ipyflow VS CPython

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

Thankfully, Python, the go-to coding language for loads of developers, is here to save the day. It's got some awesome features for diving into text sentiment analysis. With cool libraries like Tweepy, we can sift through X(Twitter) data and snag those interesting tweets about Bitcoin. And then there's TextBlob, a clever tool for understanding the sentiment in text. When it's time to clean up and organize all that data, libraries like pandas and numpy are there to help out. And let's not forget about matplotlib, the master of visualisations that can help us see the trends in sentiment crystal clear. Armed with these tools, developers can really dig deep into social media data and figure out what the general public thinks about Bitcoin.

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> It provides a meaningful performance improvement for at least one popular platform (realistically, on the order of 5%).

At first it will not provide a large boost, but it will set the foundations for larger gains in subsequent releases. They link a list of some proposed improvements already underway, with improvement estimates, at https://github.com/python/cpython/issues/115802

After that, with the basics of software engineering understood, I would move on to a wider use language, with a bigger ecosystem to employ, most likely Python. This would expose me to large system design / distributed systems and architectural challenges...

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