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 **NOTE:** For the latest stable [README.md](https://github.com/rapidsai/cudf/blob/main/README.md) ensure you are on the `main` branch.
 
+## Resources
+
+- [cuDF Reference Documentation](https://docs.rapids.ai/api/cudf/stable/): Python API reference, tutorials, and topic guides.
+- [libcudf Reference Documentation](https://docs.rapids.ai/api/libcudf/stable/): C/C++ CUDA library API reference.
+- [Getting Started](https://rapids.ai/start.html): Instructions for installing cuDF.
+- [RAPIDS Community](https://rapids.ai/community.html): Get help, contribute, and collaborate.
+- [GitHub repository](https://github.com/rapidsai/cudf): Download the cuDF source code.
+- [Issue tracker](https://github.com/rapidsai/cudf/issues): Report issues or request features.
+
+## Overview
+
 Built based on the [Apache Arrow](http://arrow.apache.org/) columnar memory format, cuDF is a GPU DataFrame library for loading, joining, aggregating, filtering, and otherwise manipulating data.
 
 cuDF provides a pandas-like API that will be familiar to data engineers & data scientists, so they can use it to easily accelerate their workflows without going into the details of CUDA programming.