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ai deep-dive

Uncle Bob Doesn't Read His Agents' Code: What He Runs Instead of Code Review

Uncle Bob's 4.18M-view post of 2026/7/23 isn't a manifesto — it's a reply to an engineer who started in 1983 asking whether needing to understand code psychologically makes him old-fashioned. And he doesn't skip the code entirely: his 6/1 four-stage pipeline post says 'I spot check the code,' with thresholds of crap ≤ 6 (convention is 30) and mutation runs that kill all survivors. Plus a breakdown of his open-sourced Acceptance-Pipeline-Specification and the three metric blind spots Grady Booch names.

ai guide

Where AI Code Review Stands Now: Lessons from Cloudflare's Multi-Agent System

Cloudflare ran a Multi-Agent Code Review system internally for 30 days — 131K reviews, median 3 minutes. This post breaks down their architecture and compares it with solutions from Anthropic, GitHub, CodeRabbit, Greptile, and others.

tech guide

How to Classify Code Review Comments? From Conventional Comments to AI Review Tool Taxonomies

Three main classification systems dominate: Conventional Comments (label-based), Google's severity prefixes (Nit/Optional/FYI), and SonarQube's four quadrants (Bug/Vulnerability/Code Smell/Hotspot). AI review tools have each developed their own taxonomies, but the core dimensions consistently converge on four areas: correctness, security, performance, and maintainability.