Merge branch 'main' into main

readme.md

@@ -103,6 +103,12 @@ This section contains libraries that are well-made and useful, but have not nece
 - [MaxText](https://github.com/google/maxtext) - A simple, performant and scalable Jax LLM written in pure Python/Jax and targeting Google Cloud TPUs. <img src="https://img.shields.io/github/stars/google/maxtext?style=social" align="center">
 - [Pax](https://github.com/google/paxml) - A Jax-based machine learning framework for training large scale models. <img src="https://img.shields.io/github/stars/google/paxml?style=social" align="center">
 - [Praxis](https://github.com/google/praxis) - The layer library for [Pax](https://github.com/google/paxml) with a goal to be usable by other JAX-based ML projects. <img src="https://img.shields.io/github/stars/google/praxis?style=social" align="center">
+- [Kernex](https://github.com/ASEM000/kernex) - Differentiable stencil decorators in JAX.
+- [purejaxrl](https://github.com/luchris429/purejaxrl) - Vectorisable, end-to-end RL algorithms in JAX. <img src="https://img.shields.io/github/stars/luchris429/purejaxrl?style=social" align="center">
+- [Lorax](https://github.com/davisyoshida/lorax) - Automatically apply LoRA to JAX models (Flax, Haiku, etc.)
+- [SCICO](https://github.com/lanl/scico) - Scientific computational imaging in JAX. <img src="https://img.shields.io/github/stars/lanl/scico?style=social" align="center">
+- [BrainPy](https://github.com/brainpy/BrainPy) - Brain Dynamics Programming in Python. <img src="https://img.shields.io/github/stars/brainpy/BrainPy?style=social" align="center">
+- [OTT-JAX](https://github.com/ott-jax/ott) - Optimal transport tools in JAX. <img src="https://img.shields.io/github/stars/ott-jax/ott?style=social" align="center">
 
 <a name="models-and-projects" />
 
@@ -236,6 +242,7 @@ This section contains papers focused on JAX (e.g. JAX-based library whitepapers,
 - [Writing a Training Loop in JAX + FLAX by Saurav Maheshkar and Soumik Rakshit](https://wandb.ai/jax-series/simple-training-loop/reports/Writing-a-Training-Loop-in-JAX-FLAX--VmlldzoyMzA4ODEy) - A tutorial on writing a simple end-to-end training and evaluation pipeline in JAX, Flax and Optax.
 - [Implementing NeRF in JAX by Soumik Rakshit and Saurav Maheshkar](https://wandb.ai/wandb/nerf-jax/reports/Implementing-NeRF-in-JAX--VmlldzoxODA2NDk2?galleryTag=jax) - A tutorial on 3D volumetric rendering of scenes represented by Neural Radiance Fields in JAX.
 - [Deep Learning tutorials with JAX+Flax by Phillip Lippe](https://uvadlc-notebooks.readthedocs.io/en/latest/tutorial_notebooks/JAX/tutorial2/Introduction_to_JAX.html) - A series of notebooks explaining various deep learning concepts, from basics (e.g. intro to JAX/Flax, activiation functions) to recent advances (e.g., Vision Transformers, SimCLR), with translations to PyTorch.
+- [Achieving 4000x Speedups with PureJaxRL](https://chrislu.page/blog/meta-disco/) - A blog post on how JAX can massively speedup RL training through vectorisation.
 
 <a name="books" />