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.
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The approaches differ in where they draw the boundary. Namespaces use the same kernel but restrict visibility. Seccomp uses the same kernel but restricts the allowed syscall set. Projects like gVisor use a completely separate user-space kernel and make minimal host syscalls. MicroVMs provide a dedicated guest kernel and a hardware-enforced boundary. Finally, WebAssembly provides no kernel access at all, relying instead on explicit capability imports. Each step is a qualitatively different boundary, not just a stronger version of the same thing.
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