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Co-authored-by: Elliot Saba <[email protected]>

README.md

@@ -12,7 +12,7 @@ Diffractor is an experimental next-generation, compiler-based AD system for Juli
 
 Design goals:
 - Ultra high performance for both scalar and array code
-- Efficient higher order derivatives though nested AD
+- Efficient higher order derivatives through nested AD
 - Reasonable compile times
 - High flexibility (like Zygote)
 - Support for forward/reverse/mixed modes
@@ -22,28 +22,27 @@ This is achieved through a combination of innovations:
 - A new lowest level interface (∂⃖ the "AD optic functor" or "diffractor"), more suited to higher order AD
 - New capabilities in Base Julia (Opaque closures, inference plugins)
 - Better integration with ChainRules.jl
-- Demand-driven forward-mode AD (Forwards-mode AD that knows which outputs are active and only ADs the path to them.)
+- Demand-driven forward-mode AD (Applying transforms to only those IR statements that contribute to relevant outputs of the function being differentiated)
 
 # Current status
-## Current Status: Forwards-Mode
-Recently forwards-mode has been a priority, and now forms a core part of some closed source products.
+## Current Status: Forward-Mode
+Currently, forward-mode is the only fully-functional mode and is now shipping in some closed source products.
 It is in a position to compete with [ForwardDiff.jl](https://github.com/JuliaDiff/TaylorDiff.jl), and with [TaylorDiff.jl](https://github.com/JuliaDiff/TaylorDiff.jl).
-It is certainly no where near as battle-tested as ForwardDiff.jl, but it has several advantages.
+It is not as battle-tested as ForwardDiff.jl, but it has several advantages.
 Primarily, as it is not an operator overloading AD, it frees one from the need to relax type-constants and worry about the types of containers.
 Furthermore, Like TaylorDiff.jl, it supports Taylor series based computation of higher order derviatives.
 
 
-One limitation over ForwardDiff.jl is a lack of chunking support, to pushforward multiple basies at once.
+One limitation over ForwardDiff.jl is a lack of chunking support, to pushforward multiple bases at once.
 
 
 
 ## Current Status: Reverse-Mode
-Reverse mode has recently been deprioritized.
-While originally, a higher priority and working well, it is now a secondary priority.<br>
+Improved reverse mode support is planned for a future release.
+While reverse mode was originally implemented and working, it has been stripped out until such a time as it can be properly implemented on top of new Julia compiler changes.<br>
 ⚠️ **Reverse Mode support should be considered experimental, and may break without warning, and may not be fixed rapidly.** ⚠️ <br>
 
 With that said, issues and PRs for reverse mode continue to be appreciated.
-
 ### Status as of last time reverse mode was worked on:
 
 The plan is to implement this in two stages: