Show HN: Parallax – Coordinate adversarial AI agents over durable streams (github.com)

Parallax, a new proof-of-concept tool, enables the coordination of independent adversarial AI agents across durable streams, enhancing collaborative research in the AI/ML community. Unlike traditional methods that utilize a single model in a single context, Parallax allows multiple agent cohorts to operate in isolation, tackling complex questions from differing perspectives. This unique approach not only fosters deeper inquiry but also combines the findings only after all agents have contributed, leading to more robust conclusions. The tool employs S2 streams, which can be dynamically created to implement various reasoning strategies based on user-defined configurations. With straightforward command-line functionalities, researchers can initialize their streams, initiate sessions, and steer agent interactions in real-time. Key features include modular agent roles (like Claude for planning and Codex for code reviews) and the ability to scale the number of groups and agents per session to address intricate topics. Parallax’s innovative framework is poised to significantly advance research methodologies in AI, allowing for more comprehensive and intricate exploration of scenarios that demand nuanced arguments from distinct viewpoints.