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Someone just learned how to add "act as an expert" to their ChatGPT prompts and suddenly thinks they've transcended human knowledge. The hubris is real. The first tweet is genuinely asking why Wikipedia exists when ChatGPT can just... make stuff up with confidence? Because nothing says "reliable information" like a large language model that occasionally hallucinates entire programming languages and historical events. Sure, let's replace peer-reviewed, sourced articles with probabilistic token generation. What could go wrong? The reply absolutely murders them with a Wall-E reference—comparing them to the humans who got so dependent on technology they literally became floating blobs in chairs. Brutal. Accurate. Chef's kiss. 💋 The irony? These are the same devs who will spend 3 hours debugging why their AI-generated code doesn't work instead of reading the docs for 5 minutes. Wikipedia isn't going anywhere, buddy.

So you used to write beautiful comments explaining every function, every variable, every decision? Yeah, those were simpler times. Then ChatGPT dropped and suddenly your entire codebase became AI-generated spaghetti that you barely understand yourself. Now your "well-commented code" is just cryptic AI outputs with maybe a desperate "TODO: figure out what this does" thrown in. The innocence is gone. The trust is shattered. You're just a prompt engineer now, copy-pasting mysterious code blocks and praying they work. Welcome to the post-2022 developer experience where comments are a luxury from a bygone era and Stack Overflow feels like ancient history.

The classic tech startup founder transformation arc, but make it AI. You start with that ambitious gleam in your eye thinking you're about to revolutionize machine learning. Then you dump your entire Series A funding into GPUs and cloud infrastructure because "we need compute power!" Next thing you know, you've automated every single position in your company including your own, because efficiency, right? The punchline? Your AI-powered product is so expensive to run that your target market can't even afford the subscription fees. Turns out training models on petabytes of data and running inference at scale costs slightly more than a Netflix subscription. Who knew that burning through millions in compute costs would make your pricing model look like a luxury yacht rental? The clown makeup progression perfectly captures the descent from "visionary entrepreneur" to "why is my AWS bill six figures this month?" The real kicker is realizing you've essentially built a very expensive solution looking for a problem that can actually pay for it.