Proton – A GitHub app that auto-fixes your PRs

Hacker News

Hey HN! We're launching 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 here'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 Would love to hear your thoughts — reply here or email us at support@proton.codes Cheers!

Visit product View on Hacker NewsApr 22, 2025

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

90%90% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

52%52% 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: code review, ide, io · Missing: https docs, excited, just released

49%49% 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, para · Missing: mobile apps, ios, personal

42%42% 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

34%34% 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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