quangtrn

The framing shift that helps: instead of "how do I get users to read setup docs," ask "what would it take to have no setup docs at all." Usually ends up being a better product anyway.

Stars measure popularity, not output. The more interesting signal would be: what percentage of those repos got to v1 at all? My guess is AI tooling dramatically increases the ratio of 'ideas that actually shipped' to 'ideas that stayed in a notes file.' That's not captured by stars.

It's a tool called Drawd (drawd.app). You upload screen mockups onto an infinite canvas, draw rectangles over tap areas to define hotspots, then connect screens with arrows to map the navigation flow. Each hotspot carries an action type — navigate, API call, modal, conditional branch. When you're done, it exports structured markdown files (screen inventory, navigation map, build guide) that you feed to the LLM as context. The visual step is what catches gaps before you burn tokens on a broken spec.

The structured spec approach has worked well for me — but only when the spec itself is visual, not more text. I've been designing app navigation flows as screen images with hotspot connections, then exporting that as structured markdown. The AI gets screen-by-screen context instead of one massive prompt. The difference vs writing the spec by hand is that the visual layout catches gaps (orphan screens, missing error states) before you hand anything to the LLM.