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.
jev-to-laya-proxy
diwakersurya
A Bun proxy exposes the TypeSafe Jev API and forwards requests to a local Laya server
ewm-laya-bonsai-lab
alexmy21
Uses notebooks to inspect a Rust Laya daemon's interactions and compare Laya with Jev and a mock
browser-agent-laya-fast
samdem-ai
Uses Laya and NuExtract to power a browser agent for automated tasks
laya-decision-engine-tutorial
manishhnnegi
A hands-on notebook tutorial for Laya’s multilingual typed-decision model
jev-kev-laya-selfhost
suarify
Provides a Dockerized, self-hosted HTTP API for Laya typed decisions with agent-skill documentation
ruby_llm-providers-laya
codenamev
A RubyLLM provider uses local ONNX Laya checkpoints to answer judge questions
laya-vs-dgpl-arena
vk-alto-none
Stages AI decision models in a game arena, including local Laya and hosted Jev
SEO-Agent-using-LAYA
07anishu12
Audits websites and uses Laya MLX inference to classify and prioritize SEO issues
synapse
Hduc
Tạo hệ thống ra quyết định và tự động hóa bằng Laya, kết hợp quy tắc, phê duyệt và phân giải giao diện động
ruby_decision_model-providers-laya
codenamev
Adds a local ONNX-backed Laya provider to the Ruby decision-model gem
laya-zeroshot-agent-guard-eval
Zephyr4772
An evaluation measures zero-shot Laya checkpoints as guards between agent steps
decision-guard
ashp15205
Adds input scanning, thresholding, and confidence calibration for Jev and Laya decision models
Smart-Support-Ticket-Router-Using-Laya
affan1311
Routes support tickets with Laya and uses Gemini to draft replies, with a benchmark against a Gemini-only pipeline
Smart-Support-Ticket-Classifier-Using-Laya
affanhyder-diggit
Routes support tickets with Laya and benchmarks the results against a Gemini-only pipeline
quarkus-langchain4j-jev-laya
kdubois
Integrates Jev and self-hosted Laya decision backends into a Quarkus LangChain4j agent
privatemode-decisions-benchmark
edgelesssys
A benchmark compares speed, accuracy, and cost for Jev, Laya, and Privatemode decisions
MacJev-322M-4K-Laya
lawrence3699
Provides a fine-tuned Laya-derived decision model for local Mac agents
jev-equivalent-research
hjl1045
Evaluates Jev, Laya, and a GPT-5.6 Luna baseline on synthetic auto-claims classification
Autonomous-Decision-Intelligence-Platform-ADIP-
Ayyankhan101
Blueprints a local Laya-based decision platform using the laya-mlx runtime
MacJev-322M-4K-Laya-GGUF
lawrence3699
Runs a quantized Laya-derived decision model through llama.cpp and a Python decision head
Sureband
TejaPriyan
Applies split conformal prediction to provide coverage guarantees for outputs from models such as Laya
Reflex
dimpu47
Routes infrastructure alerts using Jev or local Laya decision model APIs
MacJev-322M-4K-Laya-MLX
lawrence3699
Runs the MacJev typed-decision model natively on Apple silicon with MLX and compares it with Laya
Intelligent-Email-Routing-at-Local-Speed-Using-Laya
hasif154
Uses Laya to route emails with typed decisions and a checkpoint-selecting router