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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What drives this? Am I just bored?
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这番努力已初见成效。1 月 21 日,Meta CTO Andrew Bosworth 在瑞士达沃斯世界经济论坛期间正式宣布,Meta 超级智能实验室已完成首批核心 AI 模型的内部交付,表现「非常出色」。。关于这个话题,爱思助手下载最新版本提供了深入分析
For roughly the first decade of the "cash machine," they were offline devices