W.AI

Submit training data and model architecture to the W.AI network.

Network distributes training tasks across available compute providers.

Providers execute training tasks and return results.

Results are aggregated to refine the model.

Trained model is deployed for inference.

Submit data processing tasks to the W.AI network.

Network divides tasks and assigns them to compute providers.

Providers process data and return results.

Processed data is aggregated and delivered to the user.

User analyzes the processed data.

Researchers submit simulation parameters to the W.AI network.

Network allocates compute resources for the simulation.

Simulation runs on distributed devices.

Results are aggregated and returned to researchers.

Researchers analyze the simulation results.

dApp developers integrate W.AI API for compute-intensive tasks.

Tasks are offloaded to the W.AI network.

Compute providers execute the tasks.

Results are returned to the dApp.

dApp functions smoothly with reduced on-chain load.

Game developers integrate W.AI for rendering and physics processing.

Tasks are distributed across the network to reduce latency.

Real-time processing enhances game performance.

Improved user experience due to lower latency.

Scalable resources reduce lag for MMOs.