diff --git a/README.md b/README.md
index efcb12c..72c81f6 100644
--- a/README.md
+++ b/README.md
@@ -73,7 +73,7 @@ One important difference between `tree_math` and `jax.numpy` is that dot
 products in `tree_math` default to full precision on all platforms, rather
 than defaulting to bfloat16 precision on TPUs. This is useful for writing most
 numerical algorithms, and will likely be JAX's default behavior
-[in the future](https://github.com/google/jax/pull/7859).
+[in the future](https://github.com/jax-ml/jax/pull/7859).
 
 It would be nice to have a `Matrix` class to make it possible to use tree-math
 for numerical algorithms such as
@@ -86,7 +86,7 @@ feature, please comment on [this GitHub issue](https://github.com/google/tree-ma
 Here is how we could write the preconditioned conjugate gradient
 method. Notice how similar the implementation is to the [pseudocode from
 Wikipedia](https://en.wikipedia.org/wiki/Conjugate_gradient_method#The_preconditioned_conjugate_gradient_method),
-unlike the [implementation in JAX](https://github.com/google/jax/blob/b5aea7bc2da4fb5ef96c87a59bfd1486d8958dd7/jax/_src/scipy/sparse/linalg.py#L111-L121).
+unlike the [implementation in JAX](https://github.com/jax-ml/jax/blob/b5aea7bc2da4fb5ef96c87a59bfd1486d8958dd7/jax/_src/scipy/sparse/linalg.py#L111-L121).
 Both versions support arbitrary pytrees as input:
 
 ```python
diff --git a/tree_math/_src/vector.py b/tree_math/_src/vector.py
index 66fb195..b9857fc 100644
--- a/tree_math/_src/vector.py
+++ b/tree_math/_src/vector.py
@@ -104,7 +104,7 @@ def dot(left, right, *, precision="highest"):
   Note that unlike jax.numpy.dot, tree_math.dot defaults to full (highest)
   precision. This is more useful for numerical algorithms and will be the
   default for jax.numpy in the future:
-  https://github.com/google/jax/pull/7859
+  https://github.com/jax-ml/jax/pull/7859
 
   Args:
     left: left argument.