diff --git a/qdrant-landing/content/articles/neural-search-tutorial.md b/qdrant-landing/content/articles/neural-search-tutorial.md
index d8e94bcf7..233576dbc 100644
--- a/qdrant-landing/content/articles/neural-search-tutorial.md
+++ b/qdrant-landing/content/articles/neural-search-tutorial.md
@@ -7,7 +7,7 @@ social_preview_image: /articles_data/neural-search-tutorial/social_preview.jpg
 preview_dir: /articles_data/neural-search-tutorial/preview
 small_preview_image: /articles_data/neural-search-tutorial/tutorial.svg
 weight: 50
-author: Andrei Vasnetsov
+author: Andrey Vasnetsov
 author_link: https://blog.vasnetsov.com/
 date: 2021-06-10T10:18:00.000Z
 # aliases: [ /articles/neural-search-tutorial/ ]
@@ -222,7 +222,7 @@ qdrant_client.upload_collection(
 Now we have vectors, uploaded to the vector search engine.
 On the next step we will learn how to actually search for closest vectors.
 
-The full code for this step could be found [here](https://github.com/qdrant/qdrant_demo/blob/master/qdrant_demo/init_vector_search_index.py).
+The full code for this step could be found [here](https://github.com/qdrant/qdrant_demo/blob/master/qdrant_demo/init_collection_startups.py).
 
 ### Make a search API
 
diff --git a/qdrant-landing/content/articles/seed-round.md b/qdrant-landing/content/articles/seed-round.md
index cd173e690..e4c7f70d4 100644
--- a/qdrant-landing/content/articles/seed-round.md
+++ b/qdrant-landing/content/articles/seed-round.md
@@ -46,7 +46,7 @@ The current Qdrant ecosystem consists of excellent products to work with vector
 {{< figure src=/articles_data/seed-round/ecosystem.png caption="Qdrant Ecosystem" alt="Qdrant Vector Database Ecosystem" >}}
 
 
-Our plan for the current [open-source roadmap](https://github.com/qdrant/qdrant/blob/master/docs/roadmap/README.md) is to make billion-scale vector search affordable. Our recent release of the [Scalar Quantization](https://qdrant.tech/articles/scalar-quantization/) improves both memory usage (x4) as well as speed (x2). Upcoming [Product Quantization](https://lear.inrialpes.fr/pubs/2011/JDS11/jegou_searching_with_quantization.pdf) will introduce even another option with more memory saving. Stay tuned. 
+Our plan for the current [open-source roadmap](https://github.com/qdrant/qdrant/blob/master/docs/roadmap/README.md) is to make billion-scale vector search affordable. Our recent release of the [Scalar Quantization](https://qdrant.tech/articles/scalar-quantization/) improves both memory usage (x4) as well as speed (x2). Upcoming [Product Quantization](https://www.irisa.fr/texmex/people/jegou/papers/jegou_searching_with_quantization.pdf) will introduce even another option with more memory saving. Stay tuned. 
 
 Qdrant started more than two years ago with the mission of building a vector database powered by a well-thought-out tech stack. Using Rust as the system programming language and technical architecture decision during the development of the engine made Qdrant the leading and one of the most popular vector database solutions. 
 
diff --git a/qdrant-landing/content/documentation/integrations/autogen.md b/qdrant-landing/content/documentation/integrations/autogen.md
index 198992bbf..a88a2ae89 100644
--- a/qdrant-landing/content/documentation/integrations/autogen.md
+++ b/qdrant-landing/content/documentation/integrations/autogen.md
@@ -99,4 +99,4 @@ ragproxyagent.initiate_chat(assistant, problem=code_problem)
 
 ## Next steps
 
-Check out more Autogen [examples](https://microsoft.github.io/autogen/docs/Examples/AutoGen-AgentChat). You can find detailed documentation about AutoGen [here](https://microsoft.github.io/autogen/).
+Check out more Autogen [examples](https://microsoft.github.io/autogen/docs/Examples/AgentChat). You can find detailed documentation about AutoGen [here](https://microsoft.github.io/autogen/).