Models & Labs
Claude Sonnet 5.5 arrives in GitHub Copilot
GitHub Changeloghigh confidence
Why it matters
- →Developers get a more efficient coding assistant that reduces token usage without sacrificing performance.
- →Anthropic gains deeper integration into the dominant developer workflow via GitHub Copilot.
- →Efficiency gains may lower costs for enterprise users billed on usage-based pricing models.
Anthropic has made Claude Sonnet 5.5 generally available in GitHub Copilot, expanding its presence beyond standalone API usage. The model is optimized for efficiency, reportedly matching the performance of Claude Sonnet 5 on coding tasks while requiring fewer tokens and tool calls. It is accessible across a wide range of development environments including Visual Studio Code, JetBrains IDEs, Xcode, and mobile apps. Administrators for Business and Enterprise plans can manage access through new model policies in Copilot settings.
Read originalThe story around this
Earlier coverage that leads up to this article, and what followed. Lines connect each piece to the closest one after it, converging here.
More from GitHub Changelog
© GitHub ChangelogCoding Toolscoding
GitHub expands issue types to personal accounts
GitHub finally brings enterprise-grade planning structures to individual developers. Personal repositories now support customizable issue types and fields, allowing solo maintainers to standardize workflows with up to 25 distinct categories. This closes a long-standing gap where only organizations could enforce structured data schemas for bug tracking and feature management. The move signals that GitHub is treating personal open-source projects with the same operational seriousness as corporate codebases.
GitHub Changelog
© GitHub ChangelogMore in Models & Labs
Models & Labsother
llama.cpp b12340 adds CUDA 13 and ROCm 10.0
This release quietly closes the hardware gap for local inference by adding default support for CUDA 13 and ROCm 10.0 across Linux and Windows. Users with newer NVIDIA GPUs or AMD accelerators no longer need to compile from source to get performance, while Apple Silicon builds now ship KleidiAI disabled by default, shifting the focus back to standard ARM optimizations. The inclusion of Snapdragon support for Linux and Android marks a significant step toward on-device AI beyond the x86/ARM desktop ecosystem. It is less about new features and more about ensuring llama.cpp remains the universal runtime for every available accelerator.
llama.cpp Releases
Models & Labscoding
llama.cpp b12342 adds few-row MMQ for speculative decoding
Speculative decoding just got a significant speed boost on NVIDIA GPUs. This release introduces a specialized kernel for 'few-row' matrix multiplications, targeting the small batch sizes typical of verification passes in speculative decoding workflows. By optimizing these specific operations for quantized formats like Q8_0 and IQ4 variants, llama.cpp reduces latency where it matters most during token generation. The change is CUDA-only for now but leverages hardware capabilities from Ampere onwards, making local inference faster without requiring new model architectures or complex configuration changes.
