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> Remember we used to spend enormous amount of time in school and in our spare time studying computer science? Algorithms, operating systems, compilers, and etc. All kinds of insights. All kinds of fun. All kinds of hard engineering. Yet, how much time do we really need to spend in our day-to-day work implementing or using the algorithms and etc that we have learned?

Because great insight and expertise stems from foundational knowledge.

I am a huge hockey fan. In the NHL, the players do not stop practicing the fundamentals once they make it to the NHL. They practice the fundamentals even more. Many practice the same drills as youth leagues -- stick handling, passing back and forth, shooting, edgework, shooting, rebound control, etc..

The best of the NHL might not hit the hardest, have the most accurate shot, or skate the fastest. What separates them from the rest of the lot is that they are fundamentally better than everyone else.

So what drills do you practice

LeetCode Medium. :)

Yeah, but actually implementing those algorithms was extremely key to really grokking them. Testing those edge cases, seeing them fail, fixing them, learning "oh, that didn't fix it", repeat. Yeah, you can do it on paper (which I did), but the whole idea of going through the motions of writing yet another binary search algorithm was to lock in those concepts.

But that isn't the real problem here. Computer Science was always a theoretical concept, really. The meat of the issue is the rug being pulled out from the juniors and mids.

Reading code and writing it are two different skills. I believe that both are needed to maintain a codebase. It is much, much easier to understand what a service is doing when you're actually writing code that supports it, just like it's easier to remember the contents of a report that you wrote versus one that you read.

I think it follows the path of the spreadsheet. For a long while, only geeks and finance or accountants or data monkeys used spreadsheets. Then, it was such that anyone could create a spreadsheet. Vlookup was something you learned early in school or on your home computer tinkering around. There’s still some modeling gurus out there but largely everyone is developing solutions using spreadsheets everyday. And they’re doing it rather autonomously even prior to AI helping. We can talk about how they’re ugly and crappy spreadsheets but they generally solve the problem the user had.

With AI, people can build and collaborate on applications much more complex with much less technical knowledge. It might be ugly and crappy but I bet they’ll be mostly autonomous and not need to work through their IT team, or go through the hell of PM and requirements. If I know my requirements, I don’t need you. Hell, I can just start building and add requirements as I come across them. It’s not a major risk to the project like it used to be.

It will, and is, going much faster than the spreadsheet did.

> there has been little progress or few new fields in the past 10 years or so in business that ask for implementation of new algorithms

Transit routing is still moving fast in this area. We're still figuring out the best ways to return thousands-by-thousands transit time matrices with query-time truck dimensions and traffic updates. It can't be the only field!

> Engineers have done amazing work of abstracting away the hard algorithms and data structures.

Not to mention we told everyone to not roll their own anything, just use the free library for, clocks, time, crypto, auth, IPC, etc!