I built a tool that uses AI to turn messy feedback into clear insights

Hacker News

I built a tool that makes sense of all the messy feedback you get—whether it’s from users, customers, or anyone else. You know how feedback can be a total mess? Think emails, comments, reviews—all over the place. My tool uses AI to dig through that chaos and pull out the good stuff. It sorts everything, spots patterns, and gives you clear, useful insights you can actually act on. No more guessing or drowning in noise—it’s like having a smart helper that turns feedback into a simple plan. Excited to share it with you all!

Visit product View on Hacker NewsApr 24, 2025

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3points

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Launch Intel predictions

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AppSumoStrong fit for a featured deal · Strong signals: reviews, users · Missing: plus, platform, intuitive

67%67% predicted probability of success on AppSumo, 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, email · Missing: mac, agents, macos

62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

54%54% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: users · Missing: mobile apps, ios, personal

53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

22%22% 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 · 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: smart · 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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