Jev case: We are building egocentric training data for

@the_cyw Peter Wang · Sep 18, 2026 · case #739779
Original post on X
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QA'd 58,643 egocentric action labels for physical AI training data in under 3 minutes for 90 cents, using Jev's typed yes/no decisions instead of a frontier LLM.

The 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.

Engagement when collected

Views1.1K
Likes15
Bookmarks17
Reposts4
Replies1
Quotes0

Numbers are a snapshot taken from X when the case was added to the library (schema v1, collected 2026-09-19); they will not match today.

Where this case fits

Filed under classification & data extraction. In the pattern Jev is built for, the model answers a bounded question per step — and ordinary code acts on the answer, because the answer is already a value rather than a paragraph. Other posts in the same family are on the classification & data extraction page.

🗂️ Classification & data extraction

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Last updated: 2026-09-22 · sources & corrections · every card links to its author's original post