From 7fa8e7ce41554111f015e0d9e12a5947f454bdc7 Mon Sep 17 00:00:00 2001
From: Claire <143529280+ClaireSuperlinked@users.noreply.github.com>
Date: Tue, 30 Jan 2024 12:40:45 +0000
Subject: [PATCH] Update home.md

adding dates and blurbs to home page
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 docs/home.md | 16 +++++++++-------
 1 file changed, 9 insertions(+), 7 deletions(-)

diff --git a/docs/home.md b/docs/home.md
index f36b13da0..7751881d9 100644
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@@ -43,13 +43,15 @@ Here are some examples from the community, more coming soon!
 
 Subscribe to be updated when new ones come out & check the blog section.
 
-- [Personalized Search](https://hub.superlinked.com/personalized-search-harnessing-the-power-of-vector-embeddings)
-- [Recommender Systems](https://hub.superlinked.com/a-recommender-system-collaborative-filtering-with-sparse-metadata)
-- [Retrieval Augmented Generation](https://hub.superlinked.com/retrieval-augmented-generation)
-- [Enhancing RAG With A Multi-Agent System](https://hub.superlinked.com/enhancing-rag-with-a-multi-agent-system)
-- [Vector Embeddings In The Browser](https://hub.superlinked.com/vector-embeddings-in-the-browser)
-- [Answering Questions with Knowledge Embeddings](https://hub.superlinked.com/answering-questions-with-knowledge-graph-embeddings)
-- [Improving RAG performance with Knowledge Graphs](use_cases/knowledge_graphs.md) 
+- [01/25 - Improving RAG performance with Knowledge Graphs](use_cases/knowledge_graphs.md): Adding knowledge graph embeddings as contextual data to improve the performance of RAG.
+- [01/18 Representation Learning on Graph Structured Data](https://hub.superlinked.com/representation-learning-on-graph-structured-data): Understanding how combining KGEs and semantic embeddings can improve understanding of your solution.
+- [01/11 - VDB Feature Matrix](https://vdbs.superlinked.com/): Find the right Vector Database (VDB) for your use case.
+- [01/04 - Answering Questions with Knowledge Embeddings](https://hub.superlinked.com/answering-questions-with-knowledge-graph-embeddings): An introduction to knowledge graph embeddings and comparing the performance with LLMs.
+- [12/15 - Vector Embeddings In The Browser](https://hub.superlinked.com/vector-embeddings-in-the-browser): Creating an LLM powered application in browser with React.
+- [12/08 - Enhancing RAG With A Multi-Agent System](https://hub.superlinked.com/enhancing-rag-with-a-multi-agent-system): Using agents to improve the performance of your RAG with a multi-agent system to improve relevance, latency, and coherence.
+- [12/01 - Personalized Search](https://hub.superlinked.com/personalized-search-harnessing-the-power-of-vector-embeddings): How to use vector embeddings to create personalised search recommendations with user vectors.
+- [11/26 - Recommender Systems](https://hub.superlinked.com/a-recommender-system-collaborative-filtering-with-sparse-metadata): Building a recommender system using vector embeddings when you have sparce metadata.
+- [11/26 - Retrieval Augmented Generation](https://hub.superlinked.com/retrieval-augmented-generation): The basics of RAG, what it is and how to implement.
 
 ***