diff --git a/README.md b/README.md
index 3c44d90..dc5df62 100644
--- a/README.md
+++ b/README.md
@@ -1,34 +1,81 @@
 # MOSS: MObility Simulation System
 
+[![Upload Python Package](https://github.com/tsinghua-fib-lab/moss/actions/workflows/python-publish.yml/badge.svg)](https://github.com/tsinghua-fib-lab/moss/actions/workflows/python-publish.yml)
+
 A GPU-accelerated Large-scale Open Microscopic Traffic Simulation System
 
 Website: https://moss.fiblab.net
 
+Documentation: https://moss.fiblab.net/docs/introduction
+
+API Reference: https://docs.fiblab.net/moss
+
 ## Features
 
 - **Efficient**: MOSS adopts GPU as the computational engine, which accelerates 100 times compared to existing microscopic traffic simulators, allowing rapid simulation of large-scale urban road networks.
 - **Realistic**: MOSS provides the cutting-edge AIGC method to generate globally available realistic OD matrices for travel demand generation and allows the user to quickly calibrate the simulation parameters to obtain realistic simulation results.
 - **Open**: The simulator, toolchain, and sample programs will be open-sourced on Github for community access, and we hope that more people will join in the development and application of MOSS.
 
-## Related Repositories
+## Awesome MOSS
 
-- `mosstool`: The toolchain for MOSS, [URL](https://github.com/tsinghua-fib-lab/mosstool).
-- sample programs: The sample programs for MOSS, [URL](https://github.com/tsinghua-fib-lab/moss-opt-showcases).
+The related repositories of MOSS are as follows:
+- `cityproto`: The protobuf-driven data structure definition for all city simulation projects of FIBLAB, [URL](https://github.com/tsinghua-fib-lab/cityproto). The project provides C/C++, Golang, Python, and Javascript/Typescript interfaces to access the data structure. (match **python-moss>=1.0.0**)
+- `mosstool`: The toolchain for MOSS, [URL](https://github.com/tsinghua-fib-lab/mosstool). The project is a Python packages that includes map building, traffic demand generation, SUMO format conversion, and some format conversion utilities. It is the key to build the input of MOSS. (match **python-moss>=1.0.0**)
+- `routing`: A gRPC-powered routing service with A* shortest path algorithm, [URL](https://github.com/tsinghua-fib-lab/routing). The project is the necessary service for `mosstool` when generating traffic demand or just calling `pre_route` functions to compute the routes of people. (match **python-moss>=1.0.0**)
+- `moss-replay`: A internal JS/TS package for building web-based UI to visualize the simulation results, [URL](https://github.com/tsinghua-fib-lab/moss-replay). (match **python-moss>=1.0.0**)
+- `moss-webui-frontend`: A web UI to visualize the PostgreSQL output of the MOSS project, [URL](https://github.com/tsinghua-fib-lab/moss-webui-frontend). The project is only a frontend built on `moss-replay` and provide a simple 2D visualization for debugging. Users can choose the backend URL in the web settings to connect to their backend and databases. (match **python-moss>=1.0.0**, *coming soon*)
+- `moss-webui-backend`: A web backend to provide HTTP Restful API for the database output of the MOSS project, [URL](https://github.com/tsinghua-fib-lab/moss-webui-backend). The project provides Docker images to allow users to deploy the backend easily. (match **python-moss>=1.0.0**)
+- `moss-ui`: A simple UI to visualize the AVRO output of the MOSS project by adapting `moss-replay` to the AVRO output, [URL](https://github.com/tsinghua-fib-lab/moss-ui). The project is a desktop application using web technologies, but it now faces performance challenges to manage data without a database. (match **python-moss>=1.0.0**, *unstable*)
+- MOSS based transportation optimization benchmark: A benchmark for large-scale transportation optimization benchmark based on MOSS, [URL](https://github.com/tsinghua-fib-lab/moss-benchmark). The project includes simulation performance evaluation, traffic signal control optimization, dynamic lane control optimization, tidal lane control optimization, congestion pricing optimization, and road planning optimization. (match python-moss>=0.3.3,<1.0.0, **will be updated**)
+- sample programs: The sample programs for MOSS, [URL](https://github.com/tsinghua-fib-lab/moss-opt-showcases). The project includes the showcases of MOSS for traffic signal control optimization. (match python-moss==0.2.0, *deprecated*)
 
 ## Installation
 
-### Prerequisites
+#### Prerequisites
 
 - Linux
 - CUDA 11.8
 - Python >= 3.8
 
-### Install
+#### Install
 
 ```bash
 pip install python-moss
 ```
 
+#### Very Simple Demo
+
+We assume that you have the map input `map.pb` and the person input `person.pb` generated by `mosstool`. The following code is a simple demo to run the simulation.
+```python
+from moss import Engine, Verbosity
+from moss.export import DBRecorder
+
+e = Engine(
+    "name",
+    "data/map.pb",
+    "data/person.pb",
+    0,
+    1,
+    output_dir="output", # AVRO output, local directory
+    speed_stat_interval=300, # open road status statistics
+    verbose_level=Verbosity.ALL,
+)
+recorder = DBRecorder(e) # used for PostgreSQL output
+for _ in range(3600):
+    e.next_step(1)
+    recorder.record()
+    # YOU CAN DO SOMETHING HERE
+    # persons = e.fetch_persons()
+    # ...
+# save the simulation results to the database
+recorder.save(
+    "postgres://user:password@url:port/simulation",
+    "map_db.map_coll", # map collection used for webui-backend
+    "name",
+    True,
+)
+```
+
 ## FAQ
 
 Q1: How to resolve the error `ImportError: /.../libstdc++.so.6: version 'GLIBCXX_3.4.30' not found`?
@@ -37,7 +84,17 @@ A1: Run `conda install -c conda-forge libstdcxx-ng=12` in the current conda envi
 
 ## Development
 
-#### Build
+If you are interested in the development of MOSS, you can follow the instructions below.
+
+#### Prequsites
+
+- Linux
+- CUDA 11.8 or higher
+- CMake >= 3.18
+- Python >= 3.8
+- Network that can access the GitHub repository
+
+#### Compile and Build
 
 1. Install Boost
 ```bash
@@ -51,7 +108,41 @@ rm -r boost_1_86_0
 rm boost_1_86_0.tar.gz
 ```
 
-## From v0.4 to v1.0
+2. Build MOSS
+```bash
+mkdir build
+cd build
+cmake ..
+make -j
+```
+
+3. Test MOSS: You can use the compiled MOSS executable to run the simulation to skip the python package installation.
+```bash
+./build/bin/moss -h
+
+Usage: Moss [--help] [--version] --name VAR --config VAR [--gpu VAR] [--quiet]
+
+Optional arguments:
+  -h, --help     shows help message and exits 
+  -v, --version  prints version information and exits 
+  -n, --name     name of the simulation [required]
+  -c, --config   path to config file [required]
+  --gpu          GPU device ID [nargs=0..1] [default: 0]
+  -q, --quiet
+```
+
+The config file is a YAML file that contains the simulation parameters. You can refer to the [ConfigFile](examples/config.yaml) in the repository. The meanings of the parameters can be found in the python package Engine's docstring in [engine.py](python/src/moss/engine.py).
+
+4. Install Python Package
+```bash
+pip install . -v
+```
+
+5. You can submit a pull request to the repository to contribute to the project.
+
+## Version History
+
+#### From v0.4 to v1.0
 
 That is what we change and why we change it.
 - Focus on the microscopic traffic simulation only (vehicle and pedestrian), no crowd in AOI, no bus for more clear code to support community contribution.
diff --git a/examples/config.yaml b/examples/config.yaml
new file mode 100644
index 0000000..6c45144
--- /dev/null
+++ b/examples/config.yaml
@@ -0,0 +1,20 @@
+map_file: /path/to/map.pb
+person_file: /path/to/person.pb
+start_step: 21600
+total_step: 86400
+step_interval: 1
+person_limit: -1
+speed_stat_interval: 300
+seed: 43
+x_min: -1e999
+y_min: -1e999
+x_max: 1e999
+y_max: 1e999
+verbose_level: 2
+junction_tl_policy: 1
+junction_tl_duration: 30
+junction_yellow_time: 0
+phase_pressure_coeff: 1.5
+output:
+  enable: false
+  dir: "./log"