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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Keir Mackenzie,in Canterbury,
带宽提升：TSV大幅缩短互连距离，显著提升数据传输速率，能够支持HBM4等超高带宽需求；延迟降低：桥接器内部的TSV路径比传统封装走线更短，有效降低数据通信延迟；功耗优化：短路径低电容，有助于降低整体系统功耗，符合高性能芯片的PPA（功耗、性能、面积）优化目标。