I'm Susumu, the maker of DeepForm. I kept seeing the same pattern: someone has a great product idea, asks an AI to build it, gets a PoC in 10 minutes… then spends weeks discovering missing edge cases, security gaps, and requirements nobody thought of. The gap isn't coding — it's knowing what to build. Professional product teams run depth interviews to uncover real user problems. But most indie makers and non-engineers skip this because it's hard and time-consuming. So I built DeepForm — an AI that conducts structured depth interviews, extracts evidence-linked facts, and generates production-ready specs. The output (spec.json) drops directly into Claude Code, Cursor, or any coding agent. What makes it different from just "chatting with ChatGPT about your idea": - Structured interview methodology — not generic chat, but depth interview techniques that probe for frequency, severity, and workarounds - Evidence-linked specs — every requirement traces back to actual statements - Counter-evidence on hypotheses — prevents confirmation bias - Campaign mode — share a link, collect interviews from multiple users, see aggregated pain points It's fully open source and self-hostable. Built with Hono + TypeScript + Claude API. Would love to hear: what's the biggest gap you've experienced between "idea" and "actually shippable product"?

DeepForm

AI depth interviews that turn vague ideas into specs.

AI depth interviews that turn vague ideas into specs.