AI

AI Use Disclaimer

Hacker News

AI Use Disclaimer

Hi HN, Here's a little website for a hobby project I have, and maybe the AI use disclaimer on it is useful to others. I believe it's a fundamental duty of open source maintainers to disclose the extent of their AI use on a per-project basis. The usual discussions here devolve into "this is vibe coded" for tools which may or may not be, and it's not great. I greatly dislike vibe-coding, but I believe in responsible tool use, such as responsible use of AI. The linked page is CC0, feel free to copy/take inspiration from, and do whatever you like. Only link back to the original if you want to.

Visit product View on Hacker NewsJun 24, 2026

Actual performance

2points

Did not reach leaderboard

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

89%89% 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

38%38% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

36%36% 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 · Missing: mobile apps, ios, personal

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

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

10%10% 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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