[snakey]

Excellent point. Giving my perspective as a relative newbie (~5 years) to the industry with no comp. sci. background.

It was around the year 3-4 mark that I decided to knuckle down and try to improve my fundamentals (data structures, algorithms, memory models, concurrency, CPU architecture and some network fundamentals) by reading popular papers and literature and writing all of my personal projects in C and C++. I’m about two years into this study and while it’s been immensely rewarding, I’ve found it to be a huge undertaking while juggling life and a full-time job.

What I’ve also noticed is that, while I understand a lot more about what the CPU is doing, memory manipulation and how to write more efficient programs, I haven’t found it be particularly beneficial to my daily work (still Python and JS). I would love to be able to put these concepts into practice for many hours of my working day but it’s difficult to move from general web-stack development to more performance-oriented development (embedded, low-latency, OS, etc.).

My guess is that this is one of the reasons we have ended up in this situation. You can get away without knowing the fundamentals (a good sign of progress?) and that if you really do want to pursue these areas that promote building this kind of knowledge as part of your career, the barrier to entry is quite high and the positions are fewer than say a decade or two ago. I find it a shame because in my eyes, these areas are the most interesting and exciting areas of programming. It’s an art.

[lolinder]

> What I’ve also noticed is that, while I understand a lot more about what the CPU is doing, memory manipulation and how to write more efficient programs, I haven’t found it be particularly beneficial to my daily work

I think this is because of the effect OP is describing: no matter how much time you spent studying, the odds that you learned about the specific thing that will make the difference in your work this week are slim because the technology is so broad and deep. Odds are that what you choose to intentionally study is irrelevant to your work.

You've picked a slice to learn about, but those who were working on it as it got layered know it all. Once you have that kind of knowledge, the odds of being able to explain some unexpected behavior approach 100%, but acquiring that knowledge took decades.

I've found a method that does work pretty well is to dig really deep into the topics that come up in your actual work. Instead of a random sample of fundamentals, find the parts of your job that feel like magic and explain that magic. Skipping over V8 and straight to the CPU might not be useful for a JavaScript dev very often, but a deep understanding of V8 is relevant more often than you might think.

[Const-me]

I agree about the high barrier to entry, but I disagree about the modern job market.

There’re huge areas of the software industry where performance matters to this day. Examples include videogames, content creation, video processing, engineering software, science related HPC, and now AI.

However, transitioning from web development to these areas gonna be hard. That software doesn’t run in web browsers, the code is either desktop apps, deep inside web backends, or supercomputer programs. And high-performance C++ is often not enough for them, depending on the area ideally you might also need GPU graphics and/or GPGPU compute.

I was lucky I have never worked on web apps. I know TCP/IP reasonably well, I have some general understanding of HTTP and HTML, but I’m totally clueless about the modern frontend technologies.

[sgarland]

What I’ve found most helpful for understanding how something affects Python specifically (but this holds true for nearly any language) is godbolt [0].

Find a chunk of code you want to optimize, simplify it as much as possible, then put it into godbolt. Match the interpreter version to yours. Then go look up the bytecodes and see what they do. Try changing the Python version to see how it differs, or a different approach to the problem (e.g. a map instead of a list comprehension).

This takes an enormous amount of time, of course, but you can learn quite a bit.

[0]: https://godbolt.org