
GitHub has announced enhancements to its Copilot code review tool, now in public preview. The update introduces agent skills and MCP support, allowing Copilot to integrate with team-specific tools and standards. A new medium analysis tier is also available, routing complex pull requests to a higher-reasoning model for deeper analysis. These features aim to streamline code reviews by reducing reliance on senior engineers and aligning review intensity with code complexity. The update is available for Copilot Pro, Pro+, Business, and Enterprise users.
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GitHub Changelog · July 29, 2026 · Same story
GitHub Changelog · August 27, 2026 · Same story
GitHub Copilot Introduces Usage-Based Billing
5 developments
Anthropic is tightening control over Claude Code's autonomous behavior with new environment variables that let you disable 'ultrathink' or cap subagent effort levels. For teams, the introduction of pooled group budgets on the gateway allows shared monthly spending limits across members, a practical fix for enterprise cost management. The release also patches critical bugs where shell commands would hang or crash on exit, stabilizing the developer experience significantly.
This release quietly cements llama.cpp as the universal inference runtime by finally bringing ROCm 10.0 to Linux and Windows alongside CUDA 13.4, effectively closing the hardware gap for AMD users who were previously left behind. The inclusion of native Snapdragon support on Linux marks a significant step toward running local LLMs on ARM-based mobile chips without emulation overhead. While KleidiAI on Apple Silicon is disabled in this build, the broader expansion to diverse accelerators ensures that developers aren't locked into NVIDIA ecosystems anymore.
This release addresses a specific inference artifact where decision prefixes were incorrectly bleeding into output embeddings. By capping shared prefixes at the first output position, llama.cpp ensures that option tokens remain distinct for parent and child tasks. This is a targeted fix for multi-step reasoning or voting workflows rather than a performance boost. It stabilizes complex generation patterns without changing the underlying model architecture.