Anthropic released Claude Code v2.1.276 to resolve a regression introduced in v2.1.275 that caused all API requests to fail with a 400 error. The failure was triggered when the ANTHROPIC_BASE_URL environment variable pointed to a proxy or gateway, due to an invalid input tag 'advisor_20260301'. This update restores connectivity for users operating behind corporate firewalls or custom routing infrastructure.
Read originalThis release stabilizes Claude Code by fixing crashes that occurred when resuming sessions with malformed transcripts or memory files. It also improves the developer experience by syncing skills and plugins from your claude.ai account to terminal sessions, ensuring consistency across environments. Security is tightened by preventing install scripts from running on npm-sourced plugins, while usability sees gains in artifact publishing and image handling. The update addresses critical edge cases that previously broke workflows, making the tool more reliable for daily use.
This release stabilizes the core agent loop by fixing critical bugs in prompt caching and subagent resume logic that previously broke context reuse. Plugin management gets a significant upgrade with dynamic folder scanning, allowing developers to hot-load tools without restarting the session. The update also hardens security around symlink traversal and refines telemetry routing for enterprise gateways. While not feature-heavy, these fixes make Claude Code more reliable for complex, multi-step coding workflows.
This release quietly extends llama.cpp's hardware support to the latest NVIDIA and AMD stacks. By shipping native binaries for CUDA 13 and ROCm 10, it ensures compatibility with newer GPU architectures without requiring users to compile from source. The inclusion of both CUDA versions side-by-side is a pragmatic move for enterprise environments managing mixed infrastructure. While no new model formats are introduced, this update keeps the runtime relevant as hardware vendors push their latest drivers.
This release quietly cements llama.cpp as the universal inference runtime by finally supporting NVIDIA's latest CUDA 13 stack alongside AMD's ROCm 10. For the first time, users on cutting-edge hardware can run local models without being forced into legacy driver versions or waiting for vendor-specific optimizations. The inclusion of both CUDA 12 and 13 binaries side-by-side removes a major friction point for developers managing mixed environments. While no new model architectures are added, this infrastructure update ensures compatibility with the fastest consumer and data center GPUs hitting the market right now.