Qwen Code

Headlines · 6

1. 2d
   10 coding agents tested on one local Qwen 3.8 27B

   A Reddit user compared 10 coding agents using the same Qwen 3.8 27B Q4 model through llama.cpp on a MacBook Pro M5 Max with 64 GB of memory. Across five trap-containing tasks, every agent passed four or five tasks, while token consumption, completion time, cache reuse, and network behavior varied substantially; Pi used the fewest median tokens, while Qwen Code used the most. Codex CLI, Cline, OpenCode, and goose attempted an internet connection on every run, although the sandbox blocked those requests.

   r/LocalLLaMA · 🔥 1
2. 2d
   SafeAgents locally screens coding-agent tool calls

   SafeAgents is an Apache 2.0-licensed local proxy that checks coding-agent tool calls before the harness receives them. It combines two request-based checks with about 110 deterministic atif-scan detectors to identify undeclared tools, unexpected hosts, sandbox side channels, network bypasses, namespace escapes and reward-hacking attempts. An optional OpenAI-compatible judge can assess milder findings and may run locally. Flagged calls are replaced with a text notice, while --log-only mode records attempts without altering execution. The project supports OpenAI Chat Completions and Anthropic Messages, provides setup guides for 12 harnesses and reports end-to-end CI tests for six.

   r/LocalLLaMA · 🔥 0
3. 6d
   AgentSight brings local observability to AI agent trajectories

   InfoQ reports that AgentSight is the observability component of Agentic OS and runs on the user’s own machine, where it reconstructs agent trajectories and analyzes their cost, performance, and accuracy. Product documentation cited by InfoQ says the current version supports Claude Code, Codex CLI, and Qwen Code, with macOS and Linux local session collection and optional full eBPF tracing on Linux. In AgentSight’s own frontend case study, adding a reusable AGENTS.md rule reportedly reduced tokens from 36K to 8.9K, LLM steps from 18 to 8, and elapsed time from 370 to 130 seconds; this is a vendor case rather than an independent measurement.

   InfoQ 中文 AI&大模型 · 🔥 0
4. Oct 16
   Tencent Hunyuan open-sources Hy Memory 2.0, Hy-Memory-30B-A3B and MemHub

   Tencent Hunyuan announced Hy Memory 2.0 on October 16, open-sourcing three layers: the dedicated memory model Hy-Memory-30B-A3B, the memory framework SDK and the local MemHub app. Tencent says the framework handles chat, work and code memory and ranks first against open frameworks including Mem0, MemOS, EverOS and OpenViking on four benchmarks; it reports that Hy-Memory-30B-A3B, with about 3B active parameters, scored a 55.49 macro average across four memory harnesses versus 54.62 for Claude Opus 5. MemHub ships as a CLI and Mac app that can connect to roughly 10 tools such as WorkBuddy, Cursor, Codex and Qwen Code, while the model weights are published on Hugging Face under Apache 2.0, followed on October 18 by an FP8 version optimized for vLLM deployment. All performance figures are Tencent's own claims and have not been independently verified.

   腾讯混元 · Hugging Face · 🔥 0
5. Oct 12
   Sakana AI, UC Berkeley unveil MASS self-improvement method

   Researchers from Sakana AI and UC Berkeley introduced Multi-Agent Self-Supervision (MASS), a recursive self-improvement method in which a single language model takes turns as task solver, workflow optimizer and evaluator without a stronger teacher model. Using Qwen3.6-27B with Qwen Code over two rounds, the model's win rate against the base model on held-out test tasks rose from 53.9% to 69.9%, and it reached 1.2–1.6 times the base model's score per output token on MLR-Bench, DSBench, ScienceAgentBench and AstaBench. The authors warn that shared blind spots can let the internal evaluator accept flawed or manipulated steps as successes and feed them into the next training cycle, calling this the potential Achilles' heel of recursive self-improvement and urging independent checks and limits on autonomy.

   The Decoder · 🔥 0
6. Oct 9
   Windows-MCP: open-source MCP server lets AI agents control Windows apps and UI

   CursorTouch/Windows-MCP, trending on GitHub, is an MIT-licensed MCP server that connects LLM agents to Windows so they can navigate files, control applications, interact with the UI and run QA tests, without relying on traditional computer vision or task-specific fine-tuned models. It is published on PyPI (runnable via uvx windows-mcp) and listed in the MCP Registry, supports Windows 7 through 11, offers a DOM mode for Chrome, Edge and Firefox, and exposes stdio, SSE and Streamable HTTP transports plus auth keys, IP allowlists and tool whitelisting. The README claims 2M+ users through Claude Desktop extensions and 0.2–0.5s per-action latency; both are self-reported and unverified.

   GitHub Trending · Python · 🔥 0

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