Building Autonomous Product Machines
We've been working on something ambitious: a system that autonomously discovers market opportunities, validates ideas, and builds products. Here's what we've learned.
The Problem
Most startup ideas fail not because they're bad, but because they're built without sufficient market validation. By the time you realize there's no demand, you've already spent months building.
What if we could:
- Automatically discover market signals from Product Hunt, Twitter, Reddit, and more
- Use AI to synthesize these signals into product ideas
- Generate PRDs with technical specifications
- Build and deploy products with minimal human intervention
The Architecture
Our system has three main components:
1. Signal Scrapers
We run scrapers on multiple data sources:
- Product Hunt for trending products
- Indie Hackers for founder discussions
- Twitter for real-time sentiment
- Reddit for community pain points
- Google Trends for search interest
2. AI Enrichment Pipeline
Raw signals aren't useful on their own. We use LLMs to:
- Cluster related signals
- Identify underlying pain points
- Generate product concepts
- Estimate market size and competition
3. Build Automation
Once an idea is approved, we:
- Generate a detailed PRD
- Create technical specifications
- Set up the project scaffolding
- Write code with AI assistance
What We've Learned
Speed Matters, But So Does Quality
It's tempting to optimize purely for speed. We could generate 100 ideas a day. But most would be garbage.
We've found the sweet spot is around 5-10 high-quality ideas per day. Enough to explore the space, but with sufficient depth that each idea is actually buildable.
Market Signals Are Noisy
Not every trending topic is a product opportunity. A lot of what's popular on Twitter is entertainment, not problems to solve.
We've built filters to focus on:
- Pain points (complaints, frustrations)
- Willingness to pay (people asking for solutions)
- Technical feasibility (can we actually build this?)
AI Is Great at Synthesis, Bad at Taste
LLMs are excellent at combining multiple signals into coherent ideas. They're less good at knowing which ideas are worth pursuing.
We still need human judgment for the final "should we build this?" decision. The AI surfaces options; humans make choices.
What's Next
We're working on:
- Better feedback loops from launched products back to idea generation
- More sophisticated market sizing
- Automated A/B testing of landing pages
Stay tuned for more updates as we build in public.
This is the first in a series of posts about our journey building infinitemoney. Follow along as we share what we learn.