Feed Me More RAM

AI evangelists love to pitch that you can now build apps without knowing how to code. Just prompt your way to success, they say. Ship features with vibes alone. But here's the thing: AI can't fix the fact that your brain is a barren wasteland of unoriginal thoughts. You still need something worth building. Turns out the bottleneck was never the coding—it was having a single interesting idea in the first place. So congrats, you've automated the easy part and still can't ship because you're stuck staring at a blank canvas wondering what the 47th todo app should look like.

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?"

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When the CAPTCHA goes from "select all traffic lights" to a full-blown biology exam. Those duck feet at the top and nine different animals below? Clearly designed by a sadistic backend dev who got rejected by a UI designer. The real Turing test here is figuring out if you're supposed to click on birds, cats, or just give up and accept that bots have better animal anatomy knowledge than humans. Next they'll ask us to identify which semicolon is missing from a screenshot of 500 lines of JavaScript.

For decades, error handling was that thing everyone nodded about in code reviews but secretly wrapped in a try-catch that just logged "oops" to console. Nobody wrote proper error messages, nobody validated inputs, and stack traces were treated like ancient hieroglyphics. Then AI showed up and suddenly everyone's an error handling expert. Why? Because when your LLM hallucinates or your API call to GPT-4 fails, you can't just shrug and refresh the page. Now you need graceful degradation, retry logic, fallback strategies, and detailed error context. The massive book represents all the error handling knowledge we should've been using all along. The tiny pamphlet is what we actually did before AI forced us to care. Nothing motivates proper engineering practices quite like burning through your OpenAI API credits because you didn't handle rate limits correctly.

Oh look, it's the classic "I hate Nvidia but also I'm completely addicted to their GPUs" paradox! Watching the price go from $1999 to $2499 to $2999 and finally landing at a cool $5000 is like watching your bank account slowly file for bankruptcy in real-time. But here we are, Star-Lord style, pretending we're confused about why we keep crawling back to Team Green like Stockholm syndrome victims. The GPU market has basically become an abusive relationship where Nvidia keeps raising prices to absolutely BONKERS levels, everyone complains about monopolistic practices and scalper-friendly launches, and then... we all line up at 6 AM on launch day anyway because we NEED those ray-traced reflections and DLSS magic. It's fine, we're all fine, everything is fine while our wallets weep in the corner.