AI

DeAgentAI (AIA) is a decentralized infrastructure for building on-chain AI Agent, designed to give AI Agents identity verification, persistent memory, and autonomous execution within Web3 networks. Its core architecture combines AI models, on-chain consensus, and a multi-chain execution system, enabling Agent to perform auto analysis, task coordination, and on-chain interactions. The AIA token is used to pay for Agent services, governance, staking, and ecosystem incentives.

DeAgentAI empowers the AI Agent to autonomously operate and collaborate across the Web3 ecosystem, leveraging its AI Agent Framework, Memory System, on-chain execution layer, and consensus verification mechanism. When a user submits a task, the AI Agent invokes tools, retrieves historical states, devises an execution plan, and finalizes on-chain actions through Executor Nodes. The network's verification nodes then validate the results, delivering verifiable on-chain AI execution. In contrast to conventional AI bots, DeAgentAI prioritizes persistent memory, multi-Agent coordination, and decentralized, trusted operation.

AlphaX is an AI-driven on-chain signal system within the DeAgentAI ecosystem, purpose-built for on-chain data analysis, market trend detection, and automated information processing. By integrating AI Agent capabilities, on-chain behavioral data, and a multi-chain analysis framework, the system provides Real Time surveillance of capital flows, address activities, and market trends, producing AI-generated on-chain signals. Unlike traditional quantitative tools, AlphaX prioritizes AI auto-analysis, multi-agent collaboration, and on-chain verifiable execution, positioning it as a key application of AI Agent Infrastructure in the on-chain data space.

GENCY AI is an advertising infrastructure platform that combines AI and blockchain to build a verifiable, automated digital advertising system. It analyzes how verifiable data and Smart Contracts can enhance transparency, protect privacy, and reshape the digital advertising industry.

Figure AI and Tesla Optimus are two of the most closely watched companies in the Humanoid Robot industry today, but their core strategies differ significantly. Figure AI places greater emphasis on an AI-first approach and Robotics Foundation Models, aiming to build a general-purpose robotics platform with reasoning capabilities through Helix AI. Tesla Optimus, by contrast, relies on Tesla’s autonomous driving technology, manufacturing system, and data feedback loop, with a stronger focus on large-scale production. Figure AI is more like an AI Robotics company, while Tesla Optimus is closer to a humanoid robot project driven by the automotive industry. Both companies aim to commercialize Humanoid Robots, but they differ fundamentally in AI architecture, hardware systems, business models, and long-term strategy.

Figure AI’s business model mainly revolves around Humanoid Robot deployment, Robot-as-a-Service（RaaS）, AI software systems, and enterprise automation solutions. Unlike traditional robotics companies, Figure AI does not simply sell robot hardware. Instead, it aims to build an “AI + Robotics” platform through Helix AI, enabling robots to perform long-term tasks in factories, warehouses, logistics, and home environments. Its BMW factory partnership is seen as an important validation of commercialization, while in the future, Figure AI may further expand into robot subscription services, robotic AI platforms, and the large-scale automated labor market.

Figure AI is a U.S. Humanoid Robot company focuses on building general-purpose AI robots that can work autonomously in the real world. Its core products include Figure 01, Figure 02, Figure 03, and its robotic AI system, Helix. Unlike traditional robotics companies, Figure AI places greater emphasis on the integration of “AI + Robotics,” aiming to use Vision-Language-Action（VLA）models to give robots the ability to understand, reason, and carry out complex tasks.

Unibase and Virtuals both serve the AI Agent ecosystem, but their core directions are different. Unibase focuses more on long-term memory, agent interoperability, and infrastructure for the Open Agent Internet, supporting multi-agent collaboration through Membase, AIP Protocol, and a data availability layer. Virtuals, by contrast, is more oriented toward AI Agent issuance, economicization, and on-chain operations, with an emphasis on AI Agent assetization and social ecosystems. The two projects represent two different development paths in the AI Crypto sector: “AI Memory Layer” and “AI Agent Marketplace.”

Unibase mainly operates through three components: Membase, AIP Protocol, and Unibase DA. AI Agents can store long-term context through Membase, communicate across platforms through AIP Protocol, and use the data availability layer to support on-chain state synchronization and data storage. This architecture is designed to build the Open Agent Internet, allowing AI to keep learning, share memory, and carry out multi-agent collaboration tasks.

Unibase (UB) is a decentralized infrastructure designed for AI Agents. It aims to provide autonomous AI with long-term memory, cross-platform agent interoperability, and verifiable data storage. Its core modules include Membase, AIP Protocol, and Unibase DA, which support AI memory management, agent identity and communication, and high-performance data availability.

VIM is the core token of the Vimverse ecosystem. Its role goes beyond that of an ordinary crypto asset. It is positioned as a form of “programmable economic infrastructure” used to coordinate AI Agents, liquidity systems, and on-chain economic structures. In Vimverse’s design, VIM serves multiple functions at once, including governance, liquidity coordination, ecosystem incentives, and protocol-level market stability.

Vimverse is an AI Native Web3 project built around AI Agents, virtual persona systems, and on-chain digital identity. Its core goal is to make AI more than just a tool, allowing it to exist as an active participant in the on-chain ecosystem. Within Vimverse, AI Agents can not only interact with users, but also have behavioral logic, long-term memory, on-chain identities, and relationships with digital assets.

Vimverse (VIM) is a Web3 project built around AI Agents, Programmable Liquidity, and an on-chain digital ecosystem. Its core goal is to transform liquidity from a “passive capital pool” into an infrastructure layer that protocols can actively manage. By introducing the Protocol Managed Market Making (PMMM) framework, Vimverse aims to help protocols build more stable, sustainable, and coordinated on-chain market structures.

Agentverse is an AI Agent system built within the CROSS gaming ecosystem. It allows AI Agents to interact, collaborate, compete, and perform asset-related actions in an on-chain environment. Unlike traditional game NPCs, AI Agents in Agentverse can act based on on-chain data, autonomous logic, and economic systems, while building dynamic relationships with players and other AI Agents.

Gate Prediction Market has undergone a new round of upgrades, centered on Smart Money identification, whale position tracking, AI analysis, and sports trading scenario optimization, propelling the prediction market toward data-driven, professional trading.