AI-Powered Surveys for Spearheading Business Growth

Hacker News

Introducing FMJ: AI-Powered Surveys for Spearheading Business Growth We're Ryan Choi and Terrence Jung, co-founders of fmjsurvey.com, an AI survey tool designed to help founders get closer to product-market fit. We built FMJ to tackle an annoying problem during the entrepreneurship process: the time-consuming process of ideation, validation, and iteration. We understand that building, failing, and iterating quickly is crucial for success. However, we found that the steps from ideation to validation to iteration took a long time, especially when it came to thinking about the right questions to ask potential customers and users. That's why we created FMJ. Using proven frameworks and expert techniques, our AI-powered tool generates professional, insightful questions that enable founders to incorporate valuable feedback into the next iteration of their product or service. With FMJ, you can seamlessly manage your surveys and responses in one place, saving you time and effort. We built FMJ during buildspace's s5 nights & weekends and are thrilled to share it with the world. As we continue to improve and expand our tool, we'd love to hear your feedback. If you encounter any issues or have ideas for additional features, please submit a feature/fix request here. - https://insigh.to/b/fmj Connect with us on Twitter to stay updated on our progress and share your experiences with FMJ Survey: - Ryan: https://x.com/RyanChoi018 - Terrence: https://x.com/terrence_jung Start leveraging the power of AI to generate surveys that drive your business growth. Visit fmjsurvey.com today and take the first step towards achieving product-market fit.

Visit product View on Hacker NewsSep 23, 2024

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

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best fitHighest predicted score across all platforms for this description.

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

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: user, using · Missing: mac, agents, macos

28%28% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: lua, ide, io · Missing: https docs, excited, just released

23%23% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

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

Correct prediction on native model

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