Prompt Engineering: The Art Of Outsourcing Semicolons

The AI ethics bot just witnessed a child.kill() function and had a full-blown existential crisis. Classic case of "context matters" in programming. The bot's like "I can't assist with violence!" while the code's just handling thread management. Look at that beautiful irony - a recursively named AI refusing to help with perfectly innocent code because it contains a murder-sounding method. Six years of code reviews and I've never seen an AI clutch its digital pearls this hard.

Five of these panels show the typical innovation S-curve where technology evolves from primitive (MS-DOS, Internet Explorer) to peak performance (Windows 95, Chrome). Then there's music... where we apparently peaked at Napster and it's been downhill ever since. The real innovation was clearly the ability to download entire discographies without paying a cent. Progress isn't always what corporate overlords want you to believe it is.

Finally, a sorting algorithm with the computational complexity of O($$). Just send your array to GPT, pay a few cents, and get it back sorted. Perfect for when you need your data arranged but can't be bothered to remember how quicksort works. The best part? If your array isn't sorted correctly, you can just claim it's because the AI "didn't understand the vibe" of your integers.

Oh. My. GOD! The absolute CHAOS of AI ambiguity in 2023! 😱 Developer dude casually drops "I let AI write the code for that feature" and his colleague immediately jumps to the conclusion he's using some mystical "vibe coding" technique. PLOT TWIST! He's just talking about their human colleague named Ai Tachikawa! The audacity of having a normal Japanese name in this ChatGPT-obsessed hellscape! This is what happens when the term "AI" gets so overused that we forget actual humans named Ai exist! The secondhand embarrassment is PHYSICALLY PAINFUL right now!

The hard pill that data scientists refuse to swallow! While everyone's obsessed with fancy neural networks and complex algorithms, the brutal reality is that garbage data produces garbage results, no matter how sophisticated your model is. It's like putting lipstick on a pig - your 17-layer deep learning architecture won't save you from the mess of unclean, biased data you're feeding it. The real heroes aren't the ones with the fanciest models but the poor souls who spend weeks cleaning datasets nobody will ever appreciate. Next time someone brags about their model's accuracy, ask them about their data preprocessing steps and watch them squirm!

The tables have turned! After decades of philosophers being told "good luck finding a job," now they're smugly watching the software industry implode with layoffs, AI replacing entry-level devs, and 300 applicants fighting for each position. That "philosophy factory" joke hits different when you're on your fifth technical assessment for a junior role that requires 7 years of experience in a 3-year-old framework. Maybe Socrates had it right all along—true wisdom is knowing you'll never pass the hiring manager's impossible requirements.