diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
index 30f430c..0c0bd85 100644
--- a/.github/workflows/pr.yaml
+++ b/.github/workflows/pr.yaml
@@ -74,3 +74,7 @@ jobs:
       - name: Run benchmarks
         run: |
           uv run --frozen benchmark.py
+
+      - name: Run examples
+        run: |
+          uv run --frozen examples.py
diff --git a/README.md b/README.md
index 77db506..f0870e2 100644
--- a/README.md
+++ b/README.md
@@ -28,6 +28,7 @@ pip3 install dltype
 ## Usage
 
 Type hints are evaluated in a context in source-code order, so any references to dimension symbols must exist before an expression is evaluated.
+Run [./examples.py](./examples.py) `uv run examples.py` to see some basic usage patterns.
 
 ## Supported syntax
 
diff --git a/examples.py b/examples.py
new file mode 100644
index 0000000..61e49b7
--- /dev/null
+++ b/examples.py
@@ -0,0 +1,102 @@
+"""Example usages."""
+
+from __future__ import annotations
+
+from collections.abc import Iterator  # noqa: TC003
+from contextlib import contextmanager
+from typing import Annotated
+
+import numpy as np
+
+import dltype
+
+
+@contextmanager
+def _hide_internal_dltype_stacktrace(name: str) -> Iterator[None]:
+    try:
+        yield
+        msg = "Expected block to raise"
+        raise RuntimeError(msg)
+    except dltype.DLTypeError as e:
+        print(f"{name}: {e.__class__.__name__}: {e}")  # noqa: T201
+
+
+"""
+Basic usage.
+"""
+
+
+@dltype.dltyped()
+def cat_1d(
+    arr1: Annotated[np.ndarray, dltype.FloatTensor["len1"]],
+    arr2: Annotated[np.ndarray, dltype.FloatTensor["len2"]],
+) -> Annotated[np.ndarray, dltype.FloatTensor["len1+len2"]]:
+    """Concatenate 2 arrays together on the first axis."""
+    return np.concatenate((arr1, arr2), axis=0)
+
+
+@dltype.dltyped()
+def fixed_size_crop(
+    arr1: Annotated[np.ndarray, dltype.FloatTensor["batch channels=3 height width"]],
+) -> Annotated[np.ndarray, dltype.FloatTensor["batch channels min(768,height) min(1024,width)"]]:
+    """Crop the top 1024x768 pixels."""
+    return arr1[..., :768, :1024]
+
+
+@dltype.dltyped()
+def warning_for_missing_annotation(
+    # >>> UserWarning: [no_annotation] is missing a DLType hint
+    no_annotation: np.ndarray,
+) -> Annotated[np.ndarray, dltype.FloatTensor["batch channels w h"]]:
+    """Crop the top 1024x768 pixels."""
+    return no_annotation
+
+
+B = dltype.VariableAxis("batch")
+C = dltype.ConstantAxis("channels", 3)
+W = dltype.VariableAxis("width")
+H = dltype.VariableAxis("height")
+N = dltype.AnonymousAxis("ndims")
+
+# Saving an annotation as a type alias for later use
+ImgShape = dltype.Shape[B, C, W, H]
+Uint8Img = dltype.UInt8Tensor[ImgShape]
+NPImgArr = Annotated[np.ndarray, Uint8Img]
+
+
+@dltype.dltyped()
+def static_shape_stack(
+    arr: Annotated[np.ndarray, dltype.IntTensor[dltype.Shape[B, C, N]]],
+    # note the B*2, resolves to 2x the input batch dimension
+) -> Annotated[np.ndarray, dltype.IntTensor[dltype.Shape[B * 2, C, N]]]:
+    """
+    Stack an array on top of itself.
+
+    Examples of using statically defined shapes.
+    Static analyzers will catch invalid shape expressions.
+    In addition to built in operators we also support ISQRT, min, and max (imported through dltype, not the python builtin).
+    """
+    return np.concatenate((arr, arr), axis=0)
+
+
+if __name__ == "__main__":
+    assert cat_1d(np.zeros((1)), np.ones((2))).shape == (3,)
+
+    with _hide_internal_dltype_stacktrace("bad dims"):
+        # >>> DLTypeNDimsError: Invalid number of dimensions, tensor=arr2 expected ndims=1 actual=2
+        cat_1d(np.zeros((1,)), np.zeros((1, 2)))
+
+    with _hide_internal_dltype_stacktrace("bad dtype"):
+        # >>> DLTypeDtypeError: Invalid dtype, tensor=arr1 expected one of (...supported float types) got=int32
+        cat_1d(np.zeros((1,), dtype=np.int32), np.zeros((1,)))
+
+    img = np.zeros((1, 3, 800, 2048))
+    fixed_size_crop(img)
+
+    with _hide_internal_dltype_stacktrace("bad channels"):
+        # >>> DLTypeShapeError: Invalid tensor shape, tensor=arr1 dim=1 expected=3 actual=1
+        fixed_size_crop(img[:, :1, ...])
+
+    fixed_size_crop(img[..., :100])
+
+    static_shape_stack(np.zeros((1, 3, 5, 5, 9), dtype=np.int32))