Building a Complete Project Using AI as a Solo Dev
TBC
I know you can unblur it, just don't as it's pure AI slop / notes right now.
Building a Complete Project Using AI as a Solo Dev
Right now using AI is a mixure of harnessing lightning and trying to handle a greasy eel. I read a good analogy of it's rocket fuel, it can supercharge your team but if you pour it on a dumpster fire it's just going to explode and make it worse. For my AI Pet Part 2 project I built more than I've ever shipped solo: a Babylon.js game engine, a WebSocket multiplayer server, an async inference proxy, Temporal training workflows, remote compute integrations across four platforms, and a Kubernetes deployment on a Raspberry Pi cluster. I built it quickly — and AI coding tools were a central reason why.
But speed without quality is just faster mess. This post is about how I kept the codebase solid while moving fast.
The Shift AI Makes
AI doesn't just speed up writing code — it compresses the feedback loop at every stage. I can describe an architecture, get a working skeleton in minutes, and iterate on real behaviour rather than spending days on scaffolding. A feature that would have taken a week to wire up now takes an afternoon.
The risk is that you move so fast that problems pile up invisibly. Without discipline, you end up with a codebase that works now but that you're afraid to touch next month. The practices below are what I used to prevent that.
Quality Baked In
- Test-first on every feature — writing the test before the implementation keeps scope tight and gives the AI a concrete target to hit. The AI is much better at writing code that satisfies a clear spec than at guessing what the right behaviour should be.
- AI-assisted code review — after every meaningful change, a review pass catches bugs and design issues I'd miss when moving quickly. It's not a substitute for thinking, but it's a reliable second pair of eyes that doesn't get tired.
- Small, focused PRs — keeping changes atomic means the AI can reason about them accurately and I can verify correctness easily. Large PRs are where subtle bugs hide.
- Automated CI gates — type checking, linting, and test runs on every push mean broken code never accumulates; it's caught immediately. This is cheap to set up and pays off constantly.
- Explicit architecture upfront — spending 30 minutes planning the shape of a feature with the AI before writing any code avoids the expensive rewrites that come from discovering structural problems late. The AI is a good thinking partner at the design stage, not just the implementation stage.
The Compound Effect
The most important thing I noticed: the quality practices compound. Each week I ship more because I'm not paying down debt from the week before. The codebase stays clean enough that I'm not scared to change things — and that confidence frees me to take on more ambitious features.
As a solo dev, I'm also the only person who reads most of this code. AI tooling changes the equation: I now have something that can review, question, and push back on my decisions at any hour. That's a qualitatively different way of working from what solo development looked like before.
If you're a solo dev sitting on an ambitious project you keep putting off because it feels too large — I'd encourage you to try this approach. The tools have genuinely changed what one person can ship.