CASE FILE / Model & agent routing

Jev is insane

Jev is insane. We are building egocentric training data for physical AI. Jev QA'd 58,643 action labels in under 3 minutes. 90 cents. A friction cost of the Claude & GPT models. https://t.co/s7n5NpsLya

By Peter Wang @the_cyw

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Peter Wang · @the_cyw

Original post

Jev is insane. We are building egocentric training data for physical AI. Jev QA'd 58,643 action labels in under 3 minutes. 90 cents. A friction cost of the Claude & GPT models. https://t.co/s7n5NpsLya

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Chinese translation

杰夫疯了。我们正在为物理人工智能构建以自我为中心的训练数据。Jev QA在3分钟内处理了58，643个动作标签。90美分。 Claude GPT型号的摩擦成本。https://t.co/s7n5NpsLya

What this case shows

Let JEV choose the model or tool first
Auto-route on high confidence, escalate otherwise
This is the most common production pattern