X · by adithatipalli

From Jev to Laya in an AI video pipeline

A developer moves the decision layer from Jev to Laya.

Adithya Thatipalli

@adithatipalli

After seeing so much Hype around Jev, I started using it inside my Hermes as a decision and routing mechanism for a AI video pipeline I was working on But now, I am Changing that thought. Laya is a 421M local decision model by @Nandakishorm1 . Same job as Jev -> choice, score, yes/no with a probability, But it runs on my machine. No API hop. No per-token meter. ~33ms. Weights stay here. For an AI film pipeline, that is the actual bottleneck. >Not “write a better prompt.” >The expensive part is sending the wrong shot to the wrong generator. Before Seedance / Kling / Minimax / Veo / Flux / GPT Image burns a credit, Laya now answers four things in one pass: → which model gets this shot → text-only, character-ref, or first-frame → is the brief ready, or does the enhancer skill run first → how hard is identity lock Sol still writes. Kimi still helps. Video models still generate. Laya only decides. Jev was a good hosted nervous system. For shot routing I don’t need a hosted brain. I need a local one that sits in front of the generate button and says go / wait / switch model. 421M. Local first. That is the router now.

Sep 23, 2026 · 3 likesOpen on X

adithatipalli shares their experience using Jev as the decision and routing layer in Hermes, an AI video pipeline, and then switching to Laya.

Open the source

Eleven trending Jev repos

Repositories: 11

生ビール

@wmoto_ai

ローカルTypesafe Jev、できたんやないか？もうちょい速度は改善余地ありそう

XOpen source

A local Jev

Nick Khami

@skeptrune

you can make any open source model behave like jev with just a bit of inference engineering. it's shockingly easy. to prove it, we built a new endpoint we're calling deepseek-v4.1-flash-jev. see the demo below. here's how it's done: sglang (an inference engine) offers a scoring endpoint in addition to the normal generation one. in scoring mode, given an input & set of possible answers, it forces the model to produce probabilities for each one. example: > input: what is most common letter in abcccde? > possible answers: a, b, c > output: (c, 0.9), (b, 0.0.5), (a, 0.05) getting the above behavior instead of streamed output is as simple as using sglang's /v1/score endpoint instead of /generate. there's just one other trick required. for deepseek, you have to add a closing think tag before the response. this forces a direct answer instead of a reasoning trace. if you want reasoning, you can do that too, but imo that makes things too slow to be worth it. dsv4.1 flash is not as good as jev, but if we had enough spare compute to experiment with this same approach for a larger model then i think the decision quality would be at least as good, if not better. also, somewhat unrelated, i think decision-making models kill all prospecting & sourcing work. i would have absolutely killed to have jev or similar when i was recruiting @mintlify. absolutely incredible.

XOpen source

From Jev to Laya in an AI video pipeline

More like this

Eleven trending Jev repos

A local Jev

deepseek-v4.1-flash-jev