Just an anecdote and not necessarily generalizable, but I can at least give one example:
I'm in academia doing ML research where, for all intents and purposes, we work exclusively in Python. We had a massive CSV dataset which required sorting, filtering, and other data transformations. Without getting into details, we had to rerun the entire process when new data came in roughly every week. Even using every trick to speed up the Python code, it took around 3 days.
I got so annoyed by it that I decided to rewrite it in a compiled language. Since it had been a few years since I've written any C/C++, which was only for a single class in undergrad and I remember very little of, I decided to give Go a try.
I was able to learn enough of the language and write up a simple program to do the data processing in less than a few hours, which reduced the time it took from 3+ days to less than 2 hours.
I unfortunately haven't had a chance or a need to write any more Go since then. I'm sure other compiled, GC languages (e.g., Nim) would've been just as productive or performant, but I know that C/C++ would've taken me much longer to figure out and would've been much harder to read/understand for the others that work with me who pretty much only know Python. I'm fairly certain that if any of them needed to add to the program, they'd be able to do so without wasting more than a day to do so.
Of course, but the dataset was mostly strings that needed to be cross-referenced with GIS data. Tried every library under the sun. The greatest speed up I got was using polars to process the mostly-string CSVs, but didn't help much. With that said, I think polars was also just released when we were working with that dataset and I'm sure there's been a lot of performance improvements since then.
These only help if you can move the hot loop into some compiled code in those libraries. There's a lot of cases where this isn't possible and at that point there's just no way to make python fast (basically, as soon as you have a for loop in python that runs over every point in your dataset, you've lost).
I have been reviewing your comment history here, including the pastebin texts you have linked.
I find your perspective intriguing. Mind sharing the offline writings you've referred to, if there are additional ones to the three pastebin texts I've come across?
These things are developing, look out for “The Exogenous of Power”. It may be in print within the decade. There is more to come, I hope you will follow along.
You mean the data that was already public and debunking half the shit he's said? You can't mean the data he refuses to actually release, do you?
So with that in mind, let me rephrase your comment.
> It's discouraging to see people that read hackernews, act in this way, and not look at the facts, and actual data, but rather use up all their energy on just blindly praising Elon.
Is he rooting out corruption and wasteful spending though? Or is he himself corrupt and attempting to stomp out agencies for his own benefit? It seems like the latter to me, considering which agencies he's targeting, half (or more) of his statements about the spending being falsehoods, the lack of proper processes, the people he's hired, etc.
I don't understand how people can be against due process, accountability, and genuine public service. Or are you just brainwashed by right-wing media?
I think they were talking about the "model giants" like OpenAI you mentioned. Not saying they're correct, but I will concede the amount of copyrighted information someone like OpenAI would want is probably (at least) an order of magnitude more than this particular case.
I'm in academia doing ML research where, for all intents and purposes, we work exclusively in Python. We had a massive CSV dataset which required sorting, filtering, and other data transformations. Without getting into details, we had to rerun the entire process when new data came in roughly every week. Even using every trick to speed up the Python code, it took around 3 days.
I got so annoyed by it that I decided to rewrite it in a compiled language. Since it had been a few years since I've written any C/C++, which was only for a single class in undergrad and I remember very little of, I decided to give Go a try.
I was able to learn enough of the language and write up a simple program to do the data processing in less than a few hours, which reduced the time it took from 3+ days to less than 2 hours.
I unfortunately haven't had a chance or a need to write any more Go since then. I'm sure other compiled, GC languages (e.g., Nim) would've been just as productive or performant, but I know that C/C++ would've taken me much longer to figure out and would've been much harder to read/understand for the others that work with me who pretty much only know Python. I'm fairly certain that if any of them needed to add to the program, they'd be able to do so without wasting more than a day to do so.