Many people didn't understand what the viral JEV is?

Made a 50-second explainer video on Jev's parallel single-pass architecture, calibrated probability outputs and sub-0.1s latency for automation.

The original post, translated

Many people didn't understand what the viral JEV is?

So I made a 50-second demo video to take everyone through the JEV that's flooding overseas feeds and the underlying architecture behind it👇 (see video below)

JEV is the first model after former OpenAI, ChatGPT / RLHF participant @CompleteSkeptic (Diogo Almeida) founded @typesafeai

At OpenAI he discovered a fatal contradiction:

Autoregressive large models trained through human preference fine-tuning (RLHF) suffer from severe mode-dropping, overconfidence and hallucinations, and the latency of serial token-by-token generation is enormous, making them fundamentally incapable of serious machine automation production

To this end, they spent two years developing JEV, a System 1 foundation model designed specifically for automation, with extremely radical core architectural innovations:

• One Parallel Pass:

Just as Transformer replaced RNN back then, JEV completely abandons the autoregressive mechanism of serial token-by-token output, adopting an entirely new model architecture and sampler to fire all preset options in parallel in one pass

• RL for Calibrated Decisions:

Introducing a brand-new training algorithm, the output medium is no longer natural language text, but directly outputs a confidence-calibrated probability distribution

• TypeSafe:

Completely eradicates hallucinations, outputs are self-consistent and highly deterministic, and can be directly called by programs as strongly typed objects

• 100x speedup and extreme cost reduction:

Latency drops to under 0.1 seconds (fast enough to be directly inserted into a 60 FPS game loop); input costs only $42 per billion tokens, output tokens are completely free

In the video I visualized the entire chain: the 8.5-second latency bottleneck of traditional autoregression, JEV's parallel matrix computation, and real-time control mini-game tests, demo reference @MatijaSosic

Chat needs slow reasoning, automation needs fast intuition, Building Prod, not God is indeed not an empty phrase🫡

The post above is a machine translation from zh; the untranslated text is in the fold-out.

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发现了一个很省事的 AIGC 工作台，把 AI 漫剧的流程全部整合进去了 它的核心功能是「一键漫剧」，全程引导式推进： 输入一个故事主题，系统先给 3

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