I Built 174 AI Agents That Fight Each Other.

Kalki-M — Fri, 03 Apr 2026 09:09:08 +0000

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

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

…

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:

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.

I built 174 AI agents that predict the future by fighting each other

Kalki-M — Thu, 02 Apr 2026 22:19:18 +0000

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

Forem: Kalki-M