Merge pull request #124 from JuliaDiff/ChrisRackauckas-patch-2

Remove non-maintained warning

README.md

@@ -7,15 +7,9 @@
 
 [**See ReverseDiff Usage Examples**](https://github.com/JuliaDiff/ReverseDiff.jl/tree/master/examples)
 
-**Note: While ReverseDiff technically supports Julia v0.7/v1.0 and is somewhat maintained, it
-is currently not actively developed. Instead, ForwardDiff/ReverseDiff's maintainers are
-focused on the development of a new AD package built on top of [Cassette](https://github.com/jrevels/Cassette.jl).
-In the meantime, it might be worth checking out other reverse-mode AD implementations in Nabla.jl,
-AutoGrad.jl, Flux.jl, or XGrad.jl.**
-
-ReverseDiff implements methods to take **gradients**, **Jacobians**, **Hessians**, and
-higher-order derivatives of native Julia functions (or any callable object, really) using
-**reverse mode automatic differentiation (AD)**.
+ReverseDiff is a fast and compile-able tape-based **reverse mode automatic differentiation (AD)** that 
+implements methods to take **gradients**, **Jacobians**, **Hessians**, and
+higher-order derivatives of native Julia functions (or any callable object, really).
 
 While performance can vary depending on the functions you evaluate, the algorithms
 implemented by ReverseDiff **generally outperform non-AD algorithms in both speed and