i made an llm from first principles with Jev 29 yes/no

The clip posted with this case (hosted by the collector’s media CDN); original post on X · 15s.

Built an autoregressive text generator from Jev yes/no questions, picking the highest-probability next character each step.

The original post

i made an llm from first principles with Jev

29 yes/no questions per character: should the next key be a–z, space, comma, or period?

highest probability gets append to it, then fed the updated text back in & repeat

an autoregressive loop made out of a classifier

Quoted post

@CompleteSkeptic34.5M views
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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

Engagement when collected

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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.

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