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GitHub Enterprise adds credential inventory exports
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Why it matters
- →Centralizes visibility into all enterprise credentials for faster incident response.
- →Enables automated auditing via a new paginated REST API endpoint.
- →Reduces security risk by exposing unused or expired tokens across the organization.
GitHub Enterprise Cloud now allows owners to export a comprehensive inventory of all credentials, including SSH keys, personal access tokens, and OAuth app tokens. The feature provides a single-pane view of credential metadata such as scopes, expiration dates, and last-used timestamps, accessible via a new paginated REST API or CSV export. This capability enables security teams to quickly assess risk exposure and correlate credentials with audit logs during incident response. The update is available immediately for GitHub Enterprise Cloud.
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GitHub PR page GA with bulk actions
GitHub’s redesigned pull requests interface has moved from preview to general availability, shifting the focus from basic listing to active triage. The update brings bulk actions like closing or labeling multiple PRs simultaneously, a feature long requested by maintainers drowning in review queues. Advanced search now supports nested logic and AND/OR operators, while collapsible sidebars and compact modes help developers scan more context without endless scrolling. This isn't a new AI model, but it directly addresses the workflow bottleneck of code review management for millions of developers.
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Grok 4.7 arrives in GitHub Copilot for agentic coding
xAI’s Grok 4.7 has landed in GitHub Copilot, shifting the IDE experience from simple autocomplete to complex, multistep reasoning workflows. This isn't just another model swap; it targets agentic coding tasks that require holding context across multiple files and steps. Available across all major IDEs including VS Code, JetBrains, and Xcode, it brings xAI’s latest capabilities directly into the developer’s primary workspace. The gradual rollout means most users will see it soon, with enterprise admins able to manage access via policy settings. This marks a significant step in making non-OpenAI reasoning models a first-class citizen in the dominant coding environment.
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GitHub Copilot Code Review GA with Auto-Resolution
GitHub Copilot’s code review tool has reached general availability, shifting from experimental to a core part of the pull request workflow. The biggest leap is auto-resolution: Copilot now validates whether its own suggestions were actually fixed by subsequent commits and closes them out automatically, saving developers from manual cleanup. It also groups findings into clear states like 'Resolved' or 'Previously missed,' giving a real-time health check of the PR rather than just a static list of errors. This reduces context switching significantly, letting engineers focus on new issues while the AI handles the administrative burden of closing old ones.
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llama.cpp adds env var control for server args
This release solves a real operational headache: configuring llama-server parameters like temperature and penalties via environment variables instead of command-line flags. By supporting LLAMA_ARG_* prefixes, it enables clean integration with systemd EnvironmentFile directives, making deployment on Linux servers significantly more robust and reproducible. The update also refreshes documentation using llama-gen-docs to keep the readme accurate. While no new inference engines are added, this is a practical step toward treating local LLM serving as standard infrastructure rather than a manual script.
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llama.cpp b11090 fixes CUDA Volta build
This release quietly repairs a regression in the CUDA backend that broke compilation on older Volta GPUs. By generalizing the tile shape logic in the load_ldmatrix kernel, the maintainers restored support for devices like the Tesla V100 without sacrificing newer architectures. It is a necessary patch for anyone running inference on legacy hardware, ensuring the codebase remains usable across the full spectrum of NVIDIA cards rather than drifting toward only the latest silicon.
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llama.cpp b11093 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 alongside existing CUDA 12 builds. Users with newer NVIDIA GPUs or AMD accelerators no longer need to compile from source to get hardware acceleration, effectively making llama.cpp the universal runtime for consumer and enterprise AI chips. The simultaneous availability of both major driver stacks means developers can target the latest hardware without maintaining separate build pipelines. This standardization removes a significant friction point for anyone deploying models on modern, non-CUDA-12-only infrastructure.
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