Claude VS Code Extension Broken on Windows After Hardcoded Linux Path in Recent Update
A Reddit user reported that the Claude VS Code extension is broken on Windows after a recent update pushed approximately 2 hours ago. The update hardcodes a Linux path into the extension, causing it to fail entirely on Windows and potentially other non-Linux operating systems. The user resolved the issue by downgrading the extension, stating that the previous version works correctly.
This appears to be an oversight in the extension's path handling, where a platform-specific path (likely using forward slashes or /home/ style) was not wrapped in a cross-platform abstraction. For developers using the Claude extension on Windows, the workaround is to downgrade to the prior version until Anthropic releases a fix. To downgrade, you can install a specific version via VS Code's extension menu: select the Claude extension, click the gear icon, choose 'Install Another Version...', and pick a version prior to the broken update.
This issue highlights the importance of testing cross-platform paths before shipping, especially for tools that invoke local executables or access filesystem resources. If you rely on Claude AI assistance within VS Code on Windows, avoid updating the extension for now or pin to a known working version.
📖 Read the full source: r/ClaudeAI
👀 See Also
SDNY Ruling Denies Attorney-Client Privilege for AI Chat Communications
Judge Rakoff ruled in U.S. v. Heppner that communications with AI tools like ChatGPT do not qualify for attorney-client privilege, requiring disclosure of all AI-generated legal work. The court found AI lacks the human confidentiality required for privilege protection.
Hy3 LLM Tops OpenRouter Rankings: Cheapest Model or Something Else?
Hy3 preview, a Tencent open-source LLM, surged to the top of OpenRouter's model rankings by token usage, surpassing Claude and DeepSeek V4 Flash. Priced at $0.066/1M input tokens, it's the cheapest major model, but benchmarks show quality far below leaders.
Bonsai 1.7B Ternary Model Hits 442 T/s on M4 Max with Autonomously Tuned Metal Kernels
Autonomous agent ata optimized Metal kernels for Bonsai 1.7B Q2_0, achieving 442 t/s decode (+42%) and 4622 t/s prefill (+9%) on M4 Max vs unmodified llama.cpp.
Developer Prefers Qwen3.5-27B Over Proprietary Models for Its Failure Mode
A developer on r/LocalLLaMA reports preferring Qwen3.5-27B over Gemini 3.1 Pro and GPT-5.3 Codex because it gives up on problematic tasks rather than generating potentially dangerous code like unrestricted Perl or NodeJS scripts.