Preparing to make repo public

LICENSE.txt

@@ -1,4 +1,3 @@
-
                                  Apache License
                            Version 2.0, January 2004
                         http://www.apache.org/licenses/

NOTICE.txt

@@ -1,2 +1,2 @@
 Alex Klibisz
-Copyright 2019 Alex Klibisz
+Copyright 2019-2020 Alex Klibisz

readme.md

@@ -2,6 +2,13 @@
 
 ElastiKnn is an Elasticsearch plugin for exact and approximate nearest neighbors search in high-dimensional vector spaces.
 
+## Work in Progress
+
+This project is very much a work-in-progress. I've decided to go ahead and make the repo public since
+some people have expressed interest through emails and LinkedIn messages. If you want to contribute,
+you'll have to dig around quite a bit for now. The Makefile is a good place to start. I'll do my best
+to keep the readme updated and am considering making a Github project board to track my ongoing work.
+
 ## Features
 
 1. Exact nearest neighbors search. This should only be used for testing and on relatively small datasets.
@@ -17,22 +24,44 @@ ElastiKnn is an Elasticsearch plugin for exact and approximate nearest neighbors
 
 ### Install ElastiKnn on an ElasticSearch cluster
 
+TODO
+
 ### Run a Docker container with ElastiKnn already installed
 
+TODO
+
 ### Exact search using the Elasticsearch REST API
 
+TODO
+
 ### Python Client
 
+TODO
+
 ### Scala Client
 
+TODO
+
 ## Performance
 
 ### Ann-Benchmarks
 
+Currently working on this in a fork of the [Ann-Benchmarks repo here](https://github.com/alexklibisz/ann-benchmarks).
+Planning to submit a PR when all of the approximate similarities are implemented and the Docker image can be built with
+a release elastiknn zip file. 
+
 ### Million-Scale
 
+TODO
+
+Planning to implement this using one of the various word vector datasets.
+
 ### Billion-Scale
 
+TODO
+
+Not super sure of the feasability of this yet. There are some notes in benchmarks/billion.
+
 ## Development
 
 ## References