I

I built a tool to auto-accept AI slop and bigtech devs loves it

Hacker News

I built a tool to auto-accept AI slop and bigtech devs loves it

So, you know how bigtech gives nearly unlimited credits for coding tools and in the same time put a huge admin whitelist of what commands are allowed? So, instead of becoming a 100x engineers, you (including me) simply become a multi-window-enter-clicker I build a tool to solve this. It works locally, using OCR finds the "run" (or any other label you put e.g. accept, allow, fetch etc. ) and just clicks this button. Originally this was a tool for me and my team, but people seamed to love it so much, they encouraged me to share with you. I do understand that workflows in each bigtech company is different, so what worked for us !-> will work for you. So if you are interested in using this or have any question, please feel free to reach out, open issues and prs. lets make AI slop inevitable! https://github.com/Alcray/SlopeAutoAcceptor

Visit product View on Hacker NewsMay 26, 2026

Actual performance

17points

2comments

Made the leaderboard

Launch Intel predictions

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

88%88% 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: including · Missing: supports, reddit linkedin, podcasting

78%78% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

56%56% 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

45%45% predicted probability of success on TrustMRR, 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.

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 · 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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