anst

Many thanks! Incredibly versatile implementation.

Time to go amd, poor old me, Intel MB Air 2018 (zsh: exec format error, Darwin Kernel Version 22.2.0, MacOS Ventura 13.1).

Definitely. The Invincible would fit as well https://en.wikipedia.org/wiki/The_Invincible Big time for Lem.

On my old Mac: Python=133.5 s, Numba=2.61 s (parallel prange in count_primes), Taichi=1.8 s. (on ti.cpu, but fails with metal).

In a winner-takes-all finite world a tipping point may never happen...

Actually, their Julia/Mandelbrot examples work on metal (Intel).

Yeah, stir some mysterious ideas, maybe someone sees there a meaning. Quite surprised with this kind of vague magical thinking coming from lesswrong (thought they were rationalists or something).

Python + Jupyter OK, but pandas actually reads everything at once, doesn’t it. 100MB is no problem but bigger files could result in high swapping pression.

A really interesting collection for teaching Data Science. But the "WorkshopScipy" and the "scientific computation" make reference to something like scipy.org. It seems that this repo has different interests.

Stranger Things.

What "maths" is keras? Or scikit-learn? For what it's worth, to understand scikit-learn doc/tutorial I'd say you'll need Probability, Linear Algebra, Multivariate Calculus and, yeah, Stats. Not necessarily at a PhD level but still. And more you understand maths farther you can get in AL/ML.

Free will arising from quantum indeterminacy, well, that's a thought of Robert Penrose. The bad news is that even he didn't manage to convince others and the theory didn't really took off. In other words, we are not yet ready to understand such a link.

However, there is the Strong Free Will Theorem of Conway and Kochen. Essentially, they prove that "if humans have free will, then elementary particles already have their own small share of this valuable commodity", see http://www.ams.org/notices/200902/rtx090200226p.pdf

The interesting thing is not only the article itself, but also the vote count here on HN. I can't stop thinking that professionals, once strategic target for Apple Computers, appear to be dumped by Apple Inc in favor of high-end consumers. It might be stock-wise OK, but it's a significant evolution. And if I care a little less about a breathtaking design and more about memory bus speed and GPU, soon I'll have to buy or build a PC. That's a pity.

Do not forget codingame.com.

My take is from scientific computing perspective. But as my opinion isn't that interesting, I take this opportunity to cite some julia-python-language-relatedtopics on HN.

I'm still betting on Julia https://news.ycombinator.com/item?id=11516374 and, since I do not use it for "production", I'm not giving up https://news.ycombinator.com/item?id=11692155

That said, if it's all about speed, then Python can be, well, faster: https://news.ycombinator.com/item?id=10735840 (especially so if you need BigInt, these still suck in Julia.)

Recently, discovered Go(language). Clean design rules! https://news.ycombinator.com/item?id=4158865, (but you shouldn't take it for what it is not https://news.ycombinator.com/item?id=12330573)

As someone using (and loving) both Python and Julia, I found the title overpromises and the content underdelivers. More accurate header would be "Personal take on REPL/shell interactions in bpython and Julia".