A way to respect all your user feedback and your time

Hacker News

I know this is not a very common issue, but I do know that the people that have to deal with a bunch of feedback can find it hard to keep up while doing their work as well I'm not talking about answering and having actual conversations. I only talk about the feedback that report a bug or and issue, or those that request a feature you don't have. Having to keep track of it all and decide what to prioritize can be annoying I built this for myself, as a simple local running server I'd start so all my feedback would get fed into a couple of agents as soon as I received it and then into a ticket list, deduplicate, auto-group, prioritize, all that But with time, I started expanding it a bit. I integrated Mail forwarding and made it so an AI can easily access/control it and so on And today, I decided to try my luck at monetizing it, and see if some other people have this issue and are willing to pay for something like this. Let me know what you think, about the price as well, I can offer discounts if you think it's too expensive right now

Visit product View on Hacker NewsJun 24, 2026

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Actual performance

3points

Did not reach leaderboard

Launch Intel predictions

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

83%83% 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 · Strong signals: started · Missing: supports, reddit linkedin, podcasting

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

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

46%46% 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: way · Missing: mobile apps, ios, personal

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

39%39% 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

16%16% 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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