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To each their own. I want to question though, whether those simpler tools are truly simpler. Lets say you want to use notebooks instead of org mode. You will need to have the following set up, before you can start:

- python3 (OK, mostly a given, though not on Windows)

- pip and depending on your requirements you might need Poetry or Pipenv or whatever else

- Jupyter ecosystem (JupyterLab, Jupyter/notebook server and all their dependencies)

- some extensions for JupyterLab. Those could be frontend and backend. If frontend, then you will also need to have NodeJS on your machine, to have NPM, to have tsc to be able to build JupyterLab with the extension installed.

Quite a few parts there as well.




Especially for analysis tools (as I tend to use colab/Jupiter), the important part of complexity is cognitive load. Installation is a constant, upfront cost in that regard: I have literally never worried about the NPM or nodejs, as they're handled automatically by standard installers... Or already running in shared environments. (And if I'm deeply worried about binary size and dependency depth, I probably shouldn't be working python in the first place, and have other tools I can go to.)

Cognitive load when using the notebook is very low compared to emacs. As has been remarked on often, emacs is probably fine if you've been using it for thirty years, but having to keep track of different key strokes for copy paste depending on what window you're in (emacs vs the rest of the world) is pointless cognitive load. The idioms are so sprawling and out of sync with everything else that the editor is an obstacle in the way to doing what I want.

And while (historically) that perspective may just paint me as a noob, I'll just say that no amount of memorizing editor functionality or micro optimizing configurations will ever help me prove a theorem. If there's another tool that gets the job done and doesn't want to fight me before/while I use it, then it's the better tool.




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