laya-mlx plays Snake at 60 decisions per second on an M3 Max
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!
Project: laya-mlx→The Laya project directory
Laya turns any text into typed decisions in milliseconds, on your own machine. Here is what it does, and 1048 projects people built with it.
Input text
“Hi, we were billed twice for March. Please refund the duplicate today or we will cancel our plan.”
laya-mlx · 146 input tokens · 0 output tokens
Which team should handle this ticket?
choiceHow urgent is this?
scoreDoes the customer threaten to cancel or leave?
yes / noDecided in 30.7 ms · 3 typed answers · one forward pass
Real outputs from Laya, not a mock-up. Run it yourself →
Seen on X this month
All demos →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!
Project: laya-mlx→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 ->
jev + opus 5.5... i simply can't comprehend why everyone isn't building this yet. in my workflow, this cut costs and time by ~80%. i think it's one of the best ways to use it. → pick relevant project notes before loading the context → route suitable tasks to a faster worker → choose a recovery path when a tool fails → run focused checks before the full test suite opus handles the hard reasoning. jev picks from options the harness prepares and validates. i explain how to build the decision layer in the article below:
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 👇
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: Models:
Project: FluidUse→
He comes from a small village in Kasaragod, Kerala. In 2016, he was selected for IISER Mohali but couldn't afford to make the move. He eventually studied engineering at Government College of Engineering, Kannur. Last week he released Laya, an open-source AI model that challenged TypeSafe’s Jev on speed and on published decision-making scores. This is the story of Nandakishor M and how he built Laya.
laya-local-http-server
ratheesh-aot
Serves the Laya typed-decision model through a local HTTP API using MLX or PyTorch
laya-code-review-action
Mcbeer
Reviews pull-request diffs against configurable rules using Laya's typed-decision model
laya-vs-jev-traffic
ameeetgaikwad
Compares local Laya and cloud Jev in a real-time traffic-control simulation
jev-laya-classification-bench
bhushankinge
Benchmarks Jev, Laya, and Qwen on classification of federal IT solicitations
von-laya-jev-paint-compare
zhangyunting123
Creates side-by-side paintings from typed decisions by VON, Laya, and Jev
laya-coreml-vs-jev-benchmark
sallout
Compares Laya and Jev on zero-shot intent classification benchmarks
laya-v2-agent-routing
mdad-elec
Fine-tunes a Laya System-One router to select the price tier for each agent turn
testes-laya-modelos-jev-like
diegoamrg4123
Tests typed-decision models and includes a Linux Snake demo using Laya
100-days-of-jev-and-laya
kashyaprparmar
Collects 100 practical projects using Jev and local Laya for typed decisions
laya-fine-tune-bn-eco-voice
nafi-ullah
Fine-tunes multilingual Laya for typed decisions in a Bangla e-commerce voice agent
news-signal
harveybc
Classifies financial news locally with Laya and emits auditable typed results
lx
iheeb1
Prunes shell output using RTK rewrites and Laya classifications
dev
phix
Hardens and rebrands Laya as a local typed-decision engine with server and MCP support
Sent1nel
srinath1505
A guardrail SDK and API routes policy decisions through hosted Jev or self-hosted Laya
xcanv-models
attachemd
Provides opt-in ONNX Laya decision-model packs quantized for local inference
S1-chess
Talles64
Fine-tunes Laya to choose chess moves from legal options in a single forward pass
nlaut
benkya
Runs natural-language browser tests with deterministic, vision-model, and Laya-based judgments
intents
ezeike
Documents and sketches decision-model patterns for routing user input to application actions
korean-decision-benchmark
jkf87
Benchmarks SemIf, Decider, Laya, and Jev on Korean hate-speech classification
synthetic-ehr-decision-evals
MadCodeTX
Benchmarks decision models on synthetic EHR administrative-routing tasks
rlcd_x_1b-4b_instruct
mrgonzales-dev
Tests RLCD models including Laya and explores their use with 2B–4B models
stopline
mdabydeen
Uses Jev or Laya to classify browser-agent actions before applying a policy gate
jev-course-demo
clchrf
Evaluates university AI course plans in the browser using quantized Laya ONNX inference
gyra
Gowtham-R-2002
Fine-tunes Laya for fast coding-agent decisions and evaluates it against labeled tests