Buildpad – Build products that people actually want

Hacker News

Hello people of Hacker News, I’m Felix and I’ve spent the last few months building Buildpad together with my brother David. About two and a half months ago we launched Buildpad on Product Hunt and got a very positive response. Since then we’ve been constantly improving the product and grown to over 2,400 founders using the platform and 70 active paying customers. - If you’re a founder who’s wasted time building products no one wants, then Buildpad might be for you. Buildpad is an AI-guided product building platform that helps founders build products people actually want. Buildpad guides you step-by-step through phases, taking you from idea to launching your product and getting your first customers. Along your journey, you'll also get access to special tools such as problem brainstorming, Reddit validation, user persona creation, and more. Some features include: A proven 10-phase framework: organize the building of your product by using multiple chats, from ideation to building your MVP and getting your first customers, with different goals in each one. Memory: our AI learns about your project as you build, keeps the context between chats, and provides advice based on it. Problem brainstorm: if you don’t have a product idea yet you can use our brainstorming AI to help you explore different problems to solve in industries you’re interested in. Reddit validation: search through millions of Reddit discussions to determine if there's demand for your business idea. AI will help you analyze all the data gathered and give you the conclusions from it. User persona: Get to know exactly who your users are and where you can find them by generating a user persona. Marketing is easier when you have a clear picture of who you’re targeting. - We want you to experience how much easier everything becomes—from marketing to gathering feedback and getting your first customers—when you’re building a product that people actually want. Why don’t I just use ChatGPT? ChatGPT responds to what you ask, but it doesn't guide you through the crucial phases of building a business. It's reactive, not proactive. So unless you always know what to do next, making progress with ChatGPT is tough. We hope Buildpad can help some of you on your journey to building successful products! Leave a comment if you have any questions and I’ll be happy to answer them.

Visit product View on Hacker NewsDec 16, 2024

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

97%97% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, context · Missing: mac, agents, macos

89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: hacker news, ide, io · Missing: https docs, excited, just released

55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: month, users, way · Missing: mobile apps, ios, personal

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, users · Missing: plus, intuitive, reviews

26%26% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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