Anthropic released Claude Code v2.1.278, updating the default behavior of auto mode for API, Enterprise, and gateway users. The update shifts the classifier execution to the server side by default, removing the associated overhead costs from user bills. A new row in the /status command indicates whether the server-side classifier is active. Users on Bedrock, Vertex, and Foundry can opt out using the CLAUDE_CODE_AUTO_MODE_SERVER=0 variable.
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This release quietly introduces Code Mode, an opt-in feature where Claude writes JavaScript to call its own tools, marking a shift toward more autonomous agent-like behavior within the CLI. The update also hardens the plugin ecosystem by enforcing version ranges and adding strict TypeScript type checking for hooks, which should significantly reduce instability from third-party extensions. Beyond these features, the changelog is dominated by critical fixes for authentication drift in background sessions and memory leaks when handling large files, stabilizing the tool for heavy enterprise use.
This release quietly shifts the default Fable model to version 5.2 with a generous 1M context window, making long-context coding significantly more viable without manual configuration. More critically, it addresses several security gaps in MCP OAuth handling and sandbox permission checks that could have exposed sensitive tokens or bypassed safety protocols. The update also stabilizes Remote Control and background agent interactions, fixing issues where sessions would hang or lose state during network interruptions. For developers relying on Claude Code for complex, multi-step workflows, this patch reduces the friction of managing long-running tasks and ensures safer execution environments.
This release quietly patches a critical memory overflow in the mtmd module that caused crashes when loading HunyuanVL models. The issue stemmed from F64 position embeddings overflowing an F32 buffer, a subtle bug that broke multimodal inference for users relying on this specific architecture. By enforcing strict F32 requirements, llama.cpp stabilizes support for Tencent's vision-language model without adding new features or changing the core API. It’s a necessary maintenance update that keeps local multimodal workflows from breaking unexpectedly.
This release quietly extends llama.cpp’s hardware coverage with ROCm 10.0 binaries for both Linux and Windows, finally bringing AMD RDNA/CDNA users into the fold without manual compilation. The standout feature is native support for Qwen3.5 models via tensor merging during conversion, a necessary step as this new architecture gains traction in the open-source community. By shipping pre-built CUDA 13 binaries alongside the older 12.x versions, the project ensures forward compatibility with newer NVIDIA GPUs out of the box. There are no new quantization algorithms or performance breakthroughs here; it is purely an infrastructure update to keep pace with the rapidly shifting landscape of model architectures and GPU drivers.
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