The computers we use every day are almost always running well below their full potential, with processors sitting at five to ten percent capacity while we browse the internet, stream videos, or simply leave our machines switched on. At the same time, we found ourselves increasingly aware of the enormous financial and environmental cost that companies face when running artificial intelligence on massive, purpose-built data centers that consume extraordinary amounts of electricity and require constant expansion to meet growing demand. The contrast was difficult to ignore - billions of personal computers around the world are wasting the vast majority of their computing potential every single day, while businesses are simultaneously spending fortunes to build and maintain new infrastructure capable of doing that exact same work. It struck us as one of the most overlooked mismatches in modern technology and we became convinced that the solution was not to build more data centers, but to finally connect the computing power that already exists with the companies that desperately need it. This became the foundation of Corex.
Corex is a distributed compute marketplace that lets companies run their AI workloads across a network of idle personal computers instead of expensive cloud data centers. Companies submit their AI jobs through the Corex dashboard and the work gets distributed across available workers in real time. Everyday people contribute their idle CPU power through a lightweight background app, earn money for every job they complete, and get matched to jobs automatically based on their preferences and availability. Every result is verified for accuracy before it is delivered back to the company. The whole system runs without any new servers, any new data centers, or any new electricity — just computing power that already exists finally being put to use.
We built Corex as a full stack application with three main pieces working together. The orchestrator is a Python and FastAPI backend that acts as the brain of the platform It receives jobs from companies, breaks them into smaller chunks, distributes those chunks to available workers by firing webhooks using the asynchronous httpx HTTP client, verifies results using a honeypot system, and handles billing calculations. Workers run a lightweight Python and FastAPI client on their own machine that receives those webhooks, executes the jobs, and sends results back to the orchestrator. The frontend is built in React, Vite, and TypeScript with Tailwind CSS, consisting of two separate dashboards: one for companies to submit jobs and monitor progress in real time and one for workers to see incoming jobs, track their earnings, and monitor their CPU activity. Firebase handles authentication and stores all persistent data including worker accounts, company accounts. Redis paired with the rq job queue library handles the live worker pool state and chunk tracking. We used Server Sent Events through the EventSource API to push real time updates from the orchestrator to both dashboards simultaneously and the regional heatmap on the company dashboard is built using D3.js rendered over a custom SVG world map to show which regions are actively processing jobs.