diff --git a/README.rst b/README.rst
index 55be0e583f..5343aa371d 100644
--- a/README.rst
+++ b/README.rst
@@ -137,7 +137,7 @@ Flax
       for _ in range(10):
         loss, (param_grads, other_grads) = fwd_bwd_fn(params, other_variables, inp)
 
-For a more comprehensive tutorial, check out our `Quickstart Notebook <https://github.com/NVIDIA/TransformerEngine/blob/main/docs/examples/quickstart.ipynb>`_.
+For a more comprehensive tutorial, check out our `Getting Started Guide <https://docs.nvidia.com/deeplearning/transformer-engine/user-guide/getting_started.html>`_.
 
 .. overview-end-marker-do-not-remove
 
diff --git a/examples/README.md b/examples/README.md
index 004d1631f1..782dc42f58 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -23,8 +23,6 @@ Additionally, we offer [Jupyter notebook tutorials](https://github.com/NVIDIA/Tr
   - **FP8 Weight Caching**: Avoiding redundant FP8 casting during multiple gradient accumulation steps to improve efficiency.
 - [Introduction to FP8](https://github.com/NVIDIA/TransformerEngine/blob/main/docs/examples/fp8_primer.ipynb)
   - Overview of FP8 datatypes (E4M3, E5M2), mixed precision training, delayed scaling strategies, and code examples for FP8 configuration and usage.
-- [TE Quickstart](https://github.com/NVIDIA/TransformerEngine/blob/main/docs/examples/quickstart.ipynb)
-  - Introduction to TE, building a Transformer Layer using PyTorch, and instructions on integrating TE modules like Linear and LayerNorm.
 - [Basic MNIST Example](https://github.com/NVIDIA/TransformerEngine/tree/main/examples/pytorch/mnist)
 
 # JAX
@@ -34,7 +32,9 @@ Additionally, we offer [Jupyter notebook tutorials](https://github.com/NVIDIA/Tr
   - Model Parallelism: Divide a model across multiple GPUs for parallel training.
   - Multiprocessing with Model Parallelism: Multiprocessing for model parallelism, including multi-node support and hardware affinity setup.
 - [Basic MNIST Example](https://github.com/NVIDIA/TransformerEngine/tree/main/examples/jax/mnist)
- 
+- [TE JAX Integration Tutorial](https://github.com/NVIDIA/TransformerEngine/blob/main/docs/examples/te_jax_integration.ipynb)
+  - Introduction to integrating TE into an existing JAX model framework, building a Transformer Layer, and instructions on integrating TE modules like Linear and LayerNorm.
+
 # Third party
 - [Hugging Face Accelerate + TE](https://github.com/huggingface/accelerate/tree/main/benchmarks/fp8/transformer_engine)
   - Scripts for training with Accelerate and TE. Supports single GPU, and multi-GPU via DDP, FSDP, and DeepSpeed ZeRO 1-3.