Show HN: I built an 11-LLM consensus engine to detect AI hallucination

A new approach to improving local LLM reliability has emerged: running multiple models in parallel as a consensus engine. This GitHub project demonstrates how 11 different LLMs can be orchestrated together to cross-validate outputs and identify hallucinations through agreement voting.

For local deployment practitioners, this technique offers a practical solution to a critical problem—hallucination detection without external API dependencies. By running multiple smaller or quantized models locally, teams can achieve higher confidence in outputs through ensemble validation, trading modest computational overhead for significantly improved reliability.

This pattern is particularly valuable for edge inference scenarios where users need deterministic quality assurance. The approach aligns well with ongoing trends in local LLM deployment, where computational resources are increasingly sufficient to run multiple models concurrently for improved safety and accuracy.

Source: Hacker News · Relevance: 9/10