docs

docs/build/html/_sources/install.rst.txt

@@ -4,12 +4,12 @@ Build and Install
 Install from PyPI
 -----------------
 
-MLX is available at Apple's internal PyPI repository. All you have to do to use
-MLX with your own Apple silicon computer is
+MLX is available on PyPI. All you have to do to use MLX with your own Apple
+silicon computer is
 
 .. code-block:: shell
 
-    pip install apple-mlx -i https://pypi.apple.com/simple
+    pip install mlx
 
 Build from source
 -----------------
@@ -46,6 +46,17 @@ Then simply build and install it using pip:
 
    env CMAKE_BUILD_PARALLEL_LEVEL="" pip install .
 
+For developing use an editable install:
+
+.. code-block:: shell
+
+  env CMAKE_BUILD_PARALLEL_LEVEL="" pip install -e .
+
+To make sure the install is working run the tests with:
+
+.. code-block:: shell
+
+  python -m unittest discover python/tests
 
 C++ API
 ^^^^^^^

docs/build/html/_sources/quick_start.rst.txt

@@ -13,7 +13,7 @@ The main differences between MLX and NumPy are:
    and computation graph optimization.
  - **Lazy computation**: Computations in MLX are lazy. Arrays are only
    materialized when needed.
- - **Multi-device**: Operations can run on any of the suppoorted devices (CPU,
+ - **Multi-device**: Operations can run on any of the supported devices (CPU,
    GPU, ...) 
 
 The design of MLX is strongly inspired by frameworks like `PyTorch