I gave the Trump vs Kamala debate a live BS meter using Jev

The clip posted with this case (hosted by the collector’s media CDN); original post on X · 116s.

Ran 1,191 Jev yes/no calls over a Trump-Kamala debate to score each sentence on a live BS meter for $0.0497.

The original post

🚨 I gave the Trump vs Kamala debate a live BS meter using Jev

every sentence, both candidates, 5 yes/no questions each

1,191 Jev calls / 1.18M tokens / 415 ms median

total cost : $0.0497

same questions for both, clips picked by one fixed rule, not a fact-check

Quoted post

@CompleteSkeptic34.6M views
source

After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x

From the same thread

  • @chetaslua — 🚨 Open Source Jev BS meter you can use this to analyze any debate / investor call / interview / sales pitch / podcast video fact check live , for example this dario interview cost 60 Jev calls / 111K

Engagement when collected

Views165.8K
Likes1.2K
Bookmarks489
Reposts47
Replies68
Quotes27

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 general Jev builds. 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. Browse the full case library for the work it sits next to.

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