diff --git a/README.md b/README.md
index 332993bff7..db6cf38bd1 100644
--- a/README.md
+++ b/README.md
@@ -48,8 +48,6 @@ And we can change `DEBUG` to `4` to see the generated code.
 As it turns out, 90% of what you need for neural networks are a decent autograd/tensor library.
 Throw in an optimizer, a data loader, and some compute, and you have all you need.
 
-#### Neural network example (see examples/beautiful_mnist.py for the full thing)
-
 ```py
 from tinygrad import Tensor, nn
 
@@ -72,6 +70,8 @@ for i in range(10):
   print(i, loss.item())
 ```
 
+See [examples/beautiful_mnist.py](examples/beautiful_mnist.py) for the full version that gets 98% in ~5 seconds
+
 ## Accelerators
 
 tinygrad already supports numerous accelerators, including: