I spent an afternoon building a 3D visualization of my apartment building’s laundry room status. It was a collaboration with Claude Code. And it was a rollercoaster.
Here’s the thing nobody tells you about AI coding tools: they’re not a magic wand. They’re a variable multiplier. Sometimes they waste your time frustratingly. Sometimes they save you hours. On the same project. In the same session.
I had a QR code from a sticker on a washing machine. That’s it. I gave it to Claude and said, “Figure out how to get the data.” And it did. It reverse-engineered the entire API from that single URL. That moment — when the AI autonomously navigates a mess of undocumented endpoints and returns exactly what you need — is pure magic.
But the magic doesn’t come without friction. Claude would spin its wheels on trivial CSS tweaks. It would generate code that looked right but broke silently. I’d have to babysit it, feed it context, correct its assumptions. The romanticized vision of “just describe the app and it builds itself” couldn’t be further from the truth.
Yet here’s the twist: the final product is good enough. It’s a 3D interactive map of the laundry room, with clickable machines that show usage patterns and even Easter eggs. I completed it in less time than I could have done it myself — even with all the frustrating detours.
This is the real value of AI coding tools. Not raw speed. Not flawless output. But lowering the barrier to building good enough solutions for hyper-specific problems. The kind of problems that never get a dedicated app because they’re too niche — your laundry room, your apartment complex’s parking lot, your weird home automation setup.
I’ve seen this pattern before. When WordPress made building a website accessible to non-coders, we got a million mediocre blogs — and a few brilliant ones. When Canva democratized design, we got a thousand terrible presentations — and a handful of stunning visuals. The same is happening with AI coding. We’re entering the era of “bespoke software for everyone.” Not perfect software. Not enterprise-grade. But software that solves your specific, weird, personal problem — and does it in a day.
So stop waiting for the perfect AI that writes flawless code. Start using the messy, frustrating, brilliant AI that exists now. Give it a QR code. Tell it to build something weird. Let it waste your time for a bit. Because somewhere in that frustration is the moment where it changes everything.
FAQ
Q: Is AI coding actually useful for real projects, or is it just hype?
A: It's useful in a messy, non-linear way. For niche, exploratory projects where you need a 'good enough' solution fast, the net time savings are real. But you'll hit frustrating dead ends — it's not a replacement for understanding what you're building.
Q: What's the practical takeaway for someone considering using AI coding tools?
A: Don't expect perfection. Plan to iterate, babysit, and correct. The payoff comes when you have a hyper-specific problem that would normally take days to code — AI can get you a working prototype in hours, even if it takes a few wrong turns.
Q: Isn't this just a worse version of hiring a developer?
A: For a production system, yes. But for personal projects, quick experiments, or internal tools where 'good enough' is fine, AI coding is cheaper and faster than hiring anyone. It's the difference between a bespoke suit and a $50 tailor-made shirt — both have their place.