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AI-powered multi-agent system for analyzing prediction markets (Polymarket & Kalshi) by identifying consistent traders, enriching market context with live web data, and generating trading insights via LLM-based agent workflows.
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This is a 0-star, brand-new personal experiment combining off-the-shelf components (Hermes Agent framework, OpenRouter LLM API, APIFY) with public market APIs. The core idea—using LLMs to analyze prediction markets—is not novel; the execution is a straightforward wrapper around existing APIs and frameworks. No evidence of users, production deployment, or novel technical contribution. The 'multi-agent system' framing is standard agent orchestration using commodity tools. Frontier labs (OpenAI, Anthropic, Google) are already building prediction market analysis capabilities as part of broader AI agent platforms and real-time data integration features. This project directly competes with: (1) OpenAI's agents + function calling on market data, (2) Claude's native tool use capabilities, (3) Google's Gemini agent framework. A frontier lab could implement identical functionality in <1 week as a feature add-on. No network effects, no data moat, no switching costs. The code is likely tutorial-grade—glue logic between APIs without novel algorithmic or architectural insight. High frontier risk because prediction markets + LLM agents is an obvious product vector for the majors.
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