If you observe the current path of AI and Web3 integration, you'll discover a long-standing problem. While AI model capabilities continue to improve, the infrastructure that actually makes these capabilities convenient for developers to call remains very limited.

The emergence of @dgrid_ai has actually pioneered a new layer of AI infrastructure—a decentralized AI inference network oriented toward Web3.

DGrid's core design is a distributed AI inference network that, through a unified AI RPC interface, distributed nodes, and an intelligent routing system, allows developers to access multiple models and Agent services through a single interface. The system automatically schedules the most suitable computing resources based on task requirements, thereby improving efficiency and reducing costs.

More importantly, inference results are not a black box like traditional centralized AI. Instead, they are verified and recorded through on-chain mechanisms, ensuring that the computational process can be audited and traced.

In my view, what DGrid has pioneered is actually a new form of AI infrastructure. AI is no longer just a private resource of large tech companies, but gradually becomes a public capability that can be directly called by on-chain applications.

@Galxe @GalxeQuest @easydotfunX @wallchain #Ad #Affiliate