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AI Coding Agents & Skills Auto-verified translation

How the Claude Code team does code review

Engineering post by **Addy Osmani**, **Morgan Westlee Lunt**, and **Qing Wang** (the **Claude Code** team, **Anthropic**) on the *claude.dev* blog, published **October 15, 2026**; ~3,200 words. It describes the team's code review flow, in which Claude writes roughly **90%** of the code for Claude Code. From June to September, merged PRs **more than doubled** and the reverted share dropped by **more than 40%**; in Anthropic's monorepo, daily changes rose from **~2,500** (May) to **more than 30,000** (September).

#code review#code review#agentic code review

Addy Osmani · Morgan Westlee Lunt · Qing Wang — équipe Claude Code (Anthropic) ; blog *claude.dev* · catégorie Engineering.

Quality & Security Auto-verified translation

What are code reviews even for?

Issue of the **Engineering Enablement** newsletter from **DX** signed by **Brian Houck**, published on **August 5, 2026**, approximately **13,000 characters**. The text opens with a quantified observation: at **Meta**, significant lines per diff merged by a human increased by **106%** in one year and diffs per developer per month by **51%**, with more than **80%** of this growth coming from agentic AI, while the share of diffs reviewed within 24 hours declines; DX's analysis for its part measures a median pull request size up **64%**, while developers want to devote about **7%** of their time to review. (A) Before handing review over to AI, Houck reframes the usage question: *"What problem was code review solving before AI arrived?"* If the answer is defect detection, full automation becomes inevitable; the **Bacchelli & Bird** study (*Modern Code Review*, ICSE 2013) shows that defect-related comments account for only **14%** of comments actually written. (B) Part of the burden is *self-inflicted*: the 2016 Microsoft study of **911 developers** already identified turnaround time, review size, and understanding the motivation for the change as the top three obstacles, and only **26%** still wrote a detailed description. (1) **RADAR** at Meta serves as the reference deployment for risk stratification. (2) Three ordered moves: fix the fundamentals, design AI around human judgment, protect what review invisibly produces. The corpus already includes [[monperrus-end-of-code-review-agents-supersede-2026-06-11]], which pushes automation to the point of replacement, and [[laycock-thoughtworks-reviewing-all-this-code-2026-09-02]], the direct response to this text.

#code review#code review#review queue

Brian Houck · signataire de l'édition · sur la newsletter Engineering Enablement de DX ; fonction non affichée par la page.