Qwen

Skycode: Custom VS Code Extension Optimizes AI Coding Agent for Corporate Closed-Loop LLMs

A developer has released Skycode, an open-source VS Code extension that enables AI-assisted coding inside corporate closed environments using internal models such as Qwen. The project addresses strict data residency requirements in sectors like energy, where external services like GitHub Copilot or Cursor are prohibited because code would leave the perimeter. Key engineering efforts focused on aggressive context preservation within the model's roughly 100k token limit through incremental file reading, symbol-level extraction, history compression, and visual context monitoring. Additional features include tab-based parallel task handling, local voice dictation powered by Sber's GigaAM v3, and safeguards against repetitive loops or truncated file writes. The extension approach was chosen over forking the entire VS Code codebase to simplify deployment and compliance reviews, as extensions can often be installed from the Marketplace without separate approval. Skycode is positioned as a practical alternative for organizations running their own LLMs rather than relying on cloud models.

Building Secure On-Prem AI Assistants: How to Keep Corporate Data Inside Closed Contours

Many organizations hesitate to deploy AI assistants due to strict data protection rules that prohibit sending information to external clouds. The article explains how to implement AI models entirely within a company's own infrastructure, ranging from on-premise servers to fully offline laptops. It breaks down four deployment locations from public APIs to local devices and clarifies three distinct access levels: read, write, and execute. The author emphasizes that most business value comes from read-only access combined with human-in-the-loop controls for any irreversible actions. Practical recommendations include RAG over model size, quantization for local hardware, and maintaining immutable audit logs. The piece also warns that preparing clean knowledge bases often consumes more effort than the model itself.

Russian Businesses Surge Chinese LLM Usage More Than 11-Fold in First Half of 2026

Russian companies have dramatically increased their consumption of Chinese large language models, with total token usage reaching over 400 billion in the first half of 2026. This represents an 11.3-fold increase compared to the full-year 2025 figure of 39.1 billion tokens. The Qwen family maintained its dominant position, accounting for 261.1 billion tokens and 59.1% of consumption during the period. GLM secured second place with 91.2 billion tokens and 20.7% share, while Kimi overtook DeepSeek for third position at 81.4 billion tokens and 18.4%. MWS Cloud data shows that major enterprises are now integrating these models into production workflows such as chatbots, personalized advertising, customer outreach, product descriptions, and review analysis. The overall Russian LLM market is projected to grow 35% to 19.6 billion rubles in 2026, with the cloud segment approaching 1.5 billion rubles. MWS Cloud has nearly doubled its Model Hub catalog to 17 models, adding GLM 5.2, Kimi K2.6, Qwen3.6, Gemma 4, GPT OSS, plus speech recognition, synthesis tools, and rerankers accessible via a unified OpenAI-compatible API.