Programming With An AI Assistant

Remember the innocent days when AI was just about identifying cats and dogs? Fast forward to now, and suddenly we're in an arms race to create sentient beings. The escalation from "look, my model can tell a golden retriever from a tabby!" to "we're literally creating consciousness before our geopolitical rivals" happened so fast I got whiplash. Venture capital really took "move fast and break things" and applied it to the fabric of existence itself. Next quarterly goal: playing God with better ROI than the competition.

The ultimate job security hack: be so utterly useless that even AI doesn't want your position! When your only contribution to the codebase is comments like "// TODO: fix this later" and your Git commits consist mainly of whitespace changes, you've achieved immortality in the corporate hierarchy. The sweet irony of being simultaneously worthless yet irreplaceable is the tech industry's greatest paradox. That guy's walking away with a smile because he just realized his strategy of writing completely undocumented spaghetti code for the last five years wasn't laziness—it was career insurance!

The evolution of dependency management excuses is just *chef's kiss*. First we pretend it's a calculated technical decision. Then we admit we're just lazy. But that final panel? Pure gold. "LLMs don't understand it yet" is the new "works on my machine." Nothing like blaming AI for your technical debt while your package.json looks like a digital archaeological dig site. Meanwhile, your junior dev is quietly running "npm audit fix" in production.

For decades, error handling was that thing everyone nodded about in code reviews but secretly wrapped in a try-catch that just logged "oops" to console. Nobody wrote proper error messages, nobody validated inputs, and stack traces were treated like ancient hieroglyphics. Then AI showed up and suddenly everyone's an error handling expert. Why? Because when your LLM hallucinates or your API call to GPT-4 fails, you can't just shrug and refresh the page. Now you need graceful degradation, retry logic, fallback strategies, and detailed error context. The massive book represents all the error handling knowledge we should've been using all along. The tiny pamphlet is what we actually did before AI forced us to care. Nothing motivates proper engineering practices quite like burning through your OpenAI API credits because you didn't handle rate limits correctly.

Finally, someone said it. The gatekeeping energy here is *chef's kiss*. While everyone's out here letting AI autocomplete their entire codebase and calling it "productivity," this dev is out here writing actual code from scratch like it's 2015. No Copilot suggestions, no ChatGPT prompts, no MCP server wizardry—just pure, unfiltered human logic and Stack Overflow tabs. The real flex? "If it doesn't work right, I DON'T PUBLISH it." Revolutionary concept in the era of "ship fast, fix in prod." Quality control? In THIS economy? Respect the hustle, honestly. Though let's be real, we all know this person still has 47 console.logs they forgot to remove before committing.

The existential dread of leaving your IDE unattended for 5 minutes only to return and find your machine compiling Skynet. That moment when npm install decides to rebuild the entire dependency tree because you accidentally typed "y" while half asleep. Your computer's gone from "Hello World" to "Hello New World Order" real quick. The scariest part? You don't even have admin privileges in this new relationship.