Build 'ComputeFlow'—an AI-driven supercomputing workload orchestrator that optimizes resource allocation. The platform would use reinforcement learning to predict job priorities and deploy a 'Dynamic Scheduler' that auto-adjusts resource allocation based on urgency (e.g., 'Climate model simulation takes precedence over CAD rendering'). It would also offer a 'Carbon Footprint Dashboard' showing real-time energy usage and a 'Research Impact Score' to prioritize high-value projects.

Supercomputing centers face 40% idle time due to inefficient workload scheduling, costing $5M/year in wasted energy. Current batch systems cause 30% job delays, while AI workloads require 5x more resources than allocated. The U.S. DOE reports 25% of supercomputer time is lost to 'queue starvation' for urgent research (e.g., climate modeling).