Update README.md

- update with latest information and links
- copied from Gitbook "Getting started"

Author: AngelaKTE

README.md

@@ -1,15 +1,14 @@
-# Balancer AMM cadCAD model
-This repo contains the Python code, input data samples and several Python notebooks allowing users to simulate Balancer pools.
+## Welcome to **Balancer Simulations**! 
 
-It is ongoing work and repo updates all the time.
+Balancer Simulations is a project of TE-AMM and the Token Engineering Community, funded by grants from [Balancer](https://balancer.finance/) and [PowerPool](https://powerpool.finance/), and kicked off by [EthicHub](https://www.ethichub.com/en/).  
+It aims to build infrastructure and knowledge for rigorous Balancer Token Engineering to leverage the full power of Balancer Pools as a core building block in DeFi.
+We invite any project building on Balancer Pools to join our [Discord Channel](https://discord.gg/KTUPMCRZNQ), use the model, and benefit from Balancer Simulations.
 
-Roadmap:
-
-**Phase 1:** 
-Phase 01 goal is to provide Balancer AMM functionality in a cadCAD model and run a set of simulations with historical data to demonstrate model operation and produce educational materials helping to gain in-depth understanding and intuition of Balancer AMM and AMMs in general.
-
-**Phase 2:** 
-More complex cases, including: agents behavior, dynamic weights changing in the AMM equation, particular cases for EthicHub and PowerPool
-
-Google doc with specifications (in process of copying to Gitbook): https://docs.google.com/document/d/1asyGzZNVDTnsJ3CdEh3Vpv-buRXCJ8yVgNsNMyxMs8E/edit#heading=h.2fixvr78p932
+- Analyze existing Balancer Pools using on-chain transaction data,  understand pool characteristics, strengths, and weaknesses
+- Gain intuition by exploring pool variants, observe system behavior over time, derive the most valuable monitoring metrics for your use case
+- Run experiments based on historical transactions, mix historical and synthetic transactions to model particular market scenarios
+- Develop and test adaptive Dynamic AMM solutions, like Dynamic Weights Changing, test and optimize controls and feedback loops
+- Model agent behavior and apply Reinforcement Learning to run stress tests for a proposed system design
 
+All research and models are available through open source repositories, and will be further developed by TE-AMM.   
+For detailed information, please visit the [Balancer Simulations Documentation](https://token-engineering-balancer.gitbook.io/balancer-simulations/).