--- a/doc/v2_planning/use_cases.txt	Fri Sep 17 20:24:30 2010 -0400
+++ b/doc/v2_planning/use_cases.txt	Fri Sep 17 20:55:18 2010 -0400
@@ -56,8 +56,9 @@
 
 There are many ways that the training could be configured, but here is one:
 
+.. code-block:: python
 
-vm.call(
+  vm.call(
     halflife_stopper(
         # OD: is n_hidden supposed to be n_classes instead?
         initial_model=random_linear_classifier(MNIST.n_inputs, MNIST.n_hidden, r_seed=234432),
@@ -108,22 +109,25 @@
 regularly had issues in PLearn with the fact we had for instance to give the
 number of inputs when creating a neural network. I much prefer when this kind
 of thing can be figured out at runtime:
-    - Any parameter you can get rid of is a significant gain in
-      user-friendliness.
-    - It's not always easy to know in advance e.g. the dimension of your input
-      dataset. Imagine for instance this dataset is obtained in a first step
-      by going through a PCA whose number of output dimensions is set so as to
-      keep 90% of the variance.
-    - It seems to me it fits better the idea of a symbolic graph: my intuition
-      (that may be very different from what you actually have in mind) is to
-      see an experiment as a symbolic graph, which you instantiate when you
-      provide the input data. One advantage of this point of view is it makes
-      it natural to re-use the same block components on various datasets /
-      splits, something we often want to do.
+
+- Any parameter you can get rid of is a significant gain in
+  user-friendliness.
+- It's not always easy to know in advance e.g. the dimension of your input
+  dataset. Imagine for instance this dataset is obtained in a first step
+  by going through a PCA whose number of output dimensions is set so as to
+  keep 90% of the variance.
+- It seems to me it fits better the idea of a symbolic graph: my intuition
+  (that may be very different from what you actually have in mind) is to
+  see an experiment as a symbolic graph, which you instantiate when you
+  provide the input data. One advantage of this point of view is it makes
+  it natural to re-use the same block components on various datasets /
+  splits, something we often want to do.
 
 K-fold cross validation of a classifier
 ---------------------------------------
 
+.. code-block:: python
+
     splits = kfold_cross_validate(
         # OD: What would these parameters mean?
         indexlist = range(1000)