AI-driven GitHub PR reviewer

Hacker News

Hello HN readers! Our team has built an AI/LLM-driven code review tool that significantly helps improve dev velocity and code quality. Its unique features are: - Line-by-line code change suggestions: Reviews the changes line by line and provides code change suggestions that can be directly committed from the GitHub UI. - Continuous, incremental reviews: Reviews are performed on each commit within a pull request rather than a one-time review on the entire pull request. - Cost-effective and reduced noise: Incremental reviews reduce noise by tracking changed files between commits and the base of the pull request. - Chat with the bot: Supports conversation with the bot in the context of lines of code or entire files, helpful in providing context, generating test cases, and reducing code complexity. - Smart review skipping: By default, skips in-depth review for simple changes (e.g., typo fixes) and when changes look good for the most part. We would love the HN community to try it out in their GitHub repos and provide feedback! We will happily answer any technical questions regarding the sophisticated prompt engineering we did for this project.

Visit product View on Hacker NewsJul 12, 2023

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Actual performance

4points

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: context, code · 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: supports · Missing: reddit linkedin, podcasting, created

77%77% 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

57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

28%28% 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

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat, smart · Missing: web3, crypto, cryptocurrency

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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