I Propose A New Formula That Could Revolutionize The World

The perfect illustration of machine learning in its natural habitat! Our protagonist claims ML as their strength, then immediately demonstrates how it actually works—getting the first answer wrong, then blindly repeating the "correct" answer regardless of the new input. This isn't just a coding joke; it's an existential crisis for anyone who's ever built a model that confidently produces nonsense. The algorithm has spoken: 6+9=15, therefore 4+20=15. The math is wrong, but the confidence is unwavering. Just like 90% of the ML models currently running in production somewhere.

Physics professors: "Energy cannot be created or destroyed, only transformed." Meanwhile, ChatGPT's servers vaporizing 1000 liters of cooling water into the void dimension every time you ask it to explain why your crush isn't texting back. The First Law of AI Thermodynamics: Conservation of Energy applies to everything except data centers, which convert electricity directly into existential dread and slightly warmer oceans.

The eternal battle between creativity and automation! Artists are having an existential crisis over AI doing their jobs, screaming "BLASPHEMY!" like it's the end of civilization. Meanwhile, engineers are practically weeping tears of joy—finally free from the tedious parts of their work! The contrast is PERFECT. Engineers built the AI monster and now they're celebrating while artists are planning the revolution. It's basically the tech version of "I created this problem, and I'm thrilled about it!" 😂

The classic physics researcher's dilemma! Everyone's hyping AI and machine learning as the next big thing in physics, but the reality hits different. Sure, your neural network might be 1,000 times faster than traditional methods, but those 20% larger error bars? That's the part they conveniently leave out of the grant proposals. This perfectly captures the trade-off that haunts computational physics - speed vs. precision. Physics researchers everywhere are silently calculating whether shaving months off computation time is worth the awkward conversation with reviewers about those suspiciously chunky error bars.

Content Varun Khurana • @varunkhurana99 Your company's highest paid engineer showing up to meetings

The mathematical comedy show starring ChatGPT! First, it confidently declares 9.11 > 9.9 (correct). Then when asked to subtract them, it gives 0.21 (also correct). But when prompted to "use python" suddenly 9.11 - 9.9 = -0.79?! This is the AI equivalent of a student who can solve a problem on paper but completely falls apart during the practical exam. What we're witnessing is floating-point arithmetic having an existential crisis. In computers, decimal numbers are approximated, leading to these bizarre precision errors that would make any math teacher reach for the red pen... and possibly a stiff drink.