diff --git a/documentation/docs/guides/interactive-chat/index.md b/documentation/docs/guides/interactive-chat/index.mdx
similarity index 81%
rename from documentation/docs/guides/interactive-chat/index.md
rename to documentation/docs/guides/interactive-chat/index.mdx
index 97cd81656b25..607655596e9c 100644
--- a/documentation/docs/guides/interactive-chat/index.md
+++ b/documentation/docs/guides/interactive-chat/index.mdx
@@ -6,8 +6,9 @@ description: Transform text-based responses into graphical components and intera
 
 import Card from '@site/src/components/Card';
 import styles from '@site/src/components/Card/styles.module.css';
+import VideoCarousel from '@site/src/components/VideoCarousel';
 
-<h1 className={styles.pageTitle}>Rich Interactive Chat</h1>
+<h1 className={styles.pageTitle}>Rich Interactive Chat with MCP-UI</h1>
 <p className={styles.pageDescription}>
   Goose Desktop supports extensions that transform text-only responses into graphical, interactive experiences. Instead of reading through lists and descriptions, you can click, explore, and interact with UI components directly in your conversations.
 </p>
@@ -63,4 +64,21 @@ import styles from '@site/src/components/Card/styles.module.css';
       link="/blog/2025/09/08/turn-any-mcp-server-mcp-ui-compatible"
     />
   </div>
+</div>
+
+<div className={styles.categorySection}>
+  <h2 className={styles.categoryTitle}>🎥 More Videos</h2>
+
+<VideoCarousel
+  id="more-videos"
+  videos={[
+    { 
+      type: 'iframe', 
+      src: 'https://youtube.com/embed/GS-kmreZDgU',
+      title: 'Livestream - MCP-UI: The Future of Agentic Interfaces',
+      description: 'The goose team talks with MCP-UI creators about the future of visual interfaces',
+      duration: '55:32'
+    }
+  ]}
+/>
 </div>
\ No newline at end of file
diff --git a/documentation/docs/guides/subagents.md b/documentation/docs/guides/subagents.md
index 59863e9b49a5..068884c3e2db 100644
--- a/documentation/docs/guides/subagents.md
+++ b/documentation/docs/guides/subagents.md
@@ -6,6 +6,18 @@ sidebar_label: Subagents
 
 Subagents are independent instances that execute tasks while keeping your main conversation clean and focused. They bring process isolation and context preservation by offloading work to separate instances. Think of them as temporary assistants that handle specific jobs without cluttering your chat with tool execution details.
 
+<details>
+  <summary>Subagents Walkthrough</summary>
+  <iframe
+    class="aspect-ratio"
+    src="https://youtube.com/embed/Uk4TtJUykK4"
+    title="Subagents Explained"
+    frameBorder="0"
+    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
+    allowFullScreen
+  ></iframe>
+</details>
+
 ## How to Use Subagents
 
 To use subagents, ask Goose to delegate tasks using natural language. Goose automatically decides when to spawn subagents and handles their lifecycle. You can:
diff --git a/documentation/docs/tutorials/lead-worker.md b/documentation/docs/tutorials/lead-worker.md
index 310084e8c1c3..080f2b9dd09e 100644
--- a/documentation/docs/tutorials/lead-worker.md
+++ b/documentation/docs/tutorials/lead-worker.md
@@ -9,6 +9,18 @@ import TabItem from '@theme/TabItem';
 
 Goose supports a lead/worker model configuration that lets you pair two different AI models - one that's great at thinking and another that's fast at doing. This setup tackles a major pain point: premium models are powerful but expensive, while cheaper models are faster but can struggle with complex tasks. With lead/worker mode, you get the best of both worlds.
 
+<details>
+  <summary>Lead/Worker Mode Walkthrough</summary>
+  <iframe
+    class="aspect-ratio"
+    src="https://youtube.com/embed/ZyhUTsChFUw"
+    title="Lead/Worker Mode Setup and Settings Explained"
+    frameBorder="0"
+    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
+    allowFullScreen
+  ></iframe>
+</details>
+
 The lead/worker model is a smart hand-off system. The "lead" model (think: GPT-4 or Claude Opus) kicks things off, handling the early planning and big picture reasoning. Once the direction is set, Goose hands the task over to the "worker" model (like GPT-4o-mini or Claude Sonnet) to carry out the steps.
 
 If things go sideways (e.g. the worker model gets confused or keeps making mistakes), Goose notices and automatically pulls the lead model back in to recover. Once things are back on track, the worker takes over again.