App Idea Factory
Three-pipeline automation for surfacing, scoring, and specifying app ideas
Context
I generate more product ideas than I can act on. I wanted a system that would surface, score, and — on demand — deeply specify ideas, without me sitting at a keyboard for it.
What I built
A pipeline architecture with three decoupled stages running on Supabase + Vercel.
How it works
App Idea Factory — Pipeline Architecture
Stage 01
Scraper
Scheduled signals from Reddit, IndieHackers, etc.
Stage 02
Curator / Ranker
Dedupes, filters noise, scores against criteria.
Stage 03
Spec Generator
On-demand: generates full implementation spec via Claude API.
Stage 1 — Scraper. Pulls signals from sources of demand (Reddit, IndieHackers, etc.) on a schedule.
Stage 2 — Curator / Ranker. Filters noise, dedupes, and scores ideas against criteria I define (market size proxy, technical feasibility, alignment with what I'd want to build).
Stage 3 — On-demand Spec Generator. When I select an idea, this stage generates a full implementation spec ready to hand off to Claude Code.
The decoupling matters: each stage runs on its own schedule, has its own failure mode, and can be reasoned about independently. A coupled monolith would have been faster to ship but would not survive contact with real-world data quality.
Outcomes
Personal use, ongoing. The interesting general lesson is that AI-augmented automation works best when you treat the LLM as one component in a pipeline with clear boundaries — not as a magic everything-machine.