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This reminds me of a pattern I see a lot in grad students in ML -- those least comfortable with abstracting away boilerplate are usually those least familiar with what the boilerplate represents.
I mean, can I really trust a library to compute my gradients? I don't know what's going on behind the scenes... 
well... can I???
Notations are fine as long as they are universally known and accepted. Same goes for libraries. As of now, libraries are known only to a few. As they get known and used widely, the trust will increase!
I think it's the natural path, at least to me. It's not that I don't trust libraries, but things are quite confusing until I understand what they are really doing. To understand, sometimes I implement it, and then I understand and can proceed to use the abstractions.
You’ve identified a misconception. What strategies might you use to correct it?
To truly be comfortable with the abstraction requires thorough understanding of what's below. If you learn GR and assume "all those differential forms take care of indices" w/o detailed insight, you're bound to make crucial mistakes.
Yeah. That said, I still find a lot of value in tinkering often - I’m paranoid of the lamppost effect - but that said I build my own lightweight APIs anyway.
Although this doesn’t fully explain my compulsion to write monographs that drive from first principles. I think there’s some desire to construct there.
I've been surprised at how many questions appear on Quora from students of computer science who think their job is to learn algorithms and data structures, rather than how to think.
Exactly!
This reads as if you disapprove? Shouldn‘t they learn those details before using the library?
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