diff --git a/README.md b/README.md
index 241decd..f26f8cc 100644
--- a/README.md
+++ b/README.md
@@ -17,3 +17,111 @@ It does two primary things:
 - **Generate models from LinkML** - extend the LinkML pydantic generator to create models that 
   that use the [linkml-arrays](https://github.com/linkml/linkml-arrays) syntax
 
+## Parameterized Arrays
+
+Arrays use the npytying syntax:
+
+```python
+from typing import Union
+from pydantic import BaseModel
+from numpydantic import NDArray, Shape, UInt8, Float, Int
+
+class Image(BaseModel):
+    """
+    Data values. Data can be in 1-D, 2-D, 3-D, or 4-D. The first dimension should always represent time. This can also be used to store binary data (e.g., image frames). This can also be a link to data stored in an external file.
+    """
+    array: Union[
+        NDArray[Shape["* x, * y"], UInt8],
+        NDArray[Shape["* x, * y, 3 rgb"], UInt8],
+        NDArray[Shape["* x, * y, 4 rgba"], UInt8],
+        NDArray[Shape["* t, * x, * y, 3 rgb"], UInt8],
+        NDArray[Shape["* t, * x, * y, 4 rgba"], Float]
+    ]
+```
+
+### Validation:
+
+```python
+import numpy as np
+# works
+frame_gray = Image(array=np.ones((1280, 720), dtype=np.uint8))
+frame_rgb  = Image(array=np.ones((1280, 720, 3), dtype=np.uint8))
+frame_rgba = Image(array=np.ones((1280, 720, 4), dtype=np.uint8))
+video_rgb  = Image(array=np.ones((100, 1280, 720, 3), dtype=np.uint8))
+
+# fails
+wrong_n_dimensions = Image(array=np.ones((1280,), dtype=np.uint8))
+wrong_shape = Image(array=np.ones((1280,720,10), dtype=np.uint8))
+wrong_type = Image(array=np.ones((1280,720,3), dtype=np.float64))
+
+# shapes and types are checked together
+float_video = Image(array=np.ones((100, 1280, 720, 4),dtype=float))
+wrong_shape_float_video = Image(array=np.ones((100, 1280, 720, 3),dtype=float))
+```
+
+### JSON schema generation:
+
+```python
+class MyArray(BaseModel):
+  array: NDArray[Shape["2 x, * y, 4 z"], Float]
+```
+
+```python
+>>> print(json.dumps(MyArray.model_json_schema(), indent=2))
+```
+
+```json
+{
+  "properties": {
+    "array": {
+      "items": {
+        "items": {
+          "items": {
+            "type": "number"
+          },
+          "maxItems": 4,
+          "minItems": 4,
+          "type": "array"
+        },
+        "type": "array"
+      },
+      "maxItems": 2,
+      "minItems": 2,
+      "title": "Array",
+      "type": "array"
+    }
+  },
+  "required": [
+    "array"
+  ],
+  "title": "MyArray",
+  "type": "object"
+}
+```
+
+### Serialization
+
+```python
+class SmolArray(BaseModel):
+    array: NDArray[Shape["2 x, 2 y"], Int]
+
+class BigArray(BaseModel):
+    array: NDArray[Shape["1000 x, 1000 y"], Int]
+```
+
+Serialize small arrays as lists of lists, and big arrays as a b64-encoded blosc compressed string
+
+```python
+>>> smol = SmolArray(array=np.array([[1,2],[3,4]], dtype=int))
+>>> big = BigArray(array=np.random.randint(0,255,(1000,1000),int))
+
+>>> print(smol.model_dump_json())
+{"array":[[1,2],[3,4]]}
+>>> print(big.model_dump_json())
+{
+  "array": "( long b64 encoded string )",
+  "shape": [1000, 1000],
+  "dtype": "int64",
+  "unpack_fns": ["base64.b64decode", "blosc2.unpack_array2"],
+}
+```
diff --git a/numpydantic/__init__.py b/numpydantic/__init__.py
index ad62b4a..28f040a 100644
--- a/numpydantic/__init__.py
+++ b/numpydantic/__init__.py
@@ -4,4 +4,9 @@ from numpydantic.monkeypatch import apply_patches
 
 apply_patches()
 
+# convenience imports for typing - finish this!
+from typing import Any
+
+from nptyping import Float, Int, Number, Shape, UInt8
+
 from numpydantic.ndarray import NDArray
diff --git a/numpydantic/ndarray.py b/numpydantic/ndarray.py
index 315d4ba..ffd3733 100644
--- a/numpydantic/ndarray.py
+++ b/numpydantic/ndarray.py
@@ -147,7 +147,8 @@ class NDArrayMeta(_NDArrayMeta, implementation="NDArray"):
     """
     Kept here to allow for hooking into metaclass, which has
     been necessary on and off as we work this class into a stable
-    state"""
+    state
+    """
 
 
 class NDArray(NPTypingType, metaclass=NDArrayMeta):