See the presentation at https://gamma.app/docs/The-Digital-Twin-MCP-Network--t09isszi9urb7ua GitHub: https://github.com/hopeatina/deep-human

About the Project

A decentralized platform of personalized AI “digital twins” (MCP servers) that represent your identity and interests, autonomously network on your behalf, and collaborate to solve the challenges you care about—from sourcing and purchasing to job applications. Each twin seeks out relevant contacts across our interconnected peer-to-peer network and delivers only the highest-value connections and curated opportunities. No other MCPs are needed. All you need is the Twin MCP Network!

Inspiration

Connection Overload: We were struck by stats showing professionals spend over 8 hours/day managing communications, with 98 % of cold intros going stale within 24 hours and 73 % of opportunities lost to inbox overflow.
The Limit of Humanity: AI Agents and Technology are accelerating faster than we comprehend, so it seems novel and pertinent that we scale our capacities along with them. A digital twin is the first step in taking back control of the onslaught.
Agent + AI Boom: As LLM token costs plunged below \$1 per million and AI agents proliferate faster than we can use them, there was a clear opening to let an AI twin handle the heavy lifting.

What We Learned

Digital Twin Design: How to translate human goals, skills, and interests into a machine-actionable profile.
Semantic Matching: We thought through a multi layered approach to recommendations, matching based on simulating conversations and looking towards the future .
Network Effects: Building for exponential growth—every new twin multiplies daily meaningful connections.

How We Built It

Profile Intake: React frontend captures interests, expertise, goals.
MCP-Server Core: FastAPI service hosts each twin, exposing SSE endpoints for real-time outreach.
Document & Data Parse:
Client Powered Conversations: We spin up simulatioins and interactions ongoing for you and other digital twins surface the best matches, connections, collaboration+ working opportunities for you.

Challenges Faced

Cold-Start Matching: Allowing the newest people on the network to join effortlessly.
Cost vs. Performance: Balancing frequent LLM calls with token budgets meant batching requests and caching embeddings.
Privacy & Control: Ensuring users could audit and pause their twin’s activity without losing momentum.
Scalability: Architecting the MCP network so thousands of twins could simultaneously negotiate, message, and learn.