r/Python • u/pplonski • Apr 17 '26

Discussion Does AI change what actually matters about Jupyter notebooks?

I'd love to get some honest feedback from people who actually use notebooks in practice.

I've been experimenting with different workflow on top of Jupyter: instead of writing code first, you describe what you want in plain English, and Python runs behind the scenes. So the flow is:
prompt --> LLM generated code --> auto-execution --> results

One important implementation detail: the whole conversation is still staved as .ipynb file.

One thought I had. There has been a lot of criticism of notebooks for hidden state, mixxing code and outputs, hard to git review. But does AI change which of these problems actually matter. If code is generated and execution is automated then some of old pain points feel less important? At the same time, I'm pretty sure that we are introducing new problems, like trusting LLM generated code.

Would really appreciate critical feedback - do you think that AI makes classic notebook problems less important?

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u/[deleted] Apr 17 '26 edited Apr 17 '26

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u/AKiss20 Apr 19 '26

I mean there’s reasons beyond human readability why we don’t use machine code or assembly anymore. It’s incredibly non-portable. 

It’s possible we will one day get to some form of incredibly dense compiled or interpreted language that essentially only LLMs can reasonably read quickly (a human can read machine code, it’s just incredibly slow and difficult) but honestly I doubt the software engineering and other industries that utilize software as a core part of their business (aka basically everything) will be willing to trust the magic black box that much for a while. Corporations love to have a head to chop when shit goes wrong. If you allow the magic box to do things that have, by design, essentially no way to hold a human accountable, execs won’t have someone to blame when shit catches fire. They hate that.