fixed haystack logo & subtext; added changes from #1961

qdrant-landing/content/course/essentials/_index.md

@@ -58,7 +58,7 @@ Build the vector search skills that matter: hybrid retrieval, multivector rerank
 
 - Qdrant data modeling: points, payloads, and schemas
 - Embeddings, chunking, and similarity metrics
-- Indexing and retrieval tuning ([HNSW](https://qdrant.tech/articles/filtrable-hnsw/), filters, recall/latency)
+- Indexing and retrieval tuning (HNSW, filters, recall/latency)
 - Hybrid search with sparse + dense vectors and re-ranking
 - Performance optimization, compression, and quantization
 - Scaling, sharding/replication, and security
@@ -67,9 +67,9 @@ Build the vector search skills that matter: hybrid retrieval, multivector rerank
 
 ### The Path
 
-**Days 0–2**: Foundations. Connect to Qdrant Cloud, work with points and payloads, compute semantic similarity, chunk text, and tune HNSW for speed and recall.
+**Days 0-2**: Foundations. Connect to Qdrant Cloud, work with points and payloads, compute semantic similarity, chunk text, and tune HNSW for speed and recall.
 
-**Days 3–5**: Advanced retrieval. Combine dense and sparse signals, do hybrid search with server-side fusion, use multivectors (ColBERT) with the Universal Query API, and build recommendations.
+**Days 3-5**: Advanced retrieval. Combine dense and sparse signals, do hybrid search with server-side fusion, use multivectors (ColBERT) with the Universal Query API, and build recommendations.
 
 **Day 6**: Ship. Wire ingestion, hybrid retrieval, multivector re-ranking, and evaluation (Recall@10, MRR, latency P50/P95).
 
@@ -185,11 +185,11 @@ ML, backend, data, and search engineers building RAG, semantic search, or recomm
 
 ## Time commitment
 
-- Duration: 7 days at 1–2 hours/day + optional bonus day
+- Duration: 6 days at 1-2 hours/day + 1 optional bonus day
 - Video learning: ~3 hours
 - Hands-on learning: 4-5 hours
-- Final project: 2–4 hours
-- Total: 9–12 hours
+- Final project: 2-4 hours
+- Total: 9-12 hours
 
 
 {{< course-card 

qdrant-landing/content/course/essentials/certification/_index.md

@@ -5,4 +5,4 @@ weight: 100
 
 # Qdrant Essentials Certification
 
-Coming soon!
+Coming soon! [Click here](https://forms.gle/QPSfdMjs3QpUCtGT9) to be notified when certifications become available.

qdrant-landing/content/course/essentials/day-6/congratulations.md

@@ -15,7 +15,7 @@ You've built and shipped a complete vector search application and gained the exp
 
 You've progressed from vector search fundamentals to production-ready expertise:
 
-**Foundation Building** (Days 0-2): You mastered the core concepts of vector search, learned how similarity metrics work, and understood how [HNSW](https://qdrant.tech/articles/filtrable-hnsw/) indexing enables fast retrieval at scale.
+**Foundation Building** (Days 0-2): You mastered the core concepts of vector search, learned how similarity metrics work, and understood how HNSW indexing enables fast retrieval at scale.
 
 **Advanced Retrieval** (Days 3-5): You implemented hybrid search combining semantic and keyword signals, explored quantization for performance optimization, and mastered the Universal Query API with multivector reranking.
 
@@ -40,13 +40,13 @@ Your final project demonstrates several production-critical capabilities:
 {{< course-card
   title="Earn your Qdrant Essentials Certificate"
   image="/icons/outline/training-white.svg"
-  link="/course/certification/" >}}
+  link="/course/essentials/certification/" >}}
 Get recognized for completing Day 0–6 and the final project. Add it to your LinkedIn and portfolio.
 {{< /course-card >}}
 
 ## What's Next?
 
-**Explore Advanced Integrations**: Check out [Day 9 Partner Integrations](../../day-9/) to see how Qdrant works with leading AI frameworks and data platforms.
+**Explore Advanced Integrations**: Check out [Day 7 Partner Integrations](../../day-7/) to see how Qdrant works with leading AI frameworks and data platforms.
 
 **Join the Community**: Share your final project results and connect with other practitioners building vector search systems. The Qdrant community is always excited to see what people build.
 

qdrant-landing/content/course/essentials/day-7/_index.md

@@ -23,9 +23,9 @@ Learn about the Qdrant ecosystem and integration strategies.
 ## Choose Your Integration
 
 {{< cards-list >}}
-- icon: /courses/course-integrations/haystack.svg
+- icon: /courses/course-integrations/haystack.png
   title: Haystack
-  content: Build end-to-end NLP pipelines with Qdrant
+  content: Build end-to-end agentic pipelines with Qdrant
   link: haystack/
 
 - icon: /courses/course-integrations/tensorlake.svg

qdrant-landing/content/course/essentials/day-7/haystack.md

@@ -7,7 +7,7 @@ weight: 36
 
 # Integrating with Haystack
 
-Build end-to-end NLP pipelines with Haystack and Qdrant.
+Build end-to-end agentic pipelines with Qdrant.
 
 {{< youtube "lMinhPZufTc" >}}