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.
nocturn-gateway
yigiterturk-dev
Provides TypeScript and Python clients for a gateway to Jev and Laya
jev-deliberation-judge
SammySN-car
Plans a multi-agent deliberation judge using Laya jurors and vote, weight, or veto aggregation
jev-bench
brandonrc
Benchmarks Jev, Laya, Claude Haiku, and CLM on package-registry triage tasks
laya-la-ia-de-decision-rapida-ejecucion-local-y-eficiente
rubenurbano
Introduces Laya as a fast, locally executed decision AI
simplotel-guest-concierge
Itachi-1824
A hotel guest assistant combines retrieval, Laya classification, and validated availability
poi-governance
g31322543-crypto
Uses Laya for content moderation and DeepSeek or rules for POI deduplication
usejev
ali-master
Serves Laya through native ONNX inference on Bun with a TypeSafe-compatible API and bilingual playground
ai-second-brain-lab
dante01yoon
Combines an Obsidian knowledge workflow, coding-assistant hooks, local Laya classification, and a 3D Markdown viewer
decision-lab
Amine-LG
Provides a visual playground to build decisions and compare Jev, OpenJEV, and local Laya
Mark-LIII
shettysaaproductions
Provides a cross-platform desktop voice assistant with a local Laya decision model and multi-agent system
easy-open-jevs-instance
CarlosChiva
Deploys local Open-Jev and Laya inference services with Docker Compose and a test client
KVev
AdudodlaVarish
Serves cached long-document chat and uses Laya for offline routing experiments
ohmylaya
QuBiit0
Installs laya.cpp and exposes local Laya decisions to coding agents through MCP and an agent skill
decision-model-arena
sathik11
Compares Jev, Laya, and Microsoft Foundry for typed decisions in enterprise incident triage
jgent
Sube-py
Routes on-demand tool access in Pi using Jev or a local Laya model
stf-sim
JonRoosevelt
Simulates Brazilian Supreme Court cases using local Laya decisions, retrieval and bias probes
tool-output-pruning-lab
Dymyt-ry
Benchmarks Laya and other selectors for pruning tool output in coding-agent sessions
identity-jev
clawdreyhepburn
Fine-tunes Laya to rank identity and authentication standards from plain-language queries
kime-compat
tamnd
Checks whether kime supports TypeSafe and Laya APIs, clients, SDKs, and tests
narde
anliang0306
Reimplements the Laya decision engine and verifies behavioral parity against upstream
zhiyan
liuqing0224
Provides macOS chat assistance with local Laya analysis and generated reply suggestions
Wayfinder
manish-9245
Serves Laya decisions through an HTTP gateway with policies, a web console and an MCP server
model-router
gbaeke
Routes prompts to inexpensive or powerful language models using Laya complexity decisions
jev-agent-risk-gate
jayeshvpatil
Compares Jev, Claude, fine-tuned Qwen, and open Laya in experiments on agent shell-command risk gates