Remote Controlled Robo Taxi

Behold, the pinnacle of security theater! First, let's expose our database directly through an API endpoint because REST is "too complex." Then, let's sprinkle in some AI validation using GPT to check if SQL queries are safe—because regular expressions and parameterized queries are just so last decade . Nothing says "I'm a 10x developer" quite like importing five different packages, exposing your database credentials in plaintext, and asking an AI if DROP TABLE users; seems fishy. The cherry on top? That 403 error when the AI says no—as if hackers will politely accept rejection and go home. SQL injection protection via AI prompt? Congratulations, you've invented a security vulnerability with a carbon footprint!

OH. MY. GOD. The difference is SENDING ME! 💀 Left side: AI code looking like it's applying for a PhD with its perfectly commented, meticulously structured, memory-checking perfection. Like that one friend who color-coordinates their closet AND alphabetizes their spice rack. Right side: Human programmer's chaotic masterpiece with its cryptic "TODO: More chars" (which will stay there until the heat death of the universe), random variable names, and that absolutely unhinged nested loop that's probably printing ASCII art of their ex's face or something. The true signature of human code isn't elegance—it's the beautiful disaster that somehow still works despite looking like it was written during a caffeine-induced hallucination!

Spent three hours debugging only to end up asking ChatGPT, Claude, and Gemini the same question with slightly different wording hoping one of them accidentally gives you the right answer. Modern debugging isn't about knowing how to fix problems—it's about knowing which AI to sweet-talk into fixing them for you. The real skill is crafting the perfect prompt that doesn't make the AI say "That sounds challenging, have you tried reading the documentation?"

THE ABSOLUTE DRAMA of data scientists SCREAMING at the confused cat who just doesn't get it! 💅 NumPy is THE BACKBONE of scientific computing in Python, and these people are having a COMPLETE MELTDOWN because someone dared to pronounce it "num-pee" instead of "num-pie." The AUDACITY! The HORROR! As if mispronouncing a library name is going to crash the entire matrix! Meanwhile, the cat is just sitting there, judging everyone with that blank stare like "humans and their programming problems, I literally eat kibble for a living."

Ah, the eternal developer paradox. When someone shows us AI-generated code, we instantly recognize it as a tangled mess of bugs and questionable design choices. "This is brilliant," we say with thinly veiled sarcasm. But then there's our own code—equally disastrous, probably held together with duct tape and prayers—and somehow we're irrationally attached to it. "But I like this." It's like criticizing someone else's kid for being messy while your own demon spawn is literally setting the house on fire. The cognitive dissonance is strong in this profession.

Oh. My. GOD. That moment when you've been coding for 48 hours straight, fueled by nothing but energy drinks and sheer desperation, and suddenly your AI code assistant cuts you off because you've used up all your precious credits! 💀 You finally look at the absolute MONSTROSITY you've created with your own two hands and it's like meeting a demon spawn you don't even recognize! What IS this unholy abomination of nested if-statements and variable names like 'temp2Final_WORKS_DONTTOUCH'?! The primitive caveman brain takes over as you stare at your creation... confused unga bunga indeed. No AI to save you now, just you and your crimes against computer science!