The core technology architecture of Port3 revolves around BQL (Blockchain Query Language) and L2 network optimization. Its essence is to convert natural language into on-chain executable smart contract instructions while improving data processing efficiency through layered design.
1. BQL: The 'Natural Language Operating System' for On-chain Interactions
BQL, as a programming language independently developed by Port3, allows users to describe on-chain operation logic using Markdown-like syntax, such as completing complex processes like token exchanges, liquidity additions, and NFT minting with one click. This 'Code as a Service' model reduces the threshold for on-chain interaction that traditionally required specialized development skills to zero—users only need to write scripts, and BQL will automatically generate and execute transaction instructions. For example, users can define through BQL scripts, 'When the wallet balance reaches 1 ETH, automatically exchange 0.5 ETH for BTC and stake it,' and the system will monitor the on-chain status in real-time and trigger the operation.
BQL's technological breakthrough lies in process control and event-triggered mechanisms:
◦ Conditional Judgments: Supports dynamic decision-making based on on-chain data (such as price fluctuations, user behavior), for example, 'If the price of BNB falls below $300, then automatically sell and exchange for stablecoins.'
◦ Cross-chain Interaction: By aggregating data from 9 L1 and mainstream EVM networks, BQL scripts can seamlessly call cross-chain protocols (such as LayerZero) to achieve automated management of multi-chain assets.
◦ Security Isolation: BQL operates in a sandbox environment, and user private keys are authorized through smart contracts to avoid the risks of centralized custody.
2. The Efficiency Revolution of L2 Networks
Port3 has built a dedicated L2 network based on the OP Stack, using data sharding and state compression technology to reduce on-chain transaction costs by over 90%, achieving second-level confirmations. For instance, when users participate in SoQuest tasks, the L2 network can batch process thousands of on-chain interactions, with gas fees only 1/100 of L1. This design not only enhances user experience but also enables Port3 to support high-frequency on-chain data collection, providing real-time data sources for AI model training.
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