
GitHub has introduced new features to give organizations more control over their GitHub-hosted runners in Actions, focusing on macOS environments. Admins can now disable standard labels for hosted runners and add macOS runners to runner groups, allowing for more precise management of resources. This update enables organizations to restrict access to macOS runners, enforce concurrency limits, and route jobs through policy-driven runner groups. These enhancements are available on Team and Enterprise plans, although network configurations for macOS runners are not supported.
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GitHub Changelog · June 12, 2026 · Related
GitHub Changelog · September 29, 2026 · Same story
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.