Decentralised Intelligent Network (DINE)
  • DECENTRALISED INTELLIGENT NETWORK FOR EVENT (DINE)
    • Introduction to DINE
  • PROBLEM STATEMENT
    • Artificial Scarcity and Exploitation by Centralized Platforms
    • Disconnect Between Artists and Fans
    • The Absence of True Value Exchange
    • Why Web3 & AI Are the Solution
  • Key Features Of DINE
    • Personalized Content Curation
    • Live Event Discovery & Ticket Sniping
    • Brand-Creator Upselling Opportunities
    • Blockchain-Based Governance & Rewards
  • $AAA TOKEN – THE FINANCIAL PRIMITIVE BEHIND DINE
    • The Need for a Native Token
    • Core Utilities of $AAA
    • Economic Design Principles
    • Transformative Potential of $AAA
  • THE AGE OF INFOFI
    • Turning Insights into Data
    • Incentivizing Engagement
    • Sustained Value Creation
    • Equitable Ecosystem
  • CONCEPT OF $AAA
    • Community: Empowering Fans as Active Stakeholders
    • Culture: Fostering Authentic Connections Through Shared Experiences
    • Composability: Building a Modular and Interoperable Ecosystem
  • CREATING DEMAND & SCARCITY
    • Creating Demand
    • Creating Scarcity
  • AI POWERED SOCIAL GRAPH
    • Data Ownership and Monetization
    • Behavioural Insights
    • Community Engagement
  • SUSTENANCE STRATEGY FOR PRICING
    • How It Works?
    • Key Benefits
  • LIQUIDITY PROVISIONING STRATEGIES
    • Automated Market Maker (AMM) With Constant Product Formula
    • Liquidity Mining with Staking Rewards
    • Dynamic Pricing Structure
    • Staking & Liquidity Mechanisms
  • TOKEN DISTRIBUTION STRATEGY
    • Public Allocation (4.9%)
    • Investors (6.1%)
    • Team (15%)
    • Airdrop (20%)
    • Advisors (5%)
    • Treasury (20%)
    • Liquidity & Staking
  • TECHNICAL ARCHITECTURE
    • Data Collection Layer
    • Data Processing Layer
    • Machine Learning Models
    • Action Layer
    • Blockchain Layer
  • AI Stack
    • Core Technologies
    • Cloud Infrastructure
    • AI/ML Tools
    • Blockchain Components
    • Frontend/UI
  • IMPLEMENTATION ROADMAP
    • Phase 1: Public Beta – Content Curation + Airdrop + IDO
    • Phase 2: Live Alpha – Advanced Features
    • Phase 3: Scaling & Monetization – IP Tokenization
  • CHALLENGES & CONSIDERATIONS
    • Privacy & Ethics
    • Scalability
    • Adoption
  • CONCLUSION
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  1. AI Stack

AI/ML Tools

i. Data Engineering: Apache Kafka (streaming), Apache Airflow (orchestration)

ii. Model Training: Hugging Face Transformers (NLP), XGBoost/LightGBM (tabular data)

iii. Deployment: Docker containers, Kubernetes orchestration

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Last updated 28 days ago

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