Super InternX (Coming Soon)
5.1 Overview
Super InternX is a multi-agent content generation system purpose-built for Web3 projects, KOLs, and content creators. Through a modular agent architecture, it automates the entire content lifecycle — from trend monitoring and research to personalized generation and quality evaluation.
5.2 Agent Architecture
5.2.1 Central Coordination: Coordinator Agent
Acts as the central orchestrator, managing task assignments and information flow across all sub-agents to ensure coherent and efficient content output.
5.2.2 Intelligence Agents
Trend Monitor Agent Continuously monitors trending topics across platforms like X (Twitter) and Telegram to detect market sentiment shifts and viral narratives.
Fact Verification Agent Verifies on-chain data, quotes, and references in generated content to ensure factual accuracy and trustworthiness.
Deep Research Agent Retrieves in-depth data from whitepapers, blockchain sources, and knowledge repositories to enrich content with credible insights.
5.2.3 Content Generation Agents
Style Learner Agent Learns and replicates the user’s past content style — including tone, vocabulary, and brand voice — for personalized output.
Content Generator Agent Crafts tweets, posts, captions, and scripts based on real-time context, trends, and user intent.
Quality Assessor Agent Evaluates draft content for fluency, coherence, persuasiveness, and factual alignment, refining it before publishing.
5.2.4 System Infrastructure Modules
Deep Research Database A comprehensive repository of project documents, on-chain data, and curated Web3 sources accessible by all agents.
User Requirement Context Captures user preferences, goals, and behavioral history to guide content generation and style adaptation.
Shared State Management Maintains real-time task states, agent outputs, and user feedback to enable seamless multi-agent coordination.
Verification Information Cache Stores previously validated facts and references to reduce redundant verification and improve execution efficiency.
5.3 Example Use Cases
Automated campaign tweets with consistent tone for Web3 project accounts
Personalized hot-topic commentary generation for KOLs
Multi-platform, multi-language content adaptation
Real-time response to on-chain events and social sentiment spikes
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