Hey HN! I've been exploring how to guide LLMs into better thinking patterns. After working on Hegelian dialecticism (https://github.com/Hmbown/Hegelion), I've now implemented Peircean Abduction, Charles Sanders Peirce's method of "inference to the best explanation" (famously used by Sherlock Holmes).

This MCP server forces them into a detective's mindset: 1. Explicitly quantify surprise (0.0-1.0 anomaly scoring) 2. Generate multiple, distinct, testable hypotheses 3. Evaluate via Inference to Best Explanation with a "Council of Critics"

Current status: - MCP server with zero-config setup - JSON output for structured reasoning (great for analysis tasks) - Working on conversational/natural language output (for coding/chat)

My goal is to move away from "prompt engineering" and toward "logic architecture," forcing the model to make its uncertainty visible before committing to an answer. To test it, I fed it a scenario about a "defunct" satellite collision (see the README). It correctly deduced it was a dormant weapon where standard LLM responses hedged if they mentioned that possibility.

Would love to hear what use cases you come up with!