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I also have one
(media.feddit.fr)
cross-posted from: https://feddit.fr/c/microblogmemes/p/226650/i-also-have-one
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this post was submitted on 03 Jun 2026
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if it's big it's definitely not O(1)
If it’s a finite sized chair, it’s O(1)!
Not if items are covered by other items. You've got a layer of fast L1 cache that is O(1), but exceed the limit of that layer by placing an item of clothing on top of it, and some elements are effectively "pushed down" to a higher latency tier, which is going to be closer to O(log N) as you have to move some percentage of the items out of the way to get to the target item. Cache eviction (doing the laundry) will reset this.
As always careful optimisation of the cache size, in relation to the expected distribution of items to be accessed, is key to maximising performance.
Nope. Assume the chair fits at most C clothes, with C being a constant.
The efficiency is at worst O(C) = O(C * 1) = C * O(1) = O(1).
Sure, the more clothes are on top, the longer it takes. But Big O notation is only useful if the N gets very big. And considering that the naximum amount of clothes is likely very small, it can be treated as O(1).
That’s not how Big O works
The bigger the pile, the longer it takes to find an individual item (worst case). How is that O(1)?
I mean, if structure is O(1) for low N, it’s still O(1) for high N. If O “changes” from 1 to N with size then it’s never been 1. All I’m saying
is not how you identify correct O