diff --git a/build.gradle b/build.gradle
index 82aa48514..19b9d62a7 100644
--- a/build.gradle
+++ b/build.gradle
@@ -174,7 +174,7 @@ allprojects {
       pom {
         name = 'Feathr'
         description = 'An Enterprise-Grade, High Performance Feature Store'
-        url = 'https://github.com/linkedin/feathr'
+        url = 'https://github.com/feathr-ai/feathr'
         licenses {
           license {
             name = 'APL2'
@@ -190,7 +190,7 @@ allprojects {
         }
         scm {
           connection = 'scm:git@github.com:linkedin/feathr.git'
-          url = 'https://github.com/linkedin/feathr'
+          url = 'https://github.com/feathr-ai/feathr'
         }
       }
     }
diff --git a/docs/README.md b/docs/README.md
index acfae7e62..05f61f169 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -74,6 +74,26 @@ If you want to set up everything manually, you can checkout the [Feathr CLI depl
 | [Fraud Detection Demo](./samples/fraud_detection_demo.ipynb)                        | An example to demonstrate Feature Store using multiple data sources such as user account and transaction data.                        | Azure Synapse, Databricks, Local Spark |
 | [Product Recommendation Demo](./samples/product_recommendation_demo_advanced.ipynb) | Feathr Feature Store example notebook with a product recommendation scenario                                                          | Azure Synapse, Databricks, Local Spark |
 
+## 🏃 Getting Started with Feathr
+
+The easiest way to try out Feathr is to use the [Feathr Sandbox](https://feathr-ai.github.io/feathr/quickstart_local_sandbox.html) which is a self-contained container with most of Feathr's capabilities and you should be productive in 5 minutes. To use it, simply run this command:
+
+```bash
+# 80: Feathr UI 8000: Feathr API 8888: Jupyter 8080: VsCode 7080: Interpret
+docker run -it --rm -p 8888:8888  -p 8000:8000 -p 8081:80 -p 8080:8080 -p 7080:7080 --env API_BASE="api/v1" --env FEATHR_SANDBOX=True -e GRANT_SUDO=yes feathrfeaturestore/feathr-sandbox
+```
+
+And you can view default jupyter notebook:
+```bash
+http://localhost:8888/lab/workspaces/auto-w/tree/local_quickstart_notebook.ipynb
+```
+
+After running the Notebooks, all the features will be registered in the UI, and you can visit the Feathr UI at:
+
+```bash
+http://localhost:8081
+```
+
 ## 🛠️ Install Feathr Client Locally
 
 If you want to install Feathr client in a python environment, use this:
diff --git a/docs/dev_guide/creating_bacpac_file.md b/docs/dev_guide/creating_bacpac_file.md
index 02754184c..8bcb3f26c 100644
--- a/docs/dev_guide/creating_bacpac_file.md
+++ b/docs/dev_guide/creating_bacpac_file.md
@@ -13,7 +13,7 @@ In case you need to re-create the BACPAC file, follow these steps:
 1. Create a new, empty SQL database on Azure 
 ![Create Empty Database](../images/bacpac-sql-database.png)
 
-2. Connect to the database with a SQL client, such as Azure Data Studio, run the SQL script at https://github.com/linkedin/feathr/blob/main/registry/sql-registry/scripts/schema.sql 
+2. Connect to the database with a SQL client, such as Azure Data Studio, run the SQL script at https://github.com/feathr-ai/feathr/blob/main/registry/sql-registry/scripts/schema.sql 
 
 3. Now we created the tables required by the registry service, we can use “Export” function to create the BACPAC file 
 ![Bacpac Export UI](../images/bacpac-export.png)
diff --git a/docs/quickstart_local_sandbox.md b/docs/quickstart_local_sandbox.md
index e72b3a0c4..c964f744b 100644
--- a/docs/quickstart_local_sandbox.md
+++ b/docs/quickstart_local_sandbox.md
@@ -21,15 +21,25 @@ The Sandbox is ideal for:
 
 ## Getting Started
 
-To get started, simply run the command below. Note that the image is around 5GB so it might take a while to pull it from DockerHub.
+First, make sure you have docker installed successfully on your machine by running this command:
+
+```bash
+docker run hello-world
+```
+
+To get started using Feathr, simply run the command below. Note that the image is around 5GB so it might take a while to pull it from DockerHub.
 
 ```bash
 # 80: Feathr UI 8000: Feathr API 8888: Jupyter 8080: VsCode 7080: Interpret
-docker run -it --rm -p 8888:8888  -p 8000:8000 -p 80:80 -p 8080:8080 -p 7080:7080 --env CONNECTION_STR="Server=" --env API_BASE="api/v1" --env FEATHR_SANDBOX=True -e GRANT_SUDO=yes feathrfeaturestore/feathr-sandbox
+docker run -it --rm -p 8888:8888  -p 8000:8000 -p 8081:80 -p 8080:8080 -p 7080:7080 --env API_BASE="api/v1" --env FEATHR_SANDBOX=True -e GRANT_SUDO=yes feathrfeaturestore/feathr-sandbox
 ```
 
 It should pop up a Jupyter link in `http://127.0.0.1:8888/`. Double click on the notebook file to start the Jupyter Notebook, and you should be able to see the Feathr sample notebook. Click the triangle button on the Jupyter notebook and the whole notebook will run locally.
 
+If you see some errors like below, simply change the `-p 8081:80` part to `-p <new port>:80` so the Feathr UI will be redirected to the new port.
+
+`docker: Error response from daemon: driver failed programming external connectivity on endpoint hardcore_bose (ae107e924cddce6b942f96f2654369345b027ac82e5e44929a9f132e2af71746): Bind for 0.0.0.0:8081 failed: port is already allocated.`
+
 The default jupyter notebook is here:
 ```bash
 http://localhost:8888/lab/workspaces/auto-w/tree/local_quickstart_notebook.ipynb
@@ -41,7 +51,7 @@ http://localhost:8888/lab/workspaces/auto-w/tree/local_quickstart_notebook.ipynb
 After running the Notebooks, all the features will be registered in the UI, and you can visit the Feathr UI at:
 
 ```bash
-http://localhost:80
+http://localhost:8081
 ```