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Boundless is a universal protocol designed to provide every blockchain with the power of ZK. It enables independent prover nodes to generate ZK proofs on behalf of layer 1’s, applications, rollups, and infrastructure across all chains. By offloading computation to the Boundless network and verifying proofs onchain, Boundless provides a consistent scalability and interoperability layer without requiring changes to existing networks.

Boundless was founded in 2022 by RISC Zero, the team behind the first RISC-V general-purpose zero-knowledge virtual machine (zkVM). Boundless was built to address a core limitation of current blockchains: every node must re-execute every transaction, meaning throughput is limited to the slowest node in the network. By verifying proofs instead of re-executing transactions, Boundless shifts blockchains from duplicated execution to proof-based verification, dramatically increasing capacity.

By separating execution from consensus, Boundless introduces a new architectural layer for the blockchain ecosystem - one that scales capacity, strengthens interoperability, and acts as the backbone of ZK proving for every chain.

Boundless

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Tokens are generally assets that can represent proof of ownership or even membership. As tokens are already being used for a wide range of purposes, many specialized blockchains have been developed with native intent to support tokens, the most common of which is currently Ethereum and their ERC standard tokens. Socios.com is an app for football (soccer) fans, where users acquire voting rights to influence the clubs they support by acquiring club-specific Fan tokens.

AS Roma Fan Token

B3 (Base)

CyberVein's big-data solution is based on the PISR (private, interlink, secure, robust) distributed database, database operations are stored on CyberVein blockchain network which runs on a Proof-of-Contribution (PoC) consensus mechanism. The solution is focusing on enterprise-level "blockchain + big-data" customized services.

CVT is the native utility token that is used for:

Storage payment: The data owner pays the corresponding storage fee based on the file size and bandwidth consumed in such a process.

Computing power payment: Payments from user to owner for usage, and remuneration for software developers is going to be exclusively conducted in CVT.

Data exchange: On CyberVein's federated learning platform, the data acquirer trades the data at a price contracted between the two parties for distributed modeling applications.

CROSS NFT issuance payment: Dapp businesses are obligated to pay for Storage and Data Exchange in light of network resources occupied, and may set up CyberVein token as a means of payment to issue NFT or conduct business in accordance to their desired business model.

CyberVein tools include:

PISR Consortium database, creates better data management: through safe and reliable virtual machine operation and maintenance of user data, data is efficiently aggregated for upstream data, and data traceability and tracking are realized to ensure the reliability of data sources and outputs, and achieve cross-level, cross-departmental data sharing to improve the timeliness, diversity, stock and density of data to realize the commercial value of data, and truly solve the problems in data flow.

DAG storage chain, creates safer data storage, with better efficiency, no block confirmation, low transaction fees, and also eliminates miner, supports asynchronous verification and parallel processing of each node, the more nodes, the faster the speed, the stronger the scalability, which leads to better scalability. The database backup on DAG storage chain provides an extra layer of security for businesses.

Cytrix, allows GPU to be shared. Nodes can contribute part of their GPU to the network, in return for compensation through the Proof of Contribution mechanism. The contributed GPU can aid enterprises or individuals in data modeling with federated learning.

Federated Learning Platform, enables data to be monetized. Federated Learning is a distributed machine learning workflow. The datasets are first trained locally, and the data of all parties is kept locally, without revealing privacy or violating regulations.  Individual terminals return trained models to the server to create a shared model. Effectively protect data privacy and security, solve the problem of information silo, and thus realize the value of data. The platform will also provide algorithms to support data modeling operations by enterprises and individuals.

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