Pluto.jl VS ipyflow

I thought that notebook based development and package based development were diametrically opposed in the past, but Pluto.jl notebooks have changed my mind about this.

A Pluto.jl notebook is a human readable Julia source file. The Pluto.jl package is itself developed via Pluto.jl notebooks.

https://github.com/fonsp/Pluto.jl

Also, the VSCode Julia plugin tooling has really expanded in functionality and usability for me in the past year. The integrated debugging took some work to setup, but is fast enough to drop into a local frame.

https://code.visualstudio.com/docs/languages/julia

Julia is the first language I have achieved full life cycle integration between exploratory code to sharable package. It even runs quite well on my Android. 2023 is the first year I was able to solve a differential equation or render a 3D surface from a calculated mesh with the hardware in my pocket.

Re Julia there is also pluto.jl that is another notebook-like environment for julia. It's been a few years since I played with it but it looked cool, for example it handles state differently so you don't get into the same messes as with ipython notebooks. https://plutojl.org/

For this, Notebooks are often used. Julia offers a uniquely nice and interactive Pluto notebook for the web https://github.com/fonsp/Pluto.jl

I believe this is what Pluto sets out to do for Julia.

I used it as part of the “Computational Thinking” with Julia course a year or two back. Even then the beta software was very good and some of the demos the Pluto dev showed were nothing short of amazing

https://plutojl.org/

I think what you want is Pluto!

I like how Pluto.jl handles this:

> Pluto offers an environment where changed code takes effect instantly and where deleted code leaves no trace. Unlike Jupyter or Matlab, there is no mutable workspace, but rather, an important guarantee:

> At any instant, the program state is completely described by the code you see.

[1] https://github.com/fonsp/Pluto.jl

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