Coding Tools
GitHub Copilot App Now Supports Any Model Provider
Matt Wolfehigh confidence
Why it matters
- →Expands developer choice in AI model selection.
- →Increases flexibility and customization in coding workflows.
- →Represents a strategic shift in AI tool integration.
GitHub has introduced a new feature in its Copilot app that allows users to select from any AI model provider, rather than being restricted to OpenAI models like Codex. This update significantly enhances flexibility for developers using GitHub Copilot, enabling them to integrate a wider range of AI models into their coding workflows. The app's release marks a notable shift in how developers can leverage AI tools, potentially broadening the scope of AI-assisted coding.
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.
What happened next
- 1GitHub Copilot App Now Generally Available
GitHub Changelog · June 17, 2026 · Same story
- 2GitHub Copilot Expands Model Options and Features
GitHub Changelog · September 4, 2026 · Same story
Follow this story
Open the full story →GitHub Copilot Sandboxes Enter Public Preview
More in Coding Tools
Coding Toolscoding
Claude Code v2.1.323 adds experimental Code Mode and plugin safety
This release quietly introduces Code Mode, an opt-in feature where Claude writes JavaScript to call its own tools, marking a shift toward more autonomous agent-like behavior within the CLI. The update also hardens the plugin ecosystem by enforcing version ranges and adding strict TypeScript type checking for hooks, which should significantly reduce instability from third-party extensions. Beyond these features, the changelog is dominated by critical fixes for authentication drift in background sessions and memory leaks when handling large files, stabilizing the tool for heavy enterprise use.
Claude Code Releases
Coding Toolscoding
llama.cpp b12336 fixes HunyuanVL crash
This release quietly patches a critical memory overflow in the mtmd module that caused crashes when loading HunyuanVL models. The issue stemmed from F64 position embeddings overflowing an F32 buffer, a subtle bug that broke multimodal inference for users relying on this specific architecture. By enforcing strict F32 requirements, llama.cpp stabilizes support for Tencent's vision-language model without adding new features or changing the core API. It’s a necessary maintenance update that keeps local multimodal workflows from breaking unexpectedly.