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Comment by cornercasechase | Hacker News Reader

I notice that the python community seems to love workbooks/notebooks but I have to say as a non-python dev, they drive me up the wall. I just want to deal with “regular” code and not have an entire gui environment with code and text intermingling somewhere in the cloud.

I think AI really needs a de-pythoning in general.

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Please don't mix up python and notebook. The linked product is not even python! It's barely a variation of ChatGPT Web UI. And many python people love VS code too! It's a vast community with many different tastes.

That's said - As LLMs move "AI" field from self-hosted to managed, the surrounding API ecosystem seems to move away from Python, even though slowly. For example LangChain has both Python and JS implementations. So De-pythoning is kind of happening.

As a long time (16+ years) user of python and Vim, I personally find the notebook format extremely unergonomical. Maybe it would be a good option for those who haven't spent 10000 hours mastering a text editor and relating tooling, but there exist old school python devs that very much prefer a good old text editor like everyone else.

> I think AI really needs a de-pythoning in general.

Yes, absolutely. The state of the Python ecosystem is what remains after a nuclear apocalypse.

We need more native stuff, ggml.cpp for one is a super important project.

People say that, but ironically most everyone that uses it just goes with the python wrapper for llama.cpp lol. Everything except kobold anyway.

"We need more native stuff, ggml.cpp for one is a super important project."

Please, not C++

Checkout Clerk from the Clojure community [0]. Presumably it would be possible to extend a new language like Mojo (Python-superset) to support that kind of dev workflow to AI.

[0] https://github.com/nextjournal/clerk

The amount of critical production code that's straight up copy+paste from notebooks is pretty jarring too. Lots of pasta and lots of extraneous libraries (like gradio) all over the place

I suggest AI people to look into Mojo[1]. They are building a language that is fully compatible with python, but when performance is needed it can become typed and compiled. Its very early days, but that seems to be best way to depythonize

[1] https://www.modular.com/mojo

Agreed, let's bury the secrets of technological consciousness in even more opaque and hard to read layers of code.

It's a good way of separating useless crap from good code.

If the main code is in a notebook, ignore it completely.

If the code is in python but no ipynb, it may be useful.

If it's a well structured code base, and there are some ipynb files, check it out and determine if it's good software. Then the ipynb files are like only a few lines, but show decent plots and the package may end up being useful. Python can be good, it's just unfortunately less and less likely due to far too many idiots using it.

At least it's not R though.

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