I Built Feedaura to turn scattered feedback into clear insights with AI

Hacker News

Hey, I want to tell you about something I’ve been working on. A while back, I was struggling to collect feedback on one of my projects—it was a mess, and I couldn’t find any tool to fix it. So, I decided to build FeedAura. It uses AI to take that chaotic user feedback and turn it into clear insights by categorizing it for you. It’s almost ready, and I’m super excited to launch it soon!

Visit product View on Hacker NewsApr 21, 2025

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

81%81% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

59%59% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

48%48% 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, ide · Missing: https docs, just released, exist

37%37% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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 · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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