Hi - I am confident that 16 workers was the right number for that application deployed on that machine. The machine is described in the article. If you took this app and put it on a machine with 8 cores clearly it would make sense to try 32 workers - but in practice I think few Python apps are so IO bound as this one. Most of the time, just over 2 * cpu count is about the right number.

I suspect that scheduler overhead is not a realistic consideration for a Python program. My understanding is that switching executing process takes microseconds at worst, which would be too small to notice from the point of view of a Python programmer.

On "it would be the job of the programmer to yield after some time" - I'm always personally suspicious of any technique that rests on programmer diligence. My experience suggests not to require (or even expect!) programmer diligence, even from my own (I assure you, god like) programming abilities. Secondly, yielding more often probably would not help (and in fact I half-suspect part of the problem is the frequent yielding at every async/await keyword!).