Vitalik has successfully tested a privacy-preserving AI system that generates personalized health and diet advice without exposing sensitive user data to remote models. The architecture uses a local Qwen 3.8 Flash Next model for orchestration while calling frontier models as tools for advanced reasoning, ensuring private information remains on-device.
The system implements three distinct privacy layers: an identity layer where the local model constructs queries to mask writing style, a payment layer using zkAPI to conceal transaction data, and a network layer utilizing Tor for IP anonymization. While functional, Vitalik noted current limitations including high Tor latency, local model throughput of only 20-30 TPS against a target of 100+, and reduced utility from remote models under strict privacy constraints.
Vitalik Tests Three-Layer Privacy Architecture for AI Health Advice Using Local Models
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