rep.fun
  • The Intelligent Web Needs Privacy by Default
  • rep.fun: Built-in Trust
    • Mission
    • Vision
  • The Logic Layer Behind Trusted AI
    • Trusted Execution Environments (TEEs)
      • Private by Design: Protecting Data in Execution
      • Verifiable by Default: Generating Cryptographic Proofs
      • TEE Execution Lifecycle Overview:
    • Modular AI Micro-Engines
    • Cross-Chain Privacy Routing
    • Earn With TEE
      • Run & Monetize TEE Nodes
      • Deploy AI Micro-Engines
      • Join the AI & TEE Developer Program
  • Privacy, Execution, and Extensibility
    • Privacy by Default
    • TEE Node Marketplace
      • Execution Flow
    • Enterprise-Ready
  • Technical Backbone
    • AI Computation Layer
    • TEE Technology
    • Cross-Chain Communication Layer
  • Tokenomics
    • Token Allocation
    • Utility
  • Roadmap
  • FAQ
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  1. Privacy, Execution, and Extensibility

TEE Node Marketplace

The TEE Node Marketplace serves as the decentralized computing backbone of rep.fun, transforming secure execution from a static service into a dynamic, open marketplace. Rather than relying on a fixed, centralized infrastructure to handle encrypted AI workloads, rep.fun distributes this responsibility across a global network of independent node operators — each running computations within Trusted Execution Environments (TEEs).

The marketplace’s primary function is to match incoming user-submitted AI tasks with available, verified TEE nodes. This matching process is performance-driven and reputation-based, ensuring that tasks are routed to nodes with proven reliability, responsiveness, and execution integrity. This architecture provides enhanced availability, fault tolerance, and decentralization — all while maintaining a high bar for privacy and verifiability.

Participation in the marketplace is open to any operator with TEE-compatible hardware. After passing initial verification, node operators can begin accepting tasks, processing them privately within their TEEs. In exchange, they earn $REP — the protocol’s native token — based on the quantity and quality of their work. Reputation scores are updated dynamically, taking into account uptime, task throughput, attestation consistency, and feedback from the network. High-performing nodes are prioritized for future task assignments, while underperforming ones are gradually phased out or penalized, creating a merit-based incentive structure.

This decentralized approach not only strengthens rep.fun’s scalability but also democratizes its infrastructure — allowing anyone with the right capabilities to contribute to and benefit from the network.

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Last updated 7 days ago