diff --git a/examples/ml_dtypes/README.md b/examples/ml_dtypes/README.md
new file mode 100644
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+++ b/examples/ml_dtypes/README.md
@@ -0,0 +1,18 @@
+# ml_dtypes Examples
+
+Each sub-directory contains a self-contained example. The order in
+which the examples are to appear is specified in `order.json` (an
+array of directory names in the expected order).
+
+In each example directory you'll find:
+
+* `config.toml` - must conform to the specification outlined here:
+  https://docs.pyscript.net/latest/user-guide/configuration/ This is
+  parsed and ultimately turned into a JSON representation as part of
+  the package's API object.
+* `setup.py` - Python code for contextual and environmental setup,
+  NOT SEEN BY THE END USER, but is run before the `code.py` code is
+  evaluated. Allows us to create useful (IPython) shims, avoid
+  repeating boilerplate and whatnot.
+* `code.py` - the actual code added to the editor which forms the
+  practical example of using the package.
diff --git a/examples/ml_dtypes/bfloat16_basics/code.py b/examples/ml_dtypes/bfloat16_basics/code.py
new file mode 100644
index 0000000..3b6ded7
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+++ b/examples/ml_dtypes/bfloat16_basics/code.py
@@ -0,0 +1,53 @@
+"""
+A first look at ml_dtypes: the bfloat16 dtype.
+
+ml_dtypes provides NumPy dtype extensions used widely in machine
+learning (bfloat16, several float8 variants, sub-byte ints, etc.).
+Importing the package also registers the dtypes with NumPy, so you
+can use them anywhere a regular dtype is accepted.
+
+Docs: https://github.com/jax-ml/ml_dtypes
+"""
+import numpy as np
+from ml_dtypes import bfloat16
+from IPython.core.display import display, HTML
+
+heading("1. Creating a bfloat16 array")
+note(
+    "bfloat16 is a 16-bit float with the same exponent range as "
+    "float32 (8 bits) but only 7 mantissa bits. It trades precision "
+    "for range, which suits ML workloads."
+)
+
+# Construct a bfloat16 array. Once ml_dtypes is imported, NumPy
+# also recognizes the dtype by string name.
+weights_f32 = rng.normal(size=8).astype(np.float32)
+weights_bf16 = weights_f32.astype(bfloat16)
+
+note("The same eight values, side by side at different precisions:")
+comparison = np.empty(
+    (3, 8),
+    dtype=object,
+)
+comparison[0] = [f"{x:.6f}" for x in weights_f32]
+comparison[1] = [f"{x:.6f}" for x in weights_bf16.astype(np.float32)]
+comparison[2] = [f"{float(a) - float(b):+.2e}"
+                 for a, b in zip(weights_f32, weights_bf16.astype(np.float32))]
+rows = "".join(
+    "<tr>" + f"<th>{label}</th>" +
+    "".join(f"<td>{cell}</td>" for cell in comparison[i]) + "</tr>"
+    for i, label in enumerate(["float32", "bfloat16", "delta"])
+)
+display(HTML(f"<table>{rows}</table>"), append=True)
+
+heading("2. Memory savings")
+note(
+    f"float32 uses {weights_f32.nbytes} bytes for 8 values; "
+    f"bfloat16 uses {weights_bf16.nbytes}. Half the memory, "
+    "same exponent range."
+)
+
+heading("3. dtype is registered with NumPy")
+# After importing ml_dtypes, np.dtype('bfloat16') resolves.
+note(f"np.dtype('bfloat16') -> <code>{np.dtype('bfloat16')}</code>")
+note(f"itemsize: <code>{np.dtype('bfloat16').itemsize}</code> bytes")
diff --git a/examples/ml_dtypes/bfloat16_basics/config.toml b/examples/ml_dtypes/bfloat16_basics/config.toml
new file mode 100644
index 0000000..226c052
--- /dev/null
+++ b/examples/ml_dtypes/bfloat16_basics/config.toml
@@ -0,0 +1 @@
+packages = ["ml_dtypes", "numpy"]
diff --git a/examples/ml_dtypes/bfloat16_basics/setup.py b/examples/ml_dtypes/bfloat16_basics/setup.py
new file mode 100644
index 0000000..c3dcde2
--- /dev/null
+++ b/examples/ml_dtypes/bfloat16_basics/setup.py
@@ -0,0 +1,48 @@
+"""
+Shim IPython's display API onto PyScript so example code written in a
+Jupyter/IPython idiom runs unmodified in the browser.
+"""
+
+import sys
+import types
+import js
+from pyscript import window, HTML, display as _display
+
+js.alert = window.alert
+
+
+def display(*args, **kwargs):
+    """Wrap pyscript.display so output lands in the example target."""
+    return _display(
+        *args, **kwargs, target=__pyscript_display_target__,
+    )
+
+
+ipython = types.ModuleType("IPython")
+core = types.ModuleType("IPython.core")
+core_display = types.ModuleType("IPython.core.display")
+core_display.display = display
+core_display.HTML = HTML
+ipython.core = core
+core.display = core_display
+ipython.get_ipython = lambda: None
+ipython.display = core_display
+sys.modules["IPython"] = ipython
+sys.modules["IPython.core"] = core
+sys.modules["IPython.core.display"] = core_display
+sys.modules["IPython.display"] = core_display
+
+
+def heading(text, level=2):
+    display(HTML(f"<h{level}>{text}</h{level}>"), append=True)
+
+
+def note(text):
+    display(HTML(f"<p>{text}</p>"), append=True)
+
+
+# Package imports for this example.
+import numpy as np
+from ml_dtypes import bfloat16
+
+rng = np.random.default_rng(0)
diff --git a/examples/ml_dtypes/float8_landscape/code.py b/examples/ml_dtypes/float8_landscape/code.py
new file mode 100644
index 0000000..b47d100
--- /dev/null
+++ b/examples/ml_dtypes/float8_landscape/code.py
@@ -0,0 +1,80 @@
+# ---------------------------------------------------------------------
+# A tour of the smaller dtypes: float8 variants and int4.
+# ---------------------------------------------------------------------
+
+heading("How many distinct values does each format represent?")
+note(
+    "float8_e4m3fn favors precision (4 exponent, 3 mantissa). "
+    "float8_e5m2 favors range (5 exponent, 2 mantissa). "
+    "Both fit in a single byte, but they cover the number line "
+    "very differently."
+)
+
+# Enumerate every byte pattern for each 8-bit float type and decode
+# it to float32. This gives us the complete value set of each format.
+all_bytes = np.arange(256, dtype=np.uint8)
+
+
+def decode_all(dtype):
+    """Return the float32 values of all 256 byte patterns for a dtype."""
+    return all_bytes.view(dtype).astype(np.float32)
+
+
+e4m3_values = decode_all(float8_e4m3fn)
+e5m2_values = decode_all(float8_e5m2)
+
+# Drop NaNs for plotting.
+e4m3_finite = e4m3_values[np.isfinite(e4m3_values)]
+e5m2_finite = e5m2_values[np.isfinite(e5m2_values)]
+
+note(
+    f"float8_e4m3fn finite values: <strong>{len(e4m3_finite)}</strong>, "
+    f"max = {e4m3_finite.max():g}<br>"
+    f"float8_e5m2 finite values: <strong>{len(e5m2_finite)}</strong>, "
+    f"max = {e5m2_finite.max():g}"
+)
+
+# Plot the value distributions on a symlog axis so we can see how
+# each format spaces its representable points.
+fig, ax = plt.subplots(figsize=(9, 3.5))
+ax.scatter(e4m3_finite, np.full_like(e4m3_finite, 1.0),
+           s=10, color="steelblue", label="float8_e4m3fn")
+ax.scatter(e5m2_finite, np.full_like(e5m2_finite, 0.0),
+           s=10, color="crimson", label="float8_e5m2")
+ax.set_xscale("symlog", linthresh=1e-3)
+ax.set_yticks([0, 1])
+ax.set_yticklabels(["e5m2", "e4m3fn"])
+ax.set_xlabel("Representable value (symlog)")
+ax.set_title("Where each float8 format places its points")
+ax.legend(loc="upper center")
+ax.grid(True, axis="x", alpha=0.3)
+fig.tight_layout()
+display(fig, append=True)
+
+heading("Quantizing weights to int4")
+note(
+    "Sub-byte integers like int4 are stored unpacked (one per byte) "
+    "but only the low 4 bits matter. Here we quantize a small "
+    "weight vector to the int4 range [-8, 7]."
+)
+
+weights = rng.normal(scale=2.0, size=12).astype(np.float32)
+scale = np.max(np.abs(weights)) / 7.0
+quantized = np.round(weights / scale).clip(-8, 7).astype(int4)
+recovered = quantized.astype(np.float32) * scale
+
+rows = "<tr><th>original</th>" + "".join(
+    f"<td>{w:+.2f}</td>" for w in weights
+) + "</tr>"
+rows += "<tr><th>int4 code</th>" + "".join(
+    f"<td>{int(q)}</td>" for q in quantized
+) + "</tr>"
+rows += "<tr><th>dequantized</th>" + "".join(
+    f"<td>{w:+.2f}</td>" for w in recovered
+) + "</tr>"
+display(HTML(f"<table>{rows}</table>"), append=True)
+
+note(
+    f"Mean absolute error after round-trip: "
+    f"<strong>{np.abs(weights - recovered).mean():.3f}</strong>"
+)
diff --git a/examples/ml_dtypes/float8_landscape/config.toml b/examples/ml_dtypes/float8_landscape/config.toml
new file mode 100644
index 0000000..f940b3d
--- /dev/null
+++ b/examples/ml_dtypes/float8_landscape/config.toml
@@ -0,0 +1 @@
+packages = ["ml_dtypes", "numpy", "matplotlib"]
diff --git a/examples/ml_dtypes/float8_landscape/setup.py b/examples/ml_dtypes/float8_landscape/setup.py
new file mode 100644
index 0000000..1136bd5
--- /dev/null
+++ b/examples/ml_dtypes/float8_landscape/setup.py
@@ -0,0 +1,26 @@
+"""Setup for the third example. No IPython shim here."""
+import js
+from pyscript import window, HTML, display as _display
+
+js.alert = window.alert
+
+
+def display(*args, **kwargs):
+    return _display(
+        *args, **kwargs, target=__pyscript_display_target__,
+    )
+
+
+def heading(text, level=2):
+    display(HTML(f"<h{level}>{text}</h{level}>"), append=True)
+
+
+def note(text):
+    display(HTML(f"<p>{text}</p>"), append=True)
+
+
+import numpy as np
+import matplotlib.pyplot as plt
+from ml_dtypes import bfloat16, float8_e4m3fn, float8_e5m2, int4
+
+rng = np.random.default_rng(0)
diff --git a/examples/ml_dtypes/low_precision_summation/code.py b/examples/ml_dtypes/low_precision_summation/code.py
new file mode 100644
index 0000000..df4c575
--- /dev/null
+++ b/examples/ml_dtypes/low_precision_summation/code.py
@@ -0,0 +1,57 @@
+# ---------------------------------------------------------------------
+# Quirks of low-precision arithmetic: a cautionary tale.
+# ---------------------------------------------------------------------
+
+heading("Summing 10,000 small numbers in bfloat16")
+note(
+    "We draw 10,000 values uniformly from [0, 1). The true sum "
+    "is around 5,000. Watch what happens when bfloat16 accumulates."
+)
+
+samples = rng.uniform(size=10_000).astype(bfloat16)
+
+# Naive: accumulate in bfloat16. Once the running total reaches 256,
+# adding a value below 1 has no effect (the next representable
+# bfloat16 above 256 is 258).
+naive_sum = samples.sum()
+
+# Better: accumulate in float32 and cast back at the end.
+careful_sum = samples.sum(dtype=np.float32).astype(bfloat16)
+
+# Reference: full float32 sum.
+true_sum = samples.astype(np.float32).sum()
+
+note(
+    f"Naive bfloat16 sum: <strong>{float(naive_sum):.1f}</strong><br>"
+    f"Accumulated in float32, cast to bfloat16: "
+    f"<strong>{float(careful_sum):.1f}</strong><br>"
+    f"True (float32) sum: <strong>{float(true_sum):.1f}</strong>"
+)
+
+heading("Why? bfloat16's spacing grows with magnitude")
+note(
+    "Floating point numbers get sparser as they grow. Above 256, "
+    "consecutive bfloat16 values are 2 apart, so adding 0.5 is "
+    "indistinguishable from adding zero."
+)
+
+# Visualize the gap between adjacent bfloat16 values across magnitudes.
+magnitudes = np.array([2.0 ** k for k in range(-4, 16)], dtype=bfloat16)
+next_up = np.array(
+    [np.nextafter(m, bfloat16(np.inf)) for m in magnitudes],
+    dtype=bfloat16,
+)
+spacing = (next_up.astype(np.float32) - magnitudes.astype(np.float32))
+
+fig, ax = plt.subplots(figsize=(8, 4))
+ax.loglog(magnitudes.astype(np.float32), spacing, marker="o",
+          color="crimson", label="bfloat16 spacing")
+ax.axvline(256, color="gray", linestyle="--",
+           label="x = 256 (spacing crosses 1)")
+ax.set_xlabel("Value (x)")
+ax.set_ylabel("Distance to next bfloat16")
+ax.set_title("Bfloat16 quantization gap vs. magnitude")
+ax.legend()
+ax.grid(True, which="both", alpha=0.3)
+fig.tight_layout()
+display(fig, append=True)
diff --git a/examples/ml_dtypes/low_precision_summation/config.toml b/examples/ml_dtypes/low_precision_summation/config.toml
new file mode 100644
index 0000000..f940b3d
--- /dev/null
+++ b/examples/ml_dtypes/low_precision_summation/config.toml
@@ -0,0 +1 @@
+packages = ["ml_dtypes", "numpy", "matplotlib"]
diff --git a/examples/ml_dtypes/low_precision_summation/setup.py b/examples/ml_dtypes/low_precision_summation/setup.py
new file mode 100644
index 0000000..c1a20dd
--- /dev/null
+++ b/examples/ml_dtypes/low_precision_summation/setup.py
@@ -0,0 +1,26 @@
+"""Setup for the second example. No IPython shim here."""
+import js
+from pyscript import window, HTML, display as _display
+
+js.alert = window.alert
+
+
+def display(*args, **kwargs):
+    return _display(
+        *args, **kwargs, target=__pyscript_display_target__,
+    )
+
+
+def heading(text, level=2):
+    display(HTML(f"<h{level}>{text}</h{level}>"), append=True)
+
+
+def note(text):
+    display(HTML(f"<p>{text}</p>"), append=True)
+
+
+import numpy as np
+import matplotlib.pyplot as plt
+from ml_dtypes import bfloat16
+
+rng = np.random.default_rng(0)
diff --git a/examples/ml_dtypes/order.json b/examples/ml_dtypes/order.json
new file mode 100644
index 0000000..3a0096f
--- /dev/null
+++ b/examples/ml_dtypes/order.json
@@ -0,0 +1,5 @@
+[
+    "bfloat16_basics",
+    "low_precision_summation",
+    "float8_landscape"
+]