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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.

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James Milner

為了找出答案,我與兩位蘭卡斯特大學語言學習研究室(Language Learning Lab)的研究者合作:語言學與認知科學教授派屈克·雷布夏特(Patrick Rebuschat),以及心理系認知學教授 帕德瑞克·莫納漢(Padraic Monaghan)。他們讓我試做一項為反映真實世界語言學習情境而設計的實驗,並揭示我們的大腦如何接收、解讀新的單字與聲音。