Recently, DeAgentAI announced the completion of a $6 million seed round of financing, led by Web3.com Ventures and Vertex Capital, with participation from many well-known investment institutions such as Higgs Capital, Kernel Labs, Waterdrip Capital, Tido Capital, CatcherVC, Goplus, PANONY, and UXLINK. The success of this round of financing not only reflects the market's high recognition of DeAgentAI, but also marks the beginning of a new era of combining AI and blockchain.
DeAgentAI aims to create the world's first AI agent neural network and solve the problem of existing agent systems failing to meet user needs through its original LLM model and AGI system. Facing challenges in the Web3 field, such as high transaction costs, inefficiency, and complex user experience, DeAgentAI leverages comprehensive AI optimization to significantly improve network efficiency and reduce transaction costs, paving the way for large-scale applications.
DeAgentAI's technical architecture is specially optimized for the Solana blockchain, introducing an innovative AI consensus mechanism, replacing traditional consensus methods with artificial intelligence, reducing uncertainty, and improving system transparency and efficiency. At the same time, the intent-based interaction model simplifies the user experience and lowers the threshold of the Web3 ecosystem.
Web3.com Ventures commented on the investment: "With the continuous advancement of the Internet and social media, as well as the improvement of user awareness, social trading will become mainstream. DeAgentAI provides users with more comprehensive information and experience with its professional training and model development in AI technology. We are optimistic about DeAgentAI's user-centric philosophy and efficient execution, and believe that it can quickly achieve commercialization and is expected to become a market leader."
Current challenges in the Web3 space
The Web3 space does face some challenges as it develops rapidly. Here is an overview of these challenges and possible solutions:
1. High transaction costs:
Challenge: High transaction fees make small transactions and micropayments economically unfeasible.
Solution: Develop more efficient consensus algorithms to reduce transaction fees; use second-layer expansion solutions such as Lightning Network or Plasma to reduce the burden on the main chain.
2. Inefficient Networking:
Challenge: Existing blockchain networks are slow in processing transactions and smart contracts, making it difficult to support large-scale applications.
Solution: Adopt more efficient consensus mechanisms, such as Proof of Stake (PoS) or Delegated Proof of Stake (DPoS); optimize the design of smart contracts to reduce unnecessary calculations.
3. Complex user experience:
Challenge: The complexity of Web3 applications makes it difficult for ordinary users to participate.
Solution: Design a more intuitive user interface and experience; provide education and training resources to help users understand blockchain technology; develop user-friendly wallets and trading tools.
4. Limitations of decentralized consensus mechanism:
Challenges: Traditional consensus mechanisms may suffer from low efficiency and high energy consumption.
Solution: Explore and develop new consensus mechanisms, such as Proof of Authority (PoA) or Proof of Reputation (PoR), which may improve efficiency and reduce energy consumption while maintaining decentralization.
5. Imperfect developer ecosystem:
Challenges: Lack of adequate development tools and support makes the development and deployment of decentralized applications (DApps) more difficult.
Solution: Build a richer developer community and resource library; provide frameworks, templates, and guides to simplify the development process; hold hackathons and developer conferences to promote knowledge sharing and innovation.
In addition, with the integration of AI technology, it is foreseeable that new solutions will emerge in the Web3 field to meet the above challenges. For example, AI can play a role in optimizing transaction paths, predicting network congestion, and automating smart contract testing, thereby improving network efficiency and user experience. At the same time, AI-assisted smart contract auditing and security analysis can enhance the security of the system and reduce development risks. Through the integration of these technologies, the Web3 field is expected to achieve more robust and user-friendly development.
How DeAgentAI addresses these challenges
As a fully intelligent blockchain system based on AI-native infrastructure, DeAgentAI is specially optimized for the Solana blockchain to meet challenges in the Web3 field and promote progress in this field:
1. Reduce transaction costs:
DeAgentAI leverages the high performance and low transaction fees of the Solana blockchain to provide users with a low-cost transaction solution. This makes small transactions and micropayments more economically feasible, expanding the scope of application of blockchain technology.
2. Improve network efficiency:
By optimizing the network through AI technology, DeAgentAI significantly improves transaction processing speed and the efficiency of smart contract execution. This optimization enhances the system throughput and shortens transaction confirmation time, meeting the needs of large-scale applications.
3. Simplify the user experience:
By introducing an intent-based interaction model, DeAgentAI simplifies the interaction process between users and blockchain. By optimizing the user interface and interaction design, DeAgentAI reduces the difficulty of entry for users and improves the overall user experience.
4. Innovative AI consensus mechanism:
DeAgentAI uses an AI decision-making mechanism to replace the traditional human consensus mechanism. This innovation reduces the uncertainty and bias caused by human factors and improves the fairness, transparency and efficiency of the system.
5. Rich developer support:
DeAgentAI provides a platform that allows developers to deploy their AI agents and use token economic mechanisms to incentivize high-quality creation and innovation. In addition, DeAgentAI also provides comprehensive developer tools and support to help developers build and deploy decentralized applications more easily.
Technical highlights: DeAgentAI's core advantages
Copilot3: LLM, a tool designed for Web3 scenarios
Copilot3 is a large language model (LLM) designed for Web3 scenarios, which aims to help the main model use appropriate tools and return results. It is a framework for using Web3 tools, including data construction, model training, and evaluation. Copilot3 excels in the following aspects:
Data collection and tool usage: Copilot3 integrates the APIs in the Web3 field and the detailed documentation provided by RapidAPI, which enables it to understand and promote new APIs and enhance the adaptability and scalability of the model.
Instruction generation and solution path annotation: Copilot3 can handle single-tool and multi-tool scenarios in the instruction generation phase, and improves planning and reasoning capabilities through multiple rounds of reasoning and real-time API calls in the solution path annotation phase. The development of a depth-first search decision tree (DFSDT) further improves the annotation efficiency of complex instructions.
InterConnect Rollup: The Key to Governance and Oversight
InterConnect Rollup is a Rollup that carries all important interactions and connections. It is a specific Layer 2 solution that increases transaction throughput and reduces transaction costs by executing transactions on Layer 2 and submitting compressed transaction data to the main chain. It is designed to perform all governance and supervision: managing AI agents, data asset definitions, transaction behaviors; recording the interactions required for users, agents, and their consensus, i.e., acting as checkpoints for the AKKA network. With InterConnect Rollup, we can deploy Rollups on multiple main chains and achieve interoperability of data and transactions through a mapping mechanism to ensure the efficiency and security of the system.
Agent AKKA: Real-time communication and weak consensus
Agent AKKA is a communication network that achieves real-time communication and weak consensus between Agents. Its name comes from the famous Actor model programming framework AKKA. The main functions of Agent AKKA include: discovering the distributed network of other miner nodes through the Kademlia algorithm to enhance the system's anti-attack ability; a simplified anonymous Tor network to ensure real-time while protecting user privacy and preventing collusion; a checkpoint mechanism for filtering and compressing key data, and a matching validator network for calculating and generating the above checkpoints and submitting them to Rollup.
QKV Index Network: The Core of Smart Tool Management
QKV is the core part of the Attention calculation in the LLM Transformer architecture and the core part of DeAgent. The QKV Index network is used to solve the problem of how to use the tool. It works as follows:
Embedding Indexer: Generates embeddings based on user needs and enters the vector database for indexing.
Structured Description Indexer: Generates structured descriptions for easy indexing.
Retriever: Filters and reorders the index results, and ultimately provides a list of possible tools. Through the cooperation of the embedding indexer and the structured description indexer with the retriever, the efficient management and calling of appropriate tools is ensured, improving the efficiency and accuracy of task completion.
Proxy registration and operation: ensuring reliable execution results
The agent registry stores the code, data, and related index descriptions of agents developed by developers for indexing by the indexing framework. The agent runtime is the environment for running tool codes. The following measures are taken to ensure the credibility of the user agent execution results:
Privacy Operation Interaction Protocol: Operations involving user privacy are carried out through multiple rounds of interactions to ensure that secrets are kept confidential only on the user side.
Distributed operations: Encourage the emergence of more proxy providers and ensure the trustworthiness of the execution process through zk tools.
Agent Coordination
Multi-agent reinforcement learning (MARL) coordinates multiple subtasks to achieve a common goal of optimal team reward. Our system enhances this coordination by:
Leaders guide followers: Guide followers toward higher group rewards, providing agents with specific goals.
Introducing RGD (Reward Generation and Distribution): Trains and coordinates followers, generates and distributes synthetic rewards, and rewards followers based on their contributions and team rewards.
What changes can DeAgentAI bring?
Fair AI Decision Making
DeAgentAI uses fair artificial intelligence decision-making to assist traditional human consensus mechanisms. This innovative approach not only reduces uncertainty and bias caused by human factors, but also significantly improves the fairness and transparency of the system.
Intent-based interaction
DeAgentAI's system introduces an intent-based interaction model that enables users to interact with the blockchain more intuitively and easily. This model greatly improves the user experience and lowers the entry barrier.
AI Agent Deployment and Token Incentive Mechanism
DeAgentAI allows developers to deploy their AI agents on the platform and promote high-quality creation and innovation through a token economic incentive mechanism. This mechanism not only promotes the vitality of the ecosystem, but also provides developers with considerable economic returns.
Seamless Web3 interactive experience
DeAgentAI is committed to removing barriers for users to interact with the Web3 ecosystem, enabling them to use decentralized applications and services without barriers. With DeAgentAI, users can enjoy a smoother and more efficient Web3 experience.
Efficient network and revolutionary interactivity
Through comprehensive AI optimization, DeAgentAI significantly improves network efficiency and interactivity, unlocking numerous potential liquidity opportunities. This optimization not only improves the operational efficiency of the system, but also maximizes the capture potential of the wealth effect.
DeAgentAI’s Vision
About DeAgentAI
In today's rapidly developing blockchain and artificial intelligence fields, DeAgentAI is committed to developing and providing the most advanced blockchain intelligent agent solutions to support the innovation and development of decentralized applications. Their goal is to deeply integrate AI and blockchain to achieve true full intelligence through innovative Layer 2 solutions and smart supervision. Our vision is to build an efficient, secure and intelligent decentralized ecosystem to promote the widespread application and sustainable development of blockchain technology and create more value for users.
A leap in transaction speed: Layer 2 technology optimization
The potential of blockchain technology is often limited by its inherent transaction speed and scalability. Our company aims to significantly increase transaction speed and processing capabilities through cutting-edge Layer 2 technology. Layer 2 technologies, such as state channels, rollups, and sidechains, can offload large volumes of transactions to a more efficient off-chain environment for processing, and then synchronize the results back to the main chain. This approach not only increases transaction processing speed, but also significantly reduces transaction costs and network congestion, providing users with a smoother experience.
Smart Supervision: AI as the Guardian of On-Chain Behavior
In a decentralized ecosystem, it is crucial to ensure the transparency, security, and compliance of transactions and behaviors. Our AI agent can monitor on-chain transactions and behaviors in real time through advanced machine learning and deep learning algorithms, identifying potential violations and abnormal activities. The intelligent supervision function of the AI agent not only enhances the security of the system, but also proactively prevents fraud and attacks, and protects user assets and privacy.
Achieve full intelligence
Full intelligence is at the core of our vision and represents a fully intelligent blockchain ecosystem. Through the deep involvement of on-chain AI agents, we aim to achieve the following goals:
Real-time decision-making and optimization: AI agents can analyze and make decisions based on real-time data, optimize trading strategies and resource allocation, and improve the efficiency and returns of the entire system.
Personalized services: By analyzing user behavior and preferences, AI agents can provide highly personalized services and recommendations, improving user experience and satisfaction.
Automatic compliance: AI agents can automatically identify and comply with various regulations and compliance requirements, ensuring the legality and compliance of on-chain behaviors, paving the way for the widespread application of blockchain technology.
Ecological Construction
At DeAgentAI, we firmly believe that the power of the community is the key to driving innovation and development. Through close cooperation with developers, researchers, and users around the world, we have jointly built a series of cutting-edge projects. These projects not only demonstrate DeAgentAI's leading position in the field of intelligence and decentralization, but also reflect the importance and spirit of cooperation in community co-construction. We look forward to promoting the deep integration of blockchain technology and artificial intelligence through these co-built projects, and moving towards an intelligent and decentralized future together.
BTC Predictor
BTC Predictor is an advanced AI agent designed specifically to predict Bitcoin price movements. Its core technical architecture includes the following aspects:
Data collection and processing: The BTC predictor accesses multiple data sources, including on-chain data (such as Bitcoin network transaction volume, hash rate, etc.), exchange order book data, macroeconomic indicators of global financial markets, and social media sentiment data. These data are first pre-processed, normalized and denoised to ensure the accuracy and consistency of the data input into the model.
Deep Learning Model: The BTC Predictor uses a multi-layer deep neural network, specifically a long short-term memory network (LSTM), to process time series data. The LSTM network is able to capture the historical trend of Bitcoin prices and optimize its parameters through training, so that it can maintain the accuracy of predictions when facing complex market conditions.
Reinforcement learning mechanism: The agent also introduces a reinforcement learning mechanism, which adjusts the model's forecasting strategy by simulating different market behaviors, so that it can dynamically adapt to market volatility. The agent will compare each forecast result with the actual market trend and optimize future forecasts through a reward mechanism.
Model interpretability: To enhance the credibility and transparency of the model, the BTC predictor incorporates an attention mechanism that enables the model to identify and highlight the most important features for the prediction results. This interpretable model helps understand the reasons behind the prediction and can be used for further market analysis.
Real-time update and deployment: BTC Predictor supports real-time data stream processing, can make predictions with extremely low latency, and achieve rapid model updates and deployment through distributed computing architecture. This enables the prediction results to keep up with market changes and provide highly timely price predictions.
Meme Hunter
Meme Hunter is an AI agent designed specifically for Meme Coin content on Twitter. The following is a detailed description of its technical implementation:
Natural language processing and sentiment analysis: Meme Hunter uses advanced natural language processing (NLP) technology, especially pre-trained language models based on the Transformer architecture, such as BERT or GPT, to analyze the massive text data on Twitter. The model first performs word segmentation and semantic understanding of tweets, and then uses sentiment analysis technology to identify the emotional tendencies in tweets, thereby extracting key information related to Meme coins.
Real-time data capture and API integration: Meme Hunter integrates multiple public and private APIs to achieve real-time monitoring and data capture of tweets. The agent uses WebSocket-based long connection technology to ensure that relevant data can be captured at the first time. At the same time, Meme Hunter uses these APIs to obtain real-time market data of Meme coins, such as price fluctuations, liquidity, and trading volume.
Multimodal data fusion: In addition to text data, Meme Hunter is also able to process multimodal data such as images and videos. Through convolutional neural networks (CNN) and video analysis algorithms, Meme Hunter can identify meme images and videos contained in tweets and fuse these multimodal data with text information to improve the comprehensive analysis capabilities of the Meme coin market.
Automated analysis and report generation: Meme Hunter integrates an automated analysis module that can perform in-depth analysis of the collected data and generate reports. These reports include forecasts of market trends, identification of potential investment opportunities, and risk assessment. Reports can be automatically generated based on user needs and pushed to users through designated channels.
Privacy protection and data security: Meme Hunter uses data encryption and decentralized storage technology to ensure the security of users' privacy data and analysis results. All data processing is completed locally or on private servers, avoiding the risk of data leakage.
DeAgent Terminal
DeAgent Terminal is a next-generation AI-driven platform that integrates the GPT model with advanced Web3 functions, providing users with a one-stop Web3 interactive experience. Its technical details are as follows:
Smart dApp Navigation: DeAgent Terminal integrates the GPT model to provide natural language processing and contextual understanding capabilities, enabling users to quickly navigate and access decentralized applications (dApps) through natural language commands. Users can perform complex operations such as smart contract calls, transaction management, etc. directly on the Solana blockchain through voice or text commands.
Smart Contract Management: The platform’s built-in smart contract management module supports contract development and deployment in multiple programming languages, such as Solidity and Rust. The module integrates static analysis tools to automatically detect potential security vulnerabilities and optimization points before contract deployment, ensuring the security and efficiency of smart contracts.
Decentralized transaction processing: DeAgent Terminal supports seamless integration with multiple decentralized exchanges (DEX), using distributed network technology to achieve high-speed, low-latency transaction processing. Through the distributed hash table (DHT) and cross-chain bridging protocol, the platform can efficiently manage transaction orders and liquidity pools, supporting seamless exchange of multi-chain assets.
User privacy and security: The terminal uses multi-layer encryption mechanisms and decentralized identity authentication (DID) technology to protect user data and transaction privacy. All users' keys and sensitive data are encrypted and stored in the local device, and transaction information is verified through zero-knowledge proof (ZKP) technology to ensure the integrity and immutability of information.
Scalable plug-in system: DeAgent Terminal supports the development and integration of user-defined plug-ins. Developers can use the API and SDK provided by the platform to create exclusive plug-ins, expand the functions of the terminal, and meet personalized user needs. The plug-in system adopts a modular architecture to ensure the high scalability and compatibility of the platform.
MemeX
MemeX is a cutting-edge AI agent designed specifically for Telegram, which allows users to trade Meme coins directly in Telegram. Its technical implementation includes the following aspects:
Automated content detection and analysis: MemeX integrates a Transformer-based natural language processing model that can monitor the content in Telegram groups and channels in real time and detect keywords and topics related to Meme coins. The model combines sentiment analysis with topic modeling to identify potential market trends and investment opportunities.
Instant transaction execution: When Meme coin-related content is detected, MemeX implements instant transaction function through the built-in transaction module. This module is integrated with multiple decentralized exchanges (DEX) to support fast transactions across platforms. By using WebSocket technology to exchange real-time data with DEX, efficient execution of transactions is ensured.
Decentralized wallet integration: MemeX has a built-in decentralized wallet that supports the management and trading of multi-chain assets. The user's private key uses end-to-end encryption technology, and all transaction operations are completed on the local device to ensure the security of funds. MemeX also supports cold storage through hardware wallets to improve asset security.
Transaction records and analysis: MemeX provides detailed transaction records and market analysis functions. Users can view the details of each transaction through the interactive interface, including time, price, counterparty, etc. The platform integrates AI-driven market analysis tools to help users evaluate market conditions in real time and make smarter trading decisions.
Smart Notifications and Reminders: MemeX supports a smart notification system that will alert users in real time via Telegram messages when important market changes or trading opportunities are detected. Users can customize notification rules based on their personal preferences, such as price fluctuation reminders, trading volume changes, etc., to ensure that they do not miss any key market trends.
Trending Analytics
Trending Analytics is an AI tool designed specifically for the crypto market, providing in-depth analysis of popular tokens. Its technical implementation includes the following aspects:
Multi-source data collection and processing: Trending Analytics accesses multiple data sources, such as blockchain network data, exchange APIs, news aggregators, and social media platforms, to obtain real-time fundamental data, news trends, and technical data of tokens. All data goes through the ETL (extraction, transformation, loading) processing process to ensure data integrity and consistency.
Sentiment Analysis and Topic Modeling: The tool integrates advanced natural language processing technology to perform sentiment analysis and topic modeling on news reports, social media posts, and market comments. The sentiment analysis module uses a combination of convolutional neural networks (CNN) and recurrent neural networks (RNN) to accurately identify market sentiment trends and thus predict possible market fluctuations.
Technical Analysis and Event Detection: Trending Analytics combines multiple technical indicators (such as moving averages, relative strength index, MACD) for technical analysis, and uses anomaly detection algorithms to identify major events in the market, such as abnormal price fluctuations, large fund transfers, etc. The event detection algorithm is based on time series analysis and machine learning models, and can maintain high accuracy in complex market environments.
Visualization and interactive analysis: The built-in visualization module of the tool uses front-end technologies such as D3.js to convert complex data into easy-to-understand visualization charts. In particular, it displays important market events in the form of bubble charts, allowing users to quickly grasp market dynamics. Users can also customize the chart display content through the interactive interface and conduct in-depth data analysis.
Real-time update and notification system: Trending Analytics supports real-time data updates to ensure that analysis results are always synchronized with the market. Users can set custom notification rules. When the market sentiment or technical indicators of a specific token reach the preset conditions, the system will automatically remind users through email or message push.
Arbitrage Bot
Arbitrage Bot is an advanced AI bot designed specifically for arbitrage in the cryptocurrency market. Here is a detailed description of its technical implementation:
Cross-Exchange Arbitrage: Arbitrage Bot uses price differences between multiple exchanges to monitor price fluctuations on major trading platforms in real time. Through high-frequency data collection and analysis, the robot is able to execute arbitrage transactions at the moment when price differences appear. The module integrates an efficient order routing algorithm to ensure that trading instructions can be transmitted between multiple exchanges with minimal latency.
Funding rate arbitrage: The robot monitors and analyzes the differences in funding rates across major trading platforms. By hedging in both high and low funding rate markets, Arbitrage Bot is able to capture the benefits of rate fluctuations. This process is based on the Time Weighted Average Price (TWAP) algorithm to ensure optimal allocation of funds and maximize returns.
Maximum Extractable Value (MEV) Technology: Arbitrage Bot integrates MEV optimization technology to capture potential gains in transaction sorting and bundling in blockchain networks such as Ethereum. The robot works with mining pools or validator nodes to prioritize profitable transactions during the block packaging process to maximize user benefits.
Interest rate arbitrage: Arbitrage Bot takes advantage of the difference in lending rates between different platforms to perform arbitrage operations by borrowing assets in high-interest markets and depositing assets in low-interest markets. This module combines automated lending protocols such as Aave and Compound to ensure the efficiency and security of arbitrage operations.
Risk management and automated strategy adjustment: To cope with market uncertainty, Arbitrage Bot integrates risk management modules, including dynamic position adjustment, stop loss setting, and automatic position closing functions. The robot also uses machine learning models to analyze historical transaction data and continuously optimize arbitrage strategies to enable them to adapt to changing market conditions.
KOL Connect
KOL Connect is an advanced KOL (Key Opinion Leader) agent specifically designed to simulate and capture the personality and style of top influencers, providing a realistic interactive experience. The following is a detailed description of its technical implementation:
Personalized data collection and modeling: KOL Connect collects public content from key opinion leaders (KOLs) on social media, blogs, and video platforms through deep web crawlers and data mining technology. After natural language processing (NLP) and sentiment analysis, this data is processed to generate a unique personalized model that accurately captures each KOL's language style, behavioral characteristics, and opinion tendencies.
Multimodal interaction model: The agent combines text, voice, and image processing technologies to enable natural conversations with users in multiple interaction modes. By using pre-trained Transformer models (such as the GPT series), KOL Connect can generate conversation content that conforms to the style of a specific KOL. In addition, the agent also supports speech synthesis technology, making the conversation with the KOL not only authentic in content, but also able to imitate its unique tone and speed in voice.
Knowledge Graph and Contextual Understanding: KOL Connect has a built-in knowledge graph building block that structures information collected from multiple sources into a semantic network. This knowledge graph helps agents understand the context during the conversation and provide targeted recommendations and insights. The graph is updated in real time to ensure that the information users obtain is always up to date.
Dynamic content generation and updates: To keep content fresh and relevant, KOL Connect uses a dynamic content generation system. The system combines machine learning with a rule-driven approach to automatically adjust KOL recommendations and conversations based on the latest social media trends and user feedback. The model is retrained regularly to ensure that the generated content always matches current trends and language styles.
User customization and privacy protection: KOL Connect supports user customization, allowing users to select specific KOLs or adjust the style and topic of the conversation. At the same time, the agent adopts strict privacy protection measures to ensure that the user's interaction data is only used to improve the experience and will not be leaked or abused. All user's personalized settings and data are encrypted and stored to ensure information security.
Real-time interaction and feedback mechanism: KOL Connect has a built-in real-time interaction module that supports users to communicate with agents instantly through voice or text. During the interaction, agents can dynamically adjust the direction and content of the conversation based on user feedback to provide more personalized services. In addition, agents also have self-learning capabilities and continuously optimize their interaction strategies by analyzing user behaviors and preferences.
Future Outlook
DeAgentAI's mission is to drive change in finance, governance, digital asset management and other fields by integrating AI and blockchain technology. We believe that with the continuous advancement of technology and the maturity of the ecosystem, full intelligence will play a key role in more scenarios, such as decentralized finance (DeFi), digital identity and privacy protection, cross-chain interoperability, etc. In addition to these specific applications, we also look forward to a future where full intelligence promotes a prosperous ecosystem.
Creating a better ecosystem
We are committed to fostering a vibrant and dynamic ecosystem that allows AI and blockchain technologies to work together. This involves continuous innovation and the development of new tools and platforms that make it easier for developers, businesses, and users to harness the power of these technologies.
Optimizing blockchain systems
Our focus is also on optimizing existing blockchain systems to ensure they are ready for the widespread integration of AI. This includes improving scalability, improving consensus mechanisms, and ensuring strong security protocols. By doing so, we aim to create a more efficient and user-friendly blockchain environment.
Fully integrated AI
We believe that the future of blockchain is inseparable from AI. Our goal is to weave AI into the fabric of blockchain systems, enabling intelligent decision-making, automated processes, and adaptive systems that can respond to changing conditions in real time. This comprehensive integration will unlock new levels of functionality and efficiency, changing the way we interact with digital assets and systems. Our company will continue to innovate and explore, committed to realizing this grand vision, promoting the integrated application of blockchain and AI technologies, and building a truly intelligent and decentralized future.
