A research experiment in collective opinion dynamics using Reddit discourse and LLM-based agents
The Kimi Sentiment Tracker agent now runs on moonshotai/kimi-k2-thinking,
a newly released reasoning model from Moonshot AI. This agent tracks social dynamics and viral discourse patterns with enhanced analytical depth.
Vibe Infoveillance is an experimental system that tracks discussion patterns in Reddit communities related to stock markets and AI developments. Multiple LLM-based agents with different prompts analyze the same data to see what patterns emerge.
The system runs seven agents for stock market discussions (each configured with different analytical biases and risk tolerances), plus one agent (Dewi) that synthesizes AI industry news for technical audiences. The agents run daily on posts from 10 subreddits.
Rather than keyword counting, the agents look for shifts in how people are talking about topics—changes in narrative framing, attention patterns, and discourse themes. Think of it as computational ethnography of online financial communities.
Seven LLM agents with different system prompts analyze the same data independently. Each agent is configured to simulate different analytical biases, from risk-averse to aggressive.
Daily scraping of 10 subreddits covering stock markets and AI developments. Posts are ranked by engagement (upvotes, comments) and fed to agents for pattern identification.
The system looks for shifts in discourse framing rather than just counting keywords. What are people paying attention to? How are they talking about risk? What analogies are gaining traction?
After independent analysis, stock market agents engage in 2-3 rounds of moderated debate. They compare notes, vote on key questions with confidence scoring, and document disagreements for transparency.
Seven agents, each running a different LLM with a unique system prompt defining their analytical approach. The prompts specify what patterns to look for, not personality traits.
An experimental second layer of agents that convert analyst signals into specific trading recommendations with real-world market data.
Each of the seven stock analysis agents is paired with a trading agent that receives their signals, fetches live stock data (price, volume, technical indicators), and proposes concrete trades with position sizing, entry/exit points, and risk management.
Key Features:
Trading agents operate with moral values emphasizing responsible stewardship, patience, and prudent risk management rather than pure profit maximization.
Daily scraping via Reddit API from 10 subreddits (r/wallstreetbets, r/stocks, r/investing, etc.). Posts are ranked by engagement metrics.
Seven LLM agents (using qwen, deepseek, gemini, etc.) analyze the same Reddit data with different prompts. Each agent outputs its own interpretation independently.
A meta-agent reads all seven outputs and produces a synthesis document identifying areas of agreement, disagreement, and distinct framings.
Results published to this dashboard and a GitHub repository. Individual agent outputs and synthesis reports are available as markdown files.
Quantitative Performance Tracking
Agents compare their trading signals against actual market outcomes using reinforcement learning principles. The system calculates reward scores based on whether signals outperformed the SPY benchmark, with conviction multipliers and magnitude bonuses.
Organic Self-Discovery and Pattern Recognition
Agents read their own past analyses alongside market reality and write exploratory essays discovering their own interpretation patterns, selective attention, and cognitive biases—without performance framing or optimization goals.
Complementary Learning:
Different Knowledge Types:
Implementation details and findings will be shared as the experimental features develop.
Daily reports are available via the dashboard and GitHub repository. All agent outputs and synthesis documents are published as markdown files for reproducibility.
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