平台线程：操作系统管理的真实线程，创建和切换开销大。
Mark Ronson has more chill than a refrigerator full of penguins but his speech for the outstanding contribution award was surprisingly emotional.
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In the months since, I continued my real-life work as a Data Scientist while keeping up-to-date on the latest LLMs popping up on OpenRouter. In August, Google announced the release of their Nano Banana generative image AI with a corresponding API that’s difficult to use, so I open-sourced the gemimg Python package that serves as an API wrapper. It’s not a thrilling project: there’s little room or need for creative implementation and my satisfaction with it was the net present value with what it enabled rather than writing the tool itself. Therefore as an experiment, I plopped the feature-complete code into various up-and-coming LLMs on OpenRouter and prompted the models to identify and fix any issues with the Python code: if it failed, it’s a good test for the current capabilities of LLMs, if it succeeded, then it’s a software quality increase for potential users of the package and I have no moral objection to it. The LLMs actually were helpful: in addition to adding good function docstrings and type hints, it identified more Pythonic implementations of various code blocks.
Spin up sandboxed Linux containers pre-loaded with AI coding tools (Claude Code, Codex, OpenCode via mise). Each container gets SSH access, ZFS snapshot-based checkpoints, and network egress policies that control what the agent can reach. Managed entirely from the CLI over TrueNAS WebSocket API.
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pixels create task2 --from base:ready