But in practice you are padding token counts of agents reading streams of TUIs instead of leveraging standard unix pipes that have been around from day 1.

TLDR - your agent wants a CLI anyway.

Disclaimer: still a cool project and thank you to the author for sharing.

I very regularly need to interact with my work through a python interpreter. My work is scientific programming. So the variables might be arrays with millions of elements. In order to debug, optimize, verify, or improve in any way my work, I cannot rely on any other methods than interacting with the code as it's being run, or while everything is still in memory. So if I want to really leverage LLMs, especially to allow them to work semi-autonomously, they must be able to do the same.

I'm not going to dump tens of GB of stuff to a log file or send it around via pipes or whatever. Why is there a nan in an array that is the product of many earlier steps in a code that took an hour to run? Why are certain data in a 200k-variable system of equations much harder to fit than others, and which equations are in tension with each other to prevent better convergence?

Are interpreters and pdb not great, previously-existing tools for this kind of work? Does a new tool that lets LLMs/agents use them actually represent some sort of hack job because better solutions have existed for years?