SICP: The only computer science book worth reading twice? (2010)

It's for people that would like to learn rather advanced programming techniques and foundational ideas in computer science.

You may accidentally wish something you don’t yet know the true nature of, and this will create a fragile mess at the bottom. It usually does, cause algorithmic nature of things is rarely intuitive. Starting from the bottom is like starting from quarks that you have rather than from “I want magic to exist”. Well it does not. You reach the bottom and there’s quarks instead of magicules and you’ve lost all context clues on the way which could help to convert between two physics.

Both approaches have their use, because sometimes you have to be bold with your wishes to solve a deep problem. But personally I prefer magic to be packed into the before-topmost layer. I.e. build from the bottom up, and then, just before the business logic, create a convenience magic layer that translates to/from business speak. It becomes adjustable and doesn’t induce a tangled mess all the way down.

Traditionally, the field of databases is largely about solving algorithm problems in the scenario where you have much more data that can fit in memory. Data exists on disk as "pages", you have a fixed number of "page slots" in RAM. Moving pages from disk to RAM or RAM to disk is slow, so you want to do as little of that as you can. This makes trivial problems interesting -- e.g. there's no notion of a 'join' in classic computer science because it's too trivial to bother naming.

We're used to thinking of the study of algorithms as a sort of pure essence, but one could argue that algorithmic efficiency is only meaningful in a particular data and hardware context. That's part of what keeps our jobs interesting, I guess -- otherwise algorithm expertise wouldn't be as useful, since you could just apply libraries/cookbook solutions everywhere.

At my uni 10 years ago the CS program didn’t touch anything related to hardware, hell the CS program didn’t even need to take multivariable calculus. In my computer engineering program we covered solid state physics, electromagnetism, digital electronics design, digital signals processing, CPU architecture, compiler design, OS design, algorithms, software engineering, distributed systems design.

The computer engineering program took you from solid state physics and transistor design to PAXOS.

The CS program was much more focused on logic proofs and more formalism and they never touched anything hardware adjacent.

I realize this is different between programs, but from what I read and hear many CS programs these days start at Java and never go down abstraction levels.

I do agree with you that learning the fundamentals is important, but I would argue that a SICP type course is not fundamental — physics is fundamental. And once you learn how we use physics to build CPUs you learn that fancy algorithms and complex solutions are not necessary most of the time given how fast computers are today. If you can get your CPU pipelined properly with high cache hits, branch prediction hits, prefetch hits, and SIMD you can easily brute force many problems.

And for those 10% of problems which cannot be brute forced, 90% of those problems can be solved with profiling and memoization, and for the 10% of those problems you cannot solve with memoization you can solve 90% of them with b-trees.