diff --git a/include/ceres/autodiff_local_parameterization.h b/include/ceres/autodiff_local_parameterization.h
index 1099061..0aae6c7 100644
--- a/include/ceres/autodiff_local_parameterization.h
+++ b/include/ceres/autodiff_local_parameterization.h
@@ -58,7 +58,7 @@
 //
 // For example, Quaternions have a three dimensional local
 // parameterization. It's plus operation can be implemented as (taken
-// from interncal/ceres/auto_diff_local_parameterization_test.cc)
+// from internal/ceres/auto_diff_local_parameterization_test.cc)
 //
 //   struct QuaternionPlus {
 //     template<typename T>
diff --git a/include/ceres/loss_function.h b/include/ceres/loss_function.h
index 7b4af15..b99c184 100644
--- a/include/ceres/loss_function.h
+++ b/include/ceres/loss_function.h
@@ -347,19 +347,20 @@
 //
 //  CostFunction* cost_function =
 //    new AutoDiffCostFunction < UW_Camera_Mapper, 2, 9, 3>(
-//      new UW_Camera_Mapper(data->observations[2*i + 0],
-//                           data->observations[2*i + 1]));
+//      new UW_Camera_Mapper(feature_x, feature_y));
 //
 //  LossFunctionWrapper* loss_function(new HuberLoss(1.0), TAKE_OWNERSHIP);
 //
 //  problem.AddResidualBlock(cost_function, loss_function, parameters);
 //
 //  Solver::Options options;
-//  scoped_ptr<Solver::Summary> summary1(Solve(problem, options));
+//  Solger::Summary summary;
+//
+//  Solve(options, &problem, &summary)
 //
 //  loss_function->Reset(new HuberLoss(1.0), TAKE_OWNERSHIP);
 //
-//  scoped_ptr<Solver::Summary> summary2(Solve(problem, options));
+//  Solve(options, &problem, &summary)
 //
 class LossFunctionWrapper : public LossFunction {
  public:
diff --git a/include/ceres/numeric_diff_cost_function.h b/include/ceres/numeric_diff_cost_function.h
index 6c44b58..a47a66d 100644
--- a/include/ceres/numeric_diff_cost_function.h
+++ b/include/ceres/numeric_diff_cost_function.h
@@ -82,14 +82,14 @@
 //
 //   CostFunction* cost_function
 //       = new NumericDiffCostFunction<MyScalarCostFunctor, CENTRAL, 1, 2, 2>(
-//           new MyScalarCostFunctor(1.0));                          ^  ^  ^
-//                                                               |   |  |  |
-//                                   Finite Differencing Scheme -+   |  |  |
-//                                   Dimension of residual ----------+  |  |
-//                                   Dimension of x --------------------+  |
-//                                   Dimension of y -----------------------+
+//           new MyScalarCostFunctor(1.0));                    ^     ^  ^  ^
+//                                                             |     |  |  |
+//                                 Finite Differencing Scheme -+     |  |  |
+//                                 Dimension of residual ------------+  |  |
+//                                 Dimension of x ----------------------+  |
+//                                 Dimension of y -------------------------+
 //
-// In this example, there is usually an instance for each measumerent of k.
+// In this example, there is usually an instance for each measurement of k.
 //
 // In the instantiation above, the template parameters following
 // "MyScalarCostFunctor", "1, 2, 2", describe the functor as computing
@@ -126,7 +126,7 @@
 // To get a numerically differentiated cost function, define a
 // subclass of CostFunction such that the Evaluate() function ignores
 // the jacobian parameter. The numeric differentiation wrapper will
-// fill in the jacobian parameter if nececssary by repeatedly calling
+// fill in the jacobian parameter if necessary by repeatedly calling
 // the Evaluate() function with small changes to the appropriate
 // parameters, and computing the slope. For performance, the numeric
 // differentiation wrapper class is templated on the concrete cost