Vitalik Builds Three-Layer Privacy Architecture for Personalized Health Recommendations

Vitalik generates personalized dietary and exercise recommendations by combining health and travel data with cutting-edge models through personal experimentation, while protecting private information. The system orchestrates using the local model Qwen 3.8 Flash Next and calls upon powerful remote models for higher-level thinking and knowledge, employing a three-layer privacy protection architecture: the identity layer constructs query requests on behalf of users using the local model to avoid identity exposure; the payment layer uses zkAPI to conceal payment information; the network layer hides IP addresses through Tor. The system has successfully operated and provided recommendations. Vitalik pointed out major shortcomings, including: Tor's insufficient optimization for request unlinking and high latency; the local model's speed is only 20-30 TPS, while ideally it should be over 100; the stricter the data protection, the more limited the assistance that remote models can provide.

This content is provided for general informational purposes only and doesn't constitute financial, investment, legal, or tax advice. Any events, rewards, online promotions, or related information mentioned herein should not be considered a recommendation, solicitation, or invitation to purchase, sell, trade, or otherwise deal in any crypto assets. Crypto assets are highly volatile and may result in loss. The availability of WEEX services, products, and related events may vary by region. You are responsible for ensuring that your participation is in accordance with applicable local laws and regulations.