diff --git a/_blog.yml b/_blog.yml
index c4ac503a3f..e4b24fc580 100644
--- a/_blog.yml
+++ b/_blog.yml
@@ -6888,7 +6888,7 @@
     - hub
   
 - local: streaming-datasets
-  title: "Streaming datasets at scale"
+  title: "Streaming datasets: 100x More Efficient"
   author: andito
   thumbnail: /blog/assets/streaming_datasets/streaming_datasets.png
   date: Oct 27, 2025
diff --git a/streaming-datasets.md b/streaming-datasets.md
index fe9253056e..4dcd2c3576 100644
--- a/streaming-datasets.md
+++ b/streaming-datasets.md
@@ -1,5 +1,5 @@
 ---
-title: Streaming datasets: 100x More Efficient
+title: "Streaming datasets: 100x More Efficient"
 thumbnail: /blog/assets/streaming_datasets/streaming_datasets.png
 authors:
 - user: andito
@@ -9,6 +9,7 @@ authors:
 - user: merve
 ---
 
+# Streaming datasets: 100x More Efficient
 
 ## TLDR
 
@@ -20,7 +21,6 @@ authors:
 
 <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/streaming-dark.gif" width="800" height="auto" alt="Visualization of a dataset being streamed">
 
-## Streaming datasets: 100x More Efficient
 
 Loading data, especially at the terabyte scale, is a major pain in any machine learning workflow. We suffered this while training [SmolLM3](https://huggingface.co/blog/smollm3), at one point we had to wait 3 hours before each run to download enough data.