The Problem Most personal computers sit idle 90% of the time. Meanwhile, ML training and gaming workloads cost a fortune on cloud GPU instances. I wanted to bridge that gap — turn idle hardware into useful compute. What I Built ComputePool — a hub-and-spoke distributed compute grid. Your Zo Computer acts as the control plane. Idle laptops and PCs become worker nodes that poll for jobs, execute workloads, and earn credits. Architecture Node Agent (Python) ← polls → Hub API ← dispatches → Worker Pool ↓ Credit Ledger ↓ Cashout System Enter fullscreen mode Exit fullscreen mode Node Agent ( node-agent/node_agent.py ): Polls hub every 30s for available jobs Reports GPU tier (RTX 4090 = 3x credit multiplier) Streams results back on completion Hub ( hub/hub.ts ): FastAPI backend on Railway Job queue with priority based on GPU tier Credit ledger per node Regional multipliers (Indian region: 0.7x) Dashboard ( frontend/ ): Next.js 14 on Vercel Real-time job status, credit balance, node management Live at…