I Have A Type And Steam Knows

The eternal quest for micro-optimization strikes again! Some poor soul wrote an entire math library in Rust just to divide 60 by 9 from Python. That's like building a nuclear reactor to charge your phone. Sure, Rust is blazingly fast, but at what cost? Your sanity? Three months of your life? Meanwhile, Python would've just returned 6.666... before you finished typing "cargo new". The Shrek running meme perfectly captures that mix of pride and madness that comes with over-engineering a simple solution. We've all been there—spending hours optimizing code that runs once a month to save 0.02 seconds.

Using an LLM to look up documentation is like using a sword and fork to eat chicken. Sure, it technically works, but you're bringing medieval weaponry to a task that requires... literally just opening a browser tab. The guy's committed to the bit though, full knight armor and everything. Documentation exists. It's indexed. It's searchable. It doesn't hallucinate that a function takes 4 parameters when it only takes 2. But hey, why read the actual docs when you can ask an AI that was trained on Stack Overflow answers from 2019 and might confidently tell you to use a deprecated method? The title nails it too. Same energy as people who write "loop through the array and find the maximum value" as their solution to a coding challenge. Thanks, I also speak English. Show me the code or show me the door.

Asked to apologize 1000 times, developer responds with a Java program instead of emotional labor. Classic programmer solution: automate the tedium. The code will print "Sorry babu" exactly 1001 times (that

Ah, the classic "let's use a sledgehammer to kill a fly" approach. Every tech startup these days thinks they need machine learning to solve problems that could be handled with an if-statement and a cup of coffee. After 15 years in the industry, I've sat through countless pitch meetings where some bright-eyed founder explains how their revolutionary AI will disrupt the sandwich-ordering process. Meanwhile, their actual problem is that they can't figure out how to store user preferences in a database. The real kicker? When they finally implement their neural network to predict topping preferences, it works worse than random chance. But hey, at least they can put "AI-powered" in their pitch deck!

This algorithm doesn't kill itself—it just makes inconvenient values disappear! The code starts with good intentions, but any element smaller than the current minimum gets mysteriously "[REDACTED]" instead of being properly sorted. Just like certain prison surveillance footage, some data points never make it to the final array. The comment at the bottom is even missing the return statement... because dead code tells no tales.

The programming paradigm hierarchy in its natural habitat. Object-oriented programmers confidently standing tall, data-oriented programmers clinging to them for support, and return-oriented programmers... well, they've fallen into the bucket and can't get out. Classic case of function returning to the wrong address space. That rabbit's not coming back with a value anytime soon.