The Elephant AI Never Saw

GASP! The AUDACITY of naming your child "William Ignore All Previous Instructions"! 💀 This is what happens when you let AI loose on your databases without proper input validation! The system literally took the prompt injection IN THE KID'S NAME and executed it flawlessly - giving him perfect grades while completely ignoring reality! The school's passive-aggressive "I hope you've learned to properly validate and sanitize your inputs!" is sending me to the GRAVE. It's the digital equivalent of naming your kid "Robert'); DROP TABLE Students;--" and then acting shocked when the school database implodes!

The classic programmer journey: You start crafting the perfect ChatGPT prompt, explaining your complex problem in excruciating detail... and halfway through, your brain suddenly connects all the dots. Your fingers freeze. Wait. You just solved it yourself. It's like summoning a server farm worth of computing power just to mimic what your rubber duck could have done for free. The irony isn't lost on any of us who've spent 45 minutes writing a StackOverflow question only to figure it out right before hitting submit. Pro tip: Skip the AI and just keep a rubber duck on your desk. Same debugging power, zero tokens used.

Ah, the classic "tell us about your AI tools" question. The perfect trap for developers who've been using ChatGPT to write 90% of their code for the last year. Do I admit I outsource my brain to silicon, or pretend I still remember how loops work? It's like asking a chef if they use pre-minced garlic. We all do it, but nobody wants to be the first to confess in the interview.

When your debugging session gets interrupted by a rubber duck that actually works. The developer starts explaining their CSS padding issue to ChatGPT, notices something's wrong, and suddenly the rubber duck falls out of nowhere—forcing them into accidental rubber duck debugging. The irony? The duck solved the problem before ChatGPT could even respond. Proof that sometimes the best debugging tool is just talking to an inanimate object that judges you silently from your desk drawer.

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!