Modern Full Stack Dev

So 90s sci-fi had us all convinced that AI would turn into Skynet and obliterate humanity with killer robots and world domination schemes. Fast forward to 2024, and our supposedly terrifying AI overlords are out here confidently labeling cats as dogs with the same energy as a toddler pointing at a horse and yelling "big dog!" Turns out the real threat wasn't sentient machines taking over—it was image recognition models having an existential crisis over basic taxonomy. We went from fearing Terminator to debugging why our neural network thinks a chihuahua is a muffin. The apocalypse got downgraded to a comedy show.

The AI hype cycle speedrun, perfectly captured in four stages of clown makeup. Started with the promise that AI would revolutionize everything, got seduced into thinking you could skip fundamentals and just prompt your way to a senior dev salary. Then reality hit: those "free" AI tools either got paywalled harder than Adobe Creative Cloud or started running slower than a nested loop in Python. Now you're sitting there with zero transferable skills, a LinkedIn full of AI buzzwords, and the crushing realization that "prompt engineer" isn't actually a career path. The kicker? While you were vibing, the devs who actually learned their craft are still employed. Turns out you can't Ctrl+Z your way out of not knowing how a for-loop works.

The modern "full stack" - three AI tabs open in your browser while you pretend to know what you're doing. Remember when being full stack meant actually knowing multiple languages and frameworks? Now it's just knowing which AI to ask for which problem. "Yes, I'm proficient in Claude, ChatGPT, and Perplexity" should be the new line on resumes. The only stack that matters is the stack of browser tabs helping you fake your way through that ticket your PM swore was "just a small change."

You spend days crafting a pull request, refactoring everything, writing tests, adding documentation, making it absolutely beautiful. Then some bot rolls up and says "Full of AI slop, completely unhelpful" and you just... lose it. The real gut punch? Half the time the bot is right. With AI code generators flooding repos with generic solutions and copy-paste answers, human-written code is starting to look suspiciously similar to GPT's homework. We've reached the point where genuine effort gets flagged as synthetic garbage while actual AI slop sneaks through because it happened to use the right buzzwords. The Turing test has officially reversed: now we have to prove we're NOT robots.

When AI "enhancement" goes beyond fixing pixels and straight into identity theft. The algorithm looked at a low-res image and thought, "You know what would make this better? A completely different person!" It's like asking for image upscaling and getting witness protection instead. This is why developers shouldn't let their neural networks watch crime shows during training.

Someone woke up and chose violence. The goal here is to feed ChatGPT such cursed, chaotic code that it just gives up and starts hallucinating error messages. Think legacy PHP spaghetti mixed with recursive bash scripts, sprinkled with some jQuery from 2009, all wrapped in a Dockerfile that uses FROM scratch unironically. It's like trying to teach a language model by showing it only the worst code ever written. "Here GPT, analyze this 5000-line function with no comments and 47 nested if statements. Have fun!" The AI equivalent of making someone watch every JavaScript framework tutorial from the last decade simultaneously. Bonus points if the repo includes a README that just says "it works on my machine" and a package.json with 300 dependencies, half of which are deprecated.