
GitHub has announced that Claude Haiku 4.5 will be deprecated across all Copilot experiences, including Chat, inline edits, and agent modes, effective December 14, 2026. The deprecation applies to Copilot Business and Enterprise customers, though individual subscribers on annual plans are exempt. Administrators must update model policies to ensure access to supported alternatives, as the default model enablement will shift automatically unless manually disabled. GitHub Enterprise customers are advised to contact their account teams for assistance with this transition.
Read originalEarlier coverage that leads up to this article, and what followed. Lines connect each piece to the closest one after it, converging here.
GitHub Changelog · August 31, 2026 · Same story
GitHub Changelog · October 7, 2026 · Same story
Claude Code Releases · October 10, 2026 · Related
Matt Wolfe · October 19, 2026 · Related
© GitHub ChangelogThe GitHub Copilot desktop app finally reaches parity with the web interface for enterprise users. Version 1.2.0 makes GitHub Enterprise Server support generally available, allowing developers to connect their GHES accounts directly within the IDE-like environment. This closes a significant gap for organizations that cannot or will not use GitHub.com, enabling remote repository and pull request workflows without leaving the app. While cloud sessions remain unavailable for these instances, local inference on enterprise code is now fully integrated.
© GitHub ChangelogGoogle’s latest Flash model is now generally available in GitHub Copilot, targeting agentic coding and multistep workflows. It offers improved instruction following over Gemini 3.8 Flash and is billed at provider list pricing under usage-based billing. The rollout is gradual across Visual Studio Code, JetBrains IDEs, and other platforms. Administrators can manage access through model policies in Copilot settings.
© GitHub ChangelogGitHub finally gives developers programmatic control over AI-powered code scanning at the repository level. By adding a repository_scan property to existing endpoints, organizations can now automate enablement or disablement via scripts and infrastructure as code rather than clicking through the UI one by one. This closes a critical gap for security teams managing large fleets, allowing them to enforce consistent policies across public and private repos without manual overhead. It turns AI scanning from a manual toggle into an automated governance lever.
This release prioritizes stability over new features, addressing a critical zstd decompression vulnerability that could have exposed response data. Beyond security, it significantly hardens the reliability of Model Context Protocol (MCP) integrations, fixing persistent connection drops and authentication loops that plagued background tasks. The update also resolves context compaction failures on third-party gateways, ensuring sessions no longer crash when hitting token limits. For developers relying on Claude Code for complex workflows, this patch removes several friction points that previously interrupted automated coding sessions.
This release targets the persistent performance gap on AMD hardware by optimizing Vulkan Flash Attention specifically for RDNA3 and RDNA4 architectures. The changes include reducing shared memory usage through buffer reuse and fixing critical stability issues like NVIDIA hangs and sparse FA underflow bugs. While there are no new model architectures, these low-level shader optimizations directly translate to faster inference speeds for local users on modern AMD GPUs. It is a quiet but necessary engineering update that tightens the ecosystem for non-NVIDIA accelerators.
This release quietly extends llama.cpp’s hardware support to the latest NVIDIA and AMD stacks. By shipping binaries for CUDA 13.5 and ROCm 10.0, it ensures users on cutting-edge GPUs don't have to compile from source to get performance gains. The Vulkan build also fixes header inclusion issues that previously broke compilation on some Linux distributions. It’s a maintenance update rather than a feature leap, but it keeps the local inference runtime compatible with the fastest consumer and datacenter hardware available today.