diff --git a/README.md b/README.md
index c8dea370..5eceb7a9 100644
--- a/README.md
+++ b/README.md
@@ -61,24 +61,32 @@ If you found this library useful in academic research, please cite: [(arXiv link
 
 ## See also: other libraries in the JAX ecosystem
 
-[jaxtyping](https://github.com/google/jaxtyping): type annotations for shape/dtype of arrays.
+#### Always useful
 
-[Equinox](https://github.com/patrick-kidger/equinox): neural networks.
+[Equinox](https://github.com/patrick-kidger/equinox): neural networks and everything not already in core JAX!
+
+[jaxtyping](https://github.com/patrick-kidger/jaxtyping): type annotations for shape/dtype of arrays.
+
+#### Deep learning
 
 [Optax](https://github.com/deepmind/optax): first-order gradient (SGD, Adam, ...) optimisers.
 
-[Optimistix](https://github.com/patrick-kidger/optimistix): root finding, minimisation, fixed points, and least squares.
+[Orbax](https://github.com/google/orbax): checkpointing (async/multi-host/multi-device).
 
-[Lineax](https://github.com/google/lineax): linear solvers.
+[Levanter](https://github.com/stanford-crfm/levanter): scalable+reliable training of foundation models (e.g. LLMs).
 
-[BlackJAX](https://github.com/blackjax-devs/blackjax): probabilistic+Bayesian sampling.
+#### Scientific computing
 
-[Orbax](https://github.com/google/orbax): checkpointing (async/multi-host/multi-device).
+[Optimistix](https://github.com/patrick-kidger/optimistix): root finding, minimisation, fixed points, and least squares.
 
-[sympy2jax](https://github.com/google/sympy2jax): SymPy<->JAX conversion; train symbolic expressions via gradient descent.
+[Lineax](https://github.com/patrick-kidger/lineax): linear solvers.
 
-[Eqxvision](https://github.com/paganpasta/eqxvision): computer vision models.
+[BlackJAX](https://github.com/blackjax-devs/blackjax): probabilistic+Bayesian sampling.
 
-[Levanter](https://github.com/stanford-crfm/levanter): scalable+reliable training of foundation models (e.g. LLMs).
+[sympy2jax](https://github.com/patrick-kidger/sympy2jax): SymPy<->JAX conversion; train symbolic expressions via gradient descent.
 
 [PySR](https://github.com/milesCranmer/PySR): symbolic regression. (Non-JAX honourable mention!)
+
+#### Awesome JAX
+
+[Awesome JAX](https://github.com/n2cholas/awesome-jax): a longer list of other JAX projects.
diff --git a/docs/index.md b/docs/index.md
index 3db6fd1d..836c327d 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -49,16 +49,32 @@ Have a look at the [Getting Started](./usage/getting-started.md) page.
 
 ## See also: other libraries in the JAX ecosystem
 
-[Equinox](https://github.com/patrick-kidger/equinox): neural networks.
+#### Always useful
 
-[Optax](https://github.com/deepmind/optax): first-order gradient (SGD, Adam, ...) optimisers.
+[Equinox](https://github.com/patrick-kidger/equinox): neural networks and everything not already in core JAX!
 
-[Lineax](https://github.com/google/lineax): linear solvers and linear least squares.
+[jaxtyping](https://github.com/patrick-kidger/jaxtyping): type annotations for shape/dtype of arrays.
 
-[jaxtyping](https://github.com/google/jaxtyping): type annotations for shape/dtype of arrays.
+#### Deep learning
 
-[Eqxvision](https://github.com/paganpasta/eqxvision): computer vision models.
+[Optax](https://github.com/deepmind/optax): first-order gradient (SGD, Adam, ...) optimisers.
 
-[sympy2jax](https://github.com/google/sympy2jax): SymPy<->JAX conversion; train symbolic expressions via gradient descent.
+[Orbax](https://github.com/google/orbax): checkpointing (async/multi-host/multi-device).
 
 [Levanter](https://github.com/stanford-crfm/levanter): scalable+reliable training of foundation models (e.g. LLMs).
+
+#### Scientific computing
+
+[Optimistix](https://github.com/patrick-kidger/optimistix): root finding, minimisation, fixed points, and least squares.
+
+[Lineax](https://github.com/patrick-kidger/lineax): linear solvers.
+
+[BlackJAX](https://github.com/blackjax-devs/blackjax): probabilistic+Bayesian sampling.
+
+[sympy2jax](https://github.com/patrick-kidger/sympy2jax): SymPy<->JAX conversion; train symbolic expressions via gradient descent.
+
+[PySR](https://github.com/milesCranmer/PySR): symbolic regression. (Non-JAX honourable mention!)
+
+#### Awesome JAX
+
+[Awesome JAX](https://github.com/n2cholas/awesome-jax): a longer list of other JAX projects.## See also: other libraries in the JAX ecosystem