Recently, dappOS launched a spot trading function based on its intent network, which has set off a wave of enthusiasm in the Web3 field. As a person who has been working in the blockchain industry, my evaluation of this new function is complex and diverse.
Specific steps to participate in dappOS activities:
Open your Binance wallet and find the dappOS activity link.
Complete simple tasks such as minting an NFT, trading a certain amount, etc. These tasks are often described as simple and easy to do and are designed specifically for Binance Wallet users.
intentEX, the handling fee is as low as 0.1%, the loss is almost non-existent and can be ignored!
dappOS is a Web3 operating system designed to provide users with a simpler multi-chain interaction experience. According to online information, dappOS has attracted significant investors such as Sequoia, IDG, and Binance. The project released its V2 version in September 2023 and integrated with Perp, which is considered an important time point for participating in airdrops. Users can interact through the V2 version to increase their chances of being snapshot, potentially obtaining an airdrop.
First of all, the concept of the intent network in dappOS is unique. It aims to address common pain points in traditional blockchain transactions, such as transaction speed, liquidity issues, and high fees, by building an execution network centered around 'intents'. The intent network simplifies the user experience by converting user transaction intents into specific on-chain operations. This is somewhat similar to the 'one-click operation' we are accustomed to in the traditional internet, but achieved in a decentralized environment.
Intent-Based Networking (IBN) is a concept aimed at simplifying network management by understanding and automating the execution of user intents. Here are some key points about the technical details of intent-based networking:
Core Concepts
Intent Translation: The IBN system translates these 'intents' into specific network configuration commands by understanding the user's natural language or high-level strategies. This requires powerful natural language processing (NLP) and machine learning technologies to analyze and understand user needs.
Automated Execution: Once intents are identified and translated, the system automatically implements these strategies. This means that the configuration of network devices, application of strategies, and changes in network status are all automated, reducing the likelihood of human error.
Continuous Monitoring and Verification: IBN is not just about setting network configurations; it also includes continuous monitoring of network status to ensure that the executed intents are correctly implemented. If the network status deviates from the user's intent, the system will automatically make adjustments.
Technical Implementation
Software Defined Networking (SDN): IBN is often used in conjunction with SDN, as SDN allows for the separation of the network control plane from the data plane, enabling network traffic to be managed through software programs. This provides a programmable environment for IBN to achieve automated execution of intents.
Artificial Intelligence and Machine Learning: The application of AI and ML in IBN primarily focuses on understanding intents, generating strategies, and predicting and optimizing network behavior. These technologies help the system learn the intents obtained from users and adjust future decisions based on past operations.
Closed-loop Automation: This includes a feedback mechanism where the system continuously monitors the actual state of the network against the user intent and automatically adjusts network configurations when necessary. This approach ensures that the network always meets user expectations.
Specific Technical Components
Intent Engine: This is the core of IBN, responsible for parsing, translating, and executing intents.
Strategy Manager: Manages and stores strategies, ensuring consistency and correctness of strategies.
Network Abstraction Layer: Provides a high-level view of the network, simplifying the configuration and management processes.
Adaptive and Learning Systems: Utilizes AI and ML technologies to adapt to changes in the network environment and optimize network strategies.
Challenges and Considerations
Complexity: Although IBN aims to simplify network management, its implementation involves a complex tech stack, including AI, SDN, and complex automation systems.
Security and Privacy: Automated intent execution needs to ensure security to prevent unauthorized intent execution while protecting the privacy of network and user data.
Interpretability: Given the involvement of AI and ML, ensuring transparency and interpretability of system decisions is crucial for user trust.
