diff --git a/nvidia/dgx-dashboard/README.md b/nvidia/dgx-dashboard/README.md
index 60971ad..f45db66 100644
--- a/nvidia/dgx-dashboard/README.md
+++ b/nvidia/dgx-dashboard/README.md
@@ -126,6 +126,11 @@ Verify your setup by running a simple Stable Diffusion XL image generation examp
 3. Add a new cell and paste the following code:
 
 ```python
+import warnings
+warnings.filterwarnings('ignore', message='.*cuda capability.*')
+import tqdm.auto
+tqdm.auto.tqdm = tqdm.std.tqdm
+
 from diffusers import DiffusionPipeline
 import torch
 from PIL import Image
diff --git a/nvidia/trt-llm/README.md b/nvidia/trt-llm/README.md
index 806fc5b..8b4a422 100644
--- a/nvidia/trt-llm/README.md
+++ b/nvidia/trt-llm/README.md
@@ -414,7 +414,7 @@ docker rmi nvcr.io/nvidia/tensorrt-llm/release:spark-single-gpu-dev
 
 ### Step 1. Configure network connectivity
 
-Follow the network setup instructions from the [Connect two Sparks](https://build.nvidia.com/spark/stack-sparks/stacked-sparks) playbook to establish connectivity between your DGX Spark nodes.
+Follow the network setup instructions from the [Connect two Sparks](https://build.nvidia.com/spark/connect-two-sparks/stacked-sparks) playbook to establish connectivity between your DGX Spark nodes.
 
 This includes:
 - Physical QSFP cable connection