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.
Трамп определил приоритетность Украины для США20:32
，更多细节参见搜狗输入法下载
ВСУ запустили «Фламинго» вглубь России. В Москве заявили, что это британские ракеты с украинскими шильдиками16:45
記錄「新疆再教育營」的中國青年關恆在美被關押半年後獲釋：「失去自由之後，才更意識到它的重要性」
这个问题虽然可以归咎于项目文档不全，但是却切实给我造成了麻烦：文档似乎永远不够全，规范似乎永远补不完。每次 AI 写一个功能我都需要打回修改至少 3~4 次，经常比我自己动手还要费事、烦人。而且随着规则越来越多，AI 的服从性也越来越差，后来甚至开始忘记之前的规范重新犯错误。所以到最后，我基本上放弃了让 AI 写后端代码，只用它调查问题源头或是写无所谓的内容。