# AI use cases for Laya MLX (mizorewww/laya-mlx)

Repo: [mizorewww/laya-mlx](https://github.com/mizorewww/laya-mlx) | Use cases: 4 | Page: https://dedupedai.com/use-cases/mizorewww-laya-mlx/

laya-mlx is a Python runtime that runs Laya typed decision models natively on Apple Silicon using MLX, with no text generation, PyTorch or cloud API.

This file holds every use case of this repo. Each one starts with a "## " heading and ends with its URL.

- Route support tickets to a team on your Mac: https://dedupedai.com/use-cases/mizorewww-laya-mlx/route-support-tickets-locally/
- Answer several typed questions about one message: https://dedupedai.com/use-cases/mizorewww-laya-mlx/answer-several-typed-questions-per-message/
- Watch a local Snake agent make live decisions: https://dedupedai.com/use-cases/mizorewww-laya-mlx/watch-local-snake-agent-decide/
- Serve Laya over HTTP for internal apps: https://dedupedai.com/use-cases/mizorewww-laya-mlx/serve-laya-over-http-for-internal-apps/

## How to route support tickets to a team on your Mac with Laya MLX

Repo: [mizorewww/laya-mlx](https://github.com/mizorewww/laya-mlx) | Difficulty: Intermediate | Time: 30 minutes | Basis: From the README

Pick the right team for each ticket with a local typed choice decision.

**Who it is for:** A support engineer on a small team who needs each incoming ticket sent to billing, technical or sales, without a cloud API call.

**The problem:** Manual triage is slow, and every ticket needs a consistent team choice.

**The outcome:** Each ticket gets probabilities over the named teams, computed locally, and the top label can drive routing.

### Steps

1. Install the package on an Apple Silicon Mac: `pip install laya-mlx`
2. In a Python file, import the library and load the checkpoint: `import laya_mlx as laya` then `agent = laya.load("aac6fef/laya-mlx")`
3. Define a choice question with the criteria billing, technical and sales, as shown in the Quick start.
4. Call `agent.predict` with the ticket text and the question dictionary.
5. Read `result["answers"]["department"]` and pick the label with the highest probability.

### Prompt to paste into your agent

```text
In the laya-mlx repo (github.com/mizorewww/laya-mlx), write a Python script that loads aac6fef/laya-mlx and routes the ticket text in <ticket_file> to one of the teams billing, technical or sales using a choice question. Print the answers dictionary. Verify that the printed label is one of the three criteria.
```

### Caveat

The published latency figures come from one short question on an M3 Max, so longer tickets or other machines will take different times.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/mizorewww-laya-mlx/route-support-tickets-locally/

## How to answer several typed questions about one message with Laya MLX

Repo: [mizorewww/laya-mlx](https://github.com/mizorewww/laya-mlx) | Difficulty: Intermediate | Time: 30 minutes | Basis: From the README

Get a team choice, an urgency score and a refund yes/no flag in one call.

**Who it is for:** An operations lead who wants several structured answers about each customer message in a single local call.

**The problem:** Separate checks for team, urgency and intent are repetitive to write and run one at a time.

**The outcome:** One predict call returns a choice, a rubric score and a P(true) value for the same message.

### Steps

1. Install the package: `pip install laya-mlx`
2. Load the checkpoint in Python with `agent = laya.load("aac6fef/laya-mlx")`.
3. Build a question dictionary with a choice question for department, a score question for urgency and a noul question for refund intent, following the Python API example.
4. Call `agent.predict` with the message and the dictionary.
5. Print `result["answers"]` and read each field: choice probabilities, expected score and P(true).

### Prompt to paste into your agent

```text
In the laya-mlx repo (github.com/mizorewww/laya-mlx), write a script that loads aac6fef/laya-mlx and asks three questions about the message in <message_file>: a choice for department, a score for urgency with three rubric levels, and a noul for whether the customer asks for money back. Print the answers. Verify that the urgency value falls between 0 and 2.
```

### Caveat

Probabilities can differ slightly across FP16 and FP32 even when the selected label agrees, so check the dtype you choose.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/mizorewww-laya-mlx/answer-several-typed-questions-per-message/

## How to watch a local Snake agent make live decisions with Laya MLX

Repo: [mizorewww/laya-mlx](https://github.com/mizorewww/laya-mlx) | Difficulty: Intermediate | Time: 30 minutes | Basis: From the README

Run the terminal Snake demo where Laya picks every move on your Mac.

**Who it is for:** A developer or curious user who wants to see typed decisions running in real time before building with them.

**The problem:** It is hard to judge what a typed decision runtime does until you watch it act in a loop.

**The outcome:** A terminal Snake game in which each move comes from a local Laya decision, with speed controls.

### Steps

1. Install the demo extra: `pip install 'laya-mlx[demo]'`
2. Download the multilingual checkpoint before the offline demo: `hf download aac6fef/laya-multilingual-mlx`
3. Open a terminal of at least 104 × 35 cells and start the game: `laya-snake`
4. Use Space to pause, the up and down arrows to change speed, R to reset and Q to quit.
5. Optionally run `laya-snake --optimize --max-speed` to try the compiled and prefix-reuse path.

### Prompt to paste into your agent

```text
In the laya-mlx repo (github.com/mizorewww/laya-mlx), install the demo extra and start the laya-snake terminal game in a terminal of at least 104 by 35 cells. Report the moves per second shown and whether any safety intervention occurred. Verify that the game runs without an external API key or network call during play.
```

### Caveat

The Snake frame time is not the one-question latency benchmark, and the README reports its own speed results from one paired M3 Max test.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/mizorewww-laya-mlx/watch-local-snake-agent-decide/

## How to serve Laya over HTTP for internal apps with Laya MLX

Repo: [mizorewww/laya-mlx](https://github.com/mizorewww/laya-mlx) | Difficulty: Advanced | Time: 60 minutes | Basis: From the README

Run a local Laya HTTP service that other internal tools can call.

**Who it is for:** A DevOps or platform engineer who wants several internal tools to share one local Laya model on a Mac.

**The problem:** Each internal tool loading its own copy of the model wastes memory and setup time.

**The outcome:** A local HTTP service started by one command, which internal apps can call for typed decisions.

### Steps

1. Install the serve extra (inferred from the extras list and the demo install pattern): `pip install 'laya-mlx[serve]'` (inferred, check the README)
2. Start the HTTP service: `laya-mlx-serve`
3. Find the endpoint address and request format in the upstream documentation, since this README does not show them.
4. Send one test decision request from your app or a client script and confirm a response.
5. Keep the service running on the Mac that hosts the model for the other tools.

### Prompt to paste into your agent

```text
In the laya-mlx repo (github.com/mizorewww/laya-mlx), install the serve extra and start laya-mlx-serve on this Mac. Find the request format in the upstream Laya documentation and send one choice question. Verify that the service returns an answers field with probabilities for the named options.
```

### Caveat

The README names the serve entrypoint but does not document its endpoint or request format, so confirm those before building on them.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/mizorewww-laya-mlx/serve-laya-over-http-for-internal-apps/