In regard to converting ipynb files to *.py, use jupytext [1]. It will automatically convert notebooks to both *.md and *.py. This also allows to `import * from

In regard to converting ipynb files to *.py, use jupytext [1]. It will automatically convert notebooks to both *.md and *.py. This also allows to `import * from notebook1` in notbeook2, which is great for splitting long notebooks into many sequential ones. The trick is to add cell-tags to those cells that you don't want to appear in your *.py converted notebook ("active-ipynb") - e.g. you want to include method definitions to be imported, but not plot() stuff (etc.). I was a little bit disappointed by the topic focus: I was hoping to explore/use LLMs in notebooks for data processing, not for augmenting my code writing. I used stable diffusion (automatic1111 API) in Jupyter once and wrote a blog post about it [2]. However, I haven't used any LLMs so far in Jupyter Lab to do data processing. I did use OpenAI's API in Jupyter [3], but found it too limiting and too much of a black box. [1]: https://jupytext.readthedocs.io/en/latest/ https://jupytext.readthedocs.io/en/latest/ [2]: https://ad.vgiscience.org/links/posts/2023-06-27-stable-diffusion-hpc-cluster/ https://ad.vgiscience.org/links/posts/2023-06-27-stable-diff... [3]: https://kartographie.geo.tu-dresden.de/ad/2022-12-22_OpenAI_Summary/html/gpt4-summary.html https://kartographie.geo.tu-dresden.de/ad/2022-12-22_OpenAI_...