University Lied: It Was Space Complexity All Along

The numerical system paradox strikes again! The question asks what base has 10 digits in base 10, and the answer distribution is pure mathematical chaos. The trick is that any number system represents its own base as "10" - binary (base 2) writes 2 as "10", octal (base 8) writes 8 as "10", etc. So technically, every base is "base 10" when written in its own number system! The frustrated middle character screaming "no!!! it's two!!!" gets it but can't handle the semantic trickery, while the chill characters on both ends are just vibing with "it's ten" - both correct in their own way. It's the perfect trap for the pedantic programmer who lives in the binary world but has to interface with humans.

Machine learning algorithms don't question their training data—they just optimize for patterns. So when a concerned parent uses that classic "bridge jumping" argument against peer pressure, ML algorithms are like "If that's what the data shows, absolutely I'm jumping!" No moral quandaries, no self-preservation instinct, just pure statistical correlation hunting. This is why AI safety researchers lose sleep at night. Your neural network doesn't understand bridges, gravity, or death—it just knows that if input = friends_jumping, then output = yes. And this is exactly why we need to be careful what we feed these algorithms before they cheerfully optimize humanity into oblivion.

The infamous "sleep sort" algorithm—where your array gets sorted by setting timeouts based on each value. The smaller numbers wake up first, the bigger ones hit snooze longer. Technically it works (sort of), but try explaining this beauty in a coding interview and watch the interviewer's soul leave their body. "It's O(max(array)) time complexity, sir!" Absolute chaos masquerading as computer science. The perfect algorithm if your requirements include "must be completely unreliable" and "please never use in production."

Ah, the classic "let's use a sledgehammer to kill a fly" approach. Every tech startup these days thinks they need machine learning to solve problems that could be handled with an if-statement and a cup of coffee. After 15 years in the industry, I've sat through countless pitch meetings where some bright-eyed founder explains how their revolutionary AI will disrupt the sandwich-ordering process. Meanwhile, their actual problem is that they can't figure out how to store user preferences in a database. The real kicker? When they finally implement their neural network to predict topping preferences, it works worse than random chance. But hey, at least they can put "AI-powered" in their pitch deck!

Remember when video games were just... fun? Before you became a programmer and couldn't help but see the collision detection glitches, frame rate issues, and spaghetti code lurking beneath the surface? Now you're sitting there calculating the physics equations they used for that jump animation, mentally optimizing their render pipeline, and thinking "that NPC pathfinding algorithm must be A* with a custom heuristic." The curse of knowledge strikes again. You've peeked behind the curtain, and now you can't unsee the matrix. Gaming will never be the same, but hey—at least you can impress absolutely nobody by explaining why that texture is flickering.

The age-old battle between while loops and do-while loops, perfectly illustrated by Road Runner and Wile E. Coyote! The Road Runner checks conditions before running (while loop), safely avoiding the cliff edge. Meanwhile, poor Coyote executes first and checks conditions later (do-while loop), guaranteeing at least one painful fall into the canyon. This is basically every programmer's first encounter with loop selection coming back to haunt them in production. Some bugs you just can't patch mid-air!