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 952 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
- 952
- New this week
- 952
- 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:
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 👇
laya-coreml runs 99.5% of operations on ANE at 3.7 ms per decision
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→A step-by-step Jev build guide with code and architecture
everything you need to start building with jev, in one article. code, architecture, diagrams... everything you need to follow the build and make it your own.
Directory
+ Submit yours
laya-vs-dijkstra
antonellof
Compares Laya MLX pathfinding decisions with Dijkstra on seeded weighted mazes
1·GitHub repo
katai
Shiawaseu
A browser automation agent uses fine-tuned Laya models to select actions and target elements
1·GitHub repo
SystemOneSharp
pinkroosterai
Provides a .NET client for Jev and Jev-compatible local Laya servers
1·GitHub repo
code-oracle
wahyuzero
Verifies code changes with AST checks and Laya-based residual-drift evaluation
1·GitHub repo
jevtpp
wiatrM
A C++20 library for typed model-backed decisions with optional ONNX Runtime and native Laya backends
1·GitHub repo
seems-laya
ericmjl
Runs natural-language judgments in the Seems programming language using the local Laya decision model
1·GitHub repo
laya-mlx-zh
ZLHAOOO
Provides Chinese fine-tuned Laya weights, training data, and evaluations for Apple Silicon MLX
1·GitHub repo
design-os-generative-ui
jangtrinh
Builds a generative UI engine using local Laya-MLX decisions and a TypeSafe JEV cascade router
1·GitHub repo
adecider
Agents365-ai
Provides typed decision tools for coding agents with pluggable local Laya, Jev, and OpenAI-compatible backends
1·GitHub repo
B
leanest
baronunread
A TypeScript test selector uses semantic judgments from classifier.dev, Jev, or Laya
708·npm package
G
@system-one-ai/model-laya
GitHub Actions
Adds optional Laya model plugins to System One browser and Node runtimes
317·npm package
I
pi-laya-guardrails
izolight
A Pi extension sends prompts and tool calls to a local Laya guardrail server
282·npm package
N
@scruple/provider-laya
nalexpear
A local Laya decision provider for the Scruple framework
252·npm package
J
opencode-jev-compaction
jlegends
Two OpenCode plugins prune stale tool calls and optionally refine decisions with local Laya
250·npm package
G
@gobing-ai/ts-laya-mlx
GitHub Actions
A TypeScript package connects applications to a local Laya decision backend through MLX
239·npm package
I
pi-laya-model-router
izolight
Routes Pi sessions to mapped models using task classifications from a local Laya server
145·npm package
D
modsure
davespace
Moderates user-written content against site rules using Jev or a loaded Laya client
129·npm package
L
reflex-hooks-oss
lia210350
A Node provider runs the Laya decision model locally through ONNX for reflex-hooks
116·npm package
C
extra-laya-bench-ft-data
cjdd3b
Provides additional data for Laya benchmark fine-tuning
0·HF dataset
P
laya-formatting-fragility
pranaysuyash
Measures how formatting changes affect typed-decision model outputs
0·HF dataset
A
laya-marker-corpus
annelo
Provides a multilingual-task corpus for training typed-decision heads on fixed-option decisions
0·HF dataset
J
next-jev-laya-test
JonesLin
Provides multilingual evaluation data for testing Jev and Laya across text-classification tasks
0·HF dataset
S
laya-thai-trainset
servronix
Supplies Thai typed-decision examples for fine-tuning Laya
0·HF dataset
A
turkish-mmlu-laya
AhmetSemih
Converts Turkish MMLU questions into Laya choice-decision training examples
0·HF dataset