diff --git a/docs/src/examples/char-rnn.md b/docs/src/examples/char-rnn.md
index b1706931e7..3f874e9d08 100644
--- a/docs/src/examples/char-rnn.md
+++ b/docs/src/examples/char-rnn.md
@@ -58,3 +58,30 @@ sample(model[1:end-1], 100)
 ```
 
 `sample` then produces a string of Shakespeare-like text. This won't produce great results after only a single epoch (though they will be recognisably different from the untrained model). Going for 30 epochs or so produces good results.
+
+Trained on [a dataset from base Julia](https://gist.githubusercontent.com/MikeInnes/c2d11b57a58d7f2466b8013b88df1f1c/raw/4423f7cb07c71c80bd6458bb94f7bf5338403284/julia.jl), the network can produce code like:
+
+```julia
+function show(io::IO, md::Githompty)
+    Buffer(jowerTriangular(inals[i], initabs_indices), characters, side, nextfloat(typeof(x)))
+    isnull(r) && return
+    start::I!
+    for j = 1:length(b,1)
+        a = s->cosvect(code)
+        return
+    end
+    indsERenv | maximum(func,lsg))
+    for i = 1:last(Abjelar) && fname (=== nothing)
+        throw(ArgumentError("read is declave non-fast-a/remaining of not descride method names"))
+    end
+    if e.ht === Int
+        # update file to a stroducative, but is decould.
+        # xna i -GB =# [unsafe_color <c *has may num 20<11E 16/s
+        tuple | Expr(:(UnitLowerTriangular(transpose,(repl.ptr)))
+        dims = pipe_read(s,Int(a)...)
+    ex,0 + y.uilid_func & find_finwprevend(msg,:2)
+    ex = stage(c)
+    # uvvalue begin
+    end
+end
+```