README.md in monte_carlo-0.0.3 vs README.md in monte_carlo-0.0.4

@@ -1,6 +1,6 @@
-# MonteCarlo
+# MonteCarlo [![Build Status](https://travis-ci.org/agelber/monte_carlo.svg)](https://travis-ci.org/agelber/monte_carlo)
 
 A utility to write quick [Monte Carlo Method](http://en.wikipedia.org/wiki/Monte_Carlo_method) experiments.
 
 ## Installation
 
@@ -40,13 +40,27 @@
 
 # Run your experiment and get your results
 results = experiment.run
 ```
 
-Alternatively, you can write your sample and computation method as one with the shorthand block syntax:
+Another options is to use the configuration DSL, like so:
 
 ```ruby
+# Create an experiment and pass it a configuration block
+experiment = MonteCarlo::Experiment.new do
+  times 1000000
+  sample_method { rand }
+  compuation { |sample| sample > 0.5 }
+end
+
+# And run it normally
+results = experiment.run
+```
+
+Alternatively, you can write your sample and computation method as one with the shorthand block syntax and get the restults straight away:
+
+```ruby
 results = MonteCarlo::Experiment.run(100000) { rand > 0.5 }
 ```
 
 The experiment returns a `MonteCarlo::ExperimentResults` object which contains an array of `MonteCarlo::Results` as well as some other handy methods.
 
@@ -57,10 +71,10 @@
 
 If no computation method was given, `value` and `sample_value` will be the same.
 
 ## Contributing
 
-1. Fork it ( https://github.com/[agelber]/monte_carlo/fork )
+1. Fork it ( https://github.com/agelber/monte_carlo/fork )
 2. Create your feature branch (`git checkout -b my-new-feature`)
 3. Commit your changes (`git commit -am 'Add some feature'`)
 4. Push to the branch (`git push origin my-new-feature`)
 5. Create a new Pull Request