What's Everyone Else Having?

Ever opened a PR from an AI and seen comments like // This is a for loop that iterates through the array ? Welcome to the world of AI-generated documentation—where stating the painfully obvious is considered helpful. It's like having that one intern who explains what a keyboard is every time you sit down to code. Next up: ChatGPT will helpfully inform you that your function returns a value and that variables store data. Revolutionary stuff.

Data scientists out here mixing pandas, numpy, matplotlib, sklearn, and PyTorch like they're crafting some kind of cursed potion. Each library has its own quirks, data structures, and ways of doing things—pandas DataFrames, numpy arrays, PyTorch tensors—and you're constantly converting between them like some kind of data type translator. The forced smile says it all. Sure, everything's "compatible" and "works together," but deep down you know you're just duct-taping five different ecosystems together and praying nothing breaks when you run that training loop for the third time today. The shadow looming behind? That's the production environment waiting for you to deploy this Frankenstein's monster. Fun fact: The average data science notebook has approximately 47 different import statements and at least 3 dependency conflicts that somehow still work. Don't ask how. It just does.

Oh, how the times have changed! In 2020, we were writing our own isOdd() function with a cascade of if statements like absolute savages. Fast forward to 2025, and we're just outsourcing our brain cells to OpenAI's API. Sure, the 2020 approach was inefficient and borderline ridiculous (just use num % 2 !== 0 , you monsters!), but at least it didn't cost $0.002 per API call. Progress? Maybe. But our wallets are definitely feeling the difference between "free but stupid" and "smart but expensive." The real tragedy is that somewhere out there, a junior dev is actually implementing this in production right now.

When you think $500/day in LLM tokens is cheap, you've officially transcended to a higher plane of existence. My guy spent $4,536 in 30 days just asking ChatGPT to debug their code. That's like burning through 12 BILLION tokens - basically having a conversation with an AI that never shuts up. The math here is wild: take the $500k/year job and you're essentially paying $182,500/year for the privilege of using AI. Meanwhile, the $400k job with "free" tokens is actually netting you $582,500 in total compensation. But sure, let's pretend we're making a tough decision here. This is what happens when you let AI write all your code - you become so dependent on it that spending $1,356 per DAY seems reasonable. At this rate, they're probably asking GPT to write their grocery lists and compose breakup texts.

That moment when you're in the middle of a coding session with ChatGPT or GitHub Copilot and suddenly hit your API rate limit. Gandalf the White with his staff and magic? That was you 5 minutes ago, autocompleting entire functions with AI assistance. Gandalf without his powers, just an old man with a stick? That's you now, forced to actually remember syntax and write code like some kind of caveman from 2019. Welcome back to the stone age, where you have to manually type "for" loops and actually read documentation instead of asking an AI to explain it to you. Your productivity just dropped by 400% and you're questioning every life decision that led you here.

Claude just dropped the sickest Objective-C beat with four consecutive @objc decorators like it's remixing a track. And someone in the comments absolutely nailed it: "you know what kind of music it is? func ." Because nothing says "functional programming" quite like decorating your Swift method with Objective-C compatibility markers four times in a row. It's like Claude got stuck in a loop and decided to make it a feature instead of a bug. The NSLocalizedString return type is just the cherry on top of this syntactic symphony. Props to whoever set up this prompt though - "good job Claude. also free GPT did not do this" is the kind of AI shade we live for. When your paid AI assistant produces more entertaining bugs than the free one, that's value right there.