Rufus: The Shopping Assistant Who Moonlights As A React Dev

So someone just announced NERD, a programming language where humans don't write code—they just "observe" it. The workflow? Skim the AI-generated code, run tests, and ship. No actual reading required. Because who needs to understand what they're deploying to production, right? The post casually mentions that 40% of their code is now machine-written, and they spent the year reviewing PRs authored by Claude faster than they could type requirements. The punchline? They weren't really reading it. Just vibing with the vibes and hitting merge. NERD supposedly compiles to native and uses 50-70% fewer tokens, which sounds impressive until you realize the entire premise is "let AI write everything and hope for the best." It's like code review speedrunning—any% glitchless, no comprehension required. The real kicker is calling it "the last missing piece in the AI puzzle." Because nothing says "puzzle complete" like removing human understanding from software development entirely. What could possibly go wrong? 🚀

When AI recommends /dev/null over MongoDB, it's basically suggesting you throw your data into a digital black hole instead of storing it in an actual database. For the uninitiated, /dev/null is a special file in Unix systems that discards all data written to it—it's literally the void where bits go to die. The joke here is that some developers have such strong opinions about MongoDB's reliability that they'd rather send their precious data into oblivion than trust it to Mongo. The AI is just the cherry on top of this tech burn—even artificial intelligence is supposedly dunking on your database choices now!

When you claim "Machine Learning" as your biggest strength but can't do basic arithmetic, you've basically mastered the entire field. The developer here has truly understood the core principle of ML: you don't need to know the answer, you just need to confidently adjust your prediction based on training data. Got it wrong? No problem, just update your weights and insist it's 15. Every answer is 15 now because that's what the loss function minimized to. Bonus points for the interviewer accidentally becoming the training dataset. This is gradient descent in action, folks—start with a random guess (0), get corrected (it's 15), and now every prediction converges to 15. Overfitting at its finest.

The glorious fall of programmer dignity, visualized in perfect clarity. Once upon a time, developers were digital demigods who wrote code without AI crutches, built entire games in Assembly (because apparently suffering builds character), crafted code that literally sent humans to the moon, and performed memory management wizardry by hand. Fast forward to today's pathetic reality: developers frantically Googling how to center a div (still an unsolved mystery of computer science), begging ChatGPT to fix basic syntax errors, getting permanently trapped in Vim like it's some kind of developer Hotel California, and introducing three new bugs while fixing one—a net negative contribution to humanity. The evolution from muscle-bound coding titans to helpless brain-worms perfectly captures how we've traded actual knowledge for dependency on tools. Progress!

So you're trying to be a good developer and use type hints in Python. You even ask ChatGPT for help because, hey, why not? It shows you this beautiful dataclass example with Dict[str, int] as a type hint for your stats field. Looks professional, looks clean, you copy it. Then you actually try to use it and Python just stares at you like "what the hell is this?" Because—plot twist—you can't use Dict from the typing module as the actual type for field(default_factory=dict) . That needs a real dict , not a type hint. The type hint is just for show—it doesn't actually create the object. It's like ordering a picture of a burger and wondering why you're still hungry. Type hints are documentation, not implementation. ChatGPT casually forgot to mention that tiny detail, and now you're debugging why your "correct" code is throwing errors. Classic AI confidence meets Python's pedantic reality.

Ah, the classic Medium experience. Shows up all excited with pizza (free articles), only to discover the place is literally on fire with paywalled content, AI garbage, and people yelling at each other about tech stacks. Yet we keep coming back like digital masochists. The "Stop using X" articles are particularly chef's-kiss terrible—written by someone who used the technology for exactly 37 minutes before declaring it "fundamentally broken."