Marge Sort

BREAKING NEWS: AI absolutely MASSACRES basic arithmetic while showing its work! The audacity of this machine to think that if someone is 70, and their sister was half their age when they were 6, she'd be 73 now?! HONEY, NO! The sister is 67! If she was 3 when you were 6, she's always going to be 3 years younger than you! The age gap doesn't magically change with time! This is why programmers still have job security—AI can't even handle elementary school math problems without making them unnecessarily complicated. And they want this thing driving our cars?! I CAN'T EVEN! 💀

Ah, the elegant art of being simultaneously clever and ridiculous. This Python function is the programming equivalent of using a sledgehammer to kill a fly—with style. Instead of the boring old num % 2 == 0 check that peasant programmers use, this galaxy-brain developer created a string "eovdden" and indexes into it using num % 2 as the position, followed by another 2 (which does absolutely nothing). The string is cleverly arranged so position 0 gives "even" and position 1 gives "odd" — making this the most unnecessarily complex even/odd checker in existence. It's like building a Rube Goldberg machine when a light switch would do. And those sunglasses emojis? They know exactly what they've done. Pure chaotic evil masquerading as code.

When you dive into machine learning without understanding the fundamentals, things go sideways real quick. Poor Hulk thought he'd build a sophisticated image recognition system, but ended up with code so broken it's just spitting out random numbers. It's the classic journey from "I'll just follow this tutorial" to "why is my neural network predicting that cats are submarines?" The tears are real – we've all been there, staring at our monitor at 2AM wondering how our brilliant AI project turned into digital soup. Machine learning: expectation vs reality in its purest form.

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.

Python starts out all friendly and approachable, luring you in with its simple syntax and beginner-friendly reputation. "Look at me, I'm so easy to learn!" it says with that innocent dinosaur face. Then suddenly you're drowning in machine learning libraries, matrix math, and data mining frameworks that make calculus look like kindergarten finger painting. The learning curve isn't a curve at all—it's a vertical wall with spikes at the top. One day you're printing "Hello World," the next you're implementing neural networks while questioning your life choices.

The evolution of programmer excuses is a beautiful thing to witness. First, you've got "algorithm" – the fancy word we throw around when we just don't feel like explaining our spaghetti code to the junior dev. Then there's "heuristic" – perfect for when you cobbled together a solution at 3 AM that somehow works but you've genuinely forgotten how. And finally, the boss level: "machine learning" – where even YOU don't know what your code is doing anymore. The model is just vibing in n-dimensional space making decisions while you nod confidently in meetings. The progression is basically: "I won't tell you" → "I can't tell you" → "Hell if I know."