Develop secure and decentralized applications


Traditional discrete-time consensus algorithm, upgraded to continuous random sampling. A portion of all nodes get one result, via multi-round sampling to achieve full coverage. When the results of the random sampling comes to a trusted value, the consensus is reached.

The synchronous consensus

Improves the operating efficiency of the asynchronous system, and cooperates with the multi-node design to improve the concurrent performance of the system.

Random computable functions

It isn’t necessary to connect most nodes in the consensus process. Data transmission can be cut down, depending on the network of nodes. Nodes are randomly selected, the user knows if it’s selected according to the calculation, and other users can see the results.

Linear scalability

Performance is linearly accelerated as the node scale increases. The larger the node scale, the faster the convergence and the better the performance.

The exclusive asynchronous sorting technology

Transforms consensus from large-scale concurrent requests to asynchronous systems. Overall connectivity is better, it can run as normal in a non-fully connected network environment, even in systems with a network connection ratio of less than 50%.

Multiple hidden layer network

A hidden layer network approximates any continuous function. The framework replaces the single hidden layer with a deep network. The result is converged and normalized quicker during the fitting process.

Identify congestion to achieve balance

Independent decision-makers form a conductive structure to simulate Neuron, and make a joint decision.

Supernode and supervising node

BPTT, multi-layer partitioning, and fog algorithms switch network topology structure between weak centralization and decentralization. This is a combination of supernode and supervising node.