Truth Hurts: Data Over Models

That's not a white dot, that's Cursor AI sharing your plaintext password file with every hacker on the planet. Nothing says "security professional" like storing credentials in a CSV file named "passwords.csv" and then asking about UI elements while exposing it. Somewhere, a security engineer just felt a disturbance in the force and doesn't know why.

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."

The modern "full stack" - three AI tabs open in your browser while you pretend to know what you're doing. Remember when being full stack meant actually knowing multiple languages and frameworks? Now it's just knowing which AI to ask for which problem. "Yes, I'm proficient in Claude, ChatGPT, and Perplexity" should be the new line on resumes. The only stack that matters is the stack of browser tabs helping you fake your way through that ticket your PM swore was "just a small change."

The evolutionary tale of a data scientist! First, we see Statistics (elephant) and Computer Science (snake) as separate entities. Then they decide to collaborate—because obviously, elephants and snakes make natural coding partners. The snake begs for statistical knowledge, and suddenly—BOOM—they transform into a dinosaur labeled "DATA SCIENTIST." It's the perfect representation of how merging statistics with programming creates this mythical creature that everyone wants to hire but nobody can quite define. The irony? Real data scientists spend 80% of their time cleaning data, not evolving into majestic dinosaurs. Should've shown the final form as a janitor with a SQL mop.

Oh, the classic "elephant in the room" problem has evolved into the "elephant in the AI" problem! ChatGPT was asked to create an image with "absolutely no elephants" yet there's a massive pachyderm chilling in the corner like it's paying rent. This is the digital equivalent of a unit test that passes despite the glaring bug. The AI confidently declares "Here's the image of an empty room with absolutely no elephants in it" while the evidence trunk-slaps you in the face. It's like when your code compiles without errors but still manages to crash spectacularly in production.

Ah, the classic "asking AI to do your actual job" maneuver! This tweet perfectly showcases the modern developer's workflow: 1) Hear about LLMs 2) Immediately try to outsource your data parsing tasks that you're probably paid six figures to handle. The irony is that parsing documents between formats is literally what programming languages have been doing for decades. It's like asking "Is there a car specifically designed for driving?" while sitting in a Ferrari. Pro tip: Yes, there are LLMs for this. They're called "learning regex" and "using libraries that already exist." Revolutionary concept!