I may be completely out of line here, but isn't the story on ARM very very different? I vaguely recall the whole point of having stuff like weak atomics being that on x86, those don't do anything, but on ARM they are essential for cache coherency and memory ordering? But then again, I may just be conflating memory ordering and coherency.
Maybe this is a naive question, but how are "skills" different from just adding a bunch od examples of good/bad behavior into the prompt? As far as I can tell, each skill file is a bunch of good/bad examples of something. Is the difference that the model chooses when to load a certain skill into context?
One detail most comments seem to be missing is that the O(1) complexity of get/set in hash tables depends on memory access being O(1). However, if you have a memory system operating in physical space, that's just not possible (you'd have to break the speed of light). Ultimately, the larger your dataset, the more time it is going to take (on average) to perform random access on it. The only reason why we "haven't noticed" this yet that much in practice is that we mostly grow memory capacity by making it more compact (the same as CPU logic), not by adding more physical chips/RAM slots/etc. Still, memory latency has been slowly rising since the 2000s, so even shrinking can't save us indefinitely.
One more fun fact: this is also the reason why Turing machines are a popular complexity model. The tape on a Turing machine does not allow random access, so it simulates the act of "going somewhere to get your data". And as you might expect, hash table operations are not O(1) on a Turing machine.