diff --git a/README.md b/README.md
index 6c846ffd..c9d87340 100644
--- a/README.md
+++ b/README.md
@@ -7,25 +7,25 @@
exo: Run your own AI cluster at home with everyday devices. Maintained by [exo labs](https://x.com/exolabs).
-
-[](https://github.com/exo-explore/exo/stargazers)
-[](https://www.apache.org/licenses/LICENSE-2.0.html)
-
-
+
+ 
+ 
+ 
+
---
-EXO connects all your devices into an AI cluster. It pools together the resources of all your devices in order to run large models. Not only does EXO enable running models larger than would fit on a single device, but with [day-0 support for RDMA over Thunderbolt](https://x.com/exolabs/status/2001817749744476256?s=20), makes models run faster as you add more devices.
+exo connects all your devices into an AI cluster. Not only does exo enable running models larger than would fit on a single device, but with [day-0 support for RDMA over Thunderbolt](https://x.com/exolabs/status/2001817749744476256?s=20), makes models run faster as you add more devices.
## Features
-- **Automatic Device Discovery**: Devices running EXO automatically discover each other - no manual configuration.
-- **RDMA over Thunderbolt**: EXO ships with [day-0 support for RDMA over Thunderbolt 5](https://x.com/exolabs/status/2001817749744476256?s=20), enabling 99% reduction in latency between devices.
-- **Topology-Aware Auto Parallel**: EXO figures out the best way to split your model across all available devices based on a realtime view of your device topology. It takes into account device resources and network latency/bandwidth between each link.
-- **Tensor Parallelism**: EXO supports sharding models, for up to 1.8x speedup on 2 devices and 3.2x speedup on 4 devices.
-- **MLX Support**: EXO uses [MLX](https://github.com/ml-explore/mlx) as an inference backend and [MLX distributed](https://ml-explore.github.io/mlx/build/html/usage/distributed.html) for distributed communication.
+- **Automatic Device Discovery**: Devices running exo automatically discover each other - no manual configuration.
+- **RDMA over Thunderbolt**: exo ships with [day-0 support for RDMA over Thunderbolt 5](https://x.com/exolabs/status/2001817749744476256?s=20), enabling 99% reduction in latency between devices.
+- **Topology-Aware Auto Parallel**: exo figures out the best way to split your model across all available devices based on a realtime view of your device topology. It takes into account device resources and network latency/bandwidth between each link.
+- **Tensor Parallelism**: exo supports sharding models, for up to 1.8x speedup on 2 devices and 3.2x speedup on 4 devices.
+- **MLX Support**: exo uses [MLX](https://github.com/ml-explore/mlx) as an inference backend and [MLX distributed](https://ml-explore.github.io/mlx/build/html/usage/distributed.html) for distributed communication.
## Benchmarks
@@ -57,32 +57,32 @@ EXO connects all your devices into an AI cluster. It pools together the resource
## Quick Start
-Devices running EXO automatically discover each other, without needing any manual configuration. Each device provides an API and a dashboard for interacting with your cluster (runs at `http://localhost:52415`).
+Devices running exo automatically discover each other, without needing any manual configuration. Each device provides an API and a dashboard for interacting with your cluster (runs at `http://localhost:52415`).
-There are two ways to run EXO:
+There are two ways to run exo:
### Run from Source (Mac & Linux)
-Clone the repo, build the dashboard, and run EXO:
+Clone the repo, build the dashboard, and run exo:
```bash
-cd dashboard && npm install && npm run build
+# Clone exo
+git clone https://github.com/exo-explore/exo
+
+# Build dashboard
+cd exo/dashboard && npm install && npm run build && cd ..
+
+# Run exo
uv run exo
```
-**One-liner:**
-
-```bash
-git clone https://github.com/exo-explore/exo && cd exo/dashboard && npm i && npm run build && cd .. && uv run exo
-```
-
----
+This starts the exo dashboard and API at http://localhost:52415/
### macOS App
-EXO ships a macOS app that runs in the background on your Mac.
+exo ships a macOS app that runs in the background on your Mac.
-
+
The macOS app requires macOS Tahoe 26.2 or later.
@@ -90,14 +90,113 @@ Download the latest build here: [EXO-latest.dmg](https://assets.exolabs.net/EXO-
The app will ask for permission to modify system settings and install a new Network profile. Improvements to this are being worked on.
+### Using the API
+
+If you prefer to interact with exo via the API, here is a complete example using `curl` and a real, small model (`mlx-community/Llama-3.2-1B-Instruct-4bit`). All API endpoints and request shapes match `src/exo/master/api.py` and `src/exo/shared/types/api.py`.
+
+---
+
+**1. Preview instance placements**
+
+Obtain valid deployment placements for your model. This helps you choose a valid configuration:
+
+```bash
+curl "http://localhost:52415/instance/previews?model_id=mlx-community/Llama-3.2-1B-Instruct-4bit"
+```
+
+Sample response:
+
+```json
+{
+ "previews": [
+ {
+ "model_id": "mlx-community/Llama-3.2-1B-Instruct-4bit",
+ "sharding": "Pipeline",
+ "instance_meta": "MlxRing",
+ "instance": {...},
+ "memory_delta_by_node": {"local": 734003200},
+ "error": null
+ }
+ // ...possibly more placements...
+ ]
+}
+```
+
+This will return all valid placements for this model. Pick a placement that you like.
+
+---
+
+**2. Create a model instance**
+
+Send a POST to `/instance` with your placement in the `instance` field (the full payload must match types as in `CreateInstanceParams`):
+
+```bash
+curl -X POST http://localhost:52415/instance \
+ -H 'Content-Type: application/json' \
+ -d '{
+ "instance": {
+ "model_id": "mlx-community/Llama-3.2-1B-Instruct-4bit",
+ "instance_meta": "MlxRing",
+ "sharding": "Pipeline",
+ "min_nodes": 1
+ }
+ }'
+```
+
+Sample response:
+
+```json
+{
+ "message": "Command received.",
+ "command_id": "e9d1a8ab-...."
+}
+```
+
+---
+
+**3. Issue a chat completion**
+
+Now, make a POST to `/v1/chat/completions` (the same format as OpenAI's API):
+
+```bash
+curl -N -X POST http://localhost:52415/v1/chat/completions \
+ -H 'Content-Type: application/json' \
+ -d '{
+ "model": "mlx-community/Llama-3.2-1B-Instruct-4bit",
+ "messages": [
+ {"role": "user", "content": "What is Llama 3.2 1B?"}
+ ]
+ }'
+```
+
+You will receive a streamed or non-streamed JSON reply.
+
+---
+
+**4. Delete the instance**
+
+When you're done, delete the instance by its ID (find it via `/state` or `/instance` endpoints):
+
+```bash
+curl -X DELETE http://localhost:52415/instance/YOUR_INSTANCE_ID
+```
+
+**Tip:**
+
+- List all models: `curl http://localhost:52415/models`
+- Inspect instance IDs and deployment state: `curl http://localhost:52415/state`
+
+For further details, see API types and endpoints in `src/exo/master/api.py`.
+
+
---
## Hardware Accelerator Support
-On macOS, EXO uses the GPU. On Linux, EXO currently runs on CPU. We are working on extending hardware accelerator support. If you'd like support for a new hardware platform, please search for an existing feature request and add a thumbs up so we know what hardware is important to the community.
+On macOS, exo uses the GPU. On Linux, exo currently runs on CPU. We are working on extending hardware accelerator support. If you'd like support for a new hardware platform, please [search for an existing feature request](https://github.com/exo-explore/exo/issues) and add a thumbs up so we know what hardware is important to the community.
---
## Contributing
-See [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines on how to contribute to EXO.
+See [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines on how to contribute to exo.