Open-source AI code (pre)reviewer

Hacker News

Hey HN, Over the last year, I’ve reviewed more than 1000 code changes. Most of the time was spent catching obvious mistakes rather than debating complex design decisions. If we estimate ~10 minutes per review, that’s 160+ hours spent reviewing code in just one year. So I thought: could I get some of that time back using LLMs? That's why I spent the last few weekends building Presubmit.ai, an open-source AI reviewer that runs as a Github Action right when you open a Pull Request. The results so far are promising: I estimate it can reduce the review time by 50%, which in my case would mean I save 80hours (~10 working days) per year. Unlike similar SaaS solutions, the goal is not to replace the human reviewer but to highlight obvious mistakes early, spot security vulnerabilities and give more context about the change. I like to think of it as a “pre-reviewer”. Some of its features are: * Line-by-line comments * PR summarization * Title generation on request * Responds to review comments It supports all major LLMs, but I’ve found Anthropic's Claude works best for this use case. Please give it a try and share your feedback! Thanks!

Visit product View on Hacker NewsNov 26, 2024

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

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

best fitHighest predicted score across all platforms for this description.

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

89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: 000, io · Missing: https docs, excited, just released

60%60% 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

29%29% 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

28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

15%15% 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.

Incorrect prediction on native model

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