TRAST Documentation
TRAST.spaceConnect
  • Introduction
    • What is TRAST?
    • TRAST Litepaper
    • Trust System
    • Mechanics
    • Core Concepts
  • Getting Started
    • Quick Start Guide
    • Beta Information
    • Understanding Trust Scores
  • Platform Features
    • Platform Overview
    • Core Infrastructure
      • Vote Staking
      • Trust & Reviews
      • TRAST Premium
      • Verification
      • Feature Specifications
      • Tokenomics Analysis (Planned)
    • PayAI & Marketplace
      • PayAI System
      • TRAST Marketplace
      • TRAST.fun (Planned)
      • Advanced Platform Features
      • Bridge to XRPL
    • User Interaction
      • Inline Search
      • Live Feed
      • Commenting System
      • Identity Integration
      • Smart Onboarding
      • Monetization Options
    • Chat System
      • Chat Rooms
      • Specialized Rooms
      • Two-Tier Rooms
    • Platform Analytics
  • Technical Documentation
    • Technical Overview
    • Core Architecture
      • Design & Implementation
      • Security & Trust
      • Infrastructure Costs
    • AI Systems
      • Bot System
      • AI Chat Guide
      • AI Scam Radar
      • AI Review Copilot & TrustDigest
      • Content Agent
    • Advanced Features
      • Bot Outputs
      • Customization & Integrations
  • Community
    • Guidelines
      • Communication Style
      • Communication Playbook
      • Community Features
    • Engagement & Rewards
      • Community Incentives
      • Premium Features (Gem Hunters)
      • TRAST Reflections (Blog)
  • Resources
    • Use Cases
      • Overview
      • Detailed Examples
      • Real World Scenarios
    • Support & Contact
      • Contact Information & Company Details
      • Support Center
  • Token
    • Economics
      • Financial Model
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On this page
  • 1. Dynamic Trust Score
  • 2. AI-Powered Pattern Recognition
  • 3. Community Intelligence
  • 4. Gem Hunting
  • Monetizing Your Expertise
  • Interconnected Concepts
  • Next Steps
  1. Introduction

Core Concepts

Understand the fundamental concepts driving the TRAST platform, including dynamic Trust Scores, AI-driven Pattern Recognition, collective Community Intelligence, and the idea of Gem Hunting.

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Last updated 11 days ago

TRAST operates on a foundation of several key concepts designed to build a robust and reliable Web3 intelligence ecosystem. Understanding these concepts is crucial to leveraging the platform effectively.

1. Dynamic Trust Score

This is more than just a simple rating. The is a dynamic metric reflecting the perceived reliability and quality of any Web3 entity (project, token, profile). It's calculated using a multi-faceted approach that considers:

  • Community Feedback: Weighted based on reviewer reputation and time.

  • AI Analysis: Incorporating insights from and other AI systems.

  • Verification Status: Confirmed identities and verified entities carry more weight.

  • Activity History: Consistent, positive interactions contribute to higher scores.

This dynamic nature ensures scores remain relevant and resistant to manipulation.

2. AI-Powered Pattern Recognition

TRAST employs sophisticated AI algorithms to analyze vast amounts of data (on-chain activity*, social signals*, market behavior, community discussions) in real-time. This allows the platform to:

  • Identify Suspicious Behavior: Detect patterns indicative of scams, manipulation, or other risks ().

  • Highlight Emerging Trends: Spot new projects gaining traction or shifts in market sentiment.

  • Support Decision Making: Provide users with data-driven insights beyond simple ratings.

(Note: Some data sources like on-chain and full social signal analysis are part of future development.)

3. Community Intelligence

TRAST harnesses the power of its user base. Collective knowledge is gathered, validated, and shared through:

This collective approach ensures that insights are diverse, grounded in real-world experience, and continuously refined.

4. Gem Hunting

While not a formal system yet, "Gem Hunting" embodies the spirit of using TRAST to discover promising, under-the-radar Web3 opportunities. By leveraging TRAST's real-time data, community insights, and AI analysis, users can:

  • Identify Early-Stage Potential: Find new projects or tokens showing positive initial signals.

  • Conduct Efficient Due Diligence: Quickly assess risk and community sentiment.

Monetizing Your Expertise

  • Offer verified project assessments to new token launches

  • Provide community management services to growing projects

  • Test and review new platform features for rewards

  • Create educational content and promotional materials

Each contribution builds your verified skill portfolio, creating pathways to paid opportunities within the ecosystem.

Interconnected Concepts

These concepts don't exist in isolation. Pattern Recognition informs Trust Scores. Community Intelligence validates AI findings and contributes to scores. Gem Hunting utilizes all other concepts to find value. Together, they create a powerful, self-reinforcing ecosystem for navigating Web3.

Next Steps

Reviews and Comments: Users share their direct experiences and analyses ().

Reputation System: User contributions are weighted based on their verified status and track record ().

Collaborative Validation: The connects AI analysis with human expertise for enhanced accuracy.

Share Findings: Contribute discoveries back to the community, potentially earning rewards through the .

The allows users to turn their Gem Hunting and other skills into direct monetization opportunities:

See how these concepts are implemented in the .

Dive deeper into the .

Explore the .

Trust Score
Scam Radar
Scam Radar
Commenting System
Trust System
TrustMarketplace
Contributor Program
Contributor Program
Platform Mechanics
Trust System
Technical Documentation Overview