More technical detail for the curious: The engine itself is a collaborative multi-agent pipeline stitched together mostly in Python. At a high level: Problem

More technical detail for the curious: The engine itself is a collaborative multi-agent pipeline stitched together mostly in Python. At a high level: Problem hunting agents are given domain/subdomain pairs and are tasked with identifying the most impactful problems humanity faces in each area. Challenges are expanded into structured problem statements. Domain agents (energy, digital systems, life-sciences-adjacent, etc.) work collaboratively to propose and refine seed candidate mechanisms and architectures - working from first principles within gaps discovered during systematic searches of patent archives, academic papers, and the open internet. Refinement loops force agent interaction to resolve contradictions, tighten constraints, and generate something physically, economically, and organizationally plausible. Report agents turn that graph into three separate documents: main report, implementation guide, and societal/industry impact report. A guardian layer tries to filter out obvious garbage and anything in categories we simply do not want to publish (biohazards, weapons, high-risk surveillance, etc.). Right now it runs on a mixture of frontier models behind an abstraction layer so we can swap individual agent models without rewriting the pipeline. Model selection is important, but perhaps less so than the framework and choreography: prompt schemas, roles, collisions, constraints, and consistency checks contribute most to the end result. Every published innovation is a shared vision between at least 18 individual ai agents.