diff --git a/qdrant-landing/content/course/multi-vector-search/module-1/late-interaction-basics.md b/qdrant-landing/content/course/multi-vector-search/module-1/late-interaction-basics.md
index 2ee915791..ce78041d7 100644
--- a/qdrant-landing/content/course/multi-vector-search/module-1/late-interaction-basics.md
+++ b/qdrant-landing/content/course/multi-vector-search/module-1/late-interaction-basics.md
@@ -16,7 +16,7 @@ This lesson introduces the late interaction paradigm - the foundation of multi-v
 
 <div class="video">
 <iframe
-  src="https://www.youtube.com/embed/dQw4w9WgXcQ"
+  src="https://www.youtube.com/embed/xK9mV7zR4pL"
   frameborder="0"
   allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
   referrerpolicy="strict-origin-when-cross-origin"
diff --git a/qdrant-landing/content/course/multi-vector-search/module-1/maxsim-distance.md b/qdrant-landing/content/course/multi-vector-search/module-1/maxsim-distance.md
index cb0ced417..cd7321a69 100644
--- a/qdrant-landing/content/course/multi-vector-search/module-1/maxsim-distance.md
+++ b/qdrant-landing/content/course/multi-vector-search/module-1/maxsim-distance.md
@@ -16,7 +16,7 @@ Understanding MaxSim is important for working with multi-vector search effective
 
 <div class="video">
 <iframe
-  src="https://www.youtube.com/embed/dQw4w9WgXcQ"
+  src="https://www.youtube.com/embed/xK9mV7zR4pL"
   frameborder="0"
   allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
   referrerpolicy="strict-origin-when-cross-origin"
diff --git a/qdrant-landing/content/course/multi-vector-search/module-1/multi-vector-in-qdrant.md b/qdrant-landing/content/course/multi-vector-search/module-1/multi-vector-in-qdrant.md
index e22d24792..1a2880e8f 100644
--- a/qdrant-landing/content/course/multi-vector-search/module-1/multi-vector-in-qdrant.md
+++ b/qdrant-landing/content/course/multi-vector-search/module-1/multi-vector-in-qdrant.md
@@ -18,7 +18,7 @@ By the end, you'll understand the key configuration parameters, know when to use
 
 <div class="video">
 <iframe
-  src="https://www.youtube.com/embed/dQw4w9WgXcQ"
+  src="https://www.youtube.com/embed/xK9mV7zR4pL"
   frameborder="0"
   allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
   referrerpolicy="strict-origin-when-cross-origin"
diff --git a/qdrant-landing/content/course/multi-vector-search/module-1/problems-multi-vector.md b/qdrant-landing/content/course/multi-vector-search/module-1/problems-multi-vector.md
index d8360815d..e4023fc0f 100644
--- a/qdrant-landing/content/course/multi-vector-search/module-1/problems-multi-vector.md
+++ b/qdrant-landing/content/course/multi-vector-search/module-1/problems-multi-vector.md
@@ -16,7 +16,7 @@ The good news: Module 3 covers optimization techniques that address many of thes
 
 <div class="video">
 <iframe
-  src="https://www.youtube.com/embed/dQw4w9WgXcQ"
+  src="https://www.youtube.com/embed/xK9mV7zR4pL"
   frameborder="0"
   allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
   referrerpolicy="strict-origin-when-cross-origin"
diff --git a/qdrant-landing/content/course/multi-vector-search/module-1/use-cases-multi-vector.md b/qdrant-landing/content/course/multi-vector-search/module-1/use-cases-multi-vector.md
index a482ff7fd..56b2c634b 100644
--- a/qdrant-landing/content/course/multi-vector-search/module-1/use-cases-multi-vector.md
+++ b/qdrant-landing/content/course/multi-vector-search/module-1/use-cases-multi-vector.md
@@ -16,7 +16,7 @@ The answer comes down to one core capability: **fine-grained matching**. In the
 
 <div class="video">
 <iframe
-  src="https://www.youtube.com/embed/dQw4w9WgXcQ"
+  src="https://www.youtube.com/embed/xK9mV7zR4pL"
   frameborder="0"
   allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
   referrerpolicy="strict-origin-when-cross-origin"
diff --git a/qdrant-landing/content/course/multi-vector-search/module-2/colpali-family.md b/qdrant-landing/content/course/multi-vector-search/module-2/colpali-family.md
index a79f4122c..36b5276a8 100644
--- a/qdrant-landing/content/course/multi-vector-search/module-2/colpali-family.md
+++ b/qdrant-landing/content/course/multi-vector-search/module-2/colpali-family.md
@@ -16,7 +16,7 @@ Let's explore what each model offers and when to use it.
 
 <div class="video">
 <iframe
-  src="https://www.youtube.com/embed/dQw4w9WgXcQ"
+  src="https://www.youtube.com/embed/xK9mV7zR4pL"
   frameborder="0"
   allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
   referrerpolicy="strict-origin-when-cross-origin"
diff --git a/qdrant-landing/content/course/multi-vector-search/module-2/how-colpali-works.md b/qdrant-landing/content/course/multi-vector-search/module-2/how-colpali-works.md
index 9a11274d8..7f4f0ba1d 100644
--- a/qdrant-landing/content/course/multi-vector-search/module-2/how-colpali-works.md
+++ b/qdrant-landing/content/course/multi-vector-search/module-2/how-colpali-works.md
@@ -16,7 +16,7 @@ Understanding ColPali's architecture helps you leverage its full potential for m
 
 <div class="video">
 <iframe
-  src="https://www.youtube.com/embed/dQw4w9WgXcQ"
+  src="https://www.youtube.com/embed/xK9mV7zR4pL"
   frameborder="0"
   allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
   referrerpolicy="strict-origin-when-cross-origin"
@@ -92,7 +92,7 @@ For text inputs, there isn't any additional preprocessing step, but it is tokeni
 
 Vision transformers, the foundation of ColPali, don't process entire images at once. Instead, they divide images into a grid of fixed-size patches - think of it like a checkerboard overlaid on your document.
 
-ColPali pipeline starts with a document. It is usually a screenshot of a single PDF page, or anything you want to encode. While you might convert a PDF page to a high-resolution screenshot to preserve visual details during conversion, **the ColPali preprocessing itself resizes images to a fixed resolution** of **448×448 pixels** before processing.
+The ColPali pipeline starts with a document. It is usually a screenshot of a single PDF page, or anything you want to encode. While you might convert a PDF page to a high-resolution screenshot to preserve visual details during conversion, **the ColPali preprocessing itself resizes images to a fixed resolution** of **448×448 pixels** before processing.
 
 This fixed-size input is then divided into patches:
 - **Patch size**: 14×14 pixels per patch
diff --git a/qdrant-landing/content/course/multi-vector-search/module-2/visual-interpretability.md b/qdrant-landing/content/course/multi-vector-search/module-2/visual-interpretability.md
index 1e08a78af..6c94805dc 100644
--- a/qdrant-landing/content/course/multi-vector-search/module-2/visual-interpretability.md
+++ b/qdrant-landing/content/course/multi-vector-search/module-2/visual-interpretability.md
@@ -16,7 +16,7 @@ This transparency is invaluable for building trust in multi-modal search systems
 
 <div class="video">
 <iframe
-  src="https://www.youtube.com/embed/dQw4w9WgXcQ"
+  src="https://www.youtube.com/embed/xK9mV7zR4pL"
   frameborder="0"
   allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
   referrerpolicy="strict-origin-when-cross-origin"
diff --git a/qdrant-landing/content/course/multi-vector-search/module-3/evaluating-pipelines.md b/qdrant-landing/content/course/multi-vector-search/module-3/evaluating-pipelines.md
index ee8c63def..1079fb80d 100644
--- a/qdrant-landing/content/course/multi-vector-search/module-3/evaluating-pipelines.md
+++ b/qdrant-landing/content/course/multi-vector-search/module-3/evaluating-pipelines.md
@@ -16,7 +16,7 @@ This lesson provides a framework for making data-driven decisions about your mul
 
 <div class="video">
 <iframe
-  src="https://www.youtube.com/embed/dQw4w9WgXcQ"
+  src="https://www.youtube.com/embed/xK9mV7zR4pL"
   frameborder="0"
   allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
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diff --git a/qdrant-landing/content/course/multi-vector-search/module-3/multi-stage-retrieval.md b/qdrant-landing/content/course/multi-vector-search/module-3/multi-stage-retrieval.md
index 3c8d73bfb..2ecfc14a9 100644
--- a/qdrant-landing/content/course/multi-vector-search/module-3/multi-stage-retrieval.md
+++ b/qdrant-landing/content/course/multi-vector-search/module-3/multi-stage-retrieval.md
@@ -16,7 +16,7 @@ Qdrant's Universal Query API makes it easy to build sophisticated multi-stage re
 
 <div class="video">
 <iframe
-  src="https://www.youtube.com/embed/dQw4w9WgXcQ"
+  src="https://www.youtube.com/embed/xK9mV7zR4pL"
   frameborder="0"
   allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
   referrerpolicy="strict-origin-when-cross-origin"
diff --git a/qdrant-landing/content/course/multi-vector-search/module-3/muvera.md b/qdrant-landing/content/course/multi-vector-search/module-3/muvera.md
index 20bfbd9b1..4a94fb38e 100644
--- a/qdrant-landing/content/course/multi-vector-search/module-3/muvera.md
+++ b/qdrant-landing/content/course/multi-vector-search/module-3/muvera.md
@@ -16,7 +16,7 @@ Understanding MUVERA is key to scaling multi-vector search to millions of docume
 
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 <iframe
-  src="https://www.youtube.com/embed/dQw4w9WgXcQ"
+  src="https://www.youtube.com/embed/xK9mV7zR4pL"
   frameborder="0"
   allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
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diff --git a/qdrant-landing/content/course/multi-vector-search/module-3/pooling-techniques.md b/qdrant-landing/content/course/multi-vector-search/module-3/pooling-techniques.md
index c0bf44370..b4497a662 100644
--- a/qdrant-landing/content/course/multi-vector-search/module-3/pooling-techniques.md
+++ b/qdrant-landing/content/course/multi-vector-search/module-3/pooling-techniques.md
@@ -16,7 +16,7 @@ Pooling is particularly effective when combined with quantization for maximum me
 
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-  src="https://www.youtube.com/embed/dQw4w9WgXcQ"
+  src="https://www.youtube.com/embed/xK9mV7zR4pL"
   frameborder="0"
   allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
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diff --git a/qdrant-landing/content/course/multi-vector-search/module-3/quantization-techniques.md b/qdrant-landing/content/course/multi-vector-search/module-3/quantization-techniques.md
index d22159b8a..1cae34d8e 100644
--- a/qdrant-landing/content/course/multi-vector-search/module-3/quantization-techniques.md
+++ b/qdrant-landing/content/course/multi-vector-search/module-3/quantization-techniques.md
@@ -16,7 +16,7 @@ Choosing the right quantization method depends on your quality requirements and
 
 <div class="video">
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-  src="https://www.youtube.com/embed/dQw4w9WgXcQ"
+  src="https://www.youtube.com/embed/xK9mV7zR4pL"
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   allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
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