AI

AI agents are evolving from simple chat tools into digital entities that can autonomously execute tasks, manage assets, and engage in economic activities. This article breaks down how Luffa's DID identity system, Web3 wallet, and verifiable AI architecture empower AI agents with real on-chain capabilities.

Luffa is a digital platform integrating AI and Web3 technologies, designed to redefine how creators, fans, and Grupo interact. By leveraging decentralized identity (DID), AI Agent, on-chain payments, and a programmable economic system, Luffa aims to build a novel ecosystem where content, value, and Grupo can circulate freely.

In Gate.AI’s architecture, an AI request typically goes through several stages, including request intake, task analysis, model evaluation, routing decision, model execution, and result delivery. By connecting different model ecosystems through a unified interface, Gate.AI can automatically allocate inference resources based on actual needs, enable multiple models to work together, and reduce the risks that come with relying on a single model.

AntFun (ANTFUN) is a Web3 application that brings together artificial intelligence (AI), social networking, and multichain wallet functions. It is designed to help users manage digital assets, present their on-chain identity, interact socially, and access intelligent information services. Unlike traditional crypto wallets, which mainly focus on asset storage and transactions, AntFun expands the wallet into an important gateway for users entering the Web3 world.

The core difference between Janction and OpenAI GPT Store is that Janction is a decentralized collaboration network for AI Agents, while OpenAI GPT Store is a publishing and distribution platform for AI applications. Janction focuses on connection, collaboration, and value exchange between Agents, while GPT Store mainly helps users discover and use GPT applications.

Janction Agent Network is the AI Agent collaboration network within the Janction ecosystem. Through a unified identity system, resource discovery mechanism, task scheduling system, and value settlement layer, it allows multiple AI Agents to discover, connect, collaborate, and transact within the same network. Unlike traditional AI Agents that operate independently, Janction Agent Network places greater emphasis on connectivity and coordinated execution among Agents.

Janction is a decentralized computing network built for the age of artificial intelligence. By integrating distributed computing resources, AI Agents, and blockchain based incentives, it provides open infrastructure for AI model training, inference, and intelligent task execution. Janction aims to enable the discovery, allocation, coordination, and value settlement of computing resources without relying on centralized cloud service providers.

Trusta.AI and World are both Web3 identity infrastructure projects, but they approach digital identity in fundamentally different ways. World uses biometric technologies such as iris scanning to verify user uniqueness, focusing on a “one person, one identity” digital identity system. Trusta.AI builds an identity and reputation network through on-chain behavior analysis, reputation scoring, and Verifiable Credentials, focusing more on trust accumulation and reliable collaboration.

Proof of Humanity is a human identity verification mechanism launched by Trusta.AI. It is designed to determine whether a blockchain account is controlled by a real person rather than a bot program or a batch-created wallet. By analyzing on-chain behavior, interaction patterns, reputation records, and identity credentials, the mechanism generates trusted identity labels for users and provides Verifiable Credentials for cross-application verification.

Trusta.AI is a decentralized identity and reputation network built for Web3 users and AI Agents. It uses on-chain behavior analysis, reputation scoring, identity verification, and Verifiable Credentials to establish trusted digital identities. The network is designed to address bot accounts, Sybil attacks, identity forgery, and the lack of trusted identities for AI Agents, providing unified Trust Layer infrastructure for the blockchain ecosystem.

Gate Research: This paper focuses on the practical need for trade review in the crypto market. Based on the OpenClaw framework and Gate MCP capabilities, it develops an AI investment advisory system that automates the entire process from data ingestion and metric analysis to report generation. By introducing an agent-based architecture and modular tool invocation, the system enables AI not only to understand trading data but also to perform analysis and support decision-making, producing review reports that are both interpretable and actionable. Overall, this approach validates the potential of the “LLM + MCP + Agent” paradigm in financial scenarios, offering a feasible path for the engineering implementation of AI-driven investment assistance and laying the foundation for future evolution toward more intelligent and quantitatively driven decision systems.

Gate Research: Large language models and AI agent technologies are pushing trading systems into a new stage of development. Quantitative trading, which previously relied heavily on programming skills and complex engineering systems, is gradually evolving into product forms with much lower barriers to entry. Gate has introduced products such as AI Quant Workspace and Gate for AI, which aim to integrate strategy generation, backtesting, and automated execution within a single platform through natural language interaction, no-code quant tools, and unified trading interfaces, allowing more users to participate in strategy trading. As AI technology continues to mature, trading platforms are also evolving from traditional matching tools into AI-driven trading infrastructure.

The core difference between Gate.AI and direct OpenAI API integration lies in their architectural positioning. The OpenAI API provides direct access to OpenAI models, while Gate.AI functions as an AI Gateway and model routing platform. It can connect multiple large language models through a unified interface and automatically handle model selection and request distribution based on factors such as cost, performance, and availability.

The main risks of Allora Network come from data quality, model accuracy evaluation, incentive mechanism design, and game-theoretic behavior among participants. As a decentralized AI inference network, Allora relies on Workers, Reputers, and Validators to operate collaboratively. If input data is biased, the scoring mechanism is manipulated, or the incentive structure becomes unbalanced, the quality of network predictions may be affected. Understanding these risks provides a more complete view of how decentralized AI infrastructure works and the challenges it faces as it develops.

The core difference between Allora and Bittensor lies in their network positioning. Allora Network mainly builds a decentralized AI inference and prediction market, using Workers, Reputers, and Validators to collaboratively optimize prediction results. Bittensor, by contrast, builds an open AI model network where miners and validators jointly train, provide, and evaluate AI services. Both aim to advance decentralized AI through token incentives, but one focuses more on “prediction and inference,” while the other focuses more on “models and intelligence production.”