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You don't start by building. You start by finding the one assumption that would kill the idea, then build the smallest thing that tests it. Here's the full idea-to-deployed workflow from a live AI build session: research the market with ChatGPT, pick a segment on unit economics, have AI write the PRD, then vibe-code and ship with Claude Code — in a couple of hours, not a quarter.

A harness is the runtime wrapper that turns a bare language model into an agent — the layer that runs tools, holds memory, assembles context, and enforces limits. The model does the reasoning; the harness does everything the model can't do on its own. It's the part that decides whether you shipped a chatbot or a real agent — and it's why the same model feels brilliant in one tool and useless in another.

Claude Code has built-in memory — a CLAUDE.md file it reads each session — but it only holds short, hand-written notes. To make it understand a large codebase or docs set, you add a second brain: turn the repo into a knowledge graph, fold that into an Obsidian vault, and let the agent query the map instead of re-reading every file.