Rivalz Network Launches Its Intel Incentivized Testnet
Built on Dymension and Powered by Celestia’s DA layer, Rivalz aims to create a decentralized peer-to-peer network for modular AI apps, enhanced by programmatic trust and privacy. , backed by leading investors such as Delphi Ventures, Zee Prime, Stakewithus, Cogitent Ventures, D1, M6, 4RC, and GSR - is laying the foundations for the AI web3 landscape with their DePIN-based AI Intel Layer.
Users can obtain Testnet points by running Light rClients - which provides the network with core data storage & data processing facilities, completing social challenges & by participating in daily mints of Fragmentz - which are essential to obtain discounts on the node sale whitelist. The Initiating Intel Testnet is the first step we’re taking in the journey towards rolling out our AI DePIN. Our AI Intel Layer stands to become the connectivity core for the future of web3 x AI.
The Rivalz DePIN is a decentralized P2P node network that employs advanced encryption algorithms for secure data handling and blind computation for external data access. It focuses on ensuring compliant and validated data storage and private access to such data using FHE, as well as providing validated and trustless data streams for AI. are used as a settlement solution for the inflows and outflows of Data from the DePIN Layer. Validator zNodes will be sold via the zNode Key Licence Sale.
$RIZ will be the native token of Rivalz. It pays for transaction fees, coordinates worker and validator nodes, and ensures network security. It rewards users for providing valuable data, powers AI applications, and allows holders to participate in governance decisions, influencing the network's development.Disclaimer: This is sponsored content. The information on this page is not endorsed or supported by U.Today, and U.
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Rivalz Network launches it’s Intel Incentivized TestnetBuilt on Dymension and Powered by Celestia’s DA layer, Rivalz aims to create a decentralized peer-to-peer network for modular AI apps, enhanced by programmatic trust and privacy
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