Update README.md for New Era (#112)

* Update README.md

* Update README.md

Co-Authored-By: Nick Robinson <npr251@gmail.com>

* Update README.md

Co-Authored-By: Nick Robinson <npr251@gmail.com>

* Update README.md

Co-Authored-By: Nick Robinson <npr251@gmail.com>

* Update README.md

Co-Authored-By: Nick Robinson <npr251@gmail.com>

* Apply suggestions from code review

Author: oxinabox

README.md

@@ -5,22 +5,25 @@
 [![Travis](https://travis-ci.org/JuliaDiff/ChainRules.jl.svg?branch=master)](https://travis-ci.org/JuliaDiff/ChainRules.jl)
 [![Coveralls](https://coveralls.io/repos/github/JuliaDiff/ChainRules.jl/badge.svg?branch=master)](https://coveralls.io/github/JuliaDiff/ChainRules.jl?branch=master)
 
-**Docs:* 
+**Docs:**
 [![](https://img.shields.io/badge/docs-master-blue.svg)](https://JuliaDiff.github.io/ChainRules.jl/dev)
 [![](https://img.shields.io/badge/docs-stable-blue.svg)](https://JuliaDiff.github.io/ChainRules.jl/stable)
 
 The ChainRules package provides a variety of common utilities that can be used by downstream automatic differentiation (AD) tools to define and execute forward-, reverse-, and mixed-mode primitives.
 
-This package is a WIP; the framework is essentially there, but there are a bunch of TODOs, virtually no tests, etc. PRs welcome! Documentation is incoming, which should help if you'd like to contribute.
+The core logic of ChainRules is implemented in [ChainRulesCore.jl](https://github.com/JuliaDiff/ChainRulesCore.jl).
+To add ChainRules support to your package, by defining new `rrule`s or `frules`, you only need to depend on the very light-weight package ChainRulesCore.jl.
+This repository contains ChainRules.jl, which is what people actually use directly. 
+ChainRules reexports all the ChainRulesCore functionality, and has all the rules for the Julia standard library.
+and has all the rules for the julia standard library.
 
-Here are some of the basic goals for the package:
 
-- First-class support for complex differentiation via Wirtinger derivatives.
+Here are some of the core features of the package:
 
 - Mixed-mode composability without being coupled to a specific AD implementation.
-
+- Extensible rules: package authors can add rules (and thus AD support) to the functions in their packages, without needing to make a PR to ChainRules.jl .
+- Control-inverted design: rule authors can fully specify derivatives in a concise manner that supports computational efficiencies, so we will only compute as much as the user requests.
 - Propagation semantics built-in, with default implementations that allow rule authors to easily opt-in to common optimizations (fusion, increment elision, memoization, etc.).
 
-- Control-inverted design: rule authors can fully specify derivatives in a concise manner while naturally allowing the caller to compute only what they need.
 
 The ChainRules source code follows the [YASGuide](https://github.com/jrevels/YASGuide).