Models & Labs
GitHub Deprecates GPT-5.2 Models in Copilot
GitHub Changeloghigh confidence
Why it matters
- →Deprecation of GPT-5.2 models indicates a shift towards newer AI models in GitHub Copilot.
- →Users must adapt their workflows to integrate alternative models, impacting development processes.
- →This move reflects GitHub's commitment to evolving its AI tools for better performance.
GitHub has announced the deprecation of GPT-5.2 and GPT-5.2-Codex models across most Copilot experiences as of June 5, 2026. These models will no longer be available for Copilot Chat, inline edits, and code completions, though they remain accessible for Copilot code review. Users and administrators are advised to update their workflows and enable alternative models through Copilot settings. This change is part of GitHub's strategy to refine its AI offerings and encourage the use of newer models.
Read originalMore from GitHub Changelog
More in Models & Labs
Models & Labsmodels
llama.cpp b10955 release addresses heap corruption
The latest release of llama.cpp, b10955, tackles a critical issue of heap corruption by disabling the ggml-cpu precompiled header and fixing CACHE_LINE_SIZE ambiguity. This update ensures consistent CACHE_LINE_SIZE values across C++ kernels and C work-buffer sizing code, preventing heap-buffer-overflow and subsequent crashes. By restoring the natural include order and removing the std::hardware_destructive_interference_size branch, the update makes the value deterministic and include-order independent. This release is a technical fix that stabilizes the runtime environment for developers using llama.cpp.
llama.cpp Releases
Models & Labsmodels
llama.cpp b10956 release enhances SYCL backend
The latest llama.cpp release, b10956, introduces significant improvements to the SYCL backend, particularly for handling large k values in TOP_K operations. By implementing a radix select method, the update allows for efficient GPU-resident processing, avoiding previous limitations that forced operations to fall back to the CPU. This change enhances performance, especially in scenarios requiring large k values, such as qwen4exp's sparse-attention indexer. The update ensures that operations are more efficient and scalable, providing a notable boost in processing speed without regressing any measured shapes.
