LlamaPReview – AI GitHub PR reviewer that learns your codebase

Hacker News

I built LlamaPReview to solve a common frustration: most AI code reviewers either require complex setup or don't truly understand project context. Key differentiators: 1. One-click installation through GitHub Marketplace - no configuration needed 2. Analyzes your entire codebase first to understand: - Project structure - Coding patterns - Naming conventions - Architecture decisions 3. Completely free with no usage limits 4. Fully automated PR reviews with zero human intervention required Technical implementation: - Built on top of llama-github (my open source project) - Focuses on deep code understanding rather than superficial linting - Provides context-aware suggestions with explanations The goal is to handle routine reviews automatically so developers can focus on complex architectural decisions. Currently in production and processing real PRs. Try it for free: https://github.com/marketplace/llamapreview/ Looking for feedback from the HN community, especially on: - What features would make this more useful for your workflow? - How do you currently handle code review automation? - What aspects of code understanding matter most to you?

Visit product View on Hacker NewsOct 30, 2024

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Actual performance

102points

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Made the leaderboard

Launch Intel predictions

Analyze your own launch →

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

93%93% 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.

Hacker NewsStrong engagement from HN community · Strong signals: open source, llama, code review · 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.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

46%46% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

34%34% 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

27%27% 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

17%17% 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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