15:I[981,["658","static/chunks/658-a698693f56d0116a.js","183","static/chunks/183-6beeeb801c274aaa.js","474","static/chunks/app/%5Btype%5D/layout-e8e83705b5c8d3b7.js"],"Posts"] 16:T6a7,I was curious in improving code reviews because they still suck, so researching on a triage layer that you can attach to your local LLMs/api calls for better code reviews.

Most review tools dump a PR diff into a model and hope it finds bugs. The model sees added/removed lines, hunk headers, context lines. It has no idea that the function it's looking at is called by x other functions across y files, or that a type change here breaks an interface three directories away.

The triage layer parses source code into ASTs using tree-sitters, extracts semantically meaningful entities (functions, classes, methods, structs), and builds a cross-file dependency graph. It ranks every changed entity by transitive blast radius. Cuts the review surface by 80-90%, and increases the attention score on the bug significantly. Now I am sure it can be out of distribution few times but for fast code reviews this tradeoff is worth making.

Once you've narrowed the problem to "here are the n riskiest entities in this PR," you don't need a frontier model. You need a model that just knows your code. A 7B fine-tuned on your codebase knows your patterns, your conventions, your common bugs. Structural triage handles the global reasoning that results in your model handling the judgment call really well.

Commands:

- inspect diff - entity-level diff with risk scoring and blast radius

- inspect predict - show which unchanged entities are at risk of breaking

- inspect review - structural triage + LLM review

- inspect pr - review a GitHub PR

21 language parsers. Written in Rust. Open source.

Github: https://github.com/Ataraxy-Labs/inspect

How Scammers Make Fake Calls? (Step-by-Step Explained)

Structural and semantic component for improving code reviews with local models

Sonnet 4.6 Elevated Rate of Errors

China's AI Ethics Governance

Madman Theory

He Learned the Gestures

As We May Think

Why Multi-Agent Systems Need Memory Engineering

Google launched an AI dictation app that works offline

How to Use Python's Heapq for Kth Largest Problems

Japan relaxes privacy laws to make itself the 'easiest country to develop AI'

Intel is going all-in on advanced chip packaging

Inside a huge compound where 10k workers scammed people globally

The Value of a Performance Oracle

The Art of Risk Management (2017)

Monitoring Express Route Performance with AppSignal

Investigating Split Locks on x86-64

Purisaki Berberine Patches – Herbal Metabolism Support

How Trump Took the U.S. to War with Iran

We moved Railway's frontend off Next.js. Builds went from 10+ mins to under two

Temporarily disabling language support on rubygems.org

Two Years of Valkey

Optinum – finds the blind spots AI coding agents systematically miss in PR tests

Anatomy of Mercor's Data Breach

Unlocking cloud inference compute for OpenClaw

Improving Interactive In-Context Learning from Natural Language Feedback

Russians turn to cat feeders, classifieds, and vacuum cleaners to stay connected

Under the hood of MDN's new front end

Show HN: Voiceplan.it – The New Planning Mode

Energy giant Drax pulls out of UK climate plan

Potential Anthropic Price Increases