Delay Tech Singularity

The modern developer's secret weapon: copy-pasting AI-generated code without understanding a single line of it. Nothing says "senior engineer" like frantically Googling what your own code does when someone asks about it. The best part? That magical moment when you realize you've been confidently deploying code that you've essentially been taking dictation on. "It validates form data" is just fancy speak for "I have absolutely no idea what this eldritch horror does but it hasn't crashed production yet."

Oh look, it's the compiler we all secretly want but are too afraid to admit! The "AI C Compiler" (aicc) is basically what happens when you combine a programmer's desperation with their unwillingness to learn proper memory management. Instead of fixing your semicolons and memory leaks like a responsible adult, just throw your garbage code at ChatGPT and pray! "Will your code compile? Will it be fast or slow? Will it have memory leaks?" Who knows! It's like playing Russian roulette with your codebase, except all chambers are loaded and the gun is pointed at your production server. The best part? You don't even need to understand what you're doing anymore! Just make vague gestures at your requirements and let AI figure it out. Programming degree? Waste of money. Stack Overflow? Ancient history. Just whisper sweet nothings to the machine and watch it spit out code that's probably better than yours anyway.

GitHub's savage billboard burns us all to a crisp with brutal honesty. They trained their AI Copilot on our garbage code, then have the audacity to hang a building-sized sign explaining why their tool produces questionable results. It's like blaming the student for the teacher's incompetence—except we're the incompetent teachers. The ultimate "it's not me, it's you" breakup line from the tech world. Next time your PR gets rejected, just point at this billboard and say "not my fault, I'm just a product of my environment."

The AI benchmarking cult strikes again! Everyone's obsessed with those radar charts comparing Large Language Models using some bizarre "ball turning test" metric that nobody actually understands. It's just a bunch of geometric shapes that supposedly prove one model is better than another. The joke here is that these comparison charts have become so ubiquitous in AI discussions that even though they're practically meaningless to most developers, everyone nods along pretending to understand what they're looking at. Classic tech impostor syndrome - nobody wants to be the one to ask "what the heck does this actually measure?"

The handshake of deception! Both AI artists and "vibe coders" have found their career shortcut—let the machines do the heavy lifting while you collect the praise. For the uninitiated, "vibe coders" are those developers who spend more time curating their aesthetic GitHub profiles and tweaking VS Code themes than writing actual code. They're the ones who post snippets they didn't write and drop terms like "neural architecture" in casual conversation despite just copy-pasting from ChatGPT. The modern tech equivalent of putting your name on the group project after contributing absolutely nothing. But hey, fake it till you make it, right? Just don't ask them to whiteboard anything without internet access.

The tech billionaire claims they don't use Convolutional Neural Networks (CNNs) much anymore, while a deep learning pioneer questions how real-time image processing could work without them. Meanwhile, the punchline suggests former Twitter employees are manually doing what AI should be automating—literally drawing circles around objects by hand! It's the classic "we've replaced sophisticated technology with underpaid humans" move that every veteran engineer has seen at least once. Some VP somewhere is definitely calling this "an innovative hybrid approach" while the poor souls circle road signs in MS Paint for 8 hours a day.