Algorithm complexity Memes

The tech industry's selective standards are painfully real. They'll reject a fresh grad for not implementing some theoretical O(n) algorithm they'll never use again, but will happily throw billions at AI models running on brute-force compute that would make any algorithm professor have an existential crisis. For those who don't know, O(n) refers to linear time complexity - basically how efficiently an algorithm scales. Companies obsess over this in interviews then proceed to ignore efficiency completely when it comes to their shiny new toys. Next time you're rejected for not optimizing a binary tree traversal fast enough, just remember - somewhere a data center is melting the polar ice caps to generate a cat picture.

When your algorithm skills are so bad, you take logarithmic problems and somehow turn them into cubic complexity nightmares. It's like having a superpower, but the worst one possible. Your brain is essentially an inefficiency generator that would make computer scientists weep. For the uninitiated: O(log(n)) represents highly efficient algorithms that barely break a sweat as inputs grow, while O(n³) is the computational equivalent of trying to empty the ocean with a teaspoon. Congratulations on being a walking computational disaster!