Please refer to my recent AI Code review performance test include DeepSeek V3:
https://news.ycombinator.com/item?id=42547196
Readit NewsJet_Xu commented on DeepSeek v3 beats Claude sonnet 3.5 and way cheaper huggingface.co/deepseek-a... · Posted by u/helloericsf
A few weeks ago, I shared LlamaPReview (an AI PR reviewer) here on HN and received great feedback [1]. Now I'm trying to understand how experienced developers prioritize different aspects of code review to make the tool more effective.
When you open a PR, what's the first thing you check? Is it:
- Overview & Architecture Changes - Detailed Technical Analysis - Critical Findings & Issues - Security Concerns - Testing Coverage - Documentation - 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? 2. How do you balance between architectural concerns and implementation details? 3. What information do you wish was always prominently displayed?
Your insights will directly influence how we structure AI Code Review to match real developers' thought processes.
[1] Previous discussion: https://news.ycombinator.com/item?id=41996859
Jet_Xu commented on Nvidia Steps Up Hiring in China to Focus on AI-Driven Cars bloomberg.com/news/articl... · Posted by u/mfiguiere
Interesting timing, considering China just launched an antitrust probe into Nvidia [1] and there were earlier reports about H20 order suspension [2]. This hiring push seems to be a delicate balancing act:
- Expanding autonomous driving R&D while navigating export controls - Maintaining Chinese market presence despite regulatory pressures - Building local expertise when H100/A100 sales are restricted
The automotive sector might be strategically chosen as it's less impacted by current chip restrictions. Worth noting that China remains Nvidia's largest market in Asia, accounting for ~20% of their revenue.
[1] https://www.reuters.com/technology/china-investigates-nvidia... [2] https://www.tomshardware.com/tech-industry/artificial-intell...
Jet_Xu commented on Show HN: GitBook Documentation Downloader for LLMs github.com/Amal-David/git... · Posted by u/amaldavid
Interesting approach! While converting docs to markdown works, I've found that technical documentation (especially for complex repos) needs more than just text conversion for effective LLM consumption.
The key challenge is preserving repository context - like code dependencies, architectural decisions, and evolution patterns. Have others experimented with knowledge graph approaches for maintaining these relationships when processing repos for LLMs?
Interesting approach! While URL-based extraction is convenient, I've been working on a more comprehensive solution for repository knowledge retrieval (llama-github). The key challenge isn't just extracting code, but understanding the semantic relationships and evolution patterns within repositories.
A few observations from building large-scale repo analysis systems:
1. Simple text extraction often misses critical context about code dependencies and architectural decisions 2. Repository structure varies significantly across languages and frameworks - what works for Python might fail for complex C++ projects 3. Caching strategies become crucial when dealing with enterprise-scale monorepos
The real challenge is building a universal knowledge graph that captures both explicit (code, dependencies) and implicit (architectural patterns, evolution history) relationships. We've found that combining static analysis with selective LLM augmentation provides better context than pure extraction approaches.
Curious about others' experiences with handling cross-repository knowledge transfer, especially in polyrepo environments?