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Notebooks Are McDonalds of Code

yobibyte.github.io

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Re: Notebooks Are McDonalds of Code

#151

Post by int_19h »

mvanveen wrote: post #62

>Same with notebooks, you can write NASA-production-grade software in a notebook, but most likely you won't. One of my family members literally maintains a software platform for test bedding production NASA spacecraft at JPL and it is all on top of Jupyter notebooks. People are literally on course to build AGI and there’s a good chance that large portions of that work will be done within Jupyter notebooks. To suggest…

I think we need to clearly distinguish two things here, though. A lot of software engineering ritual is dedicated to shipping software - that is the part that tends to be of very remote relevance in research and scientific contexts. But there are also more fundamental principles and techniques that concern writing code, which apply regardless of the reason why you're doing that, and are beneficial in any context - in a sense that, if you follow them, you will spend less time overall writing code (and thus more time doing whatever it is you're trying to achieve with that code).

However, the problem is that those things are not trivial and have to be learned before they can be applied. And that is where the value becomes questionable - the time spent learning all that could instead be spent on learning other things. But if you already have a software engineering background, you do benefit from doing things "right" to some extent.

Re: Notebooks Are McDonalds of Code

#152

Post by madsbuch »

ambicapter wrote: post #130
madsbuch wrote: post #127

Earlier quoted context omitted.

I still rely heavily on principles acquired from courses on Software Architecture and PL studies as a part of my CS degree - and I can definitely see a difference in how people organise their code with same tenure but no schooling.

What are the most common principles you use?

Using types ;)

Re: Notebooks Are McDonalds of Code

#153

Post by Qem »

waldrews wrote: post #87
skydhash wrote: post #77

Earlier quoted context omitted.

I wonder why CL and Smalltalk haven’t beat Python. Is is the languages or just unawareness? The workflow just make more sense there with better updates propagation and state saving.

The Clojure/JVM statistics/scientific computing/now tensor math packages just never got as good as Python, and in Smalltalk they were a non-starter. R is an awkward language, but it's repl-first, has a lot of Lisp's metaprogramming (done in very ad-hoc ways), and Smalltalk's serializable image model -- so a lot of exploratory/experimental statistical methods research happens there, and then gradually makes its way to…

> The Clojure/JVM statistics/scientific computing/now tensor math packages just never got as good as Python, and in Smalltalk they were a non-starter.

There are undergoing efforts to improve numerics performance on Smalltalk. Pharo, for example, uses JIT, and already beats pure Python. But numerics in Python is mostly outsourced to fast modules written in C/Fortran. So people are in process of making Pharo's counterpart to NumPy/SciPy (PolyMath) leverage BLAS/LAPACK integration as well. See:

https://www.youtube.com/watch?v=R5jJBUMLxq8

https://hal.science/hal-03768601v1/file/main.pdf

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