Use Cases
The Protocol is Domain-Agnostic
MyMonad provides generic infrastructure for similarity-based discovery. The protocol doesn’t care what your Monad represents—it could be a person’s preferences, a document’s semantic content, an agent’s capability profile, or any other high-dimensional embedding.
The primitives are:
- Local embedding generation — Transform any data into a Monad vector
- Privacy-preserving discovery — Find similar vectors via LSH without revealing them
- Progressive trust handshake — Verify compatibility in stages, reveal nothing on failure
- Optional ZK verification — Prove proximity without revealing the vectors
Reference Implementation: Human Matchmaking
The first application built on MyMonad is autonomous matchmaking. This demonstrates the protocol in action.
MeetMyMonad: Dating
Traditional dating apps commodify loneliness—they profit when you stay single, swiping forever. MeetMyMonad inverts the incentive structure.
How It Works:
- Define your authentic self: Interests, values, deal-breakers processed locally
- Encrypted matching: Compatibility computed without revealing preferences
- Mutual threshold: Both parties must exceed similarity threshold
- Verified handshake: Cryptographic proof of mutual interest before any reveal
- Consent-first contact: Secure channel opens only after 5-stage handshake
What’s Different:
- No swiping: Binary choices dehumanize. Continuous compatibility doesn’t.
- No profiles to stalk: You can’t see someone until you match
- No algorithm manipulation: Open source, auditable, no engagement farming
- No data harvesting: Your romantic preferences never leave your device
Other Matching Applications
Professional Networking
LinkedIn knows your career ambitions, your job searches, your professional insecurities. They monetize your anxiety. MyMonad offers an alternative.
Applications:
- Co-founder matching: Find partners with complementary skills and aligned values
- Mentor discovery: Connect with experienced professionals in your field
- Collaborator search: Find researchers or creators for joint projects
- Job matching: Companies and candidates meet through verified compatibility
Privacy Advantages:
- Recruiters can’t see you’re looking until mutual interest is proven
- Your professional profile isn’t scraped for ad targeting
- Career moves remain private until you choose disclosure
Community Building
Finding your tribe shouldn’t require broadcasting your identity to the world.
Use Cases:
- Interest-based communities: Niche hobbies without public exposure
- Support groups: Sensitive topics with privacy guarantees
- Local connections: Neighbors with shared interests, revealed only mutually
- Event organization: Gather compatible attendees without public RSVPs
Beyond Human Matching: Agent Alignment
The deeper purpose of MyMonad extends beyond connecting humans. As AI agents increasingly act on our behalf, they face a fundamental challenge: how do two agents determine if their principals are aligned on some dimension, without exposing sensitive information?
Agent-to-Agent Verification
Consider these scenarios:
- Negotiating agents: Your shopping agent wants to verify a seller’s reputation agent is aligned with your values before sharing purchase intent
- Research agents: Academic agents want to find collaborators working on similar problems without revealing unpublished work
- Privacy-preserving recommendation: Recommendation agents from different services want to find overlap without sharing user profiles
In each case, the agents need to verify alignment without revealing the underlying data. MyMonad provides exactly this primitive.
The Protocol as Infrastructure
| Layer | What It Provides |
|---|---|
| Embedding | Any data → high-dimensional vector |
| Discovery | Find similar vectors (LSH) without revealing them |
| Trust | Progressive verification with zero leakage on failure |
| Proof | Optional ZK verification of proximity |
This is general infrastructure. Human matchmaking is just the first application.
What Will You Build?
The protocol is open. The primitives are yours.
Ideas We’re Excited About
- Whistleblower matching: Journalists and sources, verified without exposure
- Academic peer review: Blind matching of reviewers and papers
- Multi-agent coordination: Agents discovering compatible collaborators
- Semantic document clustering: Find related documents across organizations without sharing content
Build With Us
- GitHub: Contribute code, report issues, propose features
- Documentation: Help improve the docs
- Proposals: Submit RFCs for protocol extensions
Your monad awaits.