This is where the principle applies TTU - time to use: the value of a tool is not how intelligent it is on a benchmark, but how much time passes from task definition to a working result. GitHub got a working Rust runtime through a process built around the model: planning, breaking work into subtasks, review cycles, and reverting failed attempts.
What happened
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GitHub announced that the agent engine powering Copilot CLI, the Copilot application, and the Copilot SDK was rewritten from TypeScript/Node.js/V8 to Rust—more than 800,000 lines of production code.
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Copilot performed the rewrite itself: agents wrote most of the code.
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At the same time, a batch of ecosystem changes was released: automatic closure of code review comments, enterprise-level allowlists for MCP servers, access to Claude Opus 5 in Copilot, and the transition of GitHub MCP Server to a new protocol.
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Each of these events is routine on its own.
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Together, they show that AI tools in development have moved from the demo phase to the infrastructure phase, where they face production-level requirements.


