diff --git a/pgml-dashboard/content/blog/announcing-hnsw-support-in-our-sdk.md b/pgml-dashboard/content/blog/announcing-hnsw-support-in-our-sdk.md
new file mode 100644
index 000000000..fb64e1fb4
--- /dev/null
+++ b/pgml-dashboard/content/blog/announcing-hnsw-support-in-our-sdk.md
@@ -0,0 +1,147 @@
+---
+author: Silas Marvin
+description: HNSW indexing is the latest upgrade in vector recall performance. In this post we announce our updated SDK that utilizes HNSW indexing to give world class performance in vector search.
+image: https://postgresml.org/dashboard/static/images/blog/announcing_hnsw_support.webp
+image_alt: HNSW provides a significant improvement in recall speed compared to IVFFlat
+---
+
+# Announcing HNSW Support in Our SDK
+
+
+
+
+
Silas Marvin
+
September 21, 2023
+
+
+HNSW (hierarchical navigable small worlds) is an indexing method that greatly improves vector recall
+
+## Introducing HNSW
+
+Underneath the hood our SDK utilizes [pgvector](https://github.com/pgvector/pgvector) to store, index, and recall vectors. Up until this point our SDK used IVFFlat indexing to divide vectors into lists, search a subset of those lists, and return the closest vector matches.
+
+While the IVFFlat indexing method is fast, it is not as fast as HNSW. Thanks to the latest update of [pgvector](https://github.com/pgvector/pgvector) our SDK now utilizes HNSW indexing, creating multi-layer graphs instead of lists and removing the required training step IVFFlat imposed.
+
+The results are not disappointing.
+
+## Comparing HNSW and IVFFlat
+
+In one of our previous posts: [Tuning vector recall while generating query embeddings in the database](/blog/tuning-vector-recall-while-generating-query-embeddings-in-the-database) we were working on a dataset with over 5 million Amazon Movie Reviews, and after embedding the reviews, performed semantic similarity search to get the closest 5 reviews.
+
+Let's run that query again:
+
+!!! generic
+
+!!! code_block time="89.118 ms"
+
+```postgresql
+WITH request AS (
+ SELECT pgml.embed(
+ 'intfloat/e5-large',
+ 'query: Best 1980''s scifi movie'
+ )::vector(1024) AS embedding
+)
+
+SELECT
+ id,
+ 1 - (
+ review_embedding_e5_large <=> (
+ SELECT embedding FROM request
+ )
+ ) AS cosine_similarity
+FROM pgml.amazon_us_reviews
+ORDER BY review_embedding_e5_large <=> (SELECT embedding FROM request)
+LIMIT 5;
+```
+
+!!!
+
+!!! results
+
+| review_body | product_title | star_rating | total_votes | cosine_similarity
+| ------------------------------------------------- | ------------------------------------------------------------- | ------------- | ----------- | ------------------ |
+| best 80s SciFi movie ever | The Adventures of Buckaroo Banzai Across the Eighth Dimension | 5 | 1 | 0.9495371273162286 |
+| the best of 80s sci fi horror! | The Blob | 5 | 2 | 0.9097434758143605 |
+| Three of the best sci-fi movies of the seventies | Sci-Fi: Triple Feature (BD) [Blu-ray] | 5 | 0 | 0.9008723412875651 |
+| best sci fi movie ever | The Day the Earth Stood Still (Special Edition) [Blu-ray] | 5 | 2 | 0.8943620968858654 |
+| Great Science Fiction movie | Bloodsport / Timecop (Action Double Feature) [Blu-ray] | 5 | 0 | 0.894282454374093 |
+
+!!!
+
+!!!
+
+This query utilized IVFFlat indexing and queried through over 5 million rows in 89.118ms. Pretty fast!
+
+Let's drop our IVFFlat index and create an HNSW index.
+
+!!! generic
+
+!!! code_block time="10255099.233 ms (02:50:55.099)"
+
+```postgresql
+DROP INDEX index_amazon_us_reviews_on_review_embedding_e5_large;
+CREATE INDEX CONCURRENTLY ON pgml.amazon_us_reviews USING hnsw (review_embedding_e5_large vector_cosine_ops);
+```
+
+!!!
+
+!!! results
+
+|CREATE INDEX|
+|------------|
+
+!!!
+
+!!!
+
+Now let's try the query again utilizing the new HNSW index we created.
+
+!!! generic
+
+!!! code_block time="17.465 ms"
+
+```postgresql
+WITH request AS (
+ SELECT pgml.embed(
+ 'intfloat/e5-large',
+ 'query: Best 1980''s scifi movie'
+ )::vector(1024) AS embedding
+)
+
+SELECT
+ id,
+ 1 - (
+ review_embedding_e5_large <=> (
+ SELECT embedding FROM request
+ )
+ ) AS cosine_similarity
+FROM pgml.amazon_us_reviews
+ORDER BY review_embedding_e5_large <=> (SELECT embedding FROM request)
+LIMIT 5;
+```
+
+!!!
+
+!!! results
+
+| review_body | product_title | star_rating | total_votes | cosine_similarity
+| --------------------------------- | ------------------------------------------------------------- | ------------- | ----------- | ------------------ |
+| best 80s SciFi movie ever | The Adventures of Buckaroo Banzai Across the Eighth Dimension | 5 | 1 | 0.9495371273162286 |
+| the best of 80s sci fi horror! | The Blob | 5 | 2 | 0.9097434758143605 |
+| One of the Better 80's Sci-Fi | Krull (Special Edition) | 3 | 5 | 0.9093884940741694 |
+| Good 1980s movie | Can't Buy Me Love | 4 | 0 | 0.9090294438721961 |
+| great 80's movie | How I Got Into College | 5 | 0 | 0.9016508795301296 |
+
+!!!
+
+!!!
+
+Not only are the results better (the `cosine_similarity` is higher overall), but HNSW is over 5x faster, reducing our search and embedding time to 17.465ms.
+
+This is a massive upgrade to the recall speed utilized by our SDK and greatly improves overall performance.
+
+For a deeper dive into HNSW checkout [Jonathan Katz's excellent article on HNSW in pgvector](https://jkatz05.com/post/postgres/pgvector-hnsw-performance/).
diff --git a/pgml-dashboard/content/blog/tuning-vector-recall-while-generating-query-embeddings-in-the-database.md b/pgml-dashboard/content/blog/tuning-vector-recall-while-generating-query-embeddings-in-the-database.md
index f70054f8f..be46ec4bd 100644
--- a/pgml-dashboard/content/blog/tuning-vector-recall-while-generating-query-embeddings-in-the-database.md
+++ b/pgml-dashboard/content/blog/tuning-vector-recall-while-generating-query-embeddings-in-the-database.md
@@ -144,7 +144,7 @@ SELECT
)
) AS cosine_similarity
FROM pgml.amazon_us_reviews
-ORDER BY cosine_similarity
+ORDER BY review_embedding_e5_large <=> (SELECT embedding FROM request)
LIMIT 5;
```
diff --git a/pgml-dashboard/src/api/docs.rs b/pgml-dashboard/src/api/docs.rs
index 6245a0d2f..e256adce4 100644
--- a/pgml-dashboard/src/api/docs.rs
+++ b/pgml-dashboard/src/api/docs.rs
@@ -80,6 +80,8 @@ async fn blog_handler<'a>(path: PathBuf, cluster: &Cluster) -> Result -->
<% } %>
- Blog
+ Blog
diff --git a/pgml-dashboard/src/components/navbar_web_app/template.html b/pgml-dashboard/src/components/navbar_web_app/template.html
index c716e6d9e..72f691e58 100644
--- a/pgml-dashboard/src/components/navbar_web_app/template.html
+++ b/pgml-dashboard/src/components/navbar_web_app/template.html
@@ -54,7 +54,7 @@
- Blog
+ Blog
<% if !account_management_nav.links.is_empty() { %>
@@ -82,7 +82,7 @@
<% if !standalone_dashboard { %>
diff --git a/pgml-dashboard/static/images/blog/announcing_hnsw_support.webp b/pgml-dashboard/static/images/blog/announcing_hnsw_support.webp
new file mode 100644
index 000000000..248a08733
Binary files /dev/null and b/pgml-dashboard/static/images/blog/announcing_hnsw_support.webp differ
diff --git a/pgml-dashboard/templates/layout/nav/top.html b/pgml-dashboard/templates/layout/nav/top.html
index affb60dc2..1d0c47d24 100644
--- a/pgml-dashboard/templates/layout/nav/top.html
+++ b/pgml-dashboard/templates/layout/nav/top.html
@@ -23,7 +23,7 @@
-->
<% } %>
- Blog
+ Blog
diff --git a/pgml-dashboard/templates/layout/nav/top_web_app.html b/pgml-dashboard/templates/layout/nav/top_web_app.html
index e9d62aab6..2035ad186 100644
--- a/pgml-dashboard/templates/layout/nav/top_web_app.html
+++ b/pgml-dashboard/templates/layout/nav/top_web_app.html
@@ -53,7 +53,7 @@
- Blog
+ Blog
<% if !account_management_nav.links.is_empty() { %>
@@ -91,7 +91,7 @@
<% if !standalone_dashboard { %>
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