It’s been about three weeks since I started building with AI CLI tools, mainly using Claude CLI and APEX, and honestly — it’s been a ride. It’s fun, it’s fast, and it fixes stuff on the fly. But I’ve learned a few lessons that make working with AI way better.

First off, it’s like having a lightning-fast pair programmer that’s too eager. If I toss it a vague problem, it’ll start coding before I finish explaining — and sometimes that means building things I never asked for. So I’ve learned to slow it down. I make it explain its approach first, walk me through the plan, and then we code. That step alone has saved me from a ton of rewrites.

Unit tests have become my safety net — not something I ask the AI to write first, but something I rely on to keep both of us honest. They help confirm that what we just built does what I actually intended. It’s a nice loop: I guide the AI, it writes the code, and the tests confirm we stayed aligned.

Another big takeaway: track everything. AI can drift fast if you don’t give it guardrails. I keep a running document of what’s being built — the plan, structure, preferences, naming patterns, things I like and don’t like. Since I switch between Claude CLI and APEX, having that context written down keeps things consistent no matter which AI I’m using that day.

I’ve also realized AI can easily overcomplicate the simple stuff. It’ll throw in abstractions or patterns that don’t fit my workflow. So now I call it out early — “No, we’re keeping this simple,” or “That’s too heavy for what I need.” It listens, adapts, and remembers. Over time, it’s starting to feel like training an assistant to think the way I do.

All in all, AI CLI has been a game-changer, but you’ve got to stay in the driver’s seat. It’s not about letting it take over — it’s about having a conversation, setting expectations, and building together. That’s what makes it powerful. And honestly? It’s been pretty great.