Jet_Xu

7 ms·

1. 31.▲
   by Jet_Xu 2y ago

   Thank you for such detailed and thoughtful feedback! Really appreciate the time you took to analyze our claims and point out the areas needing more clarity. You're absolutely right about the marketing copy - we should be more precise a
2. 32.▲
   by Jet_Xu 2y ago

   Thanks for these important questions! The service runs on secure cloud infrastructure and processes code in-memory during PR reviews - we don't permanently store any source code. We use enterprise-grade LLMs (can't disclose specif
3. 33.▲
   by Jet_Xu 2y ago

   The core code of this LlamaPReview is from my open source project llama-github. But there are some other code currently still have not open source.
4. 34.▲
   by Jet_Xu 2y ago

   Maybe you could refer to my open source project llama-github
5. 35.▲
   by Jet_Xu 2y ago

   Good questions! Code review and generation are quite different tasks. Review is about pattern recognition and consistency checking, while generation requires understanding business logic and system design. LlamaPReview works best at: - Spot
6. 36.▲
   by Jet_Xu 2y ago

   Thanks for raising this important question. We will not store any code in our database. But we will leverage SaaS LLM API (e.g. GPT/Claude/Mistral) to help on the PR review - during this step, for sure we need to send code to thes
7. 37.▲
   by Jet_Xu 2y ago

   Interesting perspective on the timing of feedback. We chose PR reviews because they're a natural integration point where developers already expect feedback, and it's when context is most complete. However, we're exploring way
8. 38.▲
   by Jet_Xu 2y ago

   Thanks for mentioning PR Agent. While there are several tools in this space, LlamaPReview focuses on deep codebase understanding and context-aware reviews(advanced functions still under evolution). We'd love to hear about your experien
9. 39.▲
   by Jet_Xu 2y ago

   Thanks for raising this question. Currently, we're offering a free tier to gather community feedback and improve the service. We use enterprise-grade LLMs to ensure high-quality reviews while maintaining reasonable operational costs. O
10. 40.▲
    by Jet_Xu 2y ago

    The "learning" process involves analyzing your codebase's context during PR reviews - we don't train on your data (we even will not save them but only calculate in memory). Instead, we use advanced context retrieval to u
11. 41.▲
    by Jet_Xu 2y ago

    Great points about code review reliability. LlamaPReview is designed to be a complementary tool for senior developers, not a replacement for human review. Here's our approach: 1. It helps save senior developers' time by handling r
12. 42.▲
    by Jet_Xu 2y ago

    Thanks for raising these important questions about data privacy and security. Let me clarify: 1. Code Processing: All code analysis happens in-memory during the PR review process. We don't permanently store any of your source code. 2.
13. 43.▲
    Show HN: LlamaPReview – AI GitHub PR reviewer that learns your codebase(github.com)

    102 points by Jet_Xu 2y ago|42 comments
14. 44.▲
    by Jet_Xu 2y ago

    Thanks to everyone who has checked out llama-github so far! I would love to hear more feedback from the community. Your thoughts and suggestions are invaluable and will help improve the project. If you’ve tried it out, please share your exp
15. 45.▲
    Show HN: Llama-GitHub – LLM-Powered GitHub Context Retrieval Python Library(github.com)

    3 points by Jet_Xu 2y ago|1 comments