From cfbb37fd54b52f02b5c575151dfa2c5c69865f79 Mon Sep 17 00:00:00 2001 From: Maddie Duhon Date: Wed, 21 Jan 2026 10:49:22 -0500 Subject: [PATCH] links --- qdrant-landing/content/edge-beta/edge-beta-faq.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) 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 Quick Start guide, + and view the demo 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 Quick Start guide, and view the demo on GitHub sitemapExclude: true ---