diff --git a/README.md b/README.md
index 75bc9995..f47c0a62 100644
--- a/README.md
+++ b/README.md
@@ -4,39 +4,4 @@
 
 Flux is a refreshing approach to machine learning. It provides lightweight abstractions on top of Julia's native GPU and AD support, while remaining fully hackable (right down to the [GPU kernels](https://github.com/FluxML/CuArrays.jl)).
 
-Define a simple model using any Julia code:
-
-```julia
-using Flux.Tracker
-x, y = rand(10), rand(5) # Dummy input / output
-# `track` defines parameters that we can train
-W, b = track(randn(5,10)), track(randn(5))
-# Transform `x` and calculate the mean squared error
-loss = Flux.mse(W*x .+ b, y)
-# Calculate and store gradients of `track`ed parameters
-back!(loss)
-Tracker.grad(W) # Get the gradient of `W` wrt the loss
-```
-
-Define a larger model using high-level abstractions:
-
-```julia
-using Flux
-
-m = Chain(
-  Dense(10, 32, relu),
-  Dense(32, 10), softmax)
-
-m(rand(10))
-```
-
-Mix and match the two:
-
-```julia
-using Flux.Tracker
-x, y = rand(10), rand(5)
-d = Dense(10, 5)
-loss = Flux.mse(d(x), y)
-```
-
-See the [documentation](http://fluxml.github.io/Flux.jl/stable/) or the [model zoo](https://github.com/FluxML/model-zoo/) for more examples.
+See the [documentation](http://fluxml.github.io/Flux.jl/stable/) or the [model zoo](https://github.com/FluxML/model-zoo/) for examples.