Sam Reghenzi's product-taxonomy benchmark: a Jev Choice at every level versus a gpt-5.2 agent that descends the tree and can be sent back by a judge.
Watch out
The old pipeline used 5 threads and the new one ran sequentially, so the 7x headline is the author's generous reading. Per-call 1.30 s versus 0.43 s does not depend on that. Output tokens rose because each Choice returns a probability per criterion.
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Hacker News: A product-taxonomy migration from a gpt-5.2 agent loop to one Jev Choice per level.
sammy_rulez2 pointsReplacing an agentic classification loop with Jev: 7x fasterRead the thread on Hacker NewsPosts from builders shipping with Jev right now.
Computer use at 155× cheaper than Opus 5
i built computer use using @typesafeai ! it is 155x cheaper than opus 5, ~20x faster, and generalizes across OS's more on how it works in the vid & thread below:
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
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🚨 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 tokens / $0.0047 github.com/ChetasLua/jevm…
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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
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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
