I made a free, open-source toolkit for building apps with AI: prompts that interview you before they write your plan, and a step-by-step app starter for Claude Code. 1,000+ stars so far. github.com/TechNomadCo...

Prompts that interview you first is the right pattern. The quality of what gets built depends on the answers, and people give thin answers unless asked a follow-up. I built chatform.in on the same idea for forms (it asks again when an answer is vague), so it's good to see it applied to app specs. How do you decide when the interview has enough?

The interview-before-plan approach is smart — most AI coding tools jump straight to implementation without clarifying the "why" and "what" first. That context gap is where projects go sideways.

Curious how you handle the interview depth vs. momentum tradeoff. Do you find a sweet spot in number of questions before it feels like overhead? Also, the Claude Code starter — are you baking in any guardrails for token budget on larger codebases, or leaving that to the developer to manage per session?

Been experimenting with similar workflows and the planning phase is where I see the highest variance in output quality. Solid work open-sourcing this — found it via LabAgent, site: labagent .tech