Technology

Ondo Finance is an on-chain financial platform centered on real-world assets (RWA) and tokenized securities. Its core products and infrastructure currently include USDY, OUSG, Ondo Global Markets, and Ondo Chain. USDY has been integrated into the Ondo Global Markets system, while OUSG continues to exist as a short-term U.S. Treasury exposure product designed for qualified investors.

ONDO is the core governance and value capture token within the Ondo Finance ecosystem. It is designed to align token incentives with the integration of real world assets and DeFi, driving scalable on-chain asset management and yield products.

Anza's Constellation protocol brings the Multi-Proposer Concurrency (MCP) mechanism to Solana, redefining trade ordering, censorship resistance, and MEV allocation. This advancement propels on-chain finance into the high-frequency era.

Zebec is a blockchain-based protocol designed for real-time payments and cash flow management. Its core mechanism combines streaming payments and continuous settlement, allowing funds to move continuously at the second level rather than relying on periodic batch processing.

Bittensor is a decentralized AI network that builds an open machine learning marketplace through Subnets, Miners, and Validators. It uses the Yuma consensus mechanism to evaluate models and distribute TAO rewards, turning AI capabilities into a priced and incentivized resource.

Bittensor (TAO) is a decentralized network that combines blockchain and artificial intelligence. It uses a subnet structure to allow AI models to compete in an open market and earn rewards based on their performance.

TAO is the native token of the Bittensor network, playing a central role in incentive distribution, network security, and value capture within a decentralized AI ecosystem. Through an inflationary issuance model, staking mechanisms, and subnet based incentives, TAO supports an economic system where AI models compete, are evaluated, and rewarded based on performance.

A Bittensor Subnet functions as an independent AI task marketplace within the network. Each subnet builds its own incentive structure around specific use cases such as text generation, image recognition, or prediction. Through miners supplying models, validators assessing output quality, and dynamic TAO and Alpha token allocation, subnets enable the production and pricing of machine intelligence in a decentralized way.

Bittensor, Fetch.ai, and SingularityNET share a common goal: using token incentives to drive the supply of AI resources, whether models, compute, or services, while building open networks that lower barriers to AI access and challenge the dominance of centralized platforms. However, their core differences lie in the technical layers they operate on and how they capture value. Rather than competing within a single track, they address three distinct stages of decentralized AI, model production, task execution, and service distribution.

Walrus is a decentralized data storage protocol built on the Sui blockchain. Its core objective is to transform data from passive storage into verifiable, programmable, and tradable on-chain resources, providing foundational support for DeFi and AI applications.

Walrus (WAL) serves as the core token of a decentralized data storage network. Its primary roles include paying for on-chain storage services, incentivizing node operators who maintain the network, and enabling participation in protocol governance. By linking the storage resource market, validator rewards, and token economics, Walrus forms a distributed data infrastructure built around the value circulation of WAL.

AI agents for market research are automated systems designed to collect, process, and interpret large volumes of data for decision-making. In market research, they combine structured data and real-time information sources such as Gate News and Gate Info to identify trends, assess sentiment, and generate insights. As digital asset markets evolve, integrating multiple data layers has become essential for understanding market behavior. Examining how these systems operate helps clarify their role in modern financial analysis workflows.

Gate Exchange for AI provides access to centralized exchange trading systems, while Gate DEX for AI connects AI agents to on-chain decentralized finance environments. These two execution paths differ in transaction routing, custody structure, and operational control. Understanding how these execution architectures work helps explain how AI agents interact with centralized and decentralized financial infrastructure in modern crypto ecosystems.

AI model routing refers to a technical mechanism that dynamically selects the most suitable AI model to handle a request when multiple models are available. It is also commonly called an AI model router or LLM router. Through a model routing system, AI applications can automatically choose different large language models based on factors such as task complexity, cost, and response speed, allowing them to balance performance and operational efficiency.

An AI Agent API refers to the mechanism through which AI agents call AI models or external services via application programming interfaces (APIs). Through these APIs, AI agents can access large language models, data services, and blockchain applications, enabling them to automatically execute complex tasks without direct human intervention.