From f33c32edfb0f129c96f4eae42be9b4768895ec06 Mon Sep 17 00:00:00 2001
From: Alex Klibisz <aklibisz@gmail.com>
Date: Tue, 28 Jan 2020 20:24:43 -0500
Subject: [PATCH] Preparing to make repo public

---
 LICENSE.txt |  1 -
 NOTICE.txt  |  2 +-
 readme.md   | 29 +++++++++++++++++++++++++++++
 3 files changed, 30 insertions(+), 2 deletions(-)

diff --git a/LICENSE.txt b/LICENSE.txt
index 7a4a3ea24..f49a4e16e 100644
--- a/LICENSE.txt
+++ b/LICENSE.txt
@@ -1,4 +1,3 @@
-
                                  Apache License
                            Version 2.0, January 2004
                         http://www.apache.org/licenses/
diff --git a/NOTICE.txt b/NOTICE.txt
index 4f96a025c..84e45a3b5 100644
--- a/NOTICE.txt
+++ b/NOTICE.txt
@@ -1,2 +1,2 @@
 Alex Klibisz
-Copyright 2019 Alex Klibisz
\ No newline at end of file
+Copyright 2019-2020 Alex Klibisz
\ No newline at end of file
diff --git a/readme.md b/readme.md
index 8123ad489..397029bf4 100644
--- a/readme.md
+++ b/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
\ No newline at end of file