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. LIQUIDITY PROVISIONING STRATEGIES

Automated Market Maker (AMM) With Constant Product Formula

The most common AMM model uses the constant product formula, which ensures that the product of the reserves of two assets remains constant during trades.

For $AAA, this can be expressed as:

x y = k

Where:

  • x: Reserve of $AAA tokens in the liquidity pool.

  • y: Reserve of the paired asset (e.g., stablecoins like USDC or ETH).

  • k: A constant representing the invariant product.

When a user swaps $AAA for the paired asset, the new reserves are calculated as:

x' = x - x, y' = y + y

With the condition,

x' y' = k

This ensures that the price of $AAA adjusts dynamically based on trade volume, incentivizing arbitrageurs to correct imbalances.

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Last updated 3 months ago

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