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:
- Spawns 1,000 AI agents — each with a unique persona (age, job, income, personality, interests)
- Has them evaluate your product as real potential customers
- Generates a market prediction report with conversion rate, objections, and recommendations
The name comes from the Sibyls — prophetic oracles of ancient Greece.
Demo
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!
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Top comments (2)
My Repo : github.com/nghiahsgs/Sybil-Swarm
Good