What GitHub learned when better tools made Copilot code review worse

<p>"The Reasoning Trap." The line the authors won't quite say out loud is that the smarter your model gets at reasoning, the more likely it is to fabricate a tool that doesn't

<h2> The Numbers Don't Lie </h2> <p>The AI Engineering Report 2026 analyzed telemetry from 22,000 developers across more than 4,000 teams. The headline metrics look impressive:

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<ul> <li>GitHub repository: <a href="https://github.com/gezilinll/agent-grove" rel="noopener noreferrer">https://github.com/gezilinll/agent-grove</a> </li> <li>Online

<h2> The state of AI code review in 2026 - an industry that grew up fast </h2> <p>Two years ago, AI code review was an experiment. A handful of early-stage startups were trying to

<h2> Introduction: The Moment Manual Coding Became "Optional" </h2> <p>In January 2026, Boris Cherny, head of Claude Code at Anthropic, made a statement that reverberated across

<p>How migrating Copilot code review to shared Unix-style code exploration tools reduced review cost by reshaping agent workflows around pull request evidence.</p> <p>The post <a

<p>Your agent passed all the tests. It's been running in production for three weeks. And it has been quietly wrong the entire time.</p> <p>This is not a hypothetical. Galileo's