AI Buzzwords Be Like

OpenAI out here defending their AI training on copyrighted material by saying the race is "over" if they can't use it. Meanwhile, they're getting roasted with the car thief analogy: "10/10 car thieves agree laws are not good for business." The irony is chef's kiss. Tech companies built entire empires on intellectual property protection, patents, and licensing agreements. But suddenly when they need everyone else's data to train their models, copyright is just an inconvenient speed bump on the innovation highway. It's like watching someone argue that stealing is actually just "unauthorized borrowing for the greater good of transportation efficiency." Sure buddy, and my git commits are just "collaborative code redistribution."

The asylum of programming sins is now accepting new patients! Left to right, we have the poor soul who thought regex was a sensible XML parsing solution (narrator: it wasn't), the delusional dev who reinvented the wheel with a custom date/time library (because clearly, humanity hasn't solved that problem in the last 50 years), and finally—the pièce de résistance—the screaming maniac who blindly copy-pasted AI-generated "fixes" straight into production. The padded walls of this code asylum are the only things keeping these developers from harming themselves or others with more terrible technical decisions.

The duality of AI in the development lifecycle is painfully accurate. Junior devs are celebrating like they've discovered fire – "Look ma, no hands!" – while generating code they barely understand. Meanwhile, senior developers stare into the existential void as they're forced to review the AI-generated spaghetti mess. That thousand-yard stare says it all: "I've seen things... terrible things... like nested ternaries and 200-line functions named 'doStuff'." The circle of programming life continues – AI just made the mess arrive faster.

The corporate AI fever in a single frame! That dad just burned through millions on generative AI "proof-of-concepts" that will forever remain in the graveyard of tech demos. The son's sarcastic "What a legend" is peak engineering cynicism—he already knows these projects are the software equivalent of buying a treadmill that becomes a clothes hanger. Meanwhile, every ML engineer is nodding furiously because they've watched executives throw cash at half-baked AI ideas with the ROI strategy of "figure it out later." The real production environment was the friends we made along the way!

Content I'm going to use ChatGPT to write this document and replace "(Price]" with "$2.50" Every instance of P, r, i, c, e is the second capture group followed by ".50"

So apparently the future of coding is just naming functions like you're writing a novel and letting Copilot/ChatGPT do the heavy lifting. The function name divideMp4IntoNSegmentsOfLengthT() is so descriptive it basically is the documentation, and boom—the AI autocompletes an entire ffmpeg command that would've taken you 30 minutes of Stack Overflow archaeology to piece together. The controversial take here? Maybe we're entering an era where understanding the actual implementation matters less than being good at prompt engineering your function names. It's like pair programming, except your partner is an AI that never takes coffee breaks and doesn't judge your variable naming conventions. The real kicker is that this actually works surprisingly well for glue code and CLI wrangling. Just don't ask the AI to implement a red-black tree from scratch—it'll confidently give you something that compiles but has the time complexity of O(n²) when you sneeze.