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Hacker News - Newest: "AI"

AI can't read an investor deck AI as an attorney? Student uses ChatGPT, Gemini to sue UW over alleged racial discrimination Hacking MCP Servers in AI Systems – The Rug Pull: Tool Changes After Approval GitHub - MeepCastana/KubeezCut: Free Web based video editor Can AI judge journalism? A Thiel-backed startup says yes, even if it risks chilling whistleblowers Coming soon: 10 Things That Matter in AI Right Now DARPA built an AI to fact-check enemy weapons claims What explains heterogeneity in AI adoption? When AI Meets Muscle: Context-Aware Electrical Stimulation Promises a New Way to Guide Human Movements - Department of Computer Science AI Changed How We Build. It Did Not Change What Matters. Linux rules on using AI-generated code - Copilot is OK, but humans must take 'full responsibility for the… Meta spins up AI version of Mark Zuckerberg to engage with employees Code Mode: Let Your AI Write Programs, Not Just Call Tools | TanStack Blog GitHub - Delavalom/graft: Go framework for building AI agents. Type-safe tools, multi-provider (OpenAI, Anthropic, Gemini, Bedrock), zero vendor SDKs. India's TCS tops estimates, says new AI models did not dent services demand Gen Z's fading AI hype Strong feeling: we are in a folded AI reality GitHub - machinarii/total-recall-catalog: A reference catalog of latest knowledge retrieval, memory & RAG systems GitHub - mensfeld/code-on-incus: Give each AI agent its own isolated machine with root, Docker, and systemd. Active defense detects and stops threats automatically.. Quantization, LoRA, and the 8% Problem: Benchmarking Local LLMs for Production AI Iran war: We spoke to the man making Lego-style AI videos that experts say are powerful propaganda Powell, Bessent discussed Anthropic's Mythos AI cyber threat with major U.S. banks GitHub - immartian/bellamem: Persistent belief-graph memory for AI agents. Retrieves decisive context by importance — not recency, not RAG, not /compact. recursive-mode: The Repo-Native Operating System for AI Engineering After the attack on Sam Altman's home, will AI CEO's go on the offensive? The biggest advance in AI since the LLM Opus 4.6 vs GPT 5.4 One Prompt Unity World Generation Test “AI polls” are fake polls Client Challenge Can AI be a 'child of God'? Inside Anthropic's meeting with Christian leaders
Show HN: Vdiff – CLI to help you review AI-generated code
fforbeck · 2026-05-02 · via Hacker News - Newest: "AI"

Hey, you probably already saw that reviewing AI-generated code is a nightmare and quickly becomes a bottleneck. Everyone is using AI agents to write code fast, but the hard part is reviewing a bazillion lines.

I was thinking about building something that would guide me during development and during PR reviews. Something that would give me signals based on facts, risks, and evidence. Not just one LLM reviewing the code it generated. The initial idea was to add a deterministic review layer and combine that with LLM reasoning, and use that to find gaps in code and point me to the most important places, so I don't need to read line by line.

I ended up building a tool called vdiff, and it is working very well for me, and I'm constantly improving it. It is a CLI that analyzes your git diffs and gives you a structured report: what changed, what's risky, and what's missing. It uses tree-sitter for AST diffs and an LLM on top, so you get actual evidence for each finding, not just vibes.

Some of the output signals: - Tells you if it's safe to merge, with a risk score - Lists what's wrong, how confident it is, and shows the evidence - Dependency graph for blast radius analysis - Review memory (tracks resolved/reopened findings across sessions) - You can point it at a spec or PRD, and it checks if the changes actually match - Structural metrics (acyclicity, depth, equality, graph)

It runs locally; I didn't want the tool publishing the code to a third-party server, so your code never leaves your machine. BYOK (bring your own LLM key) - you interact directly with the provider.

If you want to give it a try:

  npm i -g @4bk/vdiff     # install globally
  pip install graphifyy   # required to generate the knowledge graph
  cd your-project         # go to a git repo
  vdiff init              # set up provider, API key, build knowledge graph
  vdiff -v                # analyze staged changes

Would love to hear if this is something helpful for you as well, and what kind of signals you'd want to see. I usually run it before each commit on a feature branch, and then on CI to verify the feature branch against main.

Any feedback is very welcome, and if it is crap, well, then just say it.

Cheers