Comment by kingstnap

I disagree. Performance is a leaky abstraction that *ALWAYS* matters.

Your cognition of it is either implicit or explicit.

Even if you didn't know for example that list appends was linear and not quadratic and fairly fast.

Even if you didn't give a shit if simple programs were for some reason 10000x slower than they needed to be because it meets some baseline level of good enough / and or you aren't the one impacted by the problems inefficacy creates.

Library authors beneath you would still know and the APIs you interact with and the pythonic code you see and the code LLMS generate will be affected by that leaky abstraction.

If you think that n^2 naive list appends is a bad example its not btw, python string appends are n^2 and that has and does affect how people do things, f strings for example are lazy.

Similarly a direct consequence of dictionaries being fast in Python is that they are used literally everywhere. The old Pycon 2017 talks from Raymond talk about this.

Ultimately what the author of the blog has provided is this sort of numerical justification for the implicit tacit sort of knowledge performance understanding gives.