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diff --git a/docs/src/tutorials/languages/static_dsl.md b/docs/src/tutorials/languages/static_dsl.md
diff --git a/docs/src/tutorials/vi.md b/docs/src/tutorials/vi.md
@@ -3,16 +3,6 @@
 Variational inference involves optimizing the parameters of a variational family to maximize a lower bound on the marginal likelihood called the ELBO.
 In Gen, variational families are represented as generative functions, and variational inference typically involves optimizing the trainable parameters of generative functions.
 
-Let's create a generative model representing a joint set of coins.
-
-Now let's fit a mean field approximation to the coins:
-
-```@example vi_tutorial
-@gen function independent_coins()
-
-end
-```
-
 ## Reparametrization trick
 
 To use the reparametrization trick to reduce the variance of gradient estimators, users currently need to write two versions of their variational family, one that is reparametrized and one that is not.