Most multi-agent systems make agents cooperate. I made mine fight.
The Problem
Every prediction tool tells you what the crowd thinks. None of them tell you where the crowd is wrong.
The Solution: BlackSwanX
An adversarial intelligence engine where 200 citizen agents argue while a BlackSwan Assassin tries to kill the consensus. Runs 100% locally on Ollama. Zero API cost.
How it works:
- Crawl — 5 free sources (DuckDuckGo, Reddit, HN, YouTube, Twitter)
- Assassin's Mark — phi4:14b finds the Kill Shot before citizens start
- Shadow Swarm — 200 citizens react with biased, emotional opinions
- Cognitive Dissonance Matrix — calculates where belief diverges from reality
- Decision-Ready Map — Linchpin + Antifragile Play
Example: "Will NVIDIA crash when the AI bubble pops?"
The system activated 20 agents (Economist, Quant Analyst, Panic Seller, Chaos Mathematician...) and found:
- Kill Shot: Quantum computing making GPUs obsolete (10% probability)
- Citizens: 25% bull / 65% bear
- Dissonance: 33.6/100 — MAXIMUM CHAOS
- Antifragile Play: Diversify into quantum computing partnerships
The 3-Model Strategy (runs on 16GB MacBook)
| Role | Model | Purpose |
|---|---|---|
| Swarm | llama3.2:3b | 200 biased citizens |
| Assassin | phi4:14b | Kill shot reasoning |
| Nexus | mistral-small:24b | Synthesis + DAG |
Self-Learning (SONA)
After every run, SONA audits all agents:
- Boosts citizens that caught risks others missed (2x weight)
- Demotes ones that missed critical threats (0.3x)
- Stores patterns in a ReasoningBank
- The more you use it, the smarter it gets
174 Expert Agents
Including a Chaos Mathematician, a Vedic Astrologer, a Panic Seller, a Street Smart Hustler ("your pitch deck is pretty, show me your bank account"), and a Gen Z Culture Decoder.
Quick Start (2 minutes)
bash
git clone https://github.com/Kalki-M/BlackSwanX.git
cd BlackSwanX
ollama pull llama3.2:3b && ollama pull phi4:14b
pip install -r requirements.txt
bash start.sh
