Say goodbye to the blind spot of AI trust! @Lagrange Official builds a solid verification line with DeepProve

When AI output results cannot prove 'untampered', and cross-chain computation is caught in the deadlock of 'who will guarantee', the 'trust crisis' in the blockchain field becomes increasingly prominent. The zero-knowledge proof (ZK) network created by @Lagrange Official , #lagrange , is breaking this dilemma with innovative technology, making 'verifiable' a standard feature of AI reasoning and cross-chain computation, equipping blockchain with a 'trust shield'.

This Web3 project focused on ZK technology has built a comprehensive technical system: the decentralized ZK proof network gathers over 85 quality operators, including top institutions like Coinbase and Kraken, ensuring the decentralization and reliability of the network; the ZK co-processor significantly lowers the threshold for on-chain queries, making operations as simple as using SQL, allowing developers to easily get started without specialized skills; the core DeepProve zkML engine further increases AI proof speed by 158 times, transforming the previously time-consuming and labor-intensive AI verification process into an efficient completion, giving cryptographic assurance to 'AI telling the truth'.

The LA token is the 'value core' of the entire ecosystem. Nodes participating in proof tasks need to stake $LA as collateral; failure to complete proof on time or errors will result in fund deductions, forcing nodes to comply with regulations; when developers call the API to use proof services, LA is the designated transaction fee; community members participating in the revision of network rules have voting rights as LA holders. Its token economic design is rigorous: total supply of 1 billion, with only 4% issued annually, and some tokens will be burned, ensuring that token value is closely tied to the actual usage demand of the network. Now, chains like ZKsync and AltLayer rely on it to complete cross-chain proofs, and DeFi protocols query historical data directly, attaching proof back to the chain to completely eliminate the risk of data tampering. #lagrange

@Lagrange Official deeply understands the pain points of developers: there is no need to study complex ZK mathematical theories; accessing the API allows for quick integration. Previously, an AI team took three months to build a proof circuit for model output verification and still had numerous vulnerabilities.