Over time, by consistently exploring the guts of anything I’m working with that seems magical, I’ve built up a broad base of knowledge about how various technical systems work. This helps me in tons of different ways. It makes it easier to track down tricky bugs across many layers of the stack. I can learn new languages and libraries quickly by pattern-matching them to what I already know. It gives me better ideas for software designs, by imitating other systems I’ve seen, or by reusing ideas or tools I’ve heard of in a different context. Maybe most importantly, it gives me the confidence that, if I run into a tricky problem, I can learn enough to solve it, instead of feeling like I’m at the mercy of a system too complex to hope to understand.
This is an excellent piece arguing why you should invest in deeply learning things you already know instead of something new and/or shiny.
It’s not shiny or esoteric, but for me, building a detailed mental model of those (and how they compare to alternatives) might be the learning that’s contributed most to my effectiveness as an engineer.
These mundane, highly-specific things are termed “blubs” for reasons you’ll find out in the piece.