Anthropic released Claude Code v2.1.289, a patch addressing stability and security regressions in the previous version. Key fixes include ensuring Bash deny and ask rules apply correctly to nested commands and environment variable prefixes, closing potential sandbox bypasses. The update also resolves terminal freezes triggered by unclosed HTML tags and fixes issues with plugin validation and UI rendering errors. Additionally, it introduces agent.spawn for teammate coordination and improves large file loading performance in the IDE plugin.
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Claude Code Releases · September 18, 2026 · Same story
Claude Code Releases · September 30, 2026 · Same story
This release stabilizes the core session management of Claude Code, specifically targeting the fragile state of resumed conversations where context or thinking traces were previously lost. It also patches critical reliability issues in the Model Context Protocol (MCP) integration, ensuring tool calls don't duplicate or hang indefinitely when remote servers misbehave. The addition of $.ui.selection() for mods and better GitHub CLI handling in cloud sessions shows a focus on developer workflow friction rather than new capabilities. These are necessary maintenance updates that make the tool more robust for heavy daily use.
Anthropic quietly fixed a critical credential leakage bug where MCP error messages were exposing raw API keys in plaintext logs. Beyond the security patch, this release stabilizes the notoriously fragile background agent system by fixing subagent hand-offs and connection stalls that previously caused silent failures. The update also tightens session management for cloud environments, ensuring large transcripts actually load instead of hanging indefinitely. It’s a maintenance-heavy release, but essential for anyone running complex, multi-step automated workflows.
This update shifts Claude Code from a simple CLI wrapper to a more extensible platform by introducing 'Claude Mods,' allowing plugins to modify deeper behavior rather than just adding tools. The inclusion of a built-in 'You should know' side agent that flags potential oversights is a notable step toward autonomous oversight within the coding workflow. Beyond features, the release addresses critical stability issues in remote sessions and significantly improves accessibility for screen reader users, making the tool more robust for enterprise and diverse developer environments.
This release tackles the notorious memory hunger of long-context inference for Qwen4-exp models by halving indexer score memory. The optimization works by computing head scores in place rather than materializing separate tensors, a change that significantly reduces VRAM pressure during heavy workloads. Beyond memory efficiency, b11372 expands hardware coverage with CUDA 13 support and Vulkan tiling for the lightning indexer. It also adds ROCm 10.0 binaries, keeping AMD users in step with NVIDIA's latest driver ecosystem. The result is a leaner runtime that handles extended contexts without hitting out-of-memory errors as quickly.
This release significantly tightens llama.cpp’s integration with Intel’s OpenVINO backend, specifically targeting Mixture of Experts (MoE) models like Qwen3.5 and Gemma-4. By fusing MoE routing and GDN normalization operations, prefill throughput on Arc GPUs jumps from 66 to over 1,600 tokens per second, effectively removing a major bottleneck for local inference on Intel hardware. The update also fixes critical stateful execution bugs that previously caused crashes or incorrect axis handling during decoding. This makes OpenVINO a far more viable option for running complex MoE architectures on consumer-grade Intel GPUs without relying on NVIDIA CUDA.
This release resolves a critical crash in Mamba SSM inference when batch cells aren't contiguous. By gathering recurrent states into a single reserve that covers every split, the engine avoids illegal graph reallocations under strict scheduling modes. It’s a quiet but essential fix for anyone running non-standard sequence lengths or complex batching logic with stateful models.