diff --git a/apps/www/_blog/2023-05-31-vecs.mdx b/apps/www/_blog/2023-05-31-vecs.mdx
index 9535b1dc250..57d839c1e25 100644
--- a/apps/www/_blog/2023-05-31-vecs.mdx
+++ b/apps/www/_blog/2023-05-31-vecs.mdx
@@ -54,7 +54,7 @@ Or, more specifically:
 create table vecs.docs (
     id text primary key,
     vec vector(3) not null,
-    metadata jsonb not null default '{}'::jsonb,
+    metadata jsonb not null default '{}'::jsonb
 );
 ```
 
@@ -124,11 +124,39 @@ or [OpenAI integration example](https://supabase.github.io/vecs/integrations_ope
 ## Deploying with Supabase
 
 As usual, if you combine [supabase/vecs](https://github.com/supabase/vecs) with the rest of Supabase, you get more than the sum of the parts.
-Once you're happy with your vecs collection, you can make it acessible to your front-end and supabase-js by exposing the collection as a view
+Once you're happy with your vecs collection, you can make it acessible to your front-end through a supabase client library by exposing the collection as a view
+in your public schema.
+
+![Expose with view](/images/blog/2023-06-01-vecs/single-database.png)
+
+For example, you could create a view
+
+```sql
+create view public.docs as
+    select
+        id,
+        embedding,
+        metadata, # Expose the metadata as JSON
+        (metadata->>'url')::text as url # Extract the URL as a string
+    from
+        vecs.docs
+```
+
+And then access it with the supabase-js client library within your applications:
+
+```js
+const { data, error } = await supabase
+  .from('docs')
+  .select('id, embedding, metadata')
+  .eq('url', '/hello-world')
+```
+
+For more deployment options, including enterprise scalable architecture, check out the [engineering for scale guide](https://supabase.com/docs/guides/ai/engineering-for-scale#simple-workloads).
+
 
 ## Future ideas
 
-Currently, `vecs` is unopinionated about where vectors come from or how their produced. While there will always be a need for generic vector
+Currently, `vecs` is unopinionated about where vectors come from or how they're produced. While there will always be a need for generic vector
 storage and querying, its becoming clear that text and image vectorization make up +95% of usage. That gives us the opportunity to
 streamline those workflows for users.
 
diff --git a/apps/www/public/images/blog/2023-06-01-vecs/single-database.png b/apps/www/public/images/blog/2023-06-01-vecs/single-database.png
new file mode 100644
index 00000000000..f129af6086c
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