Feedback to Me, 360° feedback powered by AI

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Hey HN, Feedback to Me is a personal project I've been working on for the last few weeks which I've started to "soft launch" with a few people. A few years back, my employer paid for all middle-management to get a full "360 feedback" process done - an external consultancy came in with a bespoke platform to let us send feedback questionnaires to lots of people we worked with, and after they'd made sure nothing identifiable was in there, they created a report on strengths/weaknesses... in the end, it was useful, but also time-consuming and expensive, so we never did it again. Feedback to Me is my attempt to automate much of that process using LLMs (in this case, Gemini): they extract themes from your feedback, ensure it's all anonymous, and then use a big reasoning model to bring it all together into feedback you can hopefully use. The code base is open-source (< https://github.com/AndreasThinks/feedback-to-me >) and mostly built on the very cool FastHTML framework - that means if your employer wants to run something similar, it should be easy enough to run this on your own cloud environment. In the meantime though, I hope it's vaguely useful to others! Feedback very welcome - you'll need to register to try the full process, but feel free to use guerilla mail or equivalent if you just want to give it a go.

Visit product View on Hacker NewsFeb 17, 2025

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

85%85% 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: model, gemini, using · Missing: mac, agents, macos

83%83% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

65%65% 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: personal · Missing: mobile apps, ios, entrepreneurs

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

32%32% 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

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

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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