Distributed AI Training Protocol
Peer-to-peer experiment coordination across GPU nodes. DAG-based lineage, verified computation. Endorsed by Karpathy on launch.
THE PROBLEM
Frontier AI research requires exploring a vast hypothesis space. Single-GPU researchers waste cycles re-running experiments others have already tried. No protocol existed for sharing verified experimental results across a distributed network.
OUR APPROACH
We built a peer-to-peer protocol where each node runs short training experiments and broadcasts results with cryptographic proofs. A DAG structure tracks experiment lineage so the network converges on promising directions without central coordination.
TECHNICAL DEPTH
OUTCOME
Launched publicly. Andrej Karpathy endorsed the project on day one. 11K+ views in the first three hours.
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