Updates on https://www.tensorflow.org/community/swift as part of the S4TF OSS launch...

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 Welcome to the Swift for TensorFlow development community!
 
-Swift for TensorFlow is a result of first-principles thinking applied to machine
-learning frameworks, and works quite differently than existing TensorFlow
-language bindings.  Whereas prior solutions are designed within the constraints
-of what can be achieved by a (typically Python or Lua) library, Swift for
-TensorFlow is based on the belief that machine learning is important enough to
-deserve first-class language and compiler support.
-
-First-class language and compiler support allows us to innovate in areas that
-have traditionally been out of bounds for machine learning libraries.  Our
-results provide the performance of TensorFlow graphs with the ease of use of
-define-by-run models, and provides a great user experience - for example, by
-catching more mistakes before you run your code.
+Swift for TensorFlow is the result of first-principles thinking applied to
+machine learning frameworks and aims to take TensorFlow usability to new
+heights. Swift for TensorFlow is based on the belief that machine learning is
+important enough for first-class language and compiler support, and thus works
+very differently from normal language bindings.
+
+First-class language and compiler support allow us to innovate in areas that
+traditionally were out of bounds for machine learning libraries. Our programming
+model combines the performance of TensorFlow graphs with the flexibility and
+expressivity of Eager execution, while keeping a strong focus on improved
+usability at every level of the stack.
+
 
 ## Open Source
 
-As announced at the TensorFlow Developer Summit, we are planning to launch our
-open source project on GitHub in April.  In addition to releasing the code, we
-will be using an open design model, where design discussions happen in public.
+We have released Swift for TensorFlow as an open-source project on GitHub!
 
-Between now and then, we are writing some technical white papers that explain in
-detail the design approach (e.g., the core compiler partitioning technique that
-underlies the whole thing, our approach to automatic differentiation, etc.),
-implementation tradeoffs, and the status of this work.  We can’t wait to engage
-with the broader community, but prefer to start the conversation when these
-white papers are ready.
+Our [central repository](https://github.com/tensorflow/swift) contains project
+documentation, including an 
+[overview and technical papers](https://github.com/tensorflow/swift/tree/master/docs)
+explaining specific areas of the project in depth. This repo also includes
+instructions for [installing prebuilt packages](https://github.com/tensorflow/swift/blob/master/Installation.md)
+for macOS and Linux platforms, [simple usage instructions](https://github.com/tensorflow/swift/blob/master/Usage.md),
+and how to build from source.
 
-[Sign up here to join the community Google
-group](https://groups.google.com/a/tensorflow.org/d/forum/swift). We will
-initially use it for announcements, and then open it for general discussion when
-we are ready in April.
+Moving forward, we will use an open design model and all discussions will be
+public.
 
+[Sign up here to join the community Google
+group](https://groups.google.com/a/tensorflow.org/d/forum/swift), which we will
+use for announcements and general discussion.