I don't think it's controversial that you can be much more concise with python. My experience first learning Java was that everything was 2-3x as verbose as in python. The difference is smaller if you're using type hints in python, but it's still more concise.
I talked about repl's/notebooks for other languages. They're still an especially great tool for python/data science since they make it very easy to visualize data and share analyses.
I played around with breakpoints in pycharm and I don't think it would work for me. You need to run your code from pycharm in debug mode for the breakpoint to trigger, whereas I always run things from the command line or a notebook.
I believe you that there are times when python is slower. At least it's not noticeably slower when simply calling C behind the scenes or when you're i/o blocked anyway.
Re investing, I mean, everyone has seen phenomenal returns since they were born: this century is unprecedented. Also there was the pandemic crash very recently, so everyone has experienced an extremely harsh (albeit brief) bear market too. LTCM was like 100x-leveraged, which I would not advocate for, since you'll almost certainly get wiped out if you hold that position for more than a few hours...
Eh, lots of kids have add, and like 10% of college students used adderall in 2016 according to the first hit on google. In any case they've been magic for me the few times I've tried them e.g. working 12+ good hours in a day.
> I was hoping for more DS related stuff. It almost sounds like you're learning to be a SWE!
It kind of felt that way too :). Some more data sciency things were learning how transformers work, hacking pytorch, using visualization tools like tensorboard and wandb, web scraping, better using parallelism, tuning hyperparameters (mostly the learning rate tbh), better fluency with the command line than I assume most swe's need, getting very comfortable inspecting data, making experiments more reproducible, reading lots of papers, writing papers, and trying (somewhat half-heartedly) to get published.
I did some backtesting simulations that made leveraged investing look pretty awesome. The effective borrow rate for funds like spxl is crazy low, way better than if I were to borrow myself. (Also, fwiw I was pretty conservative and am overall only around 2x-leveraged.)
The internet is very opposed to leveraged investing imo, but I think most of the concerns are pretty dumb. There was this one blog post where this guy ran ten simulations of his own, most of which showed the leveraged portfolio doing comparably to the baseline, but one a couple showed it doing worse and one saw the leveraged portfolio 100x'ing or something... and he concluded that it wasn't worth it??
People will also appeal to volatility drag as a superficially sophisticated knockdown (in short, imagine all four two-step paths in which the market goes up or down by 10% at each step. Then the baseline market averages out to (.81 + .99 + .99 + 1.21)/4 = 1, and a 3x leveraged portfolio averages out to (.49 + .91 + .91 + 1.69)/4 = 1. Volatility drag is those two middle worlds where the leveraged portfolio does badly despite the market as a whole basically ending up where it started.
Use keyword “HedgeFundie” (username that first started the discussion on Bogleheads and now most refer this strategy with his name) to search discussions about Leveraged ETFs on Bogleheads forum and Reddit. There are over 300+ pages worth of discussion on this topic only on Bogleheads.
Also checkout information about permanent portfolio put forward by Bridgewater Associates.
The remote connection to safety (did you mean security?) would be that static source analysis tools don't work as reliably with dynamic languages. That matters at Google. But you don't even have to think as hard about it: Python is simply comparatively slow and inefficient. Google's fleet is large. It pays off to use more efficient languages.
(There's also the whole thing about Python being largely single threaded and computers being very wide these days, as well as being a terrible memory hog and memory making up half the cost of servers.)
Going point by point:
It's not "he" it's "she".
I don't think it's controversial that you can be much more concise with python. My experience first learning Java was that everything was 2-3x as verbose as in python. The difference is smaller if you're using type hints in python, but it's still more concise.
I talked about repl's/notebooks for other languages. They're still an especially great tool for python/data science since they make it very easy to visualize data and share analyses.
I played around with breakpoints in pycharm and I don't think it would work for me. You need to run your code from pycharm in debug mode for the breakpoint to trigger, whereas I always run things from the command line or a notebook.
I believe you that there are times when python is slower. At least it's not noticeably slower when simply calling C behind the scenes or when you're i/o blocked anyway.
Re investing, I mean, everyone has seen phenomenal returns since they were born: this century is unprecedented. Also there was the pandemic crash very recently, so everyone has experienced an extremely harsh (albeit brief) bear market too. LTCM was like 100x-leveraged, which I would not advocate for, since you'll almost certainly get wiped out if you hold that position for more than a few hours...
Eh, lots of kids have add, and like 10% of college students used adderall in 2016 according to the first hit on google. In any case they've been magic for me the few times I've tried them e.g. working 12+ good hours in a day.