Auto-fix your GitHub PR issues with Proton for FREE

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Hey HN! We just launched Proton, a GitHub app that listens to PR review comments and suggests code changes to address them. It creates a new PR based on top of your working branch, so you can simply review and merge. How is it different from other AI code review tools? Others focus on pointing out issues, whereas Proton focuses on addressing them. Although some of them can also suggest fixes, they tend to only work on nearby lines or within a single file. But real-world feedback often involves cross-file changes like “Let’s extract this to a separate component”, or “We should follow the same pattern of doing things in file X, Y and Z”. Proton has full repo context, so it can handle these kinds of feedback. Want to see it in action? Here’s a short demo: https://youtu.be/zDEfw-R2jWc , and there’s the PR shown in the demo video: https://github.com/proton-codes/demos/pull/2 It’s free, takes two clicks to install, and works out of the box. Here’s the install link: https://github.com/apps/proton-app We'd love to get some early users and hear your thoughts — reply here or email us at [support@proton.codes](mailto:support@proton.codes) Cheers!

Visit product View on Hacker NewsApr 29, 2025

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

86%86% 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: para · Missing: supports, reddit linkedin, podcasting

55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

53%53% 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: apps, video, users · Missing: mobile apps, ios, personal

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

35%35% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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