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 # Flux
 
-Flux is an experimental machine perception / ANN library for Julia. It's most similar in philosophy to the excellent [Keras](http://keras.io). Like that and other high-level ANN libraries, Flux is designed to make experimenting with novel layer types and architectures really fast, without sacrificing speed.
+## What?
+
+Flux is an experimental machine perception / ANN library for Julia. It's designed to make experimenting with novel layer types and architectures really fast, without sacrificing speed.
+
+## Why?
 
 Flux has a few key differences from other libraries:
 
-* Flux's [graph-based DSL](https://github.com/MikeInnes/Flow.jl), which provides optimisations and automatic differentiation (à la Theano), is very tightly integrated with the language. This means nice syntax for your equations (`σ(W*x+b)` anyone?) and no unwieldy `compile` steps.
-* The graph DSL directly represents models, as opposed to computations, so custom architectures – and in particular, recurrent models – are easy to express.
-* Those fancy features are completely optional. You can implement arbitrary functionality in a Torch-like fashion if you wish, since layers are simply objects that satisfy a small interface (à la Torch).
+* Flux's [graph-based DSL](https://github.com/MikeInnes/Flow.jl), which provides optimisations and automatic differentiation, is very tightly integrated with the language. This means nice syntax for your equations (`σ(W*x+b)` anyone?) and no unwieldy `compile` steps.
+* The graph DSL directly is used to represent models (not just computations), so custom architectures – and in particular, recurrent models – are easy to express.
+* Those fancy features are completely optional. You can implement functionality in a Torch-like fashion if you wish, since layers are simply objects that satisfy a small interface.
 * Flux is written in [Julia](http://julialang.org), which means there's no "dropping down" to C. It's Julia all the way down, and you can prototype both high-level architectures and high-performance GPU kernels from the same language. This also makes the library itself very easy to understand and extend.
+
+Future work will also include:
+
+* Integration with other backends, so that models can be described using Flux and run using (say) TensorFlow.
+* Carrying out runtime optimisations of the graph, in particular to handle small matrices efficiently.
+
+## Is it any good?
+
+Yes.