a performance claim

README.md

@@ -42,6 +42,7 @@ Describe your training task with three components:
 
 	+ All data producer has an unified interface, so they can be composed and reused to perform complex preprocessing.
 	+ Allows you to process data from Python without blocking the training, thanks to multiprocess prefetch & TF Queue prefetch.
+		Even on a small CNN example, it runs [2x faster](https://gist.github.com/ppwwyyxx/8d95da79f8d97036a7d67c2416c851b6) than the equivalent Keras code.
 
 3. __Callbacks__, including everything you want to do apart from the training iterations, such as:
 	+ Change hyperparameters during training

docs/tutorial/tf-queue.md

@@ -15,6 +15,7 @@ while True:
   minimize_op.run(feed_dict={'X': X, 'y': y})
 ```
 However, when you need to load data from Python-side, this is the only available interface in frameworks such as Keras, tflearn.
+This is part of the reason why [tensorpack is fast](https://gist.github.com/ppwwyyxx/8d95da79f8d97036a7d67c2416c851b6).
 
 You should use something like this instead:
 ```python