This is a submission for the DEV April Fools Challenge
What I Built
Most multi-agent systems make agents cooperate. I made mine fight.
Meet BlackSwanX — an adversarial intelligence engine where 200 citizen AI agents argue, panic, and emotionally spiral while a BlackSwan Assassin tries to murder the consensus. It runs 100% locally on Ollama. Zero API cost. Maximum chaos.
I deployed a Vedic Astrologer, a Panic Seller, a Chaos Mathematician, a Gen Z Culture Decoder, and a Street Smart Hustler (who will tell you "your pitch deck is pretty, show me your bank account") to predict the future. Together. By fighting.
This solves zero real-world problems elegantly. It just finds where the crowd is wrong.
Demo
Quick start (2 minutes):
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
Example run — "Will NVIDIA crash when the AI bubble pops?":
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
Code
Kalki-M
/
BlackSwanX
167 AI Experts + 200 Citizen Agents. Zero API Cost. Predict Anything all in your laptop.
BlackSwanX
174 AI Experts + 200 Citizen Agents. Zero API Cost. Predict Anything — On Your Laptop.
Where the crowd is wrong, the alpha lives.
Quick Start •
Live Demo •
How It Works •
The Comparison •
Unique Agents •
Contribute
Every prediction tool tells you what the crowd thinks.
BlackSwanX tells you where the crowd is wrong.
We don't seek consensus. We seek the widest gap — the Cognitive Dissonance between what the masses believe and what the experts fear. That gap is where the alpha lives.
The Comparison
| BettaFish | MiroFish | BlackSwanX | |
|---|---|---|---|
| Cost | $$$ (7 API keys) | $$ (2 keys + Zep Cloud) | $0 (Ollama) |
| Setup time | 30+ min + PostgreSQL | 15 min + Zep account | 2 min, zero config |
| Expert agents | 5 | 0 (generic personas) | 174 domain experts |
| Citizen agents | 0 | ~100 per run (OASIS) | 200 per run (Shadow Swarm) |
| Citizen simulation | None | OASIS framework | Shadow Swarm |
How I Built It
3 models, all local, all free:
| Role | Model | Purpose |
|---|---|---|
| Swarm | llama3.2:3b | 200 biased citizens arguing |
| Assassin | phi4:14b | Kill shot reasoning |
| Nexus | mistral-small:24b | Synthesis + DAG |
The pipeline:
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
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 (and more chaotic) it gets.
Prize Category
Community Favorite — because nothing says "April Fools" like deploying a Vedic Astrologer and a Panic Seller as serious financial analysts and calling it an intelligence engine. The project is technically real, completely unhinged, and genuinely runs on your laptop.