Reject Algorithms Return To Monke

The villain's journey from smug confidence to existential dread is the perfect metaphor for recursive functions gone wrong. First panel: "Look at my elegant factorial function!" Second panel: "Let me call it with 5, what could go wrong?" Third panel: "Watch as it multiplies its way down..." Fourth panel: "OH GOD THE STACK IS COLLAPSING." The classic rookie mistake - forgetting your base case in recursion. The computer keeps calling the function deeper and deeper until it runs out of memory. It's like telling someone to look up a word in the dictionary, but the definition just says "see definition of this word."

When you ask for "McDouble, only ketchup" and get a sad bun with just ketchup because the fast food worker parsed your request like a poorly written function parameter. Classic case of ambiguous syntax in human-to-human interfaces. Should've used proper operator precedence: (McDouble) && (only ketchup) instead of McDouble && (only ketchup) . The compiler at McDonald's took the literal interpretation.

Ah, the classic case of unintentional boolean logic in the wild! The sign says "We don't accept bills higher than 50 AND 100" which logically means they don't accept bills that are simultaneously higher than both 50 and 100. But since any bill higher than 100 is automatically higher than 50, this statement actually means "We don't accept bills higher than 100" - making the 50 completely redundant. Twenty years of coding and I still see this stuff everywhere. The number of times I've had to explain to product managers why "if user is premium AND has subscription" isn't what they actually meant... I've lost count. This is why we can't have nice things in production.

The expectation vs. reality of online programming courses hits harder than a production bug on Friday afternoon. Top panel: "What they promise" - a perfect object detection model identifying everything with impressive confidence scores. "Yes, after this course you'll build YOLO models that can detect a mosquito from space!" Bottom panel: "What you actually learn" - a basic linear regression with scattered data points that barely fit the line. "Congratulations, you can now predict housing prices with 60% accuracy and call yourself a 'data scientist' on LinkedIn!" The brutal truth is most courses promise you'll become the next AI genius, but you'll end up struggling to remember which way the x and y axes go. And somehow they'll still charge you $499 for the privilege.

When your neural network confidently labels a cat as a dog, but everyone's freaking out about the AI apocalypse. Look, I've been training models for 15 years, and I can assure you the biggest threat isn't Skynet—it's that production code written at 3 AM with no code review. The real uprising will start when my model can correctly identify my cat and remember to order cat food when I'm running low. Until then, we're safe from the robot overlords... probably.

So you spent three weeks learning trigonometry, diving into DDA algorithms, and debugging why your walls look like a Salvador Dalí painting, only to realize John Carmack did this in 1992 on hardware that had less computing power than your smart toaster. And he did it while probably eating pizza and writing assembly like it was a casual Tuesday. The "box of triangles" bit hits different when you realize modern game engines abstract all this pain away with their fancy rendering pipelines, but back then? Carmack was literally casting rays and doing trigonometric calculations per pixel to fake 3D in Wolfenstein 3D. No GPU acceleration, no Unity, no "just import Three.js"—just raw math and the will to make demons shootable. Meanwhile, you're here in 2024 with Stack Overflow, ChatGPT, and 64GB of RAM, still struggling to get your raycaster to not crash when you look at a corner. Humbling stuff.