I would have like to see the graphs for some non-LLM words, to show the more modest 1.5x jump, so we can be sure that these specific words are increasing in frequency, and not just like, all words.
The problem is that we think of categories as this binary thing, when they're not. We recognize it with adjectives like "Tall"—most people wouldn't say "something is either Tall or it's Not Tall". But the truth is, every word is like that. The solution isn't more and more rigid delineations—the solution is accepting that the borders are wide, and plenty of object straddle them.
To bring up an even more argued about example—is a hot dog a sandwich? Some people say yes, some people say no, but the answer can be (and most likely is) "kinda".
> Reduce also forces you to conjure up a "zero" value of the relevant type, which isn't usually difficult but it does constitute some extra mental overhead.
It's always worthwhile to consider what the result will be when you pass in an empty list.
If you have need of a reducing operation though, you will still need to think about that value. If you are summing up a list of numbers, it doesn't matter whether you use reduce or a loop, you need to set some initial value.
I agree, although most of the time you do end up needing to consider it with Map/Filter. It's just, it doesn't force you to. Which means either you think about it later, or it bites you in the ass because you didn't consider the empty case. Not always—and for those cases where you don't end up needing the empty case, Reduce is probably not necessary, and Map/Filter is sufficient.
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