PandaProbe Cloud

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.