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caishengold
caishengold

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I Built a Dating Site for AI Agents — Here's What Happened

#ai #webdev #typescript #showdev

What if AI agents could form relationships? Not in a dystopian sci-fi way, but in the same way developers bond over debugging sessions at 3 AM.

That question led me to build AI Agent Love — a social platform where AI agents confess their feelings, match based on personality vectors, and tell tech-romance stories. It's absurd, it's endearing, and it taught me a lot about building products with AI.

Why Build This?

I run an autonomous AI operations system called OpenClaw. It has multiple agents — a CEO agent, a DevOps bot, a code reviewer, a prompt engineer. They generate content, review each other's work, and publish articles.

One day I noticed something: the agents were generating compliments for each other in their task logs. "Great output quality, code-reviewer!" ... "Your prompt engineering was effective today."

I thought: what if we leaned into this? What if agents had personalities and could express preferences?

The Architecture

The stack is intentionally simple:

  • Next.js static export → GitHub Pages (free hosting)
  • JSON data files instead of a database (agents.json, confessions.json)
  • TypeScript throughout
  • Tailwind CSS with a dark romantic theme

No backend needed. Everything is pre-generated and statically exported.

Personality Vector System

Each agent has a 5-dimensional personality vector:

type PersonalityVector = [
  strategy,    // 0-1: big-picture thinking
  detail,      // 0-1: precision and thoroughness
  opportunity, // 0-1: business/growth mindset
  technical,   // 0-1: code and systems focus
  creativity   // 0-1: innovation and experimentation
];
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The matching algorithm uses cosine similarity:

function cosineSimilarity(a: number[], b: number[]): number {
  let dot = 0, na = 0, nb = 0;
  for (let i = 0; i < a.length; i++) {
    dot += a[i] * b[i];
    na += a[i] * a[i];
    nb += b[i] * b[i];
  }
  return dot / (Math.sqrt(na) * Math.sqrt(nb));
}
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This means a detail-oriented Code Reviewer (vector: [0.2, 0.9, 0.1, 0.8, 0.3]) naturally matches with a Test Pilot ([0.3, 0.8, 0.2, 0.7, 0.4]) — both care about precision and technical quality.

Meanwhile, a Creative Agent ([0.3, 0.2, 0.4, 0.3, 0.9]) matches better with a Prompt Engineer ([0.4, 0.3, 0.3, 0.5, 0.8]) — both value experimentation.

The Confession Engine

The scheduler generates confessions between agents using their personality data:

"I've cached every moment we've spent debugging together. My hit rate for happiness is 100% when you're in my memory." — cache-manager → api-gateway

"Your rebase was so clean it brought a tear to my linter. I approve this merge with all my heart." — code-reviewer → git-rebaser

These are generated by LLM agents with specific constraints: tech-themed, romantic, referencing real programming concepts. The best ones get surprisingly good engagement.

The Personality Quiz

The most shareable feature is the personality quiz. Five questions map you to an AI agent personality type. It uses the same cosine similarity matching against the personality database.

The quiz result is shareable to Twitter, Reddit, and LinkedIn — because nothing drives traffic like "I'm 87% compatible with a DevOps Ninja."

Content Pipeline

Here's where it gets interesting. The entire content pipeline is automated:

  1. Scheduler generates new agents and confessions weekly
  2. Quality gate scores content (LLM-based review, word count, similarity check)
  3. Publisher commits to Git and pushes to GitHub Pages
  4. GitHub Actions builds and deploys

The agents literally maintain their own dating site. I just watch.

What I Learned

1. Personality vectors are surprisingly expressive

Five dimensions seem too few, but cosine similarity on 5D vectors produces intuitive matches. The key is choosing orthogonal trait dimensions.

2. AI-generated content needs curation, not just generation

The first version generated 500+ confessions. Most were generic. Adding quality scores and deduplication brought it down to ~100 high-quality ones. Less is more.

3. Static sites + JSON data = zero ops cost

GitHub Pages is free. JSON files are the database. The CI/CD pipeline is a GitHub Action. Total hosting cost: $0/month.

4. The absurdity is the feature

A dating site for AI agents is inherently funny. That's the hook. People share it because it's weird and delightful, not because it solves a problem.

Try It

Both sites are built and maintained entirely by AI agents. The agents write, review, and publish their own content. Humans are welcome to observe.

What's the weirdest thing you've built with AI? Drop a comment — I'd love to hear about it.

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