llama.cpp has released version b11000 to address a critical remote code execution vulnerability in its RPC server component. The bug allowed unauthenticated clients to trigger use-after-free conditions by freeing buffers referenced by cached compute graphs, leading to heap corruption and potential system takeover. The fix ensures all cached graphs are discarded when associated buffers are freed, forcing the client to recompute rather than reuse stale pointers. This update is essential for operators exposing the llama.cpp server over a network.
Read originalA copy-paste error in llama.cpp was corrupting matrix transpositions on Spacemit hardware, causing significant data corruption for int16 operations. This release patches the specific RVV instruction call to ensure correct computation on these RISC-V based chips. While niche, it prevents silent inference failures for users relying on this specific accelerator architecture. The update also ships binaries for CUDA 13 and ROCm 10.0, keeping the runtime current with latest driver ecosystems.
This release quietly closes the hardware gap for local inference by adding native builds for CUDA 13 and ROCm 10.0 alongside existing CUDA 12 binaries. Users with newer NVIDIA GPUs or AMD accelerators no longer need to compile from source to get hardware acceleration, while Apple Silicon support remains available but explicitly disabled in this specific binary set. The simultaneous availability of these distinct backend versions means the runtime is now truly agnostic to the underlying GPU architecture. It is a pragmatic update that removes friction for anyone trying to run models on modern, non-CUDA-12 hardware.
llama.cpp now runs the DFM Mimir 1B model, a rare example of a hierarchical recurrent transformer that alternates between two distinct processing cycles. This isn't just another standard decoder; it requires handling aliased cache slots and looped graph builds, pushing the runtime's architectural flexibility further than usual. While the decode speed is roughly four times slower than a dense model of similar width due to these 128 block passes, local inference for this specific architecture was previously impossible on consumer hardware. This release effectively opens the door for experimenting with non-standard transformer topologies in open-source environments.
This release targets the friction points that make local AI coding feel fragile. The most critical fix addresses MCP servers timing out after five minutes regardless of configuration, a major blocker for complex agent workflows. Session reliability also improves with self-healing corrupted transcripts and better handling of background agents during resumption. While not feature-heavy, these patches stabilize the environment for developers relying on long-running automated tasks.
© GitHub ChangelogGitHub Enterprise Cloud finally addresses the friction of manual SSO authorization for classic tokens and SSH keys. Admins can now delegate bulk authorization to GitHub Apps via a new API, handling up to 50 organizations in one request without exposing secrets. This shift from manual per-org clicks to automated delegation reduces the temptation to use insecure long-lived tokens. It is a practical infrastructure improvement that streamlines credential rotation for large enterprises managing complex access controls.
© GitHub ChangelogGitHub finally aligns its SCIM implementation with RFC 7643 by adding the profileUrl attribute to user responses. This small but necessary change eliminates the need for identity providers to perform extra lookups or infer account links when provisioning access. The update is additive, meaning existing integrations remain unaffected while new ones can now rely on a standardized field. It’s a quiet fix that reduces friction for enterprise IT teams managing GitHub access at scale.