I'm the founder of Replay. We've been working on a "video-to-code" engine that uses Gemini 3 Pro to analyze UI flows (not just screenshots) and reconstruct them into React + Tailwind.
One big pain point with AI generators is that they produce "dumb components" with fake data structures. You always have to rewrite the props to match your backend.
The AI connects to your Supabase (read-only) to fetch the schema. When it sees a list of items in the video, it looks at your actual tables (e.g., products) and generates code that matches your column names (price_cents vs price).
The goal is to get "run-ready" code, not just a mock.
Would love to hear your feedback on the generated code quality and the schema mapping approach!
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[ 3.2 ms ] story [ 14.2 ms ] threadOne big pain point with AI generators is that they produce "dumb components" with fake data structures. You always have to rewrite the props to match your backend.
We just shipped a feature documented here (https://replay.build/docs) that solves this:
The AI connects to your Supabase (read-only) to fetch the schema. When it sees a list of items in the video, it looks at your actual tables (e.g., products) and generates code that matches your column names (price_cents vs price).
The goal is to get "run-ready" code, not just a mock.
Would love to hear your feedback on the generated code quality and the schema mapping approach!