diff --git a/.vitepress/config.mts b/.vitepress/config.mts
index 3a85b02..8cb5c65 100644
--- a/.vitepress/config.mts
+++ b/.vitepress/config.mts
@@ -95,7 +95,7 @@ export default defineConfig({
           text: 'FAQs',
           collapsed: false,
           items: [
-            { text: 'pgvecto.rs vs. pgvector', link: '/faqs/comparison-pgvector' },
+            // { text: 'pgvecto.rs vs. pgvector', link: '/faqs/comparison-pgvector' },
             { text: 'pgvecto.rs vs. specialized vectordb', link: '/faqs/comparison-with-specialized-vectordb' },
           ]
         },
diff --git a/src/faqs/comparison-pgvector.md b/src/faqs/comparison-pgvector.md.txt
similarity index 100%
rename from src/faqs/comparison-pgvector.md
rename to src/faqs/comparison-pgvector.md.txt
diff --git a/src/getting-started/overview.md b/src/getting-started/overview.md
index b70ef8a..a200c21 100644
--- a/src/getting-started/overview.md
+++ b/src/getting-started/overview.md
@@ -8,25 +8,14 @@ pgvecto.rs is a Postgres extension that provides vector similarity search functi
 
 ## Why use pgvecto.rs
 
-- 💃 **Easy to use**: pgvecto.rs is a Postgres extension, which means that you can use it directly within your existing database. This makes it easy to integrate into your existing workflows and applications.
-- 🔗 **Async indexing**: pgvecto.rs's index is asynchronously constructed by the background threads and does not block insertions and always ready for new queries.
-- 🥅 **Filtering**: pgvecto.rs supports filtering. You can set conditions when searching or retrieving points. This is the missing feature of other postgres extensions.
-- 🧮 **Quantization**: pgvecto.rs supports scalar quantization and product qutization up to 64x.
-- 🦀 **Rewrite in Rust**: Rust's strict compile-time checks ensure memory safety, reducing the risk of bugs and security issues commonly associated with C extensions.
-
-## Comparison with pgvector
-
-|                                             | pgvecto.rs                                             | pgvector                |
-| ------------------------------------------- | ------------------------------------------------------ | ----------------------- |
-| Transaction support                         | ✅                                                      | ⚠️                       |
-| Sufficient Result with Delete/Update/Filter | ✅                                                      | ⚠️                       |
-| Vector Dimension Limit                      | 65535                                                  | 2000                    |
-| Prefilter on HNSW                           | ✅                                                      | ❌                       |
-| Parallel HNSW Index build                   | ⚡️ Linearly faster with more cores                      | 🐌 Only single core used |
-| Async Index build                           | Ready for queries anytime and do not block insertions. | ❌                       |
-| Quantization                                | Scalar/Product Quantization                            | ❌                       |
-
-More details at [📝pgvecto.rs vs. pgvector](/faqs/comparison-pgvector.md).
+- 💃 **User-Friendly**: Effortlessly incorporate pgvecto.rs into your existing database as a Postgres extension, streamlining integration with your current workflows and applications.
+- 🥅 **Join and Filter without Limitation**: Elevate your search capabilities in pgvecto.rs with VBASE filtering. Apply any filter conditions and join with other tables, achieving high recall and low latency, a distinctive edge over other vector databases.
+- 🌓 **Efficient FP16 Support**: Optimize your data storage with pgvecto.rs, supporting FP16 vector type to cut memory and storage usage by half, and boosting throughput.
+- 🧮 **Advanced Quantization**: Utilize scalar and product quantization in pgvecto.rs for up to 64x compression. Achieve up to 4x memory savings with less than 2% recall loss with scalar quantization.
+- 🔍 **Hybrid Search**: Leverage the full-text search functionality in PostgreSQL with pgvecto.rs to search text and vector data within a single query.
+- 🔗 **Async indexing**: The pgvecto.rs index is built asynchronously by background threads, allowing non-blocking inserts and always ready for new queries.
+- ⬆️ **Extended Vector Length**: pgvecto.rs supports vector length up to 65535, suitable for the latest models.
+- 🦀 **Rust-Powered Reliability**: Rust's strict compile-time checks ensure memory safety, reducing the risk of bugs and security issues commonly associated with C extensions.
 
 ## Quick start