Are we truly ready to turn AI inference into a genuinely decentralized, verifiable, and fairly incentivized network? @dgrid_ai is attempting to address this core issue. DGrid AI claims to be the world's first truly Web3 decentralized AI inference gateway aggregation layer, aiming to enable developers, node operators, model contributors, and users to perform AI inference, calls, and collaboration in an open, trust-minimized environment. Through a Web3 native architecture, it seeks to break through the limitations of centralized AI aggregators in control, incentives, and trust. In DGrid's design, Proof of Quality (PoQ) is one of the core mechanisms, a system combining cryptographic verification and game theory to ensure that every AI inference result is auditable and traceable. Validation nodes in the network are randomly sampled to recheck the output of inference tasks; if a node submits non-compliant results, it may face penalties such as staked token slashing. This mechanism ensures that decentralized AI inference is not only efficient but also trustworthy. More importantly, DGrid is not just a technical protocol; it also builds a complete ecosystem: a unified RPC interface, globally distributed node resource pools, on-chain smart contract-based settlement systems, and community governance layers. This means developers can call various models as easily as invoking smart contracts, and anyone can pay $DGAI for inference fees or stake tokens to participate in governance decisions. But the real question to ponder is: when AI inference becomes the infrastructure of this open network, can we ensure its long-term stability, security, and fairness? DGrid's vision is to transform AI inference from centralized services into a public good similar to electricity or the internet infrastructure, and this evolution undoubtedly sets new standards for the entire AI and Web3 ecosystems. @Galxe @GalxeQuest @easydotfunX