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Vdiff – CLI to help you review AI-generated code

by fforbeck·May 2, 2026·1 point·0 comments

AI Analysis

●●SolidSolve My ProblemBig Brain

AST-aware diff analysis cuts through AI noise better than standard LLM summaries.

Strengths
  • Combines deterministic AST parsing with LLM reasoning to reduce hallucinations.
  • Focuses on risk and missing context rather than just summarizing changes.
  • CLI-first workflow fits naturally into existing git review processes.
Weaknesses
  • Competes directly with integrated IDE features from Cursor and GitHub Copilot.
  • Requires separate CLI step instead of living inside pull request interfaces.
Target Audience

Developers using AI coding agents, Tech leads reviewing AI PRs

Similar To

CodeRabbit · GitHub Copilot Enterprise · Reviewable

Post Description

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

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