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A DIY Jev: typed decisions from gpt-6-luna logprobs, benchmarked head-to-head

This story is from 2026-09-28. It is preserved in the archive; the latest stories are on the live feed.

What if an ordinary LLM could return this: { "refund" : 0.72 , "fraud" : 0.21 , "other" : 0.07 } ...not by asking the model to write JSON numbers, but by turning its token logprobs into a typed, calibrated probability distribution? That is roughly the promise of TypeSafe's Jev: purpose-built models…

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  1. 2026-09-28 15:42 · DEV Community — Machine Learning
    A DIY Jev: typed decisions from gpt-6-luna logprobs, benchmarked head-to-head

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