diff --git a/src/lib/array.jl b/src/lib/array.jl
index 329676648..aed553f92 100644
--- a/src/lib/array.jl
+++ b/src/lib/array.jl
@@ -7,12 +7,23 @@
 
 Base.zero(xs::AbstractArray{Any}) = fill!(similar(xs), nothing)
 
+struct ∇getindex{T,S}
+    xs::T
+    i::S
+end
+
+function (g::∇getindex)(Δ)
+  Δ′ = zero(g.xs)
+  Δ′[g.i...] = Δ
+  (Δ′, map(_ -> nothing, g.i)...)
+end
+
+@adjoint function (g::∇getindex)(Δ)
+  g(Δ), Δ′′->(nothing, Δ′′[1][g.i...])
+end
+
 @adjoint function getindex(xs::Array, i...)
-  xs[i...], function (Δ)
-    Δ′ = zero(xs)
-    Δ′[i...] = Δ
-    (Δ′, map(_ -> nothing, i)...)
-  end
+  xs[i...], ∇getindex(xs, i)
 end
 
 @adjoint! setindex!(xs::AbstractArray, x...) = setindex!(xs, x...),
diff --git a/src/lib/base.jl b/src/lib/base.jl
index fd582f5b7..1304d41ac 100644
--- a/src/lib/base.jl
+++ b/src/lib/base.jl
@@ -16,7 +16,7 @@ end
   d[k], function (Δ)
     grad = grad_mut(__context__, d)
     grad[k] = accum(get(grad, k, nothing), Δ)
-    return (grad, nothing)
+    return (nobacksies(:getindex, grad), nothing)
   end
 end
 
diff --git a/src/lib/lib.jl b/src/lib/lib.jl
index 0944407b1..260a83671 100644
--- a/src/lib/lib.jl
+++ b/src/lib/lib.jl
@@ -18,6 +18,19 @@ accum(x::AbstractArray, y::AbstractArray) = accum.(x, y)
   Expr(:tuple, [:($f=accum(x.$f, $(grad(f)))) for f in fieldnames(x)]...)
 end
 
+"""
+  accum_sum(Δ)
+
+`accum_sum` is to `sum` as `accum` is to `+` (i.e. accum_sum treats `nothing`
+as a strong zero).
+"""
+accum_sum(Δ) = reduce(accum, Δ)
+@adjoint accum_sum(Δ) = accum_sum(Δ), Δ -> (FillArray(Δ, size(xs)),)
+
+@adjoint function (T::Type{<:FillArray})(value, size)
+  T(value, size), Δ->(nothing, accum_sum(Δ), nothing)
+end
+
 # Core functions
 
 @nograd Core.apply_type, Core.typeof, nfields, fieldtype,
@@ -41,18 +54,24 @@ end
 
 unwrap(x) = x
 
-@adjoint unwrap(x) = unwrap(x), Δ -> accum_param(__context__, x, Δ)
+@adjoint unwrap(x) = unwrap(x), Δ ->(accum_param(__context__, x, Δ); (Δ,))
+
+nobacksies(s, x) = x
+@adjoint nobacksies(s, x) = x, Δ->error("Nested AD not defined for $s")
 
 # Tuples
 
 @adjoint tuple(xs...) = xs, identity
 
+tuple_at(Δ, i, N) = ntuple(j -> i == j ? Δ : nothing, Val(N))
+@adjoint tuple_at(Δ, i, N) = tuple_at(Δ, i, N), Δ′->(Δ′[i], nothing, nothing)
+
 @adjoint getindex(xs::NTuple{N,Any}, i::Integer) where N =
-  (xs[i], Δ -> (ntuple(j -> i == j ? Δ : nothing, Val(N)), nothing))
+  (xs[i], Δ -> (tuple_at(Δ, i, N), nothing))
 
 # Needed for iteration lowering
 @adjoint Core.getfield(xs::NTuple{N,Any}, i::Integer) where N =
-  (xs[i], Δ -> (ntuple(j -> i == j ? Δ : nothing, Val(N)), nothing))
+  (xs[i], Δ -> (tuple_at(Δ, i, N), nothing))
 
 @adjoint function Base.first(xs::Tuple)
   drest = map(_->nothing, tail(xs))
@@ -79,7 +98,7 @@ unapply(t, xs) = _unapply(t, xs)[1]
   st = map(_empty, args)
   y, function (Δ)
     Δ = back(Δ)
-    (first(Δ), unapply(st, Base.tail(Δ))...)
+    (nobacksies(:apply, first(Δ)), unapply(st, Base.tail(Δ))...)
   end
 end
 
@@ -94,23 +113,39 @@ function deref!(x::Ref)
 end
 
 @generated nt_nothing(x) = Expr(:tuple, [:($f=nothing) for f in fieldnames(x)]...)
+@generated nt_nothing_type(::Type{T}) where {T} = Expr(:tuple, [:($f=nothing) for f in fieldnames(T)]...)
 
 @generated pair(::Val{k}, v) where k = :($k = v,)
 
+struct ∇getfield{T}
+    f::Symbol
+end
+
+function (g::∇getfield{T})(Δ) where {T}
+  ((;nt_nothing_type(T)...,pair(Val(g.f), Δ)...), nothing)
+end
+
+@adjoint function (g::∇getfield)(Δ)
+  g(Δ), Δ′′->(nothing, getfield(Δ′′[1], g.f))
+end
+
 # TODO make this inferrable
 # Right now constant prop is too fragile ...
 @adjoint function getfield(x, f::Symbol)
   val = getfield(x, f)
-  unwrap(val), function (Δ)
-    accum_param(__context__, val, Δ)
-    if isimmutable(x)
-      ((;nt_nothing(x)...,pair(Val(f), Δ)...), nothing)
-    else
+  back = if isimmutable(x)
+    g = ∇getfield{typeof(x)}(f)
+    isimmutable(val) ? g : Δ->(accum_param(__context__, val, Δ); g(Δ))
+  else
+    # TODO: Nested AD for mutable structs
+    function (Δ)
+      accum_param(__context__, val, Δ)
       dx = getfield(grad_mut(__context__, x), f)
       dx[] = accum(dx[], Δ)
       return
     end
   end
+  unwrap(val), back
 end
 
 # ... so we have Zygote call this version where we can.
diff --git a/src/lib/real.jl b/src/lib/real.jl
index aee1274cb..ef3c9c453 100644
--- a/src/lib/real.jl
+++ b/src/lib/real.jl
@@ -19,6 +19,10 @@ end
 
 @adjoint Base.convert(T::Type{<:Real}, x::Real) = convert(T, x), Δ -> (nothing, Δ)
 
+for T in Base.uniontypes(Core.BuiltinInts)
+    @adjoint (::Type{T})(x::Core.BuiltinInts) = T(x), Δ -> (Δ,)
+end
+
 @adjoint Base.:+(xs...) = +(xs...), Δ -> map(_ -> Δ, xs)
 
 @adjoint function sincos(x)
diff --git a/test/features.jl b/test/features.jl
index 34ebf01fd..4a9fe32a7 100644
--- a/test/features.jl
+++ b/test/features.jl
@@ -240,3 +240,29 @@ end
 if VERSION >= v"1.1"
   @test Zygote.@code_adjoint(f(1)) isa Zygote.Adjoint
 end
+@test Zygote.@code_adjoint(f(1)) isa Zygote.Adjoint
+
+# Basic nested
+f_nested(x) = x^4
+@test f_nested''(1.0) = 12.0
+
+# Nested AD for `sum`
+@test gradient([1.0, 2.0]) do x
+    gradient(x) do x
+        sin(sum(x))
+    end[1][1]
+end == -sin(3.0)
+
+# Nested AD for getindex
+@test gradient([1.0, 2.0]) do x
+   gradient(x) do x
+       sin(x[1])
+   end[1][1]
+end == -sin(1.0)
+
+# Third-order AD
+
+# Currently disabled pending improvements to Base and Zygote
+if false
+    @test sin'''(1.0) == -sin(1.0)
+end
diff --git a/test/gradcheck.jl b/test/gradcheck.jl
index db9770631..e7fb872ff 100644
--- a/test/gradcheck.jl
+++ b/test/gradcheck.jl
@@ -1,6 +1,7 @@
 using Zygote, NNlib, Test, Random, LinearAlgebra
 using Zygote: gradient
 using NNlib: conv
+import ForwardDiff
 import Random
 
 function ngradient(f, xs::AbstractArray...)
@@ -233,3 +234,27 @@ end
   @test size(Zygote.gradient((x, y)->sum(x * y), randn(1, 1), randn(1, 10))[1]) == (1, 1)
   @test size(Zygote.gradient((x, y)->sum(x * y), randn(1, 1), randn(1, 10))[2]) == (1, 10)
 end
+
+# Currently disabled pending improvements in Zygote and Base
+if false
+  @testset "third order AD (indexing)" begin
+    # Nested AD for getindex
+    grad_tracker = gradient([1.0, 2.0, 3.0]) do x
+      sum(gradient(x) do x
+        sum(gradient(x) do x
+          sum(x[1:2])^4
+        end[1])
+      end[1])
+    end[1]
+    # We compare to ForwardDiff, since the high order derivative is not
+    # numerically stable under finite differencing.
+    grad_forward = ForwardDiff.gradient([1.0, 2.0, 3.0]) do x
+      sum(ForwardDiff.gradient(x) do x
+        sum(ForwardDiff.gradient(x) do x
+          sum(x[1:2])^4
+        end)
+      end)
+    end
+    @test grad_tracker ≈ grad_forward ≈ [288.0, 288.0, 0.0]
+  end
+end