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.
omp-laya
agusbyna
An omp wrapper adds Laya decisions and prompt pre-screening to agent workflows
laya-docker
beremaran
A Docker image runs Laya as a GPU-backed, Jev-compatible HTTP server
laya-decide
Luminousyyh
A DeepSeek Harness skill gates file and command actions using a local decision model
laya-scripts
muck-stump
Shell scripts test authentication, server behavior, concurrency, and routing for Laya-compatible APIs
laya-mc-bot
LixiaoLeo123
Minecraft bots use Laya decision models alongside external LLM harnesses
laya-vision-stitch
DanielTea
A local screenshot-based game agent combines Laya, visual features, and action outputs
pi-laya-router
rabi
Adds Laya decision tools and prompt-based model routing to the pi agent
laya-steering-lab
Hantlowt
Tests methods for specializing frozen Laya models and benchmarks their accuracy and runtime
omarchy-local-laya
v3moreno
Manages a local Laya decision daemon through an Omarchy bar widget
laya-computer-use
ChenneyZhuang
Uses local Laya decisions to control macOS apps through their Accessibility trees
laya-training-log
ChenneyZhuang
Documents training and benchmark results for a fine-tuned Laya browser model
laya-first-look
cvranjith
Explores base Laya checkpoints locally on Apple M4 and reports informal behavior and latency measurements
laya-web-app
meossistant
Provides an offline web runtime for Laya with OpenVINO and PyTorch backends
laya-local-system
h7mei
Serves local Laya decisions through a web UI and Jev-compatible HTTP API
lica
np9royal
Adds typed decision packs powered by Laya to coding-agent hooks
Tenstorrent.Blackhole-convaiinnovations_laya
Thatch-cloud
Develops a Tenstorrent Blackhole inference backend and service integration for Laya
jev_and_laya_benchmarking
pavanjava
Benchmarks Jev and Laya accuracy and inference speed on typed-decision tasks
jev-laya-openai-comparison
amansahani
Benchmarks Laya and Jev against OpenAI models on financial regulatory decisions
jev-voice-browser-agent
adnankhan46
Implements a voice-controlled browser agent using Jev or Laya
decision-model-bench
SaiNarayana-B
Tests the accuracy and calibration of Laya decision-model confidence scores
decisionsmith
izam-mohammed
Uses LLM teachers to generate data for fine-tuning Jev and Laya decision models
gatelaya
Diwas2055
Adds Laya-powered multilingual firewall and routing guardrails to LiteLLM Proxy
ChenneyZhuang
ChenneyZhuang
Showcases a fine-tuned Laya browser model and local browser-agent tooling
ghosthand
ameerhmz
A local macOS voice and computer-use agent uses Laya for typed decisions