goose

Headlines · 3

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. 6d
   Blind test compares five self-hosted AI agents on Qwen 3.8 27B

   Reddit user /u/GapNew4766 reported a scripted blind test of OpenCode, goose, Atomic Agent, Hermes and OpenClaw, all running locally on an M5 Max MacBook Pro with 64GB of memory. Each used an uncensored Qwen 3.8 27B model in llama.cpp and completed the same five tasks, including merging contact lists, renaming a function across three files, fixing a bug, counting log errors and converting a receipt PDF into a spreadsheet. All five passed three tasks, while four passed the bug-fix and receipt-conversion tasks; the agent-to-label mapping remained hidden at publication.

   r/MachineLearning · 🔥 0
3. Oct 10
   GOAI 2026: AI open source shifts from code availability to reusable capability

   This InfoQ analysis argues that the value of AI open source is shifting from "what is open" to whether the released capability can be verified, reused and continuously iterated: downloadable weights do not mean a system can be reproduced, since AI projects also depend on data pipelines, prompts, tool interfaces, runtime environments and hardware, while agent systems add task planning, state management and permission control. It uses the 2026 Global Open-source AI competition (GOAI) as a sample, reporting 91 countries and regions, over 14,000 developers, 2,999 valid preliminary entries and 70 finalists, and cites the Linux Foundation's December 2025 launch of the Agentic AI Foundation (covering MCP, goose and AGENTS.md), OSI's Open Source AI Definition 1.0, Anthropic's technical article on evaluating agents through full execution traces, and workflow tracing in the OpenAI Agents SDK. It also argues that reusable open assets include data versions, evaluation scripts, run traces, experiment records and simulation environments, and describes Hangzhou's use of GOAI to link global developers with industry resources.

   InfoQ 中文 AI&大模型 · 🔥 0

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