jupyter-ai

Jupyter + LLM tools: Ipython-GPT, Elyra, jetmlgpt, jupyter-ai; CoCalc, Colab, NotebookLM, jupyterlab/jupyter-ai: https://github.com/jupyterlab/jupyter-ai "[jupyter/enhancement-proposals#128] Pre-proposal: standardize object representations for ai and a protocol to retrieve them" https://github.com/jupyter/enhancement-proposals/issues/128

Have y'all seen Jupyter AI? Seems to do the same but better (more features, more mature codebase, better UI/UX/DX) while being a JupyterLab extension https://github.com/jupyterlab/jupyter-ai

Thank you for the list - I think I've come across all of these in my research! I'll try highlight the differences for each. - https://noteable.io/ - as you say, it doesn't exist anymore - https://deepnote.com - Deepnote is closed source sadly - you can't run it locally, you can't tweak it, you need to learn a new interface and switch to it - https://github.com/jupyterlab/jupyter-ai - I actually mentioned this in the post but in my experience, the UX and features are far behind what we've built already. I'd love to hear from anyone who's tried jupyter-ai to give us a shot and let me know what we're missing! The plus side of jupyter-ai is of course that it supports way more models and the codebase is a lot more hackable than what we've built. - https://colab.google/ - closed-source, similar challenges as with Deepnote. Another big challenge

There are many other Jupyter notebooks with extensive AI integration. These are less (or not at all) open source, but more mature in some ways, having been iterated on for over a year: - https://noteable.io/ -- pretty good, but then they got acquirehired out of existence - https://deepnote.com -- also extensive AI integration and realtime collaboration - https://github.com/jupyterlab/jupyter-ai -- a very nice standard open source extension for gen AI in Jupyter, from an Amazon. JupyterLab of course also has fairly mature realtime collaboration now. - https://colab.google/ -- has great AI integration but of course only with Google-hosted models - https://cocalc.com -- very extensive AI integration everywhere with all the main hosted models, mostly free or pay as you go; also has realtime collaboration. (Disclaimer: I co-authored this.)

That’s already a thing, and works rather well: https://github.com/jupyterlab/jupyter-ai The repo README doesn’t make it obvious how it works, but if you go to the Chat Interface portion of the docs it’s illustrated: https://jupyter-ai.readthedocs.io/en/latest/users/index.html...

I think the biggest area for growth for LLM based tools for data analysis is around helping users _understand what edits they actually made_. I'm a co-founder of a non-AI data code-gen tool for data analysis -- but we also have a basic version of an LLM integration. The problem we see with tooling like Pandas AI (in practice! with real users at enterprises!) is that users make an edit like "remove NaN values" and then get a new dataframe -- but they have no way of checking if the edited dataframe is actually what they want. Maybe the LLM removed NaN values. Maybe it just deleted some random rows! The key here: how can users build an understanding of how their data changed, and confirm that the changes made by the LLM are the changes they wanted. In other words, recon! We've been experimenting more with this recon step in the AI flow (you

From core maintainers of Jupyter - https://github.com/jupyterlab/jupyter-ai