Before Was At Least Cheaper

Modern gaming is just a purple cat getting mugged from all directions. Day-one patches because who needs finished products? Lootboxes to empty your wallet through randomized "surprise mechanics." Crypto scams promising you'll totally own that JPEG of a monkey. And let's not forget the AI slop—half-baked "features" created by algorithms that somehow make games worse. Meanwhile, game studios keep recycling franchises and flipping assets while charging full price. The real game is seeing how much abuse players will tolerate before they stop throwing money at microtransactions. But hey, at least we get that dopamine hit of nostalgia bait when they remake that game you loved as a kid... for the third time.

The tables have turned! Software developers who've spent decades being called stupid by machines (compiler errors, runtime exceptions, segmentation faults) are now watching AI models struggle with basic logic and hallucinate facts. That skeleton represents the hardened dev who's been through countless debugging nightmares, now getting to say "You are stupid" to the innocent AI models that confidently generate nonsense. Sweet, sweet revenge for all those "undefined is not a function" messages that kept us awake at 3 AM.

When you're absolutely DEMOLISHING your codebase with assert(true) statements like some kind of deranged testing deity, only for Claude to walk in and question your entire existence. The kid's just sitting there, hammer in hand, gleefully spamming the most useless assertion known to humankind – because nothing says "quality assurance" like asserting that true equals true approximately 47 times. The parent's horror is palpable, the confusion is real, and honestly? The kid's response is chef's kiss. "Is this some sort of trick question?" No sweetie, it's just your descent into madness disguised as unit testing. Those assert(true) statements will ALWAYS pass, making them about as useful as a chocolate teapot. But hey, at least your code coverage metrics look fantastic! 📈

The brutal truth of modern development hits hard! This is basically every junior dev who's been using ChatGPT as a crutch instead of actually learning fundamentals. The dependency paradox strikes again—if you're completely reliant on AI to write your code, you haven't actually developed the skills to be a proper engineer. It's like claiming you're a chef because you can follow microwave instructions. The irony is that the tools that make us more efficient can sometimes prevent us from developing the mental models needed to solve problems independently. That moment when your senior dev calls you out and your entire identity as a programmer crumbles...

You know you've entered dangerous territory when Claude (or any AI assistant) starts suggesting Git commands and you just copy-paste them without reading. One git rebase -i here, a git reset --hard there, maybe a cheeky git reflog to "fix" things, and suddenly your repository has more divergent branches than the entire MCU timeline. The Loki reference is chef's kiss because just like the TVA dealing with infinite timeline variants, you're now staring at a commit graph that looks like spaghetti had a baby with a neural network diagram. Your git log --graph --all output probably needs its own scroll bar at this point. Pro tip: Maybe read what the AI suggests before executing commands that can literally rewrite history. Or don't, and enjoy explaining to your team why the main branch now exists in 47 parallel universes.

Remember when we used to carefully engineer features and tune hyperparameters for our models? Yeah, those were simpler times. Now the entire AI/ML industry has basically decided that the solution to every problem is "just throw a transformer at it and add more GPUs." Need to classify images? Transformer. Text generation? Transformer. Predict stock prices? Believe it or not, also transformer. Meanwhile, good old classical ML techniques like random forests, SVMs, and logistic regression are sitting at the bottom of the pool, completely forgotten. Sure, they're interpretable, efficient, and actually work great for tons of problems, but who cares when you can burn through your entire cloud budget training a 175 billion parameter model instead? Deep learning and neural networks are also drowning down there, which is ironic since they were the hot new thing just a few years ago. But nope, not fancy enough anymore. If your solution doesn't involve attention mechanisms and can't be described with the phrase "large language model," are you even doing AI?