
GitHub has launched a public preview of its Copilot integration with Slack, enhancing collaborative coding efforts. This integration allows users to initiate agent sessions directly from Slack by mentioning @GitHub, enabling them to plan changes, investigate issues, and manage coding tasks. The integration supports various functions, such as answering code-related questions and opening pull requests, all while maintaining GitHub's existing permissions and controls. Available to organizations on GitHub Copilot Business and Enterprise plans, this feature aims to streamline workflows and improve team collaboration.
Read originalEarlier coverage that leads up to this article, and what followed. Lines connect each piece to the closest one after it, converging here.
Matt Wolfe · June 10, 2026 · Same story
GitHub Changelog · August 21, 2026 · Same story
GitHub Changelog · September 25, 2026 · Same story
Slack introduces collaborative vibe-coding channels
5 developments
© GitHub ChangelogAnthropic’s latest lightweight model is now live inside GitHub Copilot, targeting high-volume coding tasks like subagents and terminal work. Early benchmarks suggest it matches Sonnet 5 on many coding challenges while consuming significantly fewer tokens and steps. This availability across VS Code, JetBrains, and mobile apps gives developers a faster, cheaper option for routine edits without sacrificing quality. The gradual rollout means most users will see it soon, with admin controls allowing enterprises to manage access via model policies.
© GitHub ChangelogGitHub is replacing its regex-based secret scanning with a purpose-built AI model that understands code context to catch unstructured credentials like passwords without standard token formats. This shift moves security from pattern matching to semantic understanding, catching leaks that traditional tools miss before they hit repository history via push protection. The upgrade is automatic for existing GHSP/GHAS customers, but new opt-in features in Copilot and push protection will consume AI Credits, introducing a usage-based cost layer to what was previously free. This marks a significant pivot in how developers handle security, blending LLM capabilities directly into the CI/CD pipeline.
© GitHub ChangelogGitHub Copilot finally addresses the security risks of autonomous coding by introducing local sandboxing across its CLI, app, and VS Code extensions. Powered by Microsoft’s MXC technology, this feature creates a strict execution boundary that restricts agent access to files, networks, and credentials based on developer-defined policies. This is a critical step for enterprise adoption, allowing organizations to enforce security controls without blocking the utility of agentic workflows. It shifts Copilot from a passive assistant to a controlled autonomous actor with clear operational limits.
© The Verge AIMicrosoft is selling a dedicated AI development rig for $5,999, targeting developers who need local inference power without building their own hardware. The device pairs Nvidia’s Arm-based RTX Spark platform with 128GB of unified memory, enabling it to run models exceeding 120B parameters on-device. It ships pre-configured with Windows 11 Pro and essential dev tools like VS Code and GitHub Copilot, effectively bundling the software stack with the silicon. This moves local AI from a DIY enthusiast project to a standardized enterprise-grade appliance, albeit at a premium price point that limits it to professional workflows rather than consumer hobbyists.
This release significantly tightens the security model for Claude Code plugins by exposing server tool IDs and approval ceilings to hook functions, allowing developers to build more granular permission checks. It also stabilizes long-running agent sessions by fixing critical bugs in subagent resume logic and scheduled task persistence after compaction. For plugin authors, the new validation flags ensure gating hooks are properly configured before deployment. These changes make the platform safer for enterprise use while reducing friction for complex automated workflows.
This release quietly closes the hardware gap for local inference by adding default support for CUDA 13 and ROCm 10.0 alongside existing CUDA 12 builds. NVIDIA users can now leverage newer driver stacks without manual configuration, while AMD GPU owners finally get first-class parity with the same ease of use previously reserved for CUDA. Apple Silicon KleidiAI is disabled in this specific build, a notable regression for Mac users who rely on that optimization. The inclusion of Snapdragon and OpenVINO binaries further broadens the reach to edge devices and Intel hardware. It’s less about new features and more about llama.cpp solidifying its position as the universal runtime for every major accelerator.