diff --git a/QPod/docs/intro-cn.md b/QPod/docs/intro-cn.md
index 2fb0ad3..df2292a 100644
--- a/QPod/docs/intro-cn.md
+++ b/QPod/docs/intro-cn.md
@@ -4,11 +4,11 @@ sidebar_position: 02
 
 # QPod使用指引
 
-AI/数据科学的瑞士军刀——QPod提供了一站式、开箱即用、可自由定制的，基于容器的、开源AI/数据科学开发、分析工具。
-
 ## AI/数据科学的瑞士军刀
 
-QPod将常见的、最新的开放数据科学环境和工具封装成为了docker镜像，你无需进行繁琐的环境配置即可快速开始AI/数据科学的工程，同时能够方便地复现与分享您的研究工作。您可以在QPod中使用Jupyter Notebook/JupyterLab中运行Python, R, OpenJDK, NodeJS, Go, Julia, Octave等语言，QPod也包含了VS Code, R-Studio等工具。使用QPod，您可以：
+AI/数据科学的瑞士军刀——QPod提供了一站式、开箱即用、可自由定制的，基于容器的、开源AI/数据科学开发、分析工具。
+
+QPod将常见的、最新的开放数据科学环境和工具封装成为了容器镜像，你无需进行繁琐的环境配置即可快速开始AI/数据科学的工程，同时能够方便地复现与分享您的研究工作。您可以在QPod中使用Jupyter Notebook/JupyterLab中运行Python, R, OpenJDK, NodeJS, Go, Julia, Rust等语言，QPod也封装了VS Code, R-Studio等工具。使用QPod，您可以：
 
 - 📦 避免繁琐的环境配置、安装过程，QPod已经把常用的、最新的环境和工具封装在容器镜像中，您可以开箱即用；
 - 🌍 让您的工作更容易被自己/他人`复现`——QPod让科学研究和数据分析项目成为[可复现的工作流(reproducible pipelines)](https://doi.org/10.1038/d41586-018-07196-1)，这能让你更好地和同行[分享你的工作](https://doi.org/10.1038/515151a).
@@ -18,7 +18,7 @@ QPod将常见的、最新的开放数据科学环境和工具封装成为了dock
 
 ## QPod包含了什么
 
-QPod封装、整理、维护了一些列的容器镜像，这些镜像包含了常见的开放AI/数据科学语言环境和安装包：Ptyhon, R, OpenJDK, NodeJS, Go, Julia, Octave等，同时封装了Jupyter Notebook / JupyterLab / VS Code / RStudio等IDE让用户便捷地进行开发、交互计算。QPod适用于下面的应用场景：
+QPod封装、整理、维护了一些列的容器镜像，这些镜像包含了常见的开放AI/数据科学语言环境和安装包：Ptyhon, R, OpenJDK, NodeJS, Go, Julia, Rust等，同时封装了Jupyter Notebook / JupyterLab / VS Code / RStudio等IDE让用户便捷地进行开发、交互计算。QPod适用于下面的应用场景：
 
 - 单机使用：在笔记本/台式机/工作站上使用，作为AI/数据科学开发环境；
 - 多租户使用：在服务器/集群上供多用户使用，以共享服务器计算资源（如GPU）；
@@ -26,17 +26,31 @@ QPod封装、整理、维护了一些列的容器镜像，这些镜像包含了
 
 ## 安装使用 `1-2-3-GO`🎉
 
-### 0.安装Docker
+### 0. 在服务器/笔记本上安装Docker
+
+- Linux (如: 最新版Ubuntu LTS): 直接安装 [docker-ce](https://hub.docker.com/search/?offering=community&type=edition&operating_system=linux) ( 社区版、免费 ) directly (也可以使用其他容器平台，例如podman)；
+
+- macOS: 直接安装 [docker-ce-desktop](https://hub.docker.com/editions/community/docker-ce-desktop-mac)；
 
-Linux (e.g.: Ubuntu LTS) / Windows (>=10) / macOS.
+- Windows (>=10):
 
-- CPU用户：请安装 docker-ce ( [Linux](https://hub.docker.com/search/?offering=community&type=edition&operating_system=linux) | [macOS](https://download.docker.com/mac/stable/Docker.dmg) | [Windows](https://download.docker.com/win/stable/Docker%20for%20Windows%20Installer.exe) ) 或 [docker-ee](https://hub.docker.com/search/?offering=enterprise&type=edition)
-- GPU用户：需使用Linux，在安装Docker之后还需安装[NVIDIA driver](https://github.com/NVIDIA/nvidia-docker/wiki/Frequently-Asked-Questions#how-do-i-install-the-nvidia-driver)与最新版的[nvidia-container-toolkit](https://github.com/NVIDIA/nvidia-docker#quickstart)
+  - 选项1 (推荐): 先启用WSL2并安装最新的Ubuntu发行, 再参照在Linux上安装[docker-ce](https://hub.docker.com/search/?offering=community&type=edition&operating_system=linux)的步骤即可；
+  - Option 2: 直接安装[docker-ce desktop](https://desktop.docker.com/win/stable/amd64/Docker%20Desktop%20Installer.exe)。
+
+#### GPU和cuda使用的特别提示
+
+**请安装docker-ce，直接使用yum/apt官方源安装的docker有可能不能搭配cuda使用。**
+
+在安装Docker之后还需安装下面两个组件：
+
+- [NVIDIA driver](https://github.com/NVIDIA/nvidia-docker/wiki/Frequently-Asked-Questions#how-do-i-install-the-nvidia-driver)
+- 与最新版的[nvidia-container-toolkit](https://github.com/NVIDIA/nvidia-docker#quickstart)
 
 ### 1.选择你需要的功能包和你的工作目录
 
-- 从项目主页底部的表格（QPod镜像功能列表）中，选择你需要的功能包，如果你的磁盘空间和网速满足要求则推荐CPU用户选择`full`，GPU用户选择`full-cuda`
-- 选择你的工作目录，请使用绝对路径（如`/root`,`/User/me`,`D:/work`）
+- 选择你的工作目录`WORKDIR`，请使用绝对路径（如`/root`,`/User/me`,`D:/work`）；
+
+- 从 [QPod镜像功能列表](tutorial-basics/qpod-stacks.md) 中，选择你需要的功能包，如果你的磁盘空间和网速满足要求则推荐CPU用户选择`full`，GPU用户选择`full-cuda`。
 
 ### 2.准备下载和启动容器服务
 
diff --git a/QPod/docs/intro-en.md b/QPod/docs/intro-en.md
index edd4873..3ebd2ee 100644
--- a/QPod/docs/intro-en.md
+++ b/QPod/docs/intro-en.md
@@ -6,11 +6,11 @@ sidebar_position: 01
 
 ## Your Swiss Army Knife for AI & Data Science
 
-In a nutshell, `QPod` ( [DockerHub](https://hub.docker.com/r/qpod/qpod/) | [GitHub](https://github.com/QPod/docker-images) ) is an **out-of-box Data Science / AI environment and platform** at your fingertip which you would love 💕.
+In a nutshell, `QPod` ( [DockerHub](https://hub.docker.com/u/qpod/) | [GitHub](https://github.com/QPod/) ) is an **out-of-box Data Science / AI environment and platform** at your fingertip which you would love 💕.
 
 With Docker and `QPod`, you
 
-- 📦 can start your data science / AI projects with nearly `zero configuration` - QPod puts everything about installing (latest) packages and configuring environment into standard docker images and set you free from these tedious work.
+- 📦 can start your data science / AI projects with nearly `zero configuration` - QPod puts everything about installing (latest) packages and configuring environment into standard docker images and sets you free from these tedious work.
 - 🌍 will find your work more `easy-to-reproduce` - QPod standard images make scientific research or data analysis project as [reproducible pipelines](https://doi.org/10.1038/d41586-018-07196-1) and help you [share your work with others](https://doi.org/10.1038/515151a).
 - 🆙 can easily `scale-up and scale-out` your algorithms and key innovations - QPod help you move forward smoothly from the development stage to deployment stage by re-using these images to either to provide RESTful APIs or orchestrate map/reduce operations on big data.
 
@@ -30,10 +30,26 @@ With Docker and `QPod`, you
 
 ## How to use? `1-2-3-GO`🎉
 
-### 0. Have docker installed on your laptop/server - Linux (e.g.: Ubuntu LTS) / Windows (>=10) / macOS
+### 0. Have docker installed on your laptop/server
 
-- Install **Docker >= 19.03**: `docker-ce` ( community version & free: [Linux](https://hub.docker.com/search/?offering=community&type=edition&operating_system=linux) | [macOS](https://hub.docker.com/editions/community/docker-ce-desktop-mac) | [Windows](https://desktop.docker.com/win/stable/amd64/Docker%20Desktop%20Installer.exe)   ) on your laptop/server. **Docker installed from default Ubuntu/CentOS repository probably won't work for GPU!**
-- If you want to use *NVIDIA GPUs* with `QPod`, Linux server or latest Windows WSL2 is **required**. After installing **Docker >= 19.03**, also install both the [`NVIDIA driver`](https://github.com/NVIDIA/nvidia-docker/wiki/Frequently-Asked-Questions#how-do-i-install-the-nvidia-driver) and the latest version of [`nvidia-container-toolkit`](https://github.com/NVIDIA/nvidia-docker#quickstart) to use the GPUs in containers.
+- Linux (e.g.: Ubuntu LTS): install [docker-ce](https://hub.docker.com/search/?offering=community&type=edition&operating_system=linux) ( community version & free: ) directly (or install other container services like podman).
+
+- macOS: install [docker-ce-desktop](https://hub.docker.com/editions/community/docker-ce-desktop-mac)
+
+- Windows (>=10):
+
+  - Option 1 (recommended): install WSL2 and latest Ubuntu distro, and then install [docker-ce](https://hub.docker.com/search/?offering=community&type=edition&operating_system=linux) just like on Linux.
+  - Option 2: [docker-ce desktop](https://desktop.docker.com/win/stable/amd64/Docker%20Desktop%20Installer.exe)
+
+### Special reminder for GPU and cuda users
+
+**Docker installed from default Ubuntu/CentOS repository probably won't work for GPU!**
+
+If you want to use *NVIDIA GPUs* with `QPod`, Linux server or latest Windows WSL2 is **required**.
+
+After installing **Docker >= 19.03**, also install both
+- the [`NVIDIA driver`](https://github.com/NVIDIA/nvidia-docker/wiki/Frequently-Asked-Questions#how-do-i-install-the-nvidia-driver)
+- and the latest version of [`nvidia-container-toolkit`](https://github.com/NVIDIA/nvidia-docker#quickstart) to use the GPUs in containers.
 
 ### 1. Choose the features and choose a folder on your disk
 
diff --git a/QPod/docusaurus.config.ts b/QPod/docusaurus.config.ts
index 8a6c523..b132862 100644
--- a/QPod/docusaurus.config.ts
+++ b/QPod/docusaurus.config.ts
@@ -38,14 +38,14 @@ const config: Config = {
           // Please change this to your repo.
           // Remove this to remove the "edit this page" links.
           editUrl:
-          'https://github.com/QPod/QPod.github.io/tree/main/QPod/docs/',
+          'https://github.com/QPod/QPod.github.io/tree/main/QPod/',
         },
         blog: {
           showReadingTime: true,
           // Please change this to your repo.
           // Remove this to remove the "edit this page" links.
           editUrl:
-            'https://github.com/QPod/QPod.github.io/tree/main/QPod/blog/',
+            'https://github.com/QPod/QPod.github.io/tree/main/QPod/',
         },
         theme: {
           customCss: './src/css/custom.css',