Earlier quoted context omitted.
> Why is the software engineering interview wildly different from the traditional engineering interview I have my personal theory. 1) Top companies receive way more applications than the positions they have open. Thus they standardised around very technical interview as ways to eliminate false positives. I think these companies know this method produces several false negatives, but the ratio between those (eliminatin…
Yup. And 3) is particularly interesting. Lots of companies actually need to hire people who can get things done and who can build user-friendly software, yet they thought they needed to hire people who could turn any O(N^2) algorithms into O(N) or O(Nlog(N)). And even for Google, leetcode has become noise because people simply cram them. When Microsoft started to use leetcode-style interviews, there were no interview…
And the great irony is that most software is slow as shit and resource intensive. Because yeah, knowing worst case performance is good to know, but what about mean? Or what you expect users to be doing? These can completely change the desired algorithm.
But there's the long joke "10 years of hardware advancements have been completely undone by 10 years of advancements in software."
Because people now rely on the hardware for doing things rather than trying to make software more optimal. It amazes me that gaming companies do this! And the root of the issue is trying to push things out quickly and so a lot of software is really just a Lovecraftian monster made of spaghetti and duct tape. And for what? Like Apple released the M4 today and who's going to use that power? Why did it take years for Apple to develop a fucking PDF reader that I can edit documents in? Why is it still a pain to open a PDF on my macbook and edit it on my iPad? Constantly fails and is unreliable, disconnecting despite being To bring it back to interviewing signals, I do think the rant kinda relates. Because this same degradation makes it harder to determine in groups when there's so much pressure to be a textbook. But I guess this is why so many ML enthusiasts compare LLMs to humans, because we want humans to be machines.