Jevの内部アーキテクチャを推測している技術記事(Jev’s Architecture Unmasked)からメモ。 ・本記事はJevのAPIを約1万回の呼び出して、内部構造を推測したもの ・従来の言語モデルを用いた分類やルーティングでは、トークンを1文字ずつ逐次生成するために膨大な無駄な計算コストが発生していた。 Show more
Inferring Jev's internals from 1,000 calls
Notes from a technical analysis that inferred Jev's internals from roughly 1,000 API calls, probing how probabilities and confidence behave.
- Category
- Practices & Patterns
- Published by
- Community
- Author
- iwashi86
- Added
- 2026-09-22
The post
Tagscommunityxanalysiscalibration
Highlights
- Bases its inferences on roughly 1,000 API calls.
- Probes how probabilities and confidence move across requests.
- Published in Japanese with the raw observations attached.
Watch out
Inferences about a closed model, not a published architecture; treat conclusions as hypotheses.
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From the community
Posts from builders shipping with Jev right now.
The launch post
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 Show more
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@CompleteSkeptic
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
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