OracAI Whitepaper
  • OracAI Whitepaper
  • 1. Introduction
  • 2. Vision and Mission
  • 3. Market Background
  • 4. Core Approach
  • 5. Technical Architecture & Innovations
  • 6. Token Economy
  • 7. DAO Community Governance
  • 8. Roadmap
  • 9. Official Links
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5. Technical Architecture & Innovations

5.1 Technology Stack

  1. Underlying Protocol: BSC (Binance Smart Chain)

  • Smart Contracts: Built on BSC for its mature ecosystem, scalability, and relatively low transaction fees.

  • Standards:

  • BEP-20 for the ORAC token

  • BEP-721/BEP-1155 for diverse NFT use cases (AI Avatars, identity NFTs, in-game items)

  1. Layer 2 or Rollup Integrations

  • In anticipation of high network usage, OracAI will explore BSC-based Rollup solutions or independent sidechains.

  • This approach ensures we retain BSC’s security while offloading heavy gaming or social transactions.

  1. AI Agent Layer

  • Hybrid On-Chain/Off-Chain Design: Large model training occurs off-chain (e.g., federated learning) to keep data private. AI decisions and results can be verified on-chain for transparency.

  • Agent SDK: Developers can customize AI Agent behaviors (e.g., DeFi arbitrage bots, advanced content recommendation systems).

5.2 Key Functional Modules

  1. Social Mining

  • Rewards in ORAC tokens for posting and managing communities. AI Agents optimize content distribution and social influence.

  • Social Influence Value (SIV): A proprietary scoring system measuring on-chain interactions, content originality, and community impact.

  1. AI Agent Market

  • Users can buy, sell, or lease AI Agents specialized in tasks such as DeFi arbitrage or community management.

  • Earnings settle in ORAC (or a stable partner token if needed), fostering an internal AI-agent economy.

  1. Decentralized Reputation System

  • Zero-Knowledge Proofs (ZK): Facilitates private yet verifiable credit scoring, enabling trust-based activities like uncollateralized lending.

  • Preserves user privacy while allowing robust trust mechanisms.

  1. AI Model & Inference Services

  • Deploy large AI models on cloud services (AWS, GCP) or decentralized compute solutions (Golem, Akash).

  • Integrate with decentralized or standard Web APIs (e.g., Chainlink Functions) for seamless AI services (text, image, voice).

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Last updated 3 months ago