diff --git a/README.md b/README.md
index a2d7f49..12601af 100644
--- a/README.md
+++ b/README.md
@@ -6,61 +6,24 @@ reference distributions for comparison to a broader population.
 
 ![NiChart flowchart](resources/images/NiChart_Flowchart_v2.svg)
 
-We provide both locally deployable software and a cloud application. [NiChart cloud application](https://neuroimagingchart.com/portal), hosted via Amazon Web Services (AWS), deploys
-scalable infrastructure which hosts the NiChart tools as a standard web application accessible via the user’s web browser. The cloud and desktop applications are unified at the code level through the use of the Python library [Streamlit](https://streamlit.io/). Consequently, the user experience is nearly completely identical between the cloud and desktop applications.
-
-## Components
-
-1. **Image Processing**: Utilizes tools like [DLMUSE](https://github.com/CBICA/NiChart_DLMUSE), [fMRIPrep](https://github.com/nipreps/fmriprep) [XCEPengine](https://github.com/PennLINC/xcp_d), and [QSIPrep](https://github.com/PennLINC/qsiprep) for effective image analytics.
-2. **Reference Data Curation**: Houses ISTAGING, 70000 Scans, and 14 individual studies to provide curated reference data.
-3. **Data Harmonization**: Employs [neuroharmonize](https://github.com/rpomponio/neuroHarmonize) and [Combat](https://github.com/Zheng206/ComBatFam_Pipeline) for ensuring consistent data standards.
-4. **Machine Learning Models**: Provides Supervised, Semi-supervised, and DL Models for advanced neuroimaging analysis including [SpareScore](https://github.com/CBICA/spare_score).
-5. **Data Visualization**: Features like Centile curves, direct image linking, and reference values for comprehensive data visualization.
-6. **Deployment**: Supports open-source Github components and Docker container compatibility deployed in a local environment & [AWS Cloud](https://aws.amazon.com/).
+# Main Goals:
 
+The development of nichart is guided by several core principles:
 
-## System Requirements
+1. Enabling near real-time image processing and analysis through advanced methods.
 
-For recommended system configuration, please refer to: [nnUNet hardware requirements](https://github.com/MIC-DKFZ/nnUNet/blob/master/documentation/installation_instructions.md#hardware-requirements).
+2. Integrating cutting-edge methods and enabling the continuous integration of new processing and analysis techniques to extract meaningful AI biomarkers from multi-modal neuroimaging data.
 
-## Installation Instructions
+3. Ensuring robust and reliable results through extensive data training and validation on large and diverse training datasets.
 
-1. (Optional but recommended for environment management) Mamba installation
-    [Mamba Installation Guide (Official)](https://mamba.readthedocs.io/en/latest/installation/mamba-installation.html)
+4. Providing user-friendly tools for result visualization and reporting.
 
-    Example (Linux x86):
-    ```bash
-    wget https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-Linux-x86_64.sh
+5. Developing a deployment strategy that enables easy access for users with varying technical expertise and hardware resources.
 
-    bash Mambaforge-Linux-x86_64.sh
-    mamba create -c conda-forge -c bioconda -n NCP_env python=3.12 
-    mamba activate NCP_env
-    ```
-2. Install NiChart_Project into the environment
-   ```bash
-   git clone https://github.com/CBICA/NiChart_Project.git
-   pip install -r requirements.txt
-   ```
+# Running NiChart:
 
-3. Install the proper PyTorch version for your device
-   Numpy and PyTorch have some compatibility issues which have changed variously on different platforms. To avoid frustration with these issues, please install PyTorch as noted below.
-
-   After installing all other requirements, uninstall Torch:
-   ```
-   pip uninstall torch
-   ```
-
-   Then install PyTorch using the following command. Make sure to use the correct index url for your CUDA version as specified on the [PyTorch getting started page](https://pytorch.org/get-started/locally/).
-   On Linux, use version 2.3.1. On Windows, 2.5.1 is known to work.
-   ```
-   pip install torch==2.3.1 --index-url https://download.pytorch.org/whl/cu121
-   ```
-## Run NiChart Locally (GUI)
-```bash
-cd src/viewer/
-streamlit run NiChartProject.py
-```
-The app will start in your localhost.
+We provide both a locally deployable desktop application and a cloud application. For the desktop application please see [FIXME: link to readthedocs and Github]. [NiChart cloud application](https://neuroimagingchart.com/portal), hosted via Amazon Web Services (AWS), deploys
+scalable infrastructure which hosts the NiChart tools as a standard web application accessible via the user’s web browser. The cloud and desktop applications are unified at the code level through the use of the Python library [Streamlit](https://streamlit.io/). Consequently, the user experience is nearly completely identical between the cloud and desktop applications.
 
 ## Quick Links
 
diff --git a/docs2/usage.rst b/docs2/usage.rst
index 622f5dd..64f28a7 100644
--- a/docs2/usage.rst
+++ b/docs2/usage.rst
@@ -18,10 +18,10 @@ The following steps describe how to run NiChart after installation: ::
 Cloud
 *****
 
-The cloud app can be launched at https://cloud.neuroimagingchart.com . Users need to create an account to access the cloud app.
+The cloud app can be launched at https://cloud.neuroimagingchart.com . Users need to create an account and login to access the cloud app.
 
 *****
 Usage
 *****
 
-After launching the application, users can select a pipeline and apply it to their data. For detailed instructions on each pipeline, please refer to the **Overview** and **Tutorial** pages accessible from the left-hand menu.
+After launching the application, users can select a pipeline and apply it to their data. For detailed instructions on each pipeline, please refer to the **Pipeline Overview** and **Tutorial** pages accessible from the left-hand menu.