How I Use AI as a Solo Product Builder

I ship full products solo using 5 AI tools. Not theory — this is my actual daily workflow.

The stack

Google Gemini handles research. Google has the best live data, but I don’t trust its decisions. I use it strictly as a data-gathering layer. Everything goes into an .md file for portability across tools.

ChatGPT handles initial planning and strategy. Once I have raw research, I structure requirements and expected outcomes before writing a single line of code.

Claude in Opus 4.6 extended thinking mode pressure-tests everything. I feed it research from Gemini and ChatGPT and let it grill the assumptions. It’s the most useful step for catching bad decisions before they become bad code.

Claude Code generates detailed PDFs and spreadsheets for documents and presentations. I have a custom skill so it delegates this to a subagent in the background.

Cursor handles implementation. It indexes your codebase via vector embeddings better than Claude Code. CC is sharper on quality but burns through session usage with tool calls. At Nigeria exchange rates, cost matters — Cursor handles the bulk of coding, CC refines.

Google Antigravity handles testing on critical paths. Most tenacious testing agent I’ve used. Claude Code catches quirks but eats resources. Cursor is lenient and sometimes overlooks things.

The flow: Spec → Tests → Code

Define behavior, write tests that assert it, implement until tests pass.

Full spec-first is overkill solo. I apply it on risky parts and skip it on straightforward ones. The discipline is knowing which is which.

Real example: early testers flagged a missing feature on one of my projects. We specced it, and ten minutes later it was live in their testing environment.

What this actually means

One person with the right AI stack can out-execute a small team.

The gap isn’t talent anymore — it’s knowing which tools to chain together and in what context. That’s the skill nobody talks about when they celebrate how fast AI lets you ship.