Whenever I can I strongly recommend not using Jupyter for anything more than the most transient tasks.

I don’t know whether it’s the Data Science culture or Jupyter but there is a big lack of discipline in writing maintainable code in DS and non-existent git support is part of that.

I always strongly discouraged developing models using notebooks, instead advocating for using .py files and then using notebooks for sanity checking data.

I don’t have any clever ideas for how we can move past Jupyter but the sooner we do the better.

i strongly agree with what you are saying about Jupyter, however i strongly disagree about using netobooks in general (literal programming)

one of the key things that a good notebook system must allow you to do is to mix something like markup format + LaTeX + source code. writing math-heavy documentation and explanations is simply impractical and limited (readability suffers) if done in comments. jupyter however is severely limited as it is unreadable in its raw format and therefore does not play well with a version control system such as git

instead there is a solution that allows one to do everything jupyter does good with the additional benefit that it plays with version control really well - ie org-mode [1]. the only difference is that instead of using a browser to interact with it, you use emacs. the added benefit to this is that you can also use full-featured key bindings (emacs / vim) and even integrate a language server for auto-completion [2]

EDIT: moreover the list of supported languages in orgmode far exceeds that of jupyter [3] (or did the last time i made this comparison)

[1] https://orgmode.org/

[2] https://emacs-lsp.github.io/lsp-mode/manual-language-docs/ls...

[3] https://orgmode.org/worg/org-contrib/babel/languages/index.h...