I built an open-source "focus group simulator" that spawns 1,000 AI customers to roast your startup idea

 The Problem Every founder has been there: you spend 3 months building something, launch it, and... crickets. Nobody wants it. User interviews cost time. Landing page A/B tests cost money. Survey panels cost both. What if you could get brutally honest feedback from 1,000 potential customers in 2 minutes? What I Built **Sybil Swarm** is an open-source swarm intelligence engine. You feed it your product URL or description, and it: 1. Spawns 1,000 AI agents — each with a unique persona (age, job, income, personality, interests) 2. Has them evaluate your product as real potential customers 3. Generates a market prediction report with conversion rate, objections, and recommendations The name comes from the Sibyls — prophetic oracles of ancient Greece. Demo {% youtube kUqR5mokjSs %} The Dashboard The simulation runs in real-time with: - **Canvas world** — agents move around, cluster by sentiment (buyers → right, rejectors → left) - **Sentiment heatmap** — fills up red/yellow/green as agents complete - **Live feed** — Twitter-like stream of agent reactions - **Conversion funnel** — Aware → Interested → Willing to Pay → Would Buy ## What the Report Looks Like The AI synthesis agent writes a brutally honest market prediction: - Market Viability Score (0-100) - Conversion rate prediction - Top objections (what's killing your product) - Top suggestions (what would make people buy) - Go/No-Go recommendation One of my test runs came back: *"AgriBeacon is not viable in its current form. 0% conversion rate. Its value proposition is a marketing mirage."* Ouch. But exactly what I needed to hear. ## Tech Stack - **Backend:** Python FastAPI, async parallel agent evaluation - **Frontend:** Next.js 16, Canvas 2D, Framer Motion - **LLM:** Any OpenAI-compatible API (works with free Alibaba Qwen tier) - **No agent framework** — just async batch LLM calls with semaphore. CrewAI/LangGraph are overkill for this. ## Try It ```bash git clone https://github.com/nghiahsgs/Sybil-Swarm.git cd Sybil-Swarm # Set up your API key (Qwen free tier works!) cp .env.example .env # Install & run cd backend && pip install -e . && uvicorn app.main:app --port 8000 & cd ../frontend && npm install && npm run dev Open http://localhost:3000 → paste your URL → Launch Simulation Is This Actually Useful? Honestly? It's not a replacement for talking to real humans. But it's a cheap, fast pre-filter: - If 0% of simulated customers would buy → strong signal to pivot - If 40%+ would buy → worth investing in real validation - The objections list alone is worth it — things you didn't think of What's Next - Chat with individual agents post-simulation (already built) - More provider support (OpenAI, Anthropic, Google, Qwen) - Deploy as hosted service GitHub: https://github.com/nghiahsgs/Sybil-Swarm MIT license. Stars appreciated! ---