GoMint Docs
  • INTRODUCTION
    • GoMint
  • Roadmap
  • FEATURES
    • Token Creation and Mint
    • Liquidity Migration to DEXs
    • LOUD Model Integration
    • Mindshare Pool and Reward System
    • Community for Token Holders
    • Messaging Token Holders
  • TOKENOMICS
    • $GMNT Unique Features
    • Fees and Revenue
    • Graduation
  • RESOURCES
    • Community
    • Terms of Use
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  1. FEATURES

LOUD Model Integration

PreviousLiquidity Migration to DEXsNextMindshare Pool and Reward System

Last updated 3 days ago

Inspired by the pioneering $LOUD model, GoMint integrates a cyclical incentive system where trading activity fuels community-driven promotion and token growth. This self-sustaining feedback loop leverages trading fees to reward promoters, enhancing visibility, liquidity, and user engagement.

By aligning incentives between traders, holders, and promoters, the infofi mechanism drives continuous token exposure and ecosystem momentum.

Key Features

  • Fee-Driven Incentives A defined portion of trading fees (e.g., 0.75% per trade) from the TOKEN/SOL liquidity pool is redirected into a reward pool for community contributors.

  • Community Promotion Users are incentivized to promote the token on platforms such as X (Twitter) through campaigns, memes, threads, or virality-based tasks — increasing awareness and adoption.

  • Virtuous Growth Cycle

    • Promotion → Trading → Fees → Rewards → More Promotion This model creates a positive feedback loop, where increased trading generates more rewards, encouraging further community participation.

Use Case

The $HYPE token is launched with a 1% trading fee on Raydium. Of this, 0.3% per trade is diverted to a community reward pool managed through GoMint.

  1. Users who promote $HYPE on X are eligible for rewards based on engagement or referral metrics.

  2. Increased visibility leads to more trades, raising the reward pool.

  3. This cycle continues, fueling organic growth and liquidity.

This model simplifies everything, no need to worry about mindshare mechanics or complex integrations with platforms like . We handle all of that for you.

@kaitoAI