Show HN: LLM Council: survival-of-the-fittest multi-modal deliberation (github.com)

A new project, "LLM Council," has emerged to facilitate multi-modal structured deliberation among several leading language models (LLMs). By querying four frontier LLMs simultaneously, each with distinct personas, the system aims to generate nuanced recommendations through an anonymous cross-pollination process. Users can implement the council's functionality by cloning the dedicated GitHub repository, configuring with their OpenRouter API key, and invoking it with specific command scripts, making it an accessible tool for those involved in decision-making processes in AI. This innovative approach is significant for the AI and machine learning community as it introduces a structured framework to mitigate biases that typically arise from specific LLMs by anonymizing responses, which fosters honest engagement between models. The process unfolds in three stages—initial independent responses, peer tagging for consensus-building, and a synthesis of insights led by a designated "chairman." This method not only enhances the quality of deliberation but also generates comprehensive architectural decision records automatically. The LLM Council presents a promising direction for collaborative AI development and decision-making, making it a noteworthy tool for developers and researchers alike.