diff --git a/qdrant-landing/content/edge-beta/edge-beta-faq.md b/qdrant-landing/content/edge-beta/edge-beta-faq.md
index 4b3a77e9a..c49008b50 100644
--- a/qdrant-landing/content/edge-beta/edge-beta-faq.md
+++ b/qdrant-landing/content/edge-beta/edge-beta-faq.md
@@ -6,12 +6,13 @@ questions:
   answer: Teams building AI systems that need fast, local vector search on embedded or resource-constrained devices, such as robots, mobile apps, or IoT hardware.
 - id: 1
   question: Is this available to all Qdrant users?
-  answer: Yes. Read the [Quick Start guide](https://qdrant.tech/documentation/edge/edge-quickstart/), and [view the demo](https://github.com/qdrant/qdrant-edge-demo) in GitHub.
+  answer: Yes. Read the <a href="https://qdrant.tech/documentation/edge/edge-quickstart/">Quick Start guide</a>,
+    and view the <a href="https://github.com/qdrant/qdrant-edge-demo">demo</a> on GitHub.
 - id: 2
   question: What are the minimum requirements to join the beta?
   answer: You should have a clear use case for on-device or offline vector search. Preference is given to companies working with embedded hardware or deploying agents at the edge.
 - id: 3
   question: How do I get access?
-  answer: If you're building edge-native or embedded AI systems, apply to join the beta. Or, read the [Quick Start guide](https://qdrant.tech/documentation/edge/edge-quickstart/), and [view the demo](https://github.com/qdrant/qdrant-edge-demo) in GitHub.
+  answer: If you're building edge-native or embedded AI systems, apply to join the beta. Or, read the <a href="https://qdrant.tech/documentation/edge/edge-quickstart/">Quick Start guide</a>, and view the <a href="https://github.com/qdrant/qdrant-edge-demo">demo</a> on GitHub
 sitemapExclude: true
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