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LlamaPReview-site discussion
LlamaPReview-site reviews and mentions
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Show HN: AI code reviewer that analyzes cross-module impacts
3. Is storing structural metadata (no code content) acceptable, or is Zero-knowledge Storage the only way?
Site: https://jetxu-llm.github.io/LlamaPReview-site/
I'm here for the next few hours to answer technical questions or get roasted for my monetization strategy.
- Show HN: LlamaPReview – AI Code Review Tool with Reasoning Deep Thinking LLM
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Ask HN: DeepSeek V3's AI Code Review Performance – A Reality Check with Data
- Larger parameter count (671B) didn't translate to better PR review quality
For transparency: I developed LlamaPReview (https://jetxu-llm.github.io/LlamaPReview-site/), a GitHub App for automated PR reviews, which I used its core code as the testing framework. The app is free and can help you reproduce PR review efforts.
Questions for the community:
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What do you check first in PR review? Help shape our AI Code review tool
- Deployment Impact
I've set up a quick poll here: https://github.com/JetXu-LLM/LlamaPReview-site/discussions/9
Current results show an interesting split between "Detailed Technical Analysis" and "Critical Findings", but I'd love to hear HN's perspective:
1. What makes you trust/distrust a PR at first glance?
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Show HN: LlamaPReview – AI code reviewer trusted by 2000 repos, 40%+ effective
[2] Technical deep-dive: [https://github.com/JetXu-LLM/LlamaPReview-site/discussions/3]
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A note from our sponsor - SaaSHub
www.saashub.com | 14 Aug 2026
Stats
JetXu-LLM/LlamaPReview-site is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of LlamaPReview-site is HTML.
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