diff --git a/lectures/dynamic_programming/coleman_policy_iter.md b/lectures/dynamic_programming/coleman_policy_iter.md
index 2d760c55..8acd8a98 100644
--- a/lectures/dynamic_programming/coleman_policy_iter.md
+++ b/lectures/dynamic_programming/coleman_policy_iter.md
@@ -404,7 +404,7 @@ function K!(Kg, g, grid, beta, dudc, f, f_prime, shocks)
     for (i, y) in enumerate(grid)
         function h(c)
             vals = dudc.(g_func.(f(y - c) * shocks)) .* f_prime(y - c) .* shocks
-            return dudc * c - beta * mean(vals)
+            return dudc(c) - beta * mean(vals)
         end
         Kg[i] = find_zero(h, (1e-10, y - 1e-10))
     end
diff --git a/lectures/dynamic_programming/egm_policy_iter.md b/lectures/dynamic_programming/egm_policy_iter.md
index 6036bf7e..68fb862a 100644
--- a/lectures/dynamic_programming/egm_policy_iter.md
+++ b/lectures/dynamic_programming/egm_policy_iter.md
@@ -216,7 +216,7 @@ The first step is to bring in the model that we used in the {doc}`Coleman policy
 ```{code-cell} julia
 # model
 
-function Model(alpha = 0.65, # productivity parameter
+function Model(;alpha = 0.65, # productivity parameter
                beta = 0.95, # discount factor
                gamma = 1.0,  # risk aversion
                mu = 0.0,  # lognorm(mu, sigma)