Anthropic has released Claude Code v2.1.285, focusing on security controls and bug fixes. Key additions include an environment variable to disable web fetching and settings to limit API providers. The update resolves numerous issues with cloud session synchronization, artifact publishing conflicts, and SSH plugin installations. These changes improve stability for enterprise users managing permissions and remote workflows.
Read originalAnthropic quietly upgrades its local coding agent with Sonnet 5.5 as the new default, bringing a massive 1M context window to developers' terminals. This isn't just a model swap; it fundamentally changes how much codebase history you can keep in memory without manual chunking. The release also patches critical stability issues like malformed image crashes and broken MCP reconnections, making the tool significantly more reliable for complex workflows. For builders, this means deeper context awareness and fewer interruptions during long coding sessions.
This release stabilizes Claude Code by fixing a cascade of session-breaking errors that previously caused silent data loss or API drops. The most significant fix addresses resumed conversations re-sending messages in altered forms, which was corrupting reasoning traces and breaking extended thinking workflows. It also resolves persistent login refresh loops and managed setting parsing failures that plagued enterprise deployments. While the changelog is dense with UI tweaks like scrollbar fixes and vim mode corrections, the core value lies in restoring reliability for long-running agent sessions.
This release targets a specific but costly bottleneck in Mixture-of-Experts inference on GPUs. The previous tile selection logic wasted significant compute time by misjudging the active workload per expert during dispatch. By correcting how matmul tiles are assigned, the patch ensures workers stay busy instead of idling. This is a quiet optimization that directly improves throughput for large MoE models running on Vulkan backends.
Intel's discrete GPUs have long been second-class citizens in local inference due to inefficient memory access patterns. This patch fixes that by batching F32 matrix loads two at a time, squeezing significant throughput out of the B60 architecture. Benchmarks show raw GFLOPS jumping from 153 to 221 on specific shapes, proving that driver-level optimizations matter as much as model architecture. It’s a quiet but necessary fix for anyone running llama.cpp on AMD or Intel hardware.