
GitHub has announced the deprecation of the all-platform CodeQL bundle (codeql-bundle.tar.gz/zst) starting with CLI version 2.27.0. The unified archive will be permanently removed in mid-March 2027, requiring users to download platform-specific bundles instead. Notably, Linux ARM64 binaries are already exclusive to these specific downloads and were never part of the legacy all-platform package. This shift necessitates updates to automated workflows that previously relied on a single universal binary.
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© GitHub ChangelogAnthropic’s latest flagship model is finally inside the IDE, marking a significant shift for enterprise developers who rely on GitHub Copilot. Early benchmarks suggest Opus 5.5 matches its predecessor’s accuracy while consuming fewer tokens and recovering faster from errors in complex agentic workflows. This efficiency gain matters because long-running coding tasks often hit context limits or incur high costs; reducing step count directly lowers friction for multi-file refactoring. The gradual rollout across major IDEs means builders can soon test whether this model handles their most stubborn debugging sessions better than the previous standard.
© GitHub ChangelogOpenAI is widening the GPT-6 lineup inside GitHub Copilot by adding two distinct models: Sol for complex agentic coding and Luna for fast, cheap tasks. This moves Copilot away from a single default toward a tiered strategy where developers can explicitly choose between heavy reasoning and lightweight efficiency. Sol targets multistep validation in Pro+ and Enterprise tiers, while Luna opens up to the broader Pro user base as the lowest-cost option. The gradual rollout across IDEs like VS Code and JetBrains means teams can now tune their coding assistants for specific workload profiles rather than accepting a one-size-fits-all approach.
© GitHub ChangelogGitHub is finally killing the legacy ssh-rsa signature type that relies on broken SHA-1 hashes, forcing a shift to stronger rsa-sha2 signatures. Simultaneously, they are introducing mlkem768x25519-sha256, a post-quantum key exchange method, signaling early adoption of quantum-resistant standards in mainstream developer infrastructure. This isn't just a security patch; it's a forced modernization that renders older Git clients and SSH libraries obsolete for GitHub Enterprise users. The move effectively ends the era of weak cryptographic defaults for one of the world's largest code hosting platforms.
Claude Code just got its first major model upgrade with Opus 5.5 as the new default, bringing a 1M context window and aggressive pricing that reshapes local inference economics. Beyond the headline model swap, this release quietly stabilizes the background subagent system, fixing critical issues where tool lists were rebuilt instead of cached and reports were silently lost during compaction. The UI layer also sees significant polish, with mouse support in fullscreen mode and fixes for Windows terminal rendering that had plagued power users. This is less about new features and more about making the agent runtime reliable enough for heavy, multi-step workflows.
The llama-server now binds to multiple addresses, a practical upgrade for anyone running local inference behind reverse proxies or complex network setups. This change removes the previous single-address limitation, allowing flexible routing without external workarounds. While the release includes standard binaries for CUDA 13 and ROCm 10.0, the networking feature is the real differentiator here. It makes self-hosted deployments slightly more robust for power users who need granular control over traffic flow.
This release quietly cements llama.cpp as the universal inference runtime by finally bringing full ROCm 10.0 support to both Linux and Windows, closing a long-standing gap for AMD GPU users who previously had to rely on workarounds or older versions. The inclusion of CUDA 13 builds alongside CUDA 12 ensures compatibility with the latest NVIDIA driver stacks without forcing users into beta territory. Perhaps most notably, the addition of native Snapdragon support on Linux marks a significant step toward efficient AI inference on ARM-based mobile and edge devices, expanding the hardware ecosystem beyond traditional x86 and NVIDIA dominance.