Performance as an issue is massively overstated in high level languages. CPU intensive hot paths are rarer than people think, I/O performance is a bigger deal than people think and there's more hyper optimized code written in performant languages that you can call directly than most people realize.

A lot of graduates think that compsci jobs involve optimizing algorithms or some shit coz they did that type of thing as undergrads, so they port that attitude across.

It's also the reason why pypy was massively hyped for a while and then not really used all that much. It was solving the performance problems python never really had.

It's true, that the situations in which you care about it are few. The most recent performance issue I've come up against is actually java's startup time, because for reasons unknown my client decided java AWS lambdas were the way to go, has quite a large, complex codebase built around it, and now has complaints about startup latency.

I have noticed some younger engineers on C++ projects doing some kooky things "because it's more optimal", and have taken time to explain to them that the micro-optimisations they're using impact readability, are going to be done by the compiler anyway if they're at all useful, and are likely to be several orders of magnitude less relevant than some lock somewhere, or a bit of IO, or whatever.

Pre-setting max memory seems odd these days, when everything runs in docker, with its configurable memory limits.

Handling process memory usage pre-docker wasn't so easy.