<em>Perspective</em>: Multi-shot LLMs are useful for literature summaries, but humans should remain in the loop

· · 来源:tools资讯

Основное преимущество технологии в том, что ее можно масштабировать путем разведения, а не заводского производства.

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.,更多细节参见Line官方版本下载

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Show the first 100 Google search results for any,更多细节参见safew官方下载

Trump orde

E -- F["scored JSON