Update README.md

README.md

@@ -25,12 +25,12 @@ This repository does not currently run local Jupyter notebooks, but the v3.0 rel
 
 Since [swift-jupyter](https://github.com/google/swift-jupyter) went unmaintained, Swift-Colab became the dominant "source of truth" for Jupyter notebook support. It's well-maintained and receives a high volume of internet traffic. Some users have tried running `install_swift.sh` on personal computers, with limited success. People will probably continue doing this despite the existence of [liuliu/swift-jupyter](https://github.com/liuliu/swift-jupyter). Furthermore, the repo's maintainer has a motive for supporting [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) (but not for supporting Docker\*).
 
+Local environments have faster CPUs than virtual machines, compiling Swift packages more quickly than Google Colaboratory. They can store data persistently, bypassing the bottleneck of Swift for TensorFlow's excessively long build time. Furthermore, they permit using your personal computer's GPU for machine learning (<s>only with NVIDIA/CUDA</s> an upcoming S4TF backend will support any Metal or OpenCL-capable GPU).
+
 > \*This presents a security risk: virtual machines encapsulate their code and stop it from harming the user's computer. When running vanilla JupyterLab, an ill-formed notebook could delete important files - in absence of proper security measures. Swift-Colab will harness any available mechanisms for limiting a process's access to the file system, and clearly document how it uses them.
 >
 > Docker does not provide access to the user's GPU, unless they have an NVIDIA GPU ([nvidia-docker](https://github.com/NVIDIA/nvidia-docker)). The maintainer wishes to make GPU acceleration accessible to all users, not favoring one specific platform over all others. If OpenCL ran in Docker, it would be viable and preferred to JupyterLab.
 
-Local environments have faster CPUs than virtual machines, compiling Swift packages more quickly than Google Colaboratory. They can store data persistently, bypassing the bottleneck of Swift for TensorFlow's excessively long build time. Furthermore, they permit using your personal computer's GPU for machine learning (<s>only with NVIDIA/CUDA</s> an upcoming S4TF backend will support any Metal or OpenCL-capable GPU).
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