README update

README.md

@@ -2,8 +2,14 @@
 
 ![lifecycle](https://img.shields.io/badge/lifecycle-experimental-orange.svg) [![build](https://github.com/doorisajar/ToyAD.jl/workflows/CI/badge.svg)](https://github.com/doorisajar/ToyAD.jl/actions?query=workflow%3ACI) [![codecov.io](http://codecov.io/github/doorisajar/ToyAD.jl/coverage.svg?branch=main)](http://codecov.io/github/doorisajar/ToyAD.jl?branch=main)
 
-Prototype implementation of automatic differentiation for learning purposes. Starting with forward mode, and (if time permits) moving on to reverse mode. 
+Prototype implementation of automatic differentiation for learning purposes while reading [Evaluating Derivatives: Principles & Techniques of Algorithmic Differentiation (2nd Edition)](https://www.google.com/books/edition/_/xoiiLaRxcbEC?hl=en). Starting with forward mode, and (if time permits) moving on to reverse mode. 
 
+Currently working functionality is shown in the unit tests:
+- Forward mode differentiation for scalar-valued functions utilizing a subset of operators
+- Gradients for vector-valued functions
+- Jacobians coming soon!
+
+Built without LLM assistance; references linked where appropriate. 
 
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