nb-clean VS ipyflow

What about NB clean ? https://pypi.org/project/nb-clean/

https://github.com/srstevenson/nb-clean ?

Has been my go to for this. It seems like nbdev2 is fastais own cooked solution with a bunch of other tools.

You're probably referring to nbgather (https://github.com/microsoft/gather), which shipped with VSCode for a while.

nbgather used static slicing to get all the code necessary to reconstruct some cell. I actually worked with Andrew Head (original nbgather author) and Shreya Shankar to implement something similar in ipyflow (but with dynamic slicing and a not-as-nice interface): https://github.com/ipyflow/ipyflow?tab=readme-ov-file#state-...

I have no doubt something like this will make its way into marimo's roadmap at some point :)

Np.

From https://news.ycombinator.com/context?id=35887168 re: ipyflow I learned about ReactiveX for Python (RxPY) https://rxpy.readthedocs.io/en/latest/ .

https://github.com/ipyflow/ipyflow :

> IPyflow is a next-generation Python kernel for Jupyter and other notebook interfaces that tracks dataflow relationships between symbols and cells during a given interactive session, thereby making it easier to reason about notebook state.

FWIU e.g. panda3d does not have a react or rxpy-like API, but probably does have a component tree model?

https://news.ycombinator.com/item?id=38527552 :

>> It actually looks like pygame-web (pygbag) supports panda3d and harfang in WASM

> Harfang and panda3d do 3D with WebGL, but FWIU not yet agents in SSBO/VBO/GPUBuffer

> Another giveaway is the ratio of stars to watchers / forks. I remember one project with thousands of stars but only 10 users "watching" it. They went on to raise a sizable seed round too.

Not necessarily indicative of foul play. I have two projects like this (https://github.com/smacke/ffsubsync and https://github.com/ipyflow/ipyflow) and I attribute it to not having great developer documentation.

It's not in the highlights, but one of the things that excites me most is this: https://docs.python.org/dev/whatsnew/3.12.html#pep-669-low-i...

> PEP 669 defines a new API for profilers, debuggers, and other tools to monitor events in CPython. It covers a wide range of events, including calls, returns, lines, exceptions, jumps, and more. This means that you only pay for what you use, providing support for near-zero overhead debuggers and coverage tools. See sys.monitoring for details.

Low-overhead instrumentation opens up a whole bunch of interesting interactive use cases (i.e. Jupyter etc.), and as the author of one library that relies heavily on instrumentation (https://github.com/ipyflow/ipyflow), I'm very keen to explore the possibilities here.

You’re looking for reactive kernels: https://github.com/ipyflow/ipyflow