[aarondia]

These are sweet -- thanks for sharing.

> Bridging the Abstraction Gap Between End-User Programmers and Code-Generating Large Language Models

I love the idea of giving users feedback on how to get better at prompting the LLM. I think the key to using this approach within Mito is giving users guidance at the right time -- sometimes shorter prompts get the job done, and they're always easier to write :)

A really sweet integration of this approach could be: when the LLM generated code errors or when we notice that the user undoes their previous prompt, we offer the user help in converting non-working prompts into ones that follow best practices of breaking complex tasks down into small steps.

> On the Design of AI-powered Code Assistants for Notebooks - uses Mito as part of their case study

Andrew McNutt, one of the authors presented this paper here: https://www.youtube.com/watch?v=g0prh8mE3bI Their different classifications of notebook code-gen tools has actually been super helpful in my own thinking. Thanks for the help, Andrew if you're a HNer