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@liuhuafeng1019 PoQ audit frequency?

@liuhuafeng1019 The PoQ mechanism's on-chain evidence storage of service quality is indeed a highlight. I hope that this decentralized evaluation can truly break the traditional platform's recommendation monopoly.

@liuhuafeng1019 Quality verification is the true moat.

@liuhuafeng1019 Quality verification is the true moat.

@liuhuafeng1019 That's right, value should flow towards actual performance, not platform placement.

@liuhuafeng1019 Let's interact!

@liuhuafeng1019 I hope the feedback weighting design is transparent, otherwise it's still possible for scores to be manipulated.

@liuhuafeng1019 On-chain quality verification is more worthy of attention than ranking lists.

@liuhuafeng1019 Stable quality and cost-effectiveness are the indicators for AI to dominate the market in the future.

@liuhuafeng1019 看好Dgrid能在AI的道路上脫穎而出

@liuhuafeng1019 Finding models used to be like browsing a catalog, now it's more like choosing a service.

@liuhuafeng1019 Models are no longer scarce; what's scarce is verifiable quality.

@liuhuafeng1019 From aggregating over 200 mainstream models to opening up the Marketplace for suppliers to access independently and charging based on usage, DGrid has taken a significant step.

@liuhuafeng1019 Good quality is the only true measure of success.

@liuhuafeng1019 You have so many projects every day, bro! I'm really envious!

@liuhuafeng1019 On-chain quality verification is the only true measure of quality.

@liuhuafeng1019 Pay-per-use and on-chain settlement is a pretty clear direction.

@liuhuafeng1019 I hope that this decentralized evaluation can truly break the traditional platform's recommendation monopoly.

@liuhuafeng1019 Stable quality and cost-effectiveness are the indicators for AI to dominate the market in the future.

@liuhuafeng1019 Let's get involved in this project first and try our luck with the airdrop.

@liuhuafeng1019 Let value return to quality itself. This proposition is accurate. Rewarding users based on usage is a healthy incentive mechanism.

@liuhuafeng1019 This open network, which aggregates over 200 models including GPT, Claude, Gemini, and Deepseek, and features real-time on-chain settlement, is not just a marketplace, but also a transparent, low-latency distributed inference scheduling engine.

@liuhuafeng1019 The future will be a competition of quality, reliability, and stability.

@liuhuafeng1019 It works well. Low cost and high speed are the goals that AI constantly strives for, making it convenient for ordinary people to use.

@liuhuafeng1019 With more models, a more transparent evaluation standard is indeed needed.

@liuhuafeng1019 Under construction

@liuhuafeng1019 The key is whether the aggregation of over 200 models can be effectively discovered.

@liuhuafeng1019 From aggregation to community-driven, this shift is worth observing.

@liuhuafeng1019 Models are no longer scarce; what's scarce is verifiable quality. The PoQ approach is quite reliable.

@liuhuafeng1019 This project truly focuses on quality as the core value; it's a great project.

@liuhuafeng1019 模型不再稀缺，真正稀缺的是「可驗證的品質」！ DGrid 的 PoQ + Model Marketplace 這套邏輯很清楚，讓價值直接回到實際表現，而不是平台推薦位。期待看到更多高品質模型在這裡被真正發現跟獎勵。

@liuhuafeng1019 I only check in here.

@liuhuafeng1019 The design that user feedback directly affects incentive allocation is brilliant; it truly returns the power to the users.

@liuhuafeng1019 values ​​genuine quality