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
Everything the world is building on Laya.
Laya turns any text into typed decisions in milliseconds, on your own machine. Here is what it does, and 1882 projects people built with it.
real run · laya-mlx
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?
choice✓ billing98%
technical1%
sales1%
How urgent is this?
scorenot urgent4%
soon15%
✓ blocking81%
Does the customer threaten to cancel or leave?
yes / no✓ yes84%
no16%
Decided in 30.7 ms · 3 typed answers · one forward pass
Real outputs from Laya, not a mock-up. Run it yourself →
- Projects
- 1882
- New this week
- 311
- Platforms
- 18
Seen on X this month
All demos →
Local Laya beats Jev at Tetris on a MacBook Air
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 decision layer routes tasks and recovers from tool failures
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:
Local Laya checks spider links 3× faster than cloud decision models
Local Laya beat @OpenAI's decision API 🕷 Four decision models raced down a web page ripping out links that name a spider and Laya running locally on an M5 Max took 34 ms a link so it checked 3x more than the cloud models Run decision models locally ->
Laya plays Tetris locally with decisions in about 27 milliseconds
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 👇
Unsloth Desktop serves Laya decision models locally on 4GB RAM
You can now run Laya Decision models locally on just 4GB RAM! 🔥 Works on CPU, Mac, Windows, Linux and GPU setups. Serve Laya through a Jev-compatible API via Unsloth Desktop. GitHub: Guide:
Directory
+ Submit yourscolibri
JustVugg
Runs large open models locally in pure C, including Laya for typed decisions
40.1k·GitHub repo
laya
NandhaKishorM
Runs multilingual typed-decision models and provides routing, serving, and integration options
23.9k·GitHub repo
laya-mlx
mizorewww
Runs Laya typed-decision models locally on Apple Silicon using MLX
6.3k·GitHub repo
C
laya
convaiinnovations
A multilingual-ready System 1 model that returns calibrated typed decisions over text in one pass
36.3k·HF model
laya-coreml
mizorewww
Runs Laya typed-decision models locally on Apple Silicon using Core ML and the Neural Engine
1.4k·GitHub repo
laya
receptron
Runs Laya typed-decision models from Node.js and TypeScript through ONNX Runtime
450·GitHub repo
C
laya-multilingual
convaiinnovations
A multilingual System 1 model that returns calibrated typed decisions over text in one pass
2.5k·HF model
typesafe-mcp
itsmostafa
Connects AI agents to typed-decision models through an MCP server
303·GitHub repo
openJev-verdict-2.0
Heman10x-NGU
Develops and benchmarks a non-autoregressive typed-decision model against Jev and Laya
286·GitHub repo
C
laya-demo
convaiinnovations
A Gradio demo for Laya typed decisions, multilingual routing, moderation, and RAG filtering
268·HF Space
quackd
rokbenko
A Python CLI coordinates robots using LLMs, vision-language-action models, and decision models
244·GitHub repo
laya-ultrafast
ipenywis
Runs a browser automation agent using local Laya decisions through MLX
181·GitHub repo
mu
qybaihe
Provides a coding agent with a judgment kernel that can use Laya as its local judge
179·GitHub repo
FluidUse
FluidInference
Automates form filling on Apple devices with on-device models, including Laya through Core ML
160·GitHub repo
C
laya-typed-decisions
convaiinnovations
A Laya model fine-tuned for agent traces, customer service, invoices, and security incidents
941·HF model
jev-cookbook
datawhalechina
Teaches Jev and demonstrates fine-tuning and serving the open-source Laya model
158·GitHub repo
laya-playground
wdobry
A local website combines Laya model demos, games, a benchmark, and an agent skill
157·GitHub repo
keel
codejunkie99
A macOS coding workspace uses local Laya or hosted Jev to select task routes
122·GitHub repo
A
laya-mlx
aac6fef
An MLX FP16 conversion of Laya for native inference on Apple silicon
118·HF model
laya-vs-jev
virajbhartiya
A side-by-side T-Rex game compares local Laya and hosted Jev decision models
100·GitHub repo
laya.cpp
lkarlslund
A native C++ runtime runs Laya typed-decision models across GPU and Apple backends
89·GitHub repo
jevals
openlayer-ai
Agent evaluation and guardrail tools run with local Laya, Kev, or hosted Jev
86·GitHub repo
chat-seek-vscode
fstandhartinger
A VS Code extension searches chat archives and uses Laya to rerank matches
66·GitHub repo
system1-agents
ThinkFlowLab
Prebuilt agents use Jev, Laya, or Cua-S1 for browser, computer, robotics, and game tasks
64·GitHub repo
