Vitalik Buterin tested a privacy-focused AI workflow that uses a local Qwen 3.8 Flash Next model to process personal health and travel data before seeking diet and exercise recommendations from more capable remote models. The system combines local prompt construction, zkAPI private payments and Tor routing to reduce exposure through prompt content, billing records and IP addresses. Buterin said frontier-model input improved the recommendations, but identified four limitations: Tor does not adequately separate individual requests, latency was roughly 10 to 100 times higher than desired, prompt-selection strategies need improvement, and local inference ran at about 20 to 30 tokens per second versus a target above 100. He also said withholding more personal context reduces the usefulness of remote-model advice. Separately, Buterin described Hegotá, planned for 2027, as Ethereum's likely last normal fork before development shifts toward recursive STARKs, automated formal verification, optimized consensus and quantum-safe technology.