diff --git a/README.md b/README.md
index ba34d55e..74403006 100644
--- a/README.md
+++ b/README.md
@@ -48,6 +48,7 @@ array([0, 0, 0, 0], dtype=bfloat16)
 ```
 Importing `ml_dtypes` also registers the data types with numpy, so that they may
 be referred to by their string name:
+
 ```python
 >>> np.dtype('bfloat16')
 dtype(bfloat16)
@@ -126,6 +127,7 @@ If you're exploring the use of low-precision dtypes in your code, you should be
 careful to anticipate when the precision loss might lead to surprising results.
 One example is the behavior of aggregations like `sum`; consider this `bfloat16`
 summation in NumPy (run with version 1.24.2):
+
 ```python
 >>> from ml_dtypes import bfloat16
 >>> import numpy as np
@@ -137,17 +139,20 @@ summation in NumPy (run with version 1.24.2):
 The true sum should be close to 5000, but numpy returns exactly 256: this is
 because `bfloat16` does not have the precision to increment `256` by values less than
 `1`:
+
 ```python
 >>> bfloat16(256) + bfloat16(1)
 256
 ```
 After 256, the next representable value in bfloat16 is 258:
+
 ```python
 >>> np.nextafter(bfloat16(256), bfloat16(np.inf))
 258
 ```
 For better results you can specify that the accumulation should happen in a
 higher-precision type like `float32`:
+
 ```python
 >>> vals.sum(dtype='float32').astype(bfloat16)
 4992
@@ -155,6 +160,7 @@ higher-precision type like `float32`:
 In contrast to NumPy, projects like [JAX](http://jax.readthedocs.io/) which support
 low-precision arithmetic more natively will often do these kinds of higher-precision
 accumulations automatically:
+
 ```python
 >>> import jax.numpy as jnp
 >>> jnp.array(vals).sum()