DEV Community

Cover image for I Built 174 AI Agents That Fight Each Other.
Kalki-M
Kalki-M

Posted on

I Built 174 AI Agents That Fight Each Other.

#devchallenge #418challenge #showdev

April Fools Challenge Submission ☕️🤡

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

👉 GitHub Repo — BlackSwanX

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
Enter fullscreen mode Exit fullscreen mode

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

GitHub logo Kalki-M / BlackSwanX

167 AI Experts + 200 Citizen Agents. Zero API Cost. Predict Anything all in your laptop.

BlackSwanX

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

Experts Citizens Cost Ollama Local MIT

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
…



View on GitHub


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:

  1. Crawl — 5 free sources (DuckDuckGo, Reddit, HN, YouTube, Twitter)
  2. Assassin's Mark — phi4:14b finds the Kill Shot before citizens start
  3. Shadow Swarm — 200 citizens react with biased, emotional opinions
  4. Cognitive Dissonance Matrix — calculates where belief diverges from reality
  5. 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.

Top comments (0)