diff --git a/CHANGES.md b/CHANGES.md
index 027a077c6..d834ce79d 100644
--- a/CHANGES.md
+++ b/CHANGES.md
@@ -20,3 +20,9 @@ xgboost-0.3
 * Linear booster is now parallelized, using parallel coordinated descent.
 * Add [Code Guide](src/README.md) for customizing objective function and evaluation
 * Add R module
+
+in progress version
+=====
+* Distributed version
+* Feature importance visualization in R module, thanks to Michael Benesty
+* Predict leaf inde
diff --git a/README.md b/README.md
index 8f031c9eb..c88111327 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,7 @@
 xgboost: eXtreme Gradient Boosting 
 ======
-An optimized general purpose gradient boosting library. The library is parallelized using OpenMP. It implements machine learning algorithm under gradient boosting framework, including generalized linear model and gradient boosted regression tree. 
+An optimized general purpose gradient boosting library. The library is parallelized, and also provides an optimized distributed version.
+It implements machine learning algorithm under gradient boosting framework, including generalized linear model and gradient boosted regression tree. 
 
 Contributors: https://github.com/tqchen/xgboost/graphs/contributors
 
@@ -10,6 +11,8 @@ Questions and Issues: [https://github.com/tqchen/xgboost/issues](https://github.
 
 Examples Code: [Learning to use xgboost by examples](demo)
 
+Distributed Version: [Distributed XGBoost](multi-node)
+
 Notes on the Code: [Code Guide](src)
 
 Learning about the model: [Introduction to Boosted Trees](http://homes.cs.washington.edu/~tqchen/pdf/BoostedTree.pdf)
@@ -19,11 +22,14 @@ Learning about the model: [Introduction to Boosted Trees](http://homes.cs.washin
 
 What's New
 =====
-* [Distributed XGBoost](multi-node) is now available to scale to even larger scale problems
+* [Distributed XGBoost](multi-node) is now available!!
+* New features in the lastest changes :)
+  - Distributed version that scale xgboost to even larger problems with cluster
+  - Feature importance visualization in R module, thanks to Michael Benesty
+  - Predict leaf index, see [demo/guide-python/pred_leaf_indices.py](demo/guide-python/pred_leaf_indices.py)  
 * XGBoost wins [Tradeshift Text Classification](https://kaggle2.blob.core.windows.net/forum-message-attachments/60041/1813/TradeshiftTextClassification.pdf?sv=2012-02-12&se=2015-01-02T13%3A55%3A16Z&sr=b&sp=r&sig=5MHvyjCLESLexYcvbSRFumGQXCS7MVmfdBIY3y01tMk%3D)
 * XGBoost wins [HEP meets ML Award in Higgs Boson Challenge](http://atlas.ch/news/2014/machine-learning-wins-the-higgs-challenge.html)
 * Thanks to Bing Xu, [XGBoost.jl](https://github.com/antinucleon/XGBoost.jl) allows you to use xgboost from Julia
-* See the updated [demo folder](demo) for feature walkthrough
 * Thanks to Tong He, the new [R package](R-package) is available
 
 Features
@@ -35,6 +41,9 @@ Features
 * Speed: XGBoost is very fast
   - IN [demo/higgs/speedtest.py](demo/kaggle-higgs/speedtest.py), kaggle higgs data it is faster(on our machine 20 times faster using 4 threads) than sklearn.ensemble.GradientBoostingClassifier
 * Layout of gradient boosting algorithm to support user defined objective
+* Distributed and portable
+  - The distributed version of xgboost is highly portable and can be used in different platforms
+  - It inheritates all the optimizations made in single machine mode, maximumly utilize the resources using both multi-threading and distributed computing.
 
 Build
 =====
diff --git a/multi-node/README.md b/multi-node/README.md
index 752292d9a..b94cc2c77 100644
--- a/multi-node/README.md
+++ b/multi-node/README.md
@@ -23,7 +23,7 @@ Notes
 * The multi-threading nature of xgboost is inheritated in distributed mode
   - This means xgboost efficiently use all the threads in one machine, and communicates only between machines
   - Remember to run on xgboost process per machine and this will give you maximum speedup
-* For more information about rabit and how it works, see the [tutorial](https://github.com/tqchen/rabit/tree/master/guide)
+* For more information about rabit and how it works, see the [Rabit's Tutorial](https://github.com/tqchen/rabit/tree/master/guide)
 
 Solvers
 =====