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claude.dev Blog

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开发编程 The engineering blog of the Claude Code team, sharing how they review code produced at agent scale and other development practices.

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This post stands out for documenting an internal review pipeline rather than promoting a product, covering layered automated checks, reviewer tooling, and where humans remain in the loop. It is most useful for teams already generating large volumes of AI-written code who need process ideas, though i

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claude.dev Blog is the engineering blog connected to the Claude Code team. Rather than product announcements, it publishes long-form write-ups about how the team actually builds and maintains software, including this 14-minute piece on code review. The framing question is practical: when agents produce roughly 90 percent of a codebase, how does review still work, and who catches problems when something breaks?

The article walks through the team’s review model layer by layer. It describes rethinking the purpose of code review, treating human time and attention as the scarce resource, and following a change through its lifecycle. It also explains how automated coverage and deep reviews came first, and only then did the team optimize for the minimum human review time that does not increase reverts or incidents.

A second thread covers making the reviewer better and deciding where humans stay in the loop. Sections on sharing context across the team, the principle “you land it, you own it,” and when the team changes direction show how ownership and communication are handled alongside tooling. The closing sections offer adoption guidance and a look at where the process is heading.

This is aimed at software engineers, tech leads, and platform teams experimenting with AI-generated code at scale. Readers who want concrete process patterns rather than marketing claims will get the most from it. It is less relevant for non-technical audiences or small projects with low change volume.

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