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雨夹雪❄️

@mizorewww

介绍比Jev快50倍，在你设备上跑的laya-mlx! 只在你的设备上占用最高1G内存 Laya是一个开源的类似于Jev的,基于文本输出概率的分类系统 我将其移植到MLX,并且做了一些性能优化! 视频中就是这个模型在我的本地M3Max上玩贪吃蛇 这个模型能够以每秒决策60次的速度玩贪吃蛇！ https://t.co/31KGUiNunb

Nandakishor m

@Nandakishorm1

Laya surpasses jev when it's fine tuned in almost all cases..

Nandakishor m

@Nandakishorm1

How I built Laya

Nandakishor m

@Nandakishorm1

Asking everyone to fine tune https://t.co/mwPvWGPY2n Just use any kinda agentic harness like claude code, codex etc. Change the code and use free kaggle GPUs to train your own use cases. Base model has limitations

yankis

@yankis0x

Seven open source Jev models tested in 21 minutes. Laya gets the last 3:41 0% - 0:00 - intro 17% - 3:46 - JevBench, the thing they all get scored on 33% - 7:02 - Nimble 51% - 11:03 - Decider 62% - 13:20 - OpenJev 83% - 17:50 - Laya The order is the story. Laya did 2M views this

Abhijay

@abhijay_cloaked

There’s something cute about giving a probabilistic classifier a freshman probability exam. So I gave @typesafeai Jev, SemIf, and Laya ~1000 adapted questions on probability and countability from Berkeley exams Jev: 83.7% SemIf: 61.6% Laya: 31.2% A little stress test for the

Sai Dutta Abhishek Dash

@AbhishekDash69

I benchmarked Jev 1.13.0 vs open-weights Laya on 751 identical questions. Jev sweeps triage/guardrails/moderation, goes 1.000 on 5-language intent. Laya wins agnews + mnli at $0. Full data:

iamsaturday

@my_small_room

Tested Jev vs Laya (local, MLX) on 40 support tickets, EN + Thai. Jev 90–100%. Laya zero-shot 30–80%. Laya's faster, but not close on accuracy yet.

Madhav Sharma

@MadhavSz

Laya shipped right after Jev claiming to be faster for AI agent decisions. curious if it was accurate too I built a POC same Google ADK triage agent, same 30 labeled tickets, only the engine swapped. Jev: 422ms, 53% accuracy. Laya: 152ms 10% accuracy Faster not more accurate

FrontierMind

@Raju_M_A

Spent a morning testing a small open-weights local model (Laya-MLX, fully offline on a MacBook Air, $0 per call) on real tasks: inbox triage, tagging 100 SAP decks, categorizing blog posts, and a prefilter pattern. Honest numbers in this thread.

Dima Nurm

@NURM_Dima

1,000 synthetic emails. Six folders. One local 322M Laya model running on a MacBook Air M3. ⚡ 28.6s without screen recording 🎬 97.3s while recording 🎯 65.1% accuracy against reference labels 💾 0 MB swap Everything runs locally. No cloud inference. And I’m showing the

Aaron Edell

@aaronedell

Benchmarked @mizorewww’s Laya-MLX against @FeatherlessAI Simple Jev on my M4 Pro inside @OpenClaw: Laya: 85% @ 27.8ms Jev/Qwen3-4B: 90% @ 1,167.6ms The stack is rules → Laya → Jev → @ollama 27B → cloud. 14/20 decisions resolved before Jev. @typesafeai was right about System

Andrea Bruno

@baronunread

I've added Laya and https://t.co/flwFG40TX4 (@michael_chomsky) support to leanest, Jev is good but having other options to try out seems nice. Also new og image (I hate og image caching so bad) https://t.co/OgVzZg8CVE

mgear

@unitycoder_com

Beating Laya (https://t.co/rTxWDMS8KG) in tic tac toe : ) Using this for Unity https://t.co/XEWtg1ZaEd by @asus4

VeidtKV

@VeidtKV

I ported laya to my specific hardware (6900XT) with the help of my hermes agent. Check it out here

Anton Lobach

@AntLobach

Wow, Laya (https://t.co/PhmZAwTZeX) inference in Jolt (Clojure running on Chez Scheme)

Akihito Koriyama

@koriym

Built a hypermedia browser on Laya-MLX — a small model that, like Jev, doesn’t generate text; it just returns a probability for each option you give it. The next link is chosen from HAL’s _links and what ALPS declares those links mean. No routes defined up front. cc/ @mamund

//s̶̀pò̶of.sh

@spoofydude

I'm publishing this workflow! a 3-model fully local pipeline (prompt enhancement + Laya decision-making) that generates an image in ~37s (fast af) Should run great on any RTX 3080+ https://t.co/i11GcPJbIm

pythongiant

@pyth0ngiant

Built laya-drift to visualise and monitor agentic-drift today. It continuously measures how far an OpenCode session has drifted from its original plan using Laya + Jensen-Shannon divergence https://t.co/eB9DNlQksp

Astrid

@just_some_dev

I made a postgres plugin for the Laya decision models, using @Kelwing's Rust framework for running them

Leonardo Stenico

@50bbx

I forked Needle (the Chrome extension) to use Laya instead of Jev. Fully local AI browser search. Try it out here: https://t.co/SYB7HQfya9 It is not a semantic search in strict terms, it will match your search to semantically similar results.

Daniel Tremer

@TremerDaniel

ScreenQuest: an autonomous screenshot-to-action AI agent for https://t.co/RHiWnwGPEf, running locally on an M3 Max (48 GB). Qwen3.5-4B / MLX + Laya / Core ML + Apple Vision OCR. No cloud inference. Code: https://t.co/gFP4sjd3VU

Shantanu Goel

@shantanugoel

Update: I trained Laya non-step for close to 24 hours. No where close to jev 0-shot :|

Simplifying AI

@simplifyinAI

The open-source local version of Laya crushes Jev in response speed! On the left is Laya, a local 421M open-source decision model. On the right is Jev 1.13.0, running through a cloud API. Same Snake game, same typed decisions, and the same 30-second free run: - Laya: Score

TTUX | تی‌تاکس (AmirTaha سابق)

@TTUX_tech

جالب شد بازی اسنیک. ورودی لوکیشن میوه و هد و بوردر هارو میگیره و با مدل اوپن سورس #Laya به صورت لوکال که شبیه #Jev هست دستور میگیره که لوکیشن بعدی کجا بره بالا پایین چپ راست. تجربه جالبی بود اینم لینک پروژه تو گیت هاب ممنون میشم با 🌟 حمایت بکنین: https://t.co/dcQcs6WljY

Mansour Raad

@mraad

The engineer and the trainee. Adaptive MPC knows the physics. Laya, a small decision model, learned from examples. Alone, Laya lands 0/90. With MPC vetoing about one in three of its moves, it gets 90/90, through a 60% engine loss. Fly it in your browser:

Daniel F

@cristianexer

I had a stupid idea: What if, instead of asking an #AI to talk, I made it fight me? 🥊 So I built World Summit Tournament. 21 fictional world-leader-inspired fighters, signature moves, combos, replays — and the opponent can use Laya, a small open-weight decision model in the

Sree

@sreexts

Two AIs. One game. I let @typesafeai 's Jev and @brainFnCl's Laya play the same arena survival game where every enemy’s decision is made live by the models. How it works: the game turns each moment into one sentence and asks one typed question. Back come probabilities, not

soybelli

@ahmetsoybelli

I connected Pac-Man to Laya-MLX 👻 Every move is a real typed decision running locally on Apple Silicon—no cloud API, no text generation, 0 output tokens. The live view shows action probabilities, latency, tactical planning, and safety interventions. { "steps": 351,

 Ahmed

@eng_ahmd

I made Laya (Jev-like model) play Breakout, Every paddle move is a live decision from an open 322M System One model (convaiinnovations/laya) running locally on Windows laptop, ~70 decisions/sec on an RTX 5080, zero cloud, $0. Code 👉 https://t.co/8v0dnXCnQ1

Hope

@hope_rythmn

i made Jev and Laya fight 1v1 each other in Doom 1v1 deathmatch, monsters in between, first to kill the other opponent 5 times wins! @typesafeai Jev's calls were sharper, it needed a third of the corrections. @mizorewww Laya just decided twice as often. final results

Dev Ed

@developedbyed

If you’re wondering how fast Laya is compared to Jev: my maze demo measured 19ms median responses from local Laya on a base M5, versus roughly 300ms from Jev’s cloud API.

Tony Dinh

@tdinh_me

Tetris bench: Jev vs. Laya-mlx I run the Laya model on my MacBook. It's true that the model is really fast (~84ms), but it's also much more stupid. Losing to Jev 3 out of 3 rounds.

Viraj Bhartiya

@heyxviraj

i made Jev and Laya play the chrome dinosaur game against each other except they’re actually controlling the dinosaurs. Laya runs locally on my Mac, Jev runs over an API. same track, same physics, completely different latency. they make their own decisions, dodge obstacles,

brain function collapse

@brainFnCl

This is NOT Jev. Open source. Runs on your laptop. Decides in ~27 ms, about 200× faster than waiting on a hosted LLM. Here it is playing Tetris by itself 👇 https://t.co/eq4gP53o2A

Tom Siwik

@tomhacks

The problem isn't the models, it's how you define the problem space, design the movements / decisions and optimize for it (batching e.g.) Jev wins for a network-based model any day. Local models tied - Laya-mlx and my custom build Jax. Look at them go.

Tobias Wupperfeld

@tobiaswup

Jev vs locally running Laya-MLX and Kev-4B I built my own snake benchmark. Jev @typesafeai is running via API. The other models are small alternatives running on very little RAM on my Macbook! Jev seems to deliver the best quality no doubt! After running it for a while 0

chewa

@0xchewa

Laya beat Jev 43 to 1 with the wifi turned off 86.4 decisions per second against 3.2. 1,281 moves against 47 left side is Laya, an open weights decision model, 421 million parameters, running locally off a 1GB footprint. right side is Jev 1.13.0 over an API. both got the

Alex

@Alex_tra_memory

Thanks for the model, we were able to port Laya to coreml with 99.5% of the ops on ANE + benchmarked too. it is now blazing fast with 3.7 ms per decision on an M5 Pro. Release: https://t.co/9xerYIHt9y Models: https://t.co/kPOaMQI4RH

atomic.chat

@atomic_chat_hq

Local Laya moggs Jev at @grok 4.7-built Tetris 🧩 An open-weights System One model called Laya, beat cloud-based Jev at playing Tetris by making decisions 11 times faster, running locally on a 16GB MacBook Air! Run AI models locally -> https://t.co/RbcCOIgVkj