Open source alternatives to Jev

Dario Crespo

@crespodario

Metí a Jev en un chat. 🤖 Para que no tengas que volverte loco/a adivinando cómo probarlo. probalo sin instalar nada y sin registrarte. 👉 Probalo acá: lnkd.in/dhFX5XYY Y si querés armar tu propia versión, el código está abierto: lnkd.in/dXSu6ki3 😉

Guido Pettinari

@coccoinomane

Local open-source version of Jev 🤯

netrunner

@plotarmordev

Another Jev competitor just dropped: CLM-8B from @jackyk02 and the Stanford/NVIDIA team, with open weights under Apache 2.0. They claim it's comparable to Jev with up to 9x faster inference We went from zero options to a new one almost every day!

Edwin Mesa

@edwinfmesa

Seguí con dos consultas: un código de verificación que no llega y cómo navegar con el teclado. Jev eligió seguridad y manual de usuario; Laya, privacidad y términos. En ambos casos me convencen más las elecciones de Jev, aunque Laya volvió a responder más rápido con mi setup.

Edwin Mesa

@edwinfmesa

Empecé a escribir: "¿Qué precio tiene la suscripción anual?". Antes de pulsar Enviar Jev y Laya ya sugerían planes_y_precios.md. Eso es lo que quería probar: si el agente puede tener una pista de qué consultar antes de que le llegue la pregunta, con mi setup Laya fue más velóz

Zenko | NeuralFrame Labs

@NeuralFrameLabs

Local Laya beats Jev 100% #localai #ainews #ai #laya #jev

Edwin Mesa

@edwinfmesa

Explorando qué se puede hacer con Jev y Laya, que (los de moda), me pregunté si podrían ayudar a un agente de soporte a preparar el contexto mientras el usuario escribe su mensaje. El contexto es clave para un LLM, así que armé una startup ficticia con 7 documentos. 🧵

TypeSafe AI

@typesafeai

LLMs distilling Jev like

Valvet Online

@VALVETONLINE

I RAN A DECISION MODEL ON MY LAPTOP TODAY 1. Kev: tiny Jev-style models built on Qwen3.5, 0.8B up to 9B 2. 4B/9B fit a 32GB Mac, also CUDA and ROCm 3. Answered a support ticket in 495ms with probabilities #OpenSource #LocalAI #ML

Sodeh Abadi

@irSodeh

Jev vs Laya-MLX isn't really a rivalry. Jev is the original, commercial model. Laya-MLX is a local, open-source take on the same idea, running natively on your Mac. Same core concept: "System One" models. No chatting, no text generation. Just fast, typed decisions. #Jev #Laya

WquGuru

@wquguru

Jev 发布才 8 天，开源替身已经一堆了，今天诺基亚也来蹭热闹了😅 我在 MacBook 上，让官方 Jev 和 3 个开源方案同跑 20 张中文工单🫱 Laya 25ms，比 Jev 快 50 倍 djev 509ms 官方 Jev 1.3 秒 AnyJev 将近 4 秒 仓库都在这： Laya（Mac 版）http://github.com/mizorewww/laya-mlx djev（Mac 版）http://github.com/jamescorbett/mlx-vlm AnyJev http://github.com/nokia-applied-research/AnyJev 但快的，不一定对 👇

Darshan Jain

@i_darshanjain

Jev is all over my timeline rn 👀 Went looking into it and stumbled across Laya. Open-source System 1 decision model. They even have a Jev comparison 👀 7.8× faster. 3× better calibrated. 45/51 languages. Self-hosted. And the weights are right there. 😂 Yeah… I am watching this space. https://huggingface.co/convaiinnovations/laya

Nitin.nn

@NitinthisSide_

After seeing Jev everywhere on the feed lately, we had to try this direction ourselves. We’re bringing Laya to Insight O Mate. A fast, open-source, local decision model that fits perfectly with our privacy-first approach to querying NoSQL databases. Your data stays local. Your decisions stay local. Insight O Mate + Laya is coming. Try Insight O Mate at https://insightomate.lunamic.co

Leaf Yeah!

@leaf_sanren

啊啊啊啊啊🎉🎉！JEV 黑客松已经出来啦！！！ 同时，我也见到了把 Jev 用得最对的一个项目！！ Jev 这模型最大的特点： 只会判断，一个字都写不了。所以用它的关键，就是别让它干写字的活。 这个开源的「聊天副驾」就很聪明： 你在微信里收到一句"在吗"或者"方案再想想"，它先让 Jev 一秒判出对方真实意图、危险等级 1–9、该马上回还是先晾着； 写回复这活交给另一个会写字的模型起草 3 条，最后再让 Jev 排个序。 判断归判断模型，写字归写字模型。 且它最好的一点：它永远不自动发送。 搞得我都想去闲🐟搞几台吹灰的安卓机啦！ 期待后续也能拿下 iOS、macOS、鸿蒙系统！ https://x.com/AYi_AInotes/status/2102605821456814286/video/2

lemon👑

@lemonDefi1

Laya just mogged Jev at Tetris. The interesting part isn’t just that a local model won. Laya is an open-weights System One model running locally on a 16GB MacBook Air. Jev, meanwhile, is cloud-based and built with Grok 4.7.

Ed Plese

@edplese

Jev but for images? I built a local model to try it out! 63ms for a 512x384 image and fast enough for 15fps video. Birthday 94%. Golden retriever 78%. Sprinkles 93%. Candles lit 67%. Dog about to eat the cake? 52%.

Geek Lite

@QingQ77

把本地 MLX 推理的 Laya 模型和托管 API 的 Jev 放在同一个 T-Rex 跑酷赛道上对打，用延迟、存活和回放数据对比本地推理与云 API。 github.com/virajbhartiya/…

Just AI

@thelazyyogii

上次说的 Jev： 不会陪聊，只负责拍板。 这几天又冒出个 Laya。 活差不多，路子不一样。 一起想成： 流水线上专门判题的人， 不写长文，只答「过不过 / 归哪类」。 Jev = 外面请来的裁判 开箱就能用，按量付钱， 选项多、材料长时更稳。 适合懒得自己搭、先要结果的人。 Laya = 自己厨房里养的裁判 权重能下，Apache 开源，本机跑， 更快更省隐私。 但要自己微调；选项一堆时容易糊。 适合有机器、要数据不出门、愿自己调的人。 一句人话： 不是谁取代谁—— 要省事找 Jev，要自己掌控找 Laya。 都是选择题机器，不是聊天搭子

Arthur Marques

@ArthurLabMRP

was trying to figure out how to make these 2 models fight each other, so I put jev vs laya in chess lol script picks the 10 best moves, captures, and moves that put pieces under attack. still wip but jev feels way smarter

Thinh Le

@thinh_lvv

Can someone make Laya and Jev work? 😅 I created simple Flappy Bird game and let Jev and Laya control it. But it look weird 😬

Daniel Raeder

@DanRaeder

While Jev is down for new invites, I worked with Claude last night to build Vej (a jev clone that runs locally.) It's weakest at score types, but almost 1:1 for noul and choice. You can use it to build and play with jev-based apps until you can get in. github.com/draeder/Vej

aiasssistantstore

@aiassistantstor

CLM-8B Just Dropped: The New Open AI Model That Claims to Be Up to 9× Faster Than Jev for Agents

apolinario (poli)

@multimodalart

congrats on the release! i've included xor on the Decision Index 0.2 benchmark of jev-like models we don't have a vision benchmark yet, but the text-only version got 8th place, strong in arts & human taste x.com/multimodalart/…

apolinario (poli)

@multimodalart

congrats on the release, i've added it to the jev decision index 0.2, it's the #2 open weight jev-like model 🥳 and the strongest in chess and for its size x.com/multimodalart/…

Cuth

@ItsCuthulhu

Now that I've collected so much data on System One models on my DGX Spark. Lets see if I can beat Jev on size, speed, and accuracy. It will be MIT for anybody to build on. Everything I do on here is open source.

Hassan

@nutlope

Just trained Tev1 0.8B, a tiny Jev-like classifier. Here it is running completely locally on my mac with @ollama & classifying some tasks. It's extremely fast: only ~50ms E2E latency. Video is not sped up! Releasing weights & benchmarks very soon so you can try it yourself :)