"Oh, I need to install the python package and the jupyter extension separately? And there are no breadcrumbs if I install one but not the other? And I have to figure out which versions are mutually compatible with each other and jupyterlab because 'everything latest' sure isn't? And I have to install extensions to get features that used to be built-in? And then I have to explain the install procedure to the person I'm trying to share with, twice, because he didn't believe me the first time? UGGGGGGGHHHHHHHH"

Another upside: There is a GCP gcloud one-liner to start a VM for a given Docker image, and the image is designed to work both locally and in a GCP VM, with notebooks in a git repository that gets checked out automatically on container creation, so switching from laptop to 16-core VM to churn through a large dataset from a bucket is pretty seamless.

I was always a bit frustrated by the way nbconvert pulled in pandoc as a dependency, but then didn't make use of any of its power (pandoc surely has a powerful AST). Instead they re-implemented filters and templates themselves. It struck me that it would have been much cleaner to write a proper 'ipynb > AST' Pandoc reader, and then expose pandoc AST filters and templates.

I was a bit suspicious of moving away from pandoc to sphinx, because sphinx seems markedly less powerful and general than pandoc. However, you seem to think it's the opposite! Does Sphinx have an AST? I can't see any reference?

By contrast sphinx uses docutils AST: https://github.com/live-clones/docutils/blob/master/docutils..., the documentation is not great (working on that) but it is pretty powerful.

My main concern with jupyter is that I prefer orgmode (and by a lot), but I know that it's niche and most people don't even heard about it ipnbs are preferred form of notebook exchange.

I will check how good translation to/from orgmode is then.

The only remaining tooling gap is a markdown linter implemented in python.

Can you elaborate on this a little? Do you mean via Jupyter notebooks?

https://codeocean.com/capsule/9155944/tree/v1

The nbconvert syntax is

  jupyter nbconvert --exec --to latex --template ieee.tplx --output ../results/editorial.tex editorial.ipynb

My method is roughly this:

1. Create a Python package that subclasses nbconvert.exporters.LatexExporter -- in the setup.py you register the class to 'nbconvert.exporters' in the 'entry_points' dict.

2. You can do general filtering/compiling by registering 'Exporter.filters'. The compile process can be modified by implementing a custom 'from_notebook_node()'

3. You can include a folder containing jinja template(s) in the package to implement whatever styling you want

4. I have some standard metadata that I pull from the notebook/cell metadata (using the standard Jupyter/lab frontend metadata sidebar) to define things like document titles, authors, etc.

5. Do the conversion like: jupyter nbconvert --to=myfancyexportformat notebook.ipynb