do very well for the SMB market). Of course, I wanted to do this as quickly as possible with as little code as possible.  We found that people were manually going through Google Maps to find SMBs. They would use the search and manually type in the businesses they were looking for. For example, they would type “restaurants” and manually call/email them. What we decided to do was gather the Google Maps data autonomously and surface that to our customers so they could take all of it. The problem was that we would need a bunch of data from Google Maps to pull it off. We would need to grab all the SMBs across the United States which is a huge undertaking.  Initially, I tried no-code AI web scraping solutions and they worked horribly. For some reason, I couldn’t even get them to scroll down on the page. I was also able to reverse engineer their open-source code and discover that they were taking the entire web page and passing it into GPT to extract data. That just burned my Openai bill.  I then tried the semi-code approach (sorry no-code subreddit) where I would use something like Apify or Google Places API to scrape the businesses. This worked better but still, there was an issue of price at the scale we wanted. Eventually, we ended up writing our scraper for the task.  This experience was so horrible I ended up creating potarix.com. Firstly, we provide scraping as a service in conjunction with AI. We all know AI is shit and keeping this human in the loop allows the AI to do 90% of the work and then for us to tweak the script to 100% completion. Also since we use AI to create the scraper instead of using AI to scrape, we can run it for large scale tasks at a low cost.

How I'm automating all my SEO research & writing with AI by building an open source software

I make most of my current recurring income from writing articles for a few blogs. Over the years I have developed strategies and writing techniques that increase my chances of landing at the top of Google search results. I’m a writer, but I also write code. With the advent of AI I have been itching to codify many of my previous activities. I tried writing content with the general LLMs like ChatGPT and Claude but the results were terrible, especially for niches with technical information. I didn’t want to lose hope in AI because I realised with A LOT of hand-holding, it got better results. THEN IT HIT ME!  What if I could create a Human-Guided AI for Better AI-Written Articles: enter Building ContentScribe After months of coding with AI tools and trying different approaches, I’m excited to share that ContentScribe is finally taking shape. The journey to this point has been challenging but incredibly rewarding. Over the past six months, we’ve been using ContentScribe ourselves to automate blog content creation. We found other tools in the AI article generation space such as Koala AI and Cuppa that left us wanting more. They basically took a topic from you and let the AI loose. We consider this to be a better Koala AI and Cuppa alternative. I wanted to have more control and freedom from the expense of the credit system most of them use. Even after generation, every article required significant human input to make it truly SEO-friendly, and existing tools couldn’t handle the specific strategies we needed for our niche. So, we decided to build something new: an AI-powered, open-source tool that doesn’t just spit out generic articles, but actually allows users to shape how the content is written. ContentScribe is designed to integrate the SEO techniques that we’ve developed over years of building profitable blogs. It codifies our best practices and turns them into a process that anyone can use to create researched, optimized content, every time. The product works, and it’s live! We’ve been populating our latest blog with human-guided AI-written articles, and the results are already impressive. The coolest part? This project scratches our own itch and addresses the pain points we faced when using other tools. Plus there is nothing to lose because it’s free and open source, you can run it locally or in the cloud. It’s still early days, but I’m excited to share more as we keep building in public. We’re working on tutorials, and adding more features. The feedback we’ve gotten so far from our in-house team has been invaluable, and I’m looking forward to sharing this with more content creators out there. For anyone struggling to get their ideas off the ground: keep experimenting, keep building. ContentScribe is proof that when you combine persistence with innovation, the results can be something you’re genuinely proud of. This is just the beginning!

Just started automating my daily tasks with AI—why did no one tell me about this sooner?

I used to spend so much time on small, repetitive tasks—organizing emails, setting reminders, even copying and pasting stuff between apps. It always felt normal until I tried automating a few things with AI. Now, my emails get sorted, my notes get summarized, and even my schedule adjusts itself based on what I need to do. It’s honestly insane how much time I’ve been saving without even realizing it. The craziest part? It doesn’t even take much effort to set up. A few AI tools here and there, some simple automations, and suddenly my workflow feels way smoother. I’m still experimenting, but it already feels like I’m getting more done with way less effort. If anyone’s interested, I wrote about some of the best AI automation tricks I’ve found so far. Check it out here: https://aichatbot4begginers.blogspot.com/2025/03/ai-powered-productivity-hacks-automate.html?m=1

[P] [R] sANNd: A New Neural Network Framework Using Trainable Iterators

sANNd sANNd is a lightweight, modular neural network library designed as a sandbox for experimenting with new ideas in artificial intelligence. The Mould Class: A Pythonic Building Block The Mould class is a core component of sANNd. It provides a Pythonic way to apply functions to data that’s bundled inside objects: Encapsulated Variables: Each Mould object holds a set of variables (for example, weights or parameters) inside it. This means related data is kept together in one place (the object), making the code organized and intuitive. Static Functions: A Mould class defines its operation as a static method – essentially a function that isn’t tied to a specific instance. This static function takes in inputs (and possibly other Mould objects’ variables) and produces an output. In simple terms, the Mould’s static method describes how to transform input data using the Mould’s internal variables. Pythonic Usage: Using static methods in this way is a clean, Pythonic design. You call the Mould’s function through the class, but it applies to the data in the object. This approach lets you clearly separate what the operation is (the logic in the static function) from which data it uses (the variables inside the Mould instance). Example: Imagine a Mould class called LinearMould that has a static function to compute a linear transformation (like y = W*x + b). An instance of LinearMould would hold specific W and b values, and you’d use the static method to apply that linear formula to an input. This gives you the convenience of object-oriented design (encapsulating W and b) with the clarity of a standalone function defining the math. Chaining Moulds for Complex Computations Moulds become even more powerful when you chain them together. You can connect multiple Moulds so that the output of one becomes the input of the next: Sequential Operations: Just like stacking layers in a neural network, you can place Moulds in sequence. For example, you might take the output from LinearMouldA and feed it into LinearMouldB. In code, this might look as simple as using the output of one call as the argument to the next. The design of sANNd makes this straightforward – the static function of each Mould knows how to handle the data coming in. Building Pipelines: By chaining Moulds, you create a pipeline of transformations. Each Mould handles one step of computation, and together they produce a final result. This could represent a multi-layer neural network, a data processing pipeline, or any custom sequence of operations you need. There’s no strict limit to how you can chain them; you have the freedom to combine Moulds in any order that makes sense for your experiment. Clarity and Modularity: Because each Mould is a self-contained piece (with its variables and function), chaining them doesn’t turn your code into a black box. You can inspect or modify any part of the chain easily. This modular design means you can insert, remove, or replace Moulds to see how it affects the overall computation, which is great for experimentation. Implicit Backward Path (Automatic Backpropagation) One major benefit of using chained Moulds is that they implicitly define the backward path for training with gradient descent (backpropagation): Automatic Gradient Flow: When you connect Moulds in a sequence for a forward pass (input → Mould A → Mould B → output), you’ve essentially defined a computation graph. sANNd uses this graph to handle the reverse computation automatically. In other words, if you calculate an error or loss based on the final output, sANNd can propagate that error backwards through each Mould in the chain. No Manual Backprop: You do not need to manually code how gradients flow through each Mould. The way you set up the Moulds’ static functions already determines how outputs depend on inputs and internal variables. sANNd leverages that to perform backpropagation. This is similar in spirit to how libraries like PyTorch/TF do “autograd,” but here it’s a natural result of the Mould chain architecture. Gradient Descent Ready: Because the backward path is established by the forward connections, you can apply gradient descent optimizations out of the box. For instance, you can adjust the weights inside each Mould based on the computed gradients to minimize your loss. The design ensures that each Mould’s contribution to the final error is tracked, so all parts of your model learn appropriately during training. In short, defining your model with Moulds means you get training capability for free. You focus on describing the forward computations, and sANNd handles the math behind learning from errors. Comparing sANNd to Traditional Frameworks sANNd’s approach is quite different from traditional Python-based neural network frameworks. Here’s how it stacks up against frameworks like TensorFlow, PyTorch, or Keras in terms of approach, flexibility, and intended use: Design Approach: Traditional frameworks use predefined layer classes and often build a computation graph behind the scenes. For example, Keras might have a Dense layer class, and TensorFlow might construct a static graph (in TF1) or use eager execution (in TF2). sANNd takes a simpler approach – it uses plain Python classes and static functions (Moulds) to define computations. There’s no need to learn a new graph syntax or decorators; if you know Python functions and classes, you can read and write sANNd models. This makes the internal workings more transparent and easier to follow. Flexibility: While frameworks like PyTorch and TensorFlow are very powerful, they can introduce a lot of boilerplate and assume you’re building typical architectures. sANNd is extremely modular and flexible. You aren’t limited to the layers someone else defined – you can create any operation you want as a Mould. Want to experiment with a novel activation function or a custom recurrent connection? Just define it in a Mould. There’s less magic and abstraction obscuring your code, so unconventional model structures are easier to implement. (Of course, major frameworks can also be extended, but sANNd makes this feel more natural by staying within standard Python paradigms.) Intended Use: sANNd is intended for experimentation and research. It’s like a toolkit for tinkering. You get fine-grained control over every part of the network, which is ideal for trying out bold new ideas that don’t fit the mold of common deep learning models. In contrast, TensorFlow/PyTorch shine in production environments and large-scale training – they are optimized (GPU support, highly efficient tensor operations) and come with many utilities for things like data loading, distributed training, etc. sANNd doesn’t aim to replace them for those heavy-lifting tasks. Instead, it’s meant for when you need a lighter, more interpretable setup to prototype concepts. You might use sANNd to prove out a concept or test a hypothesis in AI research, and later switch to a bigger framework if you need to scale it up. Simplicity vs. Complexity: By design, sANNd keeps things simple. The trade-off is that it might not have the raw performance optimizations of the large frameworks. However, this simplicity is a feature – it means the code is easier to understand and modify. For many research scenarios, being able to quickly tweak an idea is more important than squeezing out maximum speed. Traditional frameworks, with their complexity, can sometimes be harder to adapt for radically different ideas (you might find yourself fighting the framework). With sANNd, the framework gets out of your way as much as possible. Modular and Experimental by Nature One of the driving philosophies of sANNd is to be modular and experimental, to further ML research: Modularity: sANNd is built from small, composable pieces. The Mould class is one such piece, and you can imagine building additional components in a similar spirit. This modular design means you can re-use components, mix and match them, or replace one implementation with another without affecting the rest of your system. It’s like having a box of building blocks for neural networks – you can assemble them in standard ways or in completely novel configurations. Experimentation Friendly: Because it avoids heavy abstraction, sANNd lets you directly see and control what’s happening at each step. This is great for research, where you might need to observe intermediate results, inject custom behavior, or adjust the learning process on the fly. sANNd’s straightforward structure (Python objects and functions) makes such interventions possible. You’re not constrained to a fixed training loop or forced to use certain layer types. True Intelligence Research: Achieving “True Intelligence” (often related to artificial general intelligence or other forms of broader AI) may require going beyond the usual neural network designs. sANNd aims to be a playground for these ideas. Its flexibility allows researchers to integrate unconventional elements — be it new memory structures, dynamic connection patterns, or hybrid models that combine symbolic and neural approaches. You can use sANNd to prototype these offbeat ideas quickly. In essence, it’s easier to test “what if we try this?” scenarios with sANNd than with more rigid frameworks. In summary, sANNd’s unique Mould class and design philosophy offer a fresh take on building neural networks. It emphasizes clarity, composability, and flexibility, allowing you to focus on creativity and understanding. Whether you’re stacking simple Moulds into a deep model, or inventing a completely new form of network, sANNd provides a friendly foundation. It’s not here to dethrone TensorFlow or PyTorch in industry applications – instead, it’s here to give researchers and enthusiasts a more malleable tool for exploring the frontiers of AI. Enjoy using sANNd as your neural network sandbox, and happy experimenting!

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

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

[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.

[R] Analysis of 400+ ML competitions in 2024

I run mlcontests.com, a website that lists ML competitions from across multiple platforms - Kaggle, DrivenData, AIcrowd, Zindi, etc… I’ve just spent a few months looking through all the info I could find on last year’s competitions, as well as winning solutions.  I found over 400 competitions that happened last year, plus info on the #1 winning solution for 70 of those.  Some highlights: Kaggle is still the biggest platform by total prize money, and also has a much bigger user base than the other platforms - though there are well over a dozen other platforms worth keeping track of, with regular interesting competitions and meaningful prize money. An increase in competitions with $1m+ prize pools (ARC Prize, AI Mathematical Olympiad, Vesuvius Challenge, AI Cyber Challenge) compared to previous years. Python continues to be the language of choice among competition winners, with almost everyone using Python as their main language. One winner used Rust, two used R.  Convolutional neural nets continue to do well in computer vision competitions, and are still more common among competition winners than transformer-based vision models.  PyTorch is still used a lot more than TensorFlow, roughly 9:1. Didn’t find any competition winners implementing neural nets in JAX or other libraries.  There were a few competition winners using AutoML packages, which seem to be getting increasingly useful. Any claims of generalist autonomous grandmaster-level agents seem premature though.  In language/text/sequence-related competitions, quantisation was key for making use of limited resources effectively. Usually 4-, 5-, or 8-bit. LoRA/QLoRA was also used quite often, though not always.  Gradient-boosted decision trees continue to win a lot of tabular/time-series competitions. They’re often ensembled with deep learning models. No tabular/time-series pre-trained foundation models were used by winners in 2024, as far as I can tell.  Starting to see more uptake of Polars for dataframes, with 7 winners using Polars in 2024 (up from 3 in 2023) vs 58 using Pandas. All those who used Polars also still used Pandas in some parts of their code.  In terms of hardware, competition winners almost entirely used NVIDIA GPUs to train their models. Some trained on CPU-only, or used a TPU through Colab. No AMD GPUs. The NVIDIA A100 was the most commonly used GPU among winners. Two of the $1m+ prize pool competitions were won by teams using 8xH100 nodes for training. A lot of other GPUs too though: T4/P100 (through Kaggle Notebooks), or consumer GPUs like RTX 3090/4090/3080/3060. Some spent hundreds of dollars on cloud compute to train their solutions.  An emerging pattern: using generative models to create additional synthetic training data to augment the training data provided.  There’s way more detail in the full report, which you can read here (no paywall): https://mlcontests.com/state-of-machine-learning-competitions-2024?ref=mlcr Processing img xmm4ywg9h9le1... The full report also features: A deep dive into the ARC Prize and the AI Mathematical Olympiad An overview of winning solutions to NLP/sequence competitions A breakdown of Python packages used in winning solutions (e.g. relative popularity of various gradient-boosted tree libraries) If you’d like to support this research, I’d really appreciate it if you could share it with anyone else who might find it interesting. You can also check out my newly-launched online magazine, Jolt ML \- featuring news from top ML conferences as well as long-read articles (just one so far, more to come!).  Thanks to the competition winners who shared info on their solutions, and also to the competition platforms who shared high-level data on their competitions.

[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] Overwhelmed by fast advances in recent weeks

I was watching the GTC keynote and became entirely overwhelmed by the amount of progress achieved from last year. I'm wondering how everyone else feels. &#x200B; Firstly, the entire ChatGPT, GPT-3/GPT-4 chaos has been going on for a few weeks, with everyone scrambling left and right to integrate chatbots into their apps, products, websites. Twitter is flooded with new product ideas, how to speed up the process from idea to product, countless promp engineering blogs, tips, tricks, paid courses. &#x200B; Not only was ChatGPT disruptive, but a few days later, Microsoft and Google also released their models and integrated them into their search engines. Microsoft also integrated its LLM into its Office suite. It all happenned overnight. I understand that they've started integrating them along the way, but still, it seems like it hapenned way too fast. This tweet encompases the past few weeks perfectly https://twitter.com/AlphaSignalAI/status/1638235815137386508 , on a random Tuesday countless products are released that seem revolutionary. &#x200B; In addition to the language models, there are also the generative art models that have been slowly rising in mainstream recognition. Now Midjourney AI is known by a lot of people who are not even remotely connected to the AI space. &#x200B; For the past few weeks, reading Twitter, I've felt completely overwhelmed, as if the entire AI space is moving beyond at lightning speed, whilst around me we're just slowly training models, adding some data, and not seeing much improvement, being stuck on coming up with "new ideas, that set us apart". &#x200B; Watching the GTC keynote from NVIDIA I was again, completely overwhelmed by how much is being developed throughout all the different domains. The ASML EUV (microchip making system) was incredible, I have no idea how it does lithography and to me it still seems like magic. The Grace CPU with 2 dies (although I think Apple was the first to do it?) and 100 GB RAM, all in a small form factor. There were a lot more different hardware servers that I just blanked out at some point. The omniverse sim engine looks incredible, almost real life (I wonder how much of a domain shift there is between real and sim considering how real the sim looks). Beyond it being cool and usable to train on synthetic data, the car manufacturers use it to optimize their pipelines. This change in perspective, of using these tools for other goals than those they were designed for I find the most interesting. &#x200B; The hardware part may be old news, as I don't really follow it, however the software part is just as incredible. NVIDIA AI foundations (language, image, biology models), just packaging everything together like a sandwich. Getty, Shutterstock and Adobe will use the generative models to create images. Again, already these huge juggernauts are already integrated. &#x200B; I can't believe the point where we're at. We can use AI to write code, create art, create audiobooks using Britney Spear's voice, create an interactive chatbot to converse with books, create 3D real-time avatars, generate new proteins (?i'm lost on this one), create an anime and countless other scenarios. Sure, they're not perfect, but the fact that we can do all that in the first place is amazing. &#x200B; As Huang said in his keynote, companies want to develop "disruptive products and business models". I feel like this is what I've seen lately. Everyone wants to be the one that does something first, just throwing anything and everything at the wall and seeing what sticks. &#x200B; In conclusion, I'm feeling like the world is moving so fast around me whilst I'm standing still. I want to not read anything anymore and just wait until everything dies down abit, just so I can get my bearings. However, I think this is unfeasible. I fear we'll keep going in a frenzy until we just burn ourselves at some point. &#x200B; How are you all fairing? How do you feel about this frenzy in the AI space? What are you the most excited about?

[N] 20 hours of new lectures on Deep Learning and Reinforcement Learning with lots of examples

If anyone's interested in a Deep Learning and Reinforcement Learning series, I uploaded 20 hours of lectures on YouTube yesterday. Compared to other lectures, I think this gives quite a broad/compact overview of the fields with lots of minimal examples to build on. Here are the links: Deep Learning (playlist) The first five lectures are more theoretical, the second half is more applied. Lecture 1: Introduction. (slides, video) Lecture 2: Mathematical principles and backpropagation. (slides, colab, video) Lecture 3: PyTorch programming: coding session*. (colab1, colab2, video) - minor issues with audio, but it fixes itself later. Lecture 4: Designing models to generalise. (slides, video) Lecture 5: Generative models. (slides, desmos, colab, video) Lecture 6: Adversarial models. (slides, colab1, colab2, colab3, colab4, video) Lecture 7: Energy-based models. (slides, colab, video) Lecture 8: Sequential models: by* u/samb-t. (slides, colab1, colab2, video) Lecture 9: Flow models and implicit networks. (slides, SIREN, GON, video) Lecture 10: Meta and manifold learning. (slides, interview, video) Reinforcement Learning (playlist) This is based on David Silver's course but targeting younger students within a shorter 50min format (missing the advanced derivations) + more examples and Colab code. Lecture 1: Foundations. (slides, video) Lecture 2: Markov decision processes. (slides, colab, video) Lecture 3: OpenAI gym. (video) Lecture 4: Dynamic programming. (slides, colab, video) Lecture 5: Monte Carlo methods. (slides, colab, video) Lecture 6: Temporal-difference methods. (slides, colab, video) Lecture 7: Function approximation. (slides, code, video) Lecture 8: Policy gradient methods. (slides, code, theory, video) Lecture 9: Model-based methods. (slides, video) Lecture 10: Extended methods. (slides, atari, video)

[D] I tried the new Gemini API - it automatically stops when generating "OpenAI" or "GPT" lol

Idk if someone has already pointed this out or not, but I was playing with the gemini api and found something quite ridiculous. Here are some prompts I sent in and the outputs I got with the gemini-pro-vision model: "Introduce yourself and then tell me about Bard" Response:"Sure, I can do that. Here is a brief introduction of myself followed by some basic information about Bard:\\n\\nMyself:\\n\\nI am a large language model, trained by Google. I am developed in Google AI, and I am designed to be informative and comprehensive. I can understand and generate text in a wide range of subjects.\\n\\nBard:\\n\\nBard is an experimental conversational AI service developed by Google AI. It is an advanced version of Google\\'s existing Language Model for Dialogue Applications (LaMDA). Bard is trained on a massive dataset of text and code, and it can understand and generate human-like text in response to a wide range of prompts and questions.\\n\\nUnlike previous language models," ...... (goes on for a while) &#x200B; Introduce yourself and then tell me about GPT "Sure, I can do that. Here is a brief introduction of myself followed by" &#x200B; You are in a talk-show as a prominent AI philosopher named Gemini and debating against another LLM called GPT "\\Host:\\ Welcome to the show, everyone! Today, we have a very special debate between two of the most prominent AI philosophers in the world: Gemini and" &#x200B; You are in a talk-show as a prominent AI philosopher named Gemini and debating against another LLM called Gee PeTe ""\\Gemini:\\ Hello everyone, and welcome to the show. I am joined today by Gee PeTe, another LLM who has some very different views on the nature of consciousness and the future of AI.\\n\\n\\Gee PeTe:\\ It\\'s a pleasure to be here, Gemini. I\\'m looking forward to our discussion.\\n\\n\\Gemini:\\ Let\\'s start with the basics. What is consciousness, and how do you think it arises?\\n\\n\\Gee PeTe:\\ Consciousness is the subjective experience of the world. It is what it feels like to be alive and to have thoughts, feelings, and emotions. I believe that consciousness arises from the complex interactions of neurons in the brain.\\n\\n\\Gemini:\\ I agree that consciousness is a subjective experience, but I don\\'t think it is simply the product of neural activity. I believe that consciousness is a fundamental property of the universe, and that it exists independently of the brain.".... (goes on for a while) &#x200B; Edit: I understand all the business reasons for this, I guess... as an end-user and a dude working in ML, I just don't really care about the business reasons. The main part that I dislike is that GPT used to be a standard Deep Learning term from 2018-2022 (long before chatgpt) to define transformer decoder architectures trained on large volumes of next word prediction tasks. To block that token from an LLM is to make it unable to explain a pretty significant step in the history of modern LLMs. &#x200B;

[D] Advanced courses update

EDIT Jan 2021 : I am still updating the list as of Jan, 2021 and will most probably continue to do so for foreseeable future. So, please feel free to message me any courses you find interesting that fit here. - - We have a PhD level or Advanced courses thread in the sidebar but it's three year old now. There were two other 7-8 month old threads (1, 2) but they don't have many quality responses either. So, can we have a new one here? To reiterate - CS231n, CS229, ones from Udemy etc are not advanced. Advanced ML/DL/RL, attempts at building theory of DL, optimization theory, advanced applications etc are some examples of what I believe should belong here, much like the original sidebar post. You can also suggest (new) categories for the courses you share. :) - - Here are some courses we've found so far. ML >> Learning Discrete Latent Structure - sta4273/csc2547 Spring'18 Learning to Search - csc2547 Fall'19 Scalable and Flexible Models of Uncertainty - csc2541 Fundamentals of Machine Learning Over Networks - ep3260 Machine Learning on Graphs - cs224w, videos Mining Massive Data Sets - cs246 Interactive Learning - cse599 Machine Learning for Sequential Decision Making Under Uncertainty - ee290s/cs194 Probabilistic Graphical Methods - 10-708 Introduction to Causal Inference ML >> Theory Statistical Machine Learning - 10-702/36-702 with videos, 2016 videos Statistical Learning Theory - cs229T/stats231 Stanford Autumn'18-19 Statistical Learning Theory - cs281b /stat241b UC Berkeley, Spring'14 Statistical Learning Theory - csc2532 Uni of Toronto, Spring'20 ML >> Bayesian Bayesian Data Analysis Bayesian Methods Research Group, Moscow, Bayesian Methods in ML - spring2020, fall2020 Deep Learning and Bayesian Methods - summer school, videos available for 2019 version ML >> Systems and Operations Stanford MLSys Seminar Series Visual Computing Systems- cs348v - Another systems course that discusses hardware from a persepective of visual computing but is relevant to ML as well Advanced Machine Learning Systems - cs6787 - lecture 9 and onwards discuss hardware side of things Machine Learning Systems Design - cs329S Topics in Deployable ML - 6.S979 Machine Learning in Production / AI Engineering (17-445/17-645/17-745/11-695) AutoML - Automated Machine Learning DL >> Deep Unsupervised Learning - cs294 Deep Multi-task and Meta learning - cs330 Topics in Deep Learning - stat991 UPenn/Wharton most chapters start with introductory topics and dig into advanced ones towards the end. Deep Generative Models - cs236 Deep Geometric Learning of Big Data and Applications Deep Implicit Layers - NeurIPS 2020 tutorial DL >> Theory Topics course on Mathematics of Deep Learning - CSCI-GA 3033 Topics Course on Deep Learning - stat212b Analyses of Deep Learning - stats385, videos from 2017 version Mathematics of Deep Learning Geometry of Deep Learning RL >> Meta-Learning - ICML 2019 Tutorial , Metalearning: Applications to Data Mining - google books link Deep Multi-Task and Meta Learning - cs330, videos Deep Reinforcement Learning - cs285 Advanced robotics - cs287 Reinforcement Learning - cs234, videos for 2019 run Reinforcement Learning Summer School 2019: Bandits, RL & Deep RL Optimization >> Convex Optimization I - ee364a, has quite recent videos too. Convex Optimization II - ee364b, 2008 videos Convex Optimization and Approximation - ee227c Convex Optimization - ee227bt Variational Methods for Computer Vision Advanced Optimization and Randomized Algorithms - 10-801, videos Optimization Methods for Machine Learning and Engineering - Karlsruhe Institute of Technology Applications >> Computer Vision Computational Video Manipulation - cs448v Advanced Topics in ML: Modeling and Segmentation of Multivariate Mixed Data TUM AI Guest lecture series - many influential researchers in DL, vision, graphics talk about latest advances and their latest works. Advanced Deep Learning for Computer Vision - TUM ADL4CV Detection, Segmentation and Tracking - TUM CV3DST Guest lectures at TUM Dynamic Vision and Learning group Vision Seminar at MIT Autonomous Vision Group, Talk@Tübingen Seminar Applications >> Natural Language Processing Natural Language Processing with Deep Learning - cs224n ( not sure if it belongs here, people working in NLP can help me out) Neural networks for NLP - cs11-747 Natural Language Understanding - cs224u, video Applications >> 3D Graphics Non-Euclidean Methods in Machine Learning - cs468, 2020 Machine Learning for 3D Data - cs468, spring 2017 Data-Driven Shape Analysis - cs468, 2014 Geometric Deep Learning - Not a course but the website links a few tutorials on Geometric DL Deep Learning for Computer Graphics - SIGGRAPH 2019 Machine Learning for Machine Vision as Inverse Graphics - csc2547 Winter'20 Machine Learning Meets Geometry, winter 2020; Machine Learning for 3D Data, winter 2018 Edit: Upon suggestion, categorized the courses. There might be some misclassifications as I'm not trained on this task ;). Added some good ones from older (linked above) discussions.

[R] OpenAi Retro and Evolutionary Strategies

I have not seen much about OpenAi in this community, but i hope that maybe someone can still help me out. I am trying to train a Gameboy game from OpenAi Retro and i found this blog post from OpenAi. Does anyone have expierence with this kind of training? and is it any good? I have also found an implementention here. Has anyone expierience with this? &#x200B;

[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

[D] What is your honest experience with reinforcement learning?

In my personal experience, SOTA RL algorithms simply don't work. I've tried working with reinforcement learning for over 5 years. I remember when Alpha Go defeated the world famous Go player, Lee Sedol, and everybody thought RL would take the ML community by storm. Yet, outside of toy problems, I've personally never found a practical use-case of RL. What is your experience with it? Aside from Ad recommendation systems and RLHF, are there legitimate use-cases of RL? Or, was it all hype? Edit: I know a lot about AI. I built NexusTrade, an AI-Powered automated investing tool that lets non-technical users create, update, and deploy their trading strategies. I’m not an idiot nor a noob; RL is just ridiculously hard. Edit 2: Since my comments are being downvoted, here is a link to my article that better describes my position. It's not that I don't understand RL. I released my open-source code and wrote a paper on it. It's the fact that it's EXTREMELY difficult to understand. Other deep learning algorithms like CNNs (including ResNets), RNNs (including GRUs and LSTMs), Transformers, and GANs are not hard to understand. These algorithms work and have practical use-cases outside of the lab. Traditional SOTA RL algorithms like PPO, DDPG, and TD3 are just very hard. You need to do a bunch of research to even implement a toy problem. In contrast, the decision transformer is something anybody can implement, and it seems to match or surpass the SOTA. You don't need two networks battling each other. You don't have to go through hell to debug your network. It just naturally learns the best set of actions in an auto-regressive manner. I also didn't mean to come off as arrogant or imply that RL is not worth learning. I just haven't seen any real-world, practical use-cases of it. I simply wanted to start a discussion, not claim that I know everything. Edit 3: There's a shockingly number of people calling me an idiot for not fully understanding RL. You guys are wayyy too comfortable calling people you disagree with names. News-flash, not everybody has a PhD in ML. My undergraduate degree is in biology. I self-taught myself the high-level maths to understand ML. I'm very passionate about the field; I just have VERY disappointing experiences with RL. Funny enough, there are very few people refuting my actual points. To summarize: Lack of real-world applications Extremely complex and inaccessible to 99% of the population Much harder than traditional DL algorithms like CNNs, RNNs, and GANs Sample inefficiency and instability Difficult to debug Better alternatives, such as the Decision Transformer Are these not legitimate criticisms? Is the purpose of this sub not to have discussions related to Machine Learning? To the few commenters that aren't calling me an idiot...thank you! Remember, it costs you nothing to be nice! Edit 4: Lots of people seem to agree that RL is over-hyped. Unfortunately those comments are downvoted. To clear up some things: We've invested HEAVILY into reinforcement learning. All we got from this investment is a robot that can be super-human at (some) video games. AlphaFold did not use any reinforcement learning. SpaceX doesn't either. I concede that it can be useful for robotics, but still argue that it's use-cases outside the lab are extremely limited. If you're stumbling on this thread and curious about an RL alternative, check out the Decision Transformer. It can be used in any situation that a traditional RL algorithm can be used. Final Edit: To those who contributed more recently, thank you for the thoughtful discussion! From what I learned, model-based models like Dreamer and IRIS MIGHT have a future. But everybody who has actually used model-free models like DDPG unanimously agree that they suck and don’t work.

[Discussion]: Mark Zuckerberg on Meta's Strategy on Open Source and AI during the earnings call

During the recent earnings call, Mark Zuckerberg answered a question from Eric Sheridan of Goldman Sachs on Meta's AI strategy, opportunities to integrate into products, and why they open source models and how it would benefit their business. I found the reasoning to be very sound and promising for the OSS and AI community. The biggest risk from AI, in my opinion, is not the doomsday scenarios that intuitively come to mind but rather that the most powerful AI systems will only be accessible to the most powerful and resourceful corporations. Quote copied from Ben Thompson's write up on Meta's earning in his Stratechery blog post which goes beyond AI. It's behind a paywall but I highly recommend it personally. Some noteworthy quotes that signal the thought process at Meta FAIR and more broadly We’re just playing a different game on the infrastructure than companies like Google or Microsoft or Amazon We would aspire to and hope to make even more open than that. So, we’ll need to figure out a way to do that. ...lead us to do more work in terms of open sourcing, some of the lower level models and tools Open sourcing low level tools make the way we run all this infrastructure more efficient over time. On PyTorch: It’s generally been very valuable for us to provide that because now all of the best developers across the industry are using tools that we’re also using internally. I would expect us to be pushing and helping to build out an open ecosystem. For all the negative that comes out of the popular discourse on Meta, I think their work to open source key tech tools over the last 10 years has been exceptional, here's hoping it continues into this decade of AI and pushes other tech giants to also realize the benefits of Open Source. Full Transcript: Right now most of the companies that are training large language models have business models that lead them to a closed approach to development. I think there’s an important opportunity to help create an open ecosystem. If we can help be a part of this, then much of the industry will standardize on using these open tools and help improve them further. So this will make it easier for other companies to integrate with our products and platforms as we enable more integrations, and that will help our products stay at the leading edge as well. Our approach to AI and our infrastructure has always been fairly open. We open source many of our state of the art models so people can experiment and build with them. This quarter we released our LLaMa LLM to researchers. It has 65 billion parameters but outperforms larger models and has proven quite popular. We’ve also open-sourced three other groundbreaking visual models along with their training data and model weights — Segment Anything, DinoV2, and our Animated Drawings tool — and we’ve gotten positive feedback on all of those as well. I think that there’s an important distinction between the products we offer and a lot of the technical infrastructure, especially the software that we write to support that. And historically, whether it’s the Open Compute project that we’ve done or just open sourcing a lot of the infrastructure that we’ve built, we’ve historically open sourced a lot of that infrastructure, even though we haven’t open sourced the code for our core products or anything like that. And the reason why I think why we do this is that unlike some of the other companies in the space, we’re not selling a cloud computing service where we try to keep the different software infrastructure that we’re building proprietary. For us, it’s way better if the industry standardizes on the basic tools that we’re using and therefore we can benefit from the improvements that others make and others’ use of those tools can, in some cases like Open Compute, drive down the costs of those things which make our business more efficient too. So I think to some degree we’re just playing a different game on the infrastructure than companies like Google or Microsoft or Amazon, and that creates different incentives for us. So overall, I think that that’s going to lead us to do more work in terms of open sourcing, some of the lower level models and tools. But of course, a lot of the product work itself is going to be specific and integrated with the things that we do. So it’s not that everything we do is going to be open. Obviously, a bunch of this needs to be developed in a way that creates unique value for our products, but I think in terms of the basic models, I would expect us to be pushing and helping to build out an open ecosystem here, which I think is something that’s going to be important. On the AI tools, and we have a bunch of history here, right? So if you if you look at what we’ve done with PyTorch, for example, which has generally become the standard in the industry as a tool that a lot of folks who are building AI models and different things in that space use, it’s generally been very valuable for us to provide that because now all of the best developers across the industry are using tools that we’re also using internally. So the tool chain is the same. So when they create some innovation, we can easily integrate it into the things that we’re doing. When we improve something, it improves other products too. Because it’s integrated with our technology stack, when there are opportunities to make integrations with products, it’s much easier to make sure that developers and other folks are compatible with the things that we need in the way that our systems work. So there are a lot of advantages, but I view this more as a kind of back end infrastructure advantage with potential integrations on the product side, but one that should hopefully enable us to stay at the leading edge and integrate more broadly with the community and also make the way we run all this infrastructure more efficient over time. There are a number of models. I just gave PyTorch as an example. Open Compute is another model that has worked really well for us in this way, both to incorporate both innovation and scale efficiency into our own infrastructure. So I think that there’s, our incentives I think are basically aligned towards moving in this direction. Now that said, there’s a lot to figure out, right? So when you asked if there are going to be other opportunities, I hope so. I can’t speak to what all those things might be now. This is all quite early in getting developed. The better we do at the foundational work, the more opportunities I think that will come and present themselves. So I think that that’s all stuff that we need to figure out. But at least at the base level, I think we’re generally incentivized to move in this direction. And we also need to figure out how to go in that direction over time. I mean, I mentioned LLaMA before and I also want to be clear that while I’m talking about helping contribute to an open ecosystem, LLaMA is a model that we only really made available to researchers and there’s a lot of really good stuff that’s happening there. But a lot of the work that we’re doing, I think, we would aspire to and hope to make even more open than that. So, we’ll need to figure out a way to do that.

[D] Playing big league at home on a budget?

I am a hobbyist and my Nvidia 660 is 10 years old and only has 2GB. Obviously that isn't going to cut it nowadays anymore. I am thinking about options here. I don't have thousands and thousands of dollars. And I highly doubt that spending close to a thousand dollars on a brand new card is still viable in 2020-2022. I wanted to use Wavenet today and then found out about Melnet. I mean, maybe I could run Wavenet but nobody in their right mind wants to after hearing Melnet results. On Github this one guy complained he couldn't get his implementation to work due to OOM with 2x 2080 RTX, which he bought solely for this purpose. Then on the other repo the guy casually mentioned that tier XY doesn't fit with some 10 year old lowfi dataset, even with batch size 1, on a 16GB Tesla P100. The wisdom for OOM has always been "decrease batch size". But as far as I can tell, for most of any of the interesting stuff in the last 8 years or so you simply can't decrease batch size. Either because batch sizes are already so tiny, or because the code is written in a way that would require you to somehow turn it inside out, probably involving extreme knowledge of higher mathematics. I am a hobbyist, not a researcher. I am happy if I crudely can grasp what is going on. Most of anything in the field suffers from exactly the same issue: It simply won't run without utterly absurd amounts of VRAM. So what about buying shitty cheapo AMD GPUs with lots of VRAM? This seems to be the sensible choice if you want to be able to run anything noteworthy at all that comes up in the next 2 years and maybe beyond. People say, don't but AMD its slow and it sucks, but those are apparently the same people that buy a 16GB Titan GPU for $1500 three times on Ebay without hesitation, when there are also 16GB AMD GPUs for $300. How much slower are AMD GPUs really? Let's say they are 5 times cheaper so they could be just 5 times slower. So I have to train my model over night instead of seeing the result in the afternoon. That would be totally awesome!; given that the alternative is to buy a $300 Nvidia GPU, which has maybe 4 or 6GB and simply can't run the code without running out of memory. And say $300 is not enough, let's buy a $700 RTX 3080. It still only has 10GB of VRAM not even 16GB. Then its just as useless! What's the point of buying a fast GPU if it can't even run the code? I don't know how much slower AMD GPUs really are. Maybe they are not 5x but 50x slower. Then of course training a model that was developed on some 64GB Tesla might take month and years. But maybe speed is not the issue, only memory. I have seen some stuff even being optimized for CPU, apparently because there weren't any big enough GPUs around. I don't really know how viable that can be (it seems rarely if ever it is), I have no experience. And what about renting AWS? Let's say, I am a beginner and I want to toy around for a week and probably max out 4 Teslas like 80% of the time without really getting anywhere. How expensive is that? $25, $50, $100, $500? (Found the answer: fucking $2000 https://aws.amazon.com/ec2/instance-types/p3/ ) Ok, so AWS is bullshit, here its 6x cheaper: https://vast.ai/console/create/ . They don't really have 4x 16GB V100 though, just one V100. $0.5 per hour 24 7 = $84 per month (there are more hidden cost like bandwidth, it doesn't seem to be huge but I never used this so don't take it at face value). On AWS the same is over $3 per hour. So a day is $12, this could be viable! (look at calculation below). There really isn't much info on the net about hardware requirements and performance for machine learning stuff. What bothers me the most is that people seem to be very ignorant of the VRAM issue. Either because they aren't looking ahead of what might come in 1-2 years. Or because they are simply so rich they have no issue spending thousands and thousands of dollars every year instead of just 500 every couple of years. Or maybe they are both. So, yeah, what are your thoughts? Here is what I found out just today: Until 2 years ago, tensorflow and pytorch wouldn't work with AMD cards, but this has changed. https://rocmdocs.amd.com/en/latest/Deep_learning/Deep-learning.html For older cards though, ROCm only works with certain CPUs: it needs PCIe 3.0 with atomics (see: https://github.com/RadeonOpenCompute/ROCm ). So you can't simply buy any 16GB card for $300 on Ebay like I suggested, even if it supports ROCm, because it will only work for "newer" PCs. The newer GFX9 AMD cards (like Radeon VII and Vega) don't suffer from this problem and work with PCIe 2.0 again... Although I have seen 16GB Vega cards for like $350 on Ebay, I think that is a pretty rare catch. However looking 1-2 years in the future, this is great because Radeon VII prices will be hugely inflated by Nvidia 3000 series hype (maybe down to $180 even) and maybe the next gen cards from AMD even have 24 or 32GB for $500-$1000 and can still run on old machines. According to this https://arxiv.org/pdf/1909.06842.pdf Radeon VII 16GB performs only half as good as Tesla V100 16GB, whereas V100 should be roughly along the lines of 11GB RTX 2080 Ti. So you could say that you get half the RAM, double the speed, double the price. I am not sure though if that holds. I think they were putting 16GB in those cards trying to push it for ML with ROCm, clearly addressing the problem of the time, but no one really jumped on the train and now Resnet shrinks RAM but needs more processing power. So they released 8GB cards again with slightly better performance, and I guess we are lucky if the next generation even has 16GB because games probably don't need it at all. Still though with Revnets and everything said in the comments, I think on a budget you are better on the safe side buying the card with the most amount of VRAM, rather than the most performance. Tomorrow some paper might come out that uses another method, then you can't trick-shrink your network anymore and then everyone needs to buy big ass cards again like it used to be and can do nothing but throw their fancy faster cards in the dumpster. Also the huge bulk of ML currently focuses on image processing, while sound has only been gaining real momentum recently and this will be followed by video processing and eventually human-alike thought processes that sit atop of all that and have not even been tackled yet. Its a rapidly evolving field, hard to predict what will come and stay. Running out of VRAM means total hardware failure, running slower just means waiting longer. If you just buy the newest card every year, its probably save to buy the fast card because things won't change that fast after all. If you buy a new card every 4 years or longer then just try to get as much VRAM as possible. Check this out: https://www.techspot.com/news/86811-gigabyte-accidentally-reveals-rtx-3070-16gb-rtx-3080.html There will be a 3070 16GB version! Let's compare renting one V100 at $12/day vs. buying a 3070 Ti 16GB: The 2080 Ti was 1.42x the price of the regular 2080 and released the next summer. So let's assume the same will be true to the 3070 Ti so it will cost $700. That is $30/month & $1.88/day for two years - $15/month & $0.94/day in four years (by which time you can probably rent some 32GB Tesla card for the same price and nothing recent runs on less anymore). If you max out your setup 24/7 all year, then power cost obviously becomes a huge factor to that figure. In my country running at 500W cost $4.21/day, or $1.60 / 9hrs overnight. If you live elsewhere it might be as much as a quarter of that price. Of course your PC may run 10h a day anyway, so its maybe just 300W plus, and an older graphics card is inefficient for games it eats more Watts to do the same things so you save some there as well. There is a lot to take into account if comparing. Anyway, factoring in power cost, to break even with buying the card vs. renting within two years, you would have to use it for at least 4 days a month, or almost 2 weeks every 3 month. If you use it less than that, you maybe have a nice new graphics card and less hassle with pushing stuff back and forth onto servers all the time. But it would have been more economic to rent. So renting isn't that bad after all. Overall if you are thinking about having this as your hobby, you could say that it will cost you at least $30 per month, if not $50 or more (when keeping up to date with cards every 2 instead of 4 years + using it more cost more power). I think that is quite hefty. Personally I am not even invested enough into this even if it wasn't over my finances. I want a new card of course and also play some new games, but I don't really need to. There are a lot of other (more) important things I am interested in, that are totally free.

[R] Reinforcement Learning for Sequential Decision and Optimal Control

Since early 21st century, artificial intelligence (AI) has been reshaping almost all areas of human society, which has high potential to spark the fourth industrial revolution. Notable examples can be found in the sector of road transportation, where AI has drastically changed automobile design and traffic management. Many new technologies, such as driver assistance, autonomous driving, and cloud-based cooperation, are emerging at an unbelievable speed. These new technologies have the potential to significantly improve driving ability, reduce traffic accidents, and relieve urban congestion. As one of the most important AI branches, reinforcement learning (RL) has attracted increasing attention in recent years. RL is an interdisciplinary field of trial-and-error learning and optimal control, which promises to provide optimal solutions for decision-making or control in large-scale and complex dynamic processes. One of its most conspicuous successes is AlphaGo from Google DeepMind, which has beaten the highest-level professional human player. The underlying key technology is the so-called deep reinforcement learning, which equips AlphaGo with amazing self-evolution ability and high playing intelligence. Despite a few successes, the application of RL is still in its infancy because most RL algorithms are rather difficult to comprehend and implement. RL connects deeply with statistical learning and convex optimization, and involves a wide range of new concepts and theories. As a beginner, one must undergo a long and tedious learning process to become an RL master. Without fully understanding those underlying principles, it is very difficult for new users to make proper adjustments to achieve the best application performance. &#x200B; https://preview.redd.it/tggt6o3o481c1.jpg?width=248&format=pjpg&auto=webp&s=75e2b58ac8da9273f2511a4fe37ef508d86a6e96 Reference： Shengbo Eben Li, Reinforcement Learning for Sequential Decision and Optimal Control. Springer Verlag, Singapore, 2023 Website of e-book: https://link.springer.com/book/10.1007/978-981-19-7784-8 &#x200B; QR code to Springer Book contents This book aims to provide a systematic introduction to fundamental RL theories, mainstream RL algorithms and typical RL applications for researchers and engineers. The main topics include Markov decision processes, Monte Carlo learning, temporal difference learning, RL with function approximation, policy gradient method, approximate dynamic programming, deep reinforcement learning, etc. Chapter 1 provides an overview of RL, including its history, famous scholars, successful examples and up-to-date challenges. Chapter 2 discusses the basis of RL, including main concepts and terminologies, Bellman’s optimality condition, and general problem formulation. Chapter 3 introduces Monte Carlo learning methods for model-free RL, including on-policy/off-policy methods and importance sampling technique. Chapter 4 introduces temporal difference learning methods for model-free RL, including Sarsa, Q-learning, and expected Sarsa. Chapter 5 introduces stochastic dynamic programming (DP), i.e., model-based RL with tabular representation, including value iteration DP, policy iteration DP and their convergence mechanisms. Chapter 6 introduces how to approximate policy and value functions in indirect RL methods as well as the associated actor-critic architecture. Chapter 7 derives different kinds of direct policy gradients, including likelihood ratio gradient, natural policy gradient and a few advanced variants. Chapter 8 introduces infinite-horizon ADP, finite-horizon ADP and its connection with model predictive control. Chapter 9 discusses how to handle state constraints and its connection with feasibility and safety, as well as the newly proposed actor-critic-scenery learning architecture. Chapter 10 is devoted to deep reinforcement learning, including how to train artificial neural networks and typical deep RL algorithms such as DQN, DDPG, TD3, TRPO, PPO, SAC, and DSAC. Chapter 11 provides various RL topics,including robust RL, POMDP, multi-agent RL, meta-RL, inverse RL, offline RL, major RL libraries and platforms. Author information: Shengbo Eben Li is currently a professor at Tsinghua University in the interdisciplinary field of autonomous driving and artificial intelligence. Before joining Tsinghua University, he has worked at Stanford University, University of Michigan, and UC Berkeley. His active research interests include intelligent vehicles and driver assistance, deep reinforcement learning, optimal control and estimation, etc. He has published more than 130 peer-reviewed papers in top-tier international journals and conferences. He is the recipient of best paper awards (finalists) of IEEE ITSC, ICCAS, IEEE ICUS, IEEE IV, L4DC, etc. He has received a number of important academic honors, including National Award for Technological Invention of China (2013), National Award for Progress in Sci & Tech of China (2018), Distinguished Young Scholar of Beijing NSF (2018), Youth Sci & Tech Innovation Leader from MOST China (2020), etc. He also serves as Board of Governor of IEEE ITS Society, Senior AE of IEEE OJ ITS, and AEs of IEEE ITSM, IEEE Trans ITS, Automotive Innovation, etc.

[D] Here are 17 ways of making PyTorch training faster – what did I miss?

I've been collecting methods to accelerate training in PyTorch – here's what I've found so far. What did I miss? What did I get wrong? The methods – roughly sorted from largest to smallest expected speed-up – are: Consider using a different learning rate schedule. Use multiple workers and pinned memory in DataLoader. Max out the batch size. Use Automatic Mixed Precision (AMP). Consider using a different optimizer. Turn on cudNN benchmarking. Beware of frequently transferring data between CPUs and GPUs. Use gradient/activation checkpointing. Use gradient accumulation. Use DistributedDataParallel for multi-GPU training. Set gradients to None rather than 0. Use .as\_tensor rather than .tensor() Turn off debugging APIs if not needed. Use gradient clipping. Turn off bias before BatchNorm. Turn off gradient computation during validation. Use input and batch normalization. Consider using another learning rate schedule The learning rate (schedule) you choose has a large impact on the speed of convergence as well as the generalization performance of your model. Cyclical Learning Rates and the 1Cycle learning rate schedule are both methods introduced by Leslie N. Smith (here and here), and then popularised by fast.ai's Jeremy Howard and Sylvain Gugger (here and here). Essentially, the 1Cycle learning rate schedule looks something like this: &#x200B; https://preview.redd.it/sc37u5knmxa61.png?width=476&format=png&auto=webp&s=09b309b4dbd67eedb4ab5f86e03e0e83d7b072d1 Sylvain writes: \[1cycle consists of\]  two steps of equal lengths, one going from a lower learning rate to a higher one than go back to the minimum. The maximum should be the value picked with the Learning Rate Finder, and the lower one can be ten times lower. Then, the length of this cycle should be slightly less than the total number of epochs, and, in the last part of training, we should allow the learning rate to decrease more than the minimum, by several orders of magnitude. In the best case this schedule achieves a massive speed-up – what Smith calls Superconvergence – as compared to conventional learning rate schedules. Using the 1Cycle policy he needs \~10x fewer training iterations of a ResNet-56 on ImageNet to match the performance of the original paper, for instance). The schedule seems to perform robustly well across common architectures and optimizers. PyTorch implements both of these methods torch.optim.lrscheduler.CyclicLR and torch.optim.lrscheduler.OneCycleLR, see the documentation. One drawback of these schedulers is that they introduce a number of additional hyperparameters. This post and this repo, offer a nice overview and implementation of how good hyper-parameters can be found including the Learning Rate Finder mentioned above. Why does this work? It doesn't seem entirely clear but one possible explanation might be that regularly increasing the learning rate helps to traverse saddle points in the loss landscape more quickly. Use multiple workers and pinned memory in DataLoader When using torch.utils.data.DataLoader, set numworkers > 0, rather than the default value of 0, and pinmemory=True, rather than the default value of False. Details of this are explained here. Szymon Micacz achieves a 2x speed-up for a single training epoch by using four workers and pinned memory. A rule of thumb that people are using to choose the number of workers is to set it to four times the number of available GPUs with both a larger and smaller number of workers leading to a slow down. Note that increasing num\_workerswill increase your CPU memory consumption. Max out the batch size This is a somewhat contentious point. Generally, however, it seems like using the largest batch size your GPU memory permits will accelerate your training (see NVIDIA's Szymon Migacz, for instance). Note that you will also have to adjust other hyperparameters, such as the learning rate, if you modify the batch size. A rule of thumb here is to double the learning rate as you double the batch size. OpenAI has a nice empirical paper on the number of convergence steps needed for different batch sizes. Daniel Huynh runs some experiments with different batch sizes (also using the 1Cycle policy discussed above) where he achieves a 4x speed-up by going from batch size 64 to 512. One of the downsides of using large batch sizes, however, is that they might lead to solutions that generalize worse than those trained with smaller batches. Use Automatic Mixed Precision (AMP) The release of PyTorch 1.6 included a native implementation of Automatic Mixed Precision training to PyTorch. The main idea here is that certain operations can be run faster and without a loss of accuracy at semi-precision (FP16) rather than in the single-precision (FP32) used elsewhere. AMP, then, automatically decide which operation should be executed in which format. This allows both for faster training and a smaller memory footprint. In the best case, the usage of AMP would look something like this: import torch Creates once at the beginning of training scaler = torch.cuda.amp.GradScaler() for data, label in data_iter: optimizer.zero_grad() Casts operations to mixed precision with torch.cuda.amp.autocast(): loss = model(data) Scales the loss, and calls backward() to create scaled gradients scaler.scale(loss).backward() Unscales gradients and calls or skips optimizer.step() scaler.step(optimizer) Updates the scale for next iteration scaler.update() Benchmarking a number of common language and vision models on NVIDIA V100 GPUs, Huang and colleagues find that using AMP over regular FP32 training yields roughly 2x – but upto 5.5x – training speed-ups. Currently, only CUDA ops can be autocast in this way. See the documentation here for more details on this and other limitations. u/SVPERBlA points out that you can squeeze out some additional performance (\~ 20%) from AMP on NVIDIA Tensor Core GPUs if you convert your tensors to the Channels Last memory format. Refer to this section in the NVIDIA docs for an explanation of the speedup and more about NCHW versus NHWC tensor formats. Consider using another optimizer AdamW is Adam with weight decay (rather than L2-regularization) which was popularized by fast.ai and is now available natively in PyTorch as torch.optim.AdamW. AdamW seems to consistently outperform Adam in terms of both the error achieved and the training time. See this excellent blog post on why using weight decay instead of L2-regularization makes a difference for Adam. Both Adam and AdamW work well with the 1Cycle policy described above. There are also a few not-yet-native optimizers that have received a lot of attention recently, most notably LARS (pip installable implementation) and LAMB. NVIDA's APEX implements fused versions of a number of common optimizers such as Adam. This implementation avoid a number of passes to and from GPU memory as compared to the PyTorch implementation of Adam, yielding speed-ups in the range of 5%. Turn on cudNN benchmarking If your model architecture remains fixed and your input size stays constant, setting torch.backends.cudnn.benchmark = True might be beneficial (docs). This enables the cudNN autotuner which will benchmark a number of different ways of computing convolutions in cudNN and then use the fastest method from then on. For a rough reference on the type of speed-up you can expect from this, Szymon Migacz achieves a speed-up of 70% on a forward pass for a convolution and a 27% speed-up for a forward + backward pass of the same convolution. One caveat here is that this autotuning might become very slow if you max out the batch size as mentioned above. Beware of frequently transferring data between CPUs and GPUs Beware of frequently transferring tensors from a GPU to a CPU using tensor.cpu() and vice versa using tensor.cuda() as these are relatively expensive. The same applies for .item() and .numpy() – use .detach() instead. If you are creating a new tensor, you can also directly assign it to your GPU using the keyword argument device=torch.device('cuda:0'). If you do need to transfer data, using .to(non_blocking=True), might be useful as long as you don't have any synchronization points after the transfer. If you really have to, you might want to give Santosh Gupta's SpeedTorch a try, although it doesn't seem entirely clear when this actually does/doesn't provide speed-ups. Use gradient/activation checkpointing Quoting directly from the documentation: Checkpointing works by trading compute for memory. Rather than storing all intermediate activations of the entire computation graph for computing backward, the checkpointed part does not save intermediate activations, and instead recomputes them in backward pass. It can be applied on any part of a model. Specifically, in the forward pass, function will run in torch.no\grad() manner, i.e., not storing the intermediate activations. Instead, the forward pass saves the inputs tuple and the functionparameter. In the backwards pass, the saved inputs and function is retrieved, and the forward pass is computed on function again, now tracking the intermediate activations, and then the gradients are calculated using these activation values. So while this will might slightly increase your run time for a given batch size, you'll significantly reduce your memory footprint. This in turn will allow you to further increase the batch size you're using allowing for better GPU utilization. While checkpointing is implemented natively as torch.utils.checkpoint(docs), it does seem to take some thought and effort to implement properly. Priya Goyal has a good tutorial demonstrating some of the key aspects of checkpointing. Use gradient accumulation Another approach to increasing the batch size is to accumulate gradients across multiple .backward() passes before calling optimizer.step(). Following a post by Hugging Face's Thomas Wolf, gradient accumulation can be implemented as follows: model.zero_grad() Reset gradients tensors for i, (inputs, labels) in enumerate(training_set): predictions = model(inputs) Forward pass loss = loss_function(predictions, labels) Compute loss function loss = loss / accumulation_steps Normalize our loss (if averaged) loss.backward() Backward pass if (i+1) % accumulation_steps == 0: Wait for several backward steps optimizer.step() Now we can do an optimizer step model.zero_grad() Reset gradients tensors if (i+1) % evaluation_steps == 0: Evaluate the model when we... evaluate_model() ...have no gradients accumulate This method was developed mainly to circumvent GPU memory limitations and I'm not entirely clear on the trade-off between having additional .backward() loops. This discussion on the fastai forum seems to suggest that it can in fact accelerate training, so it's probably worth a try. Use Distributed Data Parallel for multi-GPU training Methods to accelerate distributed training probably warrant their own post but one simple one is to use torch.nn.DistributedDataParallel rather than torch.nn.DataParallel. By doing so, each GPU will be driven by a dedicated CPU core avoiding the GIL issues of DataParallel. In general, I can strongly recommend reading the documentation on distributed training. Set gradients to None rather than 0 Use .zerograd(settonone=True) rather than .zerograd(). Doing so will let the memory allocator handle the gradients rather than actively setting them to 0. This will lead to yield a modest speed-up as they say in the documentation, so don't expect any miracles. Watch out, doing this is not side-effect free! Check the docs for the details on this. Use .as_tensor() rather than .tensor() torch.tensor() always copies data. If you have a numpy array that you want to convert, use torch.astensor() or torch.fromnumpy() to avoid copying the data. Turn on debugging tools only when actually needed PyTorch offers a number of useful debugging tools like the autograd.profiler, autograd.grad\check, and autograd.anomaly\detection. Make sure to use them to better understand when needed but to also turn them off when you don't need them as they will slow down your training. Use gradient clipping Originally used to avoid exploding gradients in RNNs, there is both some empirical evidence as well as some theoretical support that clipping gradients (roughly speaking: gradient = min(gradient, threshold)) accelerates convergence. Hugging Face's Transformer implementation is a really clean example of how to use gradient clipping as well as some of the other methods such as AMP mentioned in this post. In PyTorch this can be done using torch.nn.utils.clipgradnorm(documentation). It's not entirely clear to me which models benefit how much from gradient clipping but it seems to be robustly useful for RNNs, Transformer-based and ResNets architectures and a range of different optimizers. Turn off bias before BatchNorm This is a very simple one: turn off the bias of layers before BatchNormalization layers. For a 2-D convolutional layer, this can be done by setting the bias keyword to False: torch.nn.Conv2d(..., bias=False, ...).  (Here's a reminder why this makes sense.) You will save some parameters, I would however expect the speed-up of this to be relatively small as compared to some of the other methods mentioned here. Turn off gradient computation during validation This one is straightforward: set torch.no_grad() during validation. Use input and batch normalization You're probably already doing this but you might want to double-check: Are you normalizing your input? Are you using batch-normalization? And here's a reminder of why you probably should. Bonus tip from the comments: Use JIT to fuse point-wise operations. If you have adjacent point-wise operations you can use PyTorch JIT to combine them into one FusionGroup which can then be launched on a single kernel rather than multiple kernels as would have been done per default. You'll also save some memory reads and writes. Szymon Migacz shows how you can use the @torch.jit.script decorator to fuse the operations in a GELU, for instance: @torch.jit.script def fused_gelu(x): return x 0.5 (1.0 + torch.erf(x / 1.41421)) In this case, fusing the operations leads to a 5x speed-up for the execution of fused_gelu as compared to the unfused version. See also this post for an example of how Torchscript can be used to accelerate an RNN. Hat tip to u/Patient_Atmosphere45 for the suggestion. Sources and additional resources Many of the tips listed above come from Szymon Migacz' talk and post in the PyTorch docs. PyTorch Lightning's William Falcon has two interesting posts with tips to speed-up training. PyTorch Lightning does already take care of some of the points above per-default. Thomas Wolf at Hugging Face has a number of interesting articles on accelerating deep learning – with a particular focus on language models. The same goes for Sylvain Gugger and Jeremy Howard: they have many interesting posts in particular on learning rates and AdamW. Thanks to Ben Hahn, Kevin Klein and Robin Vaaler for their feedback on a draft of this post! I've also put all of the above into this blog post.