Me: Optimize a sort & match method in backend because users complain it's a bit slow.

Coworker: These algorithms are both O(n), so they're identical *closes PR*

Me: *start zoom call* "Heeeeeeeeeey Iiiiiiiiiii wouuuuuuuld liiiiiiiiike toooooo diiiiiisscuuuuus thaaaaaaaat puuuuuuulllll reeeeeequuuueeest yooooouuuuu cloooooossseeeed"

Coworker: "wtf are you doing, why are you talking so slow"

Me: "No matter whether I talk fast or slow, the information still reaches you in O(n) time, so why are you complaining"

I fucking hate it when people misunderstand the purpose of (or abuse) big O notation. It's an estimate of how an algorithm SCALES once the set increases in size, in which case you leave out both less significant terms and constant factors.

But those terms and factors are important when you're talking about the DIRECT PERFORMANCE of the algorithm on fixed-size sets, instead of SCALING to larger sets.

1n and 10n are both O(n), but 10x performance on a job that used to take 10 minutes is still significant.

Me: *Watching a movie*

Main Character: "Oh no, we have to hack the CIA to figure out how this machine works! Hacker girl, do the stuff"

Hacker Girl: "Consider it done!"

Hacker Girl: *Opens Linux bash*

Hacker Girl: *types 'mkdir Hack_CIA'

Hacker Girl: "They have two-factor authentication in place, this is going to be a hard one."

Hacker Girl: *Types 'cd Hack_CIA'*

Hacker Girl: "I'm in!"

Me: "..."

Friend: "Wow, so well done, so realistic!"

Me: *Dies*

OOPs It worked LOL

Good Night

Doing StackOverflow.com survey in the honest way.