diff --git a/_posts/2015-05-21-rnn-effectiveness.markdown b/_posts/2015-05-21-rnn-effectiveness.markdown
index 7b4190654..e541fc714 100644
--- a/_posts/2015-05-21-rnn-effectiveness.markdown
+++ b/_posts/2015-05-21-rnn-effectiveness.markdown
@@ -658,7 +658,7 @@ I hope I've convinced you that training character-level language models is a ver
 1. CPU/GPU transparent Tensor library with a lot of functionality (slicing, array/matrix operations, etc. )
 2. An entirely separate code base in a scripting language (ideally Python) that operates over Tensors and implements all Deep Learning stuff (forward/backward, computation graphs, etc)
 3. It should be possible to easily share pretrained models (Caffe does this well, others don't), and crucially 
-4. NO compilation step (or at least not as currently done in Theano). The trend in Deep Learning is towards larger, more complex networks that are are time-unrolled in complex graphs. It is critical that these do not compile for a long time or development time greatly suffers. Second, by compiling one gives up interpretability and the ability to log/debug effectively. If there is an *option* to compile the graph once it has been developed for efficiency in prod that's fine.
+4. NO compilation step (or at least not as currently done in Theano). The trend in Deep Learning is towards larger, more complex networks that are time-unrolled in complex graphs. It is critical that these do not compile for a long time or development time greatly suffers. Second, by compiling one gives up interpretability and the ability to log/debug effectively. If there is an *option* to compile the graph once it has been developed for efficiency in prod that's fine.
 
 ## Further Reading