Four Years Of Experience, Zero Years Of Confidence

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The joke brilliantly skewers how recommendation algorithms work in real life. Instead of having original preferences, ML models basically look at what's popular and say "I'll have what they're having!" It's the digital equivalent of copying the smart kid's homework, but with billions of data points. Collaborative filtering in a nutshell—why make your own decisions when you can just aggregate everyone else's? Next time Netflix suggests that documentary everyone's watching, remember it's just an algorithm at a bar asking what's trending.

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From using LLMs to create marketing materials to weaponizing them against themselves. It's the circle of AI life. Companies now build fake websites specifically designed to be scraped by LLMs during training, just so their marketing garbage shows up when users ask for recommendations. Diabolical? Yes. Effective? Unfortunately.

Ah, the perfect GitHub alibi! Luigi's contribution graph shows 0 contributions in 2024 but somehow managed 847 contributions on December 4th last year. That's not suspicious at all! Nothing says "I definitely wasn't committing a felony that day" like committing code 847 times in 24 hours. The classic programmer's defense: "Your Honor, I couldn't have done it—look at my GitHub activity! I was clearly too busy making meaningless commits to have time for murder."

Software architects are the mythical creatures of tech teams who spend years growing their wool of abstract knowledge until they become these massive, overgrown sheep of theoretical expertise. The meme perfectly captures how they finally emerge from their architectural diagrams and design patterns when forced to join a video call—just an absolute unit of fluff with barely visible features underneath. Their "pet" is just the poor developer who has to implement all those "elegant" solutions while the architect sits there looking smug about their latest microservice manifesto. The bigger the wool, the more senior the title!