diff --git a/examples/box.jl b/examples/box.jl
index 535fa3d080..51e93bb6ab 100644
--- a/examples/box.jl
+++ b/examples/box.jl
@@ -72,6 +72,7 @@ using Enzyme
 # once. 
 
 struct ModelParameters
+
     ## handy to have constants 
     day::Float64
     year::Float64
@@ -192,43 +193,56 @@ function compute_density(state, params)
 end
 
 ## lastly, a function that takes one step forward 
-##       Input: T_now = [T1(t), T2(t), ..., S3(t)]
-##              T_old = [T1(t-dt), ..., S3(t-dt)]
+##       Input: state_now = [T1(t), T2(t), ..., S3(t)]
+##              state_old = [T1(t-dt), ..., S3(t-dt)]
 ##              u = transport(t)
 ##              dt = time step
-##       Output: T_new = [T1(t+dt), ..., S3(t+dt)]
-
-function compute_update(T_now, T_old, u, params, dt)
-    Ṫ_now = zeros(6)
-    T_new = zeros(6)
+##       Output: state_new = [T1(t+dt), ..., S3(t+dt)]
 
-    V = params.boxvol
-    Tstar = params.Tstar
+function compute_update(state_now, state_old, u, params, dt)
+    dstate_now_dt = zeros(6)
+    state_new = zeros(6)
 
     ## first computing the time derivatives of the various temperatures and salinities
     if u > 0
-        Ṫ_now[1] = u * (T_now[3] - T_now[1]) / V[1] + params.gamma * (Tstar[1] - T_now[1])
-        Ṫ_now[2] = u * (T_now[1] - T_now[2]) / V[2] + params.gamma * (Tstar[2] - T_now[2])
-        Ṫ_now[3] = u * (T_now[2] - T_now[3]) / V[3]
-
-        Ṫ_now[4] = u * (T_now[6] - T_now[4]) / V[1] + params.FW[1] / V[1]
-        Ṫ_now[5] = u * (T_now[4] - T_now[5]) / V[2] + params.FW[2] / V[2]
-        Ṫ_now[6] = u * (T_now[5] - T_now[6]) / V[3]
+        dstate_now_dt[1] =
+            u * (state_now[3] - state_now[1]) / params.boxvol[1] +
+            params.gamma * (params.Tstar[1] - state_now[1])
+        dstate_now_dt[2] =
+            u * (state_now[1] - state_now[2]) / params.boxvol[2] +
+            params.gamma * (params.Tstar[2] - state_now[2])
+        dstate_now_dt[3] = u * (state_now[2] - state_now[3]) / params.boxvol[3]
+
+        dstate_now_dt[4] =
+            u * (state_now[6] - state_now[4]) / params.boxvol[1] +
+            params.FW[1] / params.boxvol[1]
+        dstate_now_dt[5] =
+            u * (state_now[4] - state_now[5]) / params.boxvol[2] +
+            params.FW[2] / params.boxvol[2]
+        dstate_now_dt[6] = u * (state_now[5] - state_now[6]) / params.boxvol[3]
 
     elseif u <= 0
-        Ṫ_now[1] = u * (T_now[2] - T_now[1]) / V[1] + params.gamma * (Tstar[1] - T_now[1])
-        Ṫ_now[2] = u * (T_now[3] - T_now[2]) / V[2] + params.gamma * (Tstar[2] - T_now[2])
-        Ṫ_now[3] = u * (T_now[1] - T_now[3]) / V[3]
-
-        Ṫ_now[4] = u * (T_now[5] - T_now[4]) / V[1] + params.FW[1] / V[1]
-        Ṫ_now[5] = u * (T_now[6] - T_now[5]) / V[2] + params.FW[2] / V[2]
-        Ṫ_now[6] = u * (T_now[4] - T_now[6]) / V[3]
+        dstate_now_dt[1] =
+            u * (state_now[2] - state_now[1]) / params.boxvol[1] +
+            params.gamma * (params.Tstar[1] - state_now[1])
+        dstate_now_dt[2] =
+            u * (state_now[3] - state_now[2]) / params.boxvol[2] +
+            params.gamma * (params.Tstar[2] - state_now[2])
+        dstate_now_dt[3] = u * (state_now[1] - state_now[3]) / params.boxvol[3]
+
+        dstate_now_dt[4] =
+            u * (state_now[5] - state_now[4]) / params.boxvol[1] +
+            params.FW[1] / params.boxvol[1]
+        dstate_now_dt[5] =
+            u * (state_now[6] - state_now[5]) / params.boxvol[2] +
+            params.FW[2] / params.boxvol[2]
+        dstate_now_dt[6] = u * (state_now[4] - state_now[6]) / params.boxvol[3]
     end
 
     ## update fldnew using a version of Euler's method  
-    T_new .= T_old + 2.0 * dt * Ṫ_now
+    state_new .= state_old + 2.0 * dt * dstate_now_dt
 
-    return T_new
+    return state_new
 end
 
 # ## Define forward functions
@@ -239,27 +253,28 @@ end
 # Let's start with the standard forward function. This is just going to be used
 # to store the states at every timestep:
 
-function integrate(T_now, T_old, dt, M, parameters)
+function integrate(state_now, state_old, dt, M, parameters)
 
     ## Because of the adjoint problem we're setting up, we need to store both the states before 
     ## and after the Robert filter smoother has been applied 
-    states_before = [T_old]
-    states_after = [T_old]
+    states_before = [state_old]
+    states_after = [state_old]
 
     for t in 1:M
-        rho = compute_density(T_now, parameters)
+        rho = compute_density(state_now, parameters)
         u = compute_transport(rho, parameters)
-        T_new = compute_update(T_now, T_old, u, parameters, dt)
+        state_new = compute_update(state_now, state_old, u, parameters, dt)
 
         ## Applying the Robert filter smoother (needed for stability)
-        T_new_smoothed = T_now + parameters.rf_coeff * (T_new - 2.0 * T_now + T_old)
+        state_new_smoothed =
+            state_now + parameters.rf_coeff * (state_new - 2.0 * state_now + state_old)
 
-        push!(states_after, T_new_smoothed)
-        push!(states_before, T_new)
+        push!(states_after, state_new_smoothed)
+        push!(states_before, state_new)
 
         ## cycle the "now, new, old" states 
-        T_old = T_new_smoothed
-        T_now = T_new
+        state_old = state_new_smoothed
+        state_now = state_new
     end
 
     return states_after, states_before
@@ -269,17 +284,18 @@ end
 # that runs a single step of the model forward rather than the whole integration. 
 # This would allow us to save as many of the adjoint variables as we wish when running the adjoint method, 
 # although for the example we'll discuss later we technically only need one of them
-function one_step_forward(T_now, T_old, out_now, out_old, parameters, dt)
-    T_new_smoothed = zeros(6)
-    rho = compute_density(T_now, parameters)                             ## compute density
+function one_step_forward(state_now, state_old, out_now, out_old, parameters, dt)
+    state_new_smoothed = zeros(6)
+    rho = compute_density(state_now, parameters)                             ## compute density
     u = compute_transport(rho, parameters)                                   ## compute transport 
-    T_new = compute_update(T_now, T_old, u, parameters, dt)      ## compute new state values
+    state_new = compute_update(state_now, state_old, u, parameters, dt)      ## compute new state values
 
     ## Robert filter smoother 
-    T_new_smoothed[:] = T_now + parameters.rf_coeff * (T_new - 2.0 * T_now + T_old)
+    state_new_smoothed[:] =
+        state_now + parameters.rf_coeff * (state_new - 2.0 * state_now + state_old)
 
-    out_old[:] = T_new_smoothed
-    out_now[:] = T_new
+    out_old[:] = state_new_smoothed
+    out_now[:] = state_new
 
     return nothing
 end
@@ -306,8 +322,8 @@ states_after_smoother, states_before_smoother = integrate(
 )
 
 ## Run Enzyme one time on `one_step_forward``
-dT_now = zeros(6)
-dT_old = zeros(6)
+dstate_now = zeros(6)
+dstate_old = zeros(6)
 out_now = zeros(6);
 dout_now = ones(6);
 out_old = zeros(6);
@@ -316,8 +332,8 @@ dout_old = ones(6);
 autodiff(
     Reverse,
     one_step_forward,
-    Duplicated([Tbar; Sbar], dT_now),
-    Duplicated([Tbar; Sbar], dT_old),
+    Duplicated([Tbar; Sbar], dstate_now),
+    Duplicated([Tbar; Sbar], dstate_old),
     Duplicated(out_now, dout_now),
     Duplicated(out_old, dout_old),
     Const(parameters),
@@ -338,24 +354,24 @@ autodiff(
 @show states_before_smoother[2], states_after_smoother[2]
 
 # we see that Enzyme has computed and stored exactly the output of the 
-# forward step. Next, let's look at `dT_now`: 
+# forward step. Next, let's look at `dstate_now`: 
 
-@show dT_now
+@show dstate_now
 
 # Just a few numbers, but this is what makes AD so nice: Enzyme has exactly computed
-# the derivative of all outputs with respect to the input `T_now`, evaluated at
-# `T_now`, and acted with this gradient on what we gave as `dout_now` (in our case,
+# the derivative of all outputs with respect to the input `state_now`, evaluated at
+# `state_now`, and acted with this gradient on what we gave as `dout_now` (in our case,
 # all ones). Using AD notation for reverse mode, this is
 
 # ```math 
 # \overline{\text{state\_now}} = \frac{\partial \text{out\_now}}{\partial \text{state\_now}}\right|_\text{state\_now} \overline{\text{out\_now} + \frac{\partial \text{out\_old}}{\partial \text{state\_now}}\right|_\text{state\_now} \overline{\text{out\_old}
 # ```
 
-# We note here that had we initialized `dT_now` and `dT_old` as something else, our results 
+# We note here that had we initialized `dstate_now` and `dstate_old` as something else, our results 
 # will change. Let's multiply them by two and see what happens. 
 
-dT_now_new = zeros(6)
-dT_old_new = zeros(6)
+dstate_now_new = zeros(6)
+dstate_old_new = zeros(6)
 out_now = zeros(6);
 dout_now = 2 * ones(6);
 out_old = zeros(6);
@@ -363,22 +379,22 @@ dout_old = 2 * ones(6);
 autodiff(
     Reverse,
     one_step_forward,
-    Duplicated([Tbar; Sbar], dT_now_new),
-    Duplicated([Tbar; Sbar], dT_old_new),
+    Duplicated([Tbar; Sbar], dstate_now_new),
+    Duplicated([Tbar; Sbar], dstate_old_new),
     Duplicated(out_now, dout_now),
     Duplicated(out_old, dout_old),
     Const(parameters),
     Const(10 * parameters.day),
 )
 
-# Now checking `dT_now` and `dT_old` we see
+# Now checking `dstate_now` and `dstate_old` we see
 
-@show dT_now_new
+@show dstate_now_new
 
 # What happened? Enzyme is actually taking the computed gradient and acting on what we
 # give as input to `dout_now` and `dout_old`. Checking this, we see
 
-@show 2 * dT_now
+@show 2 * dstate_now
 
 # and they match the new results. This exactly matches what we'd expect to happen since 
 # we scaled `dout_now` by two. 
@@ -423,14 +439,14 @@ function compute_adjoint_values(
     dout_old = [1.0; 0.0; 0.0; 0.0; 0.0; 0.0]
 
     for j in M:-1:1
-        dT_now = zeros(6)
-        dT_old = zeros(6)
+        dstate_now = zeros(6)
+        dstate_old = zeros(6)
 
         autodiff(
             Reverse,
             one_step_forward,
-            Duplicated(states_before_smoother[j], dT_now),
-            Duplicated(states_after_smoother[j], dT_old),
+            Duplicated(states_before_smoother[j], dstate_now),
+            Duplicated(states_after_smoother[j], dstate_old),
             Duplicated(zeros(6), dout_now),
             Duplicated(zeros(6), dout_old),
             Const(parameters),
@@ -438,11 +454,11 @@ function compute_adjoint_values(
         )
 
         if j == 1
-            return dT_now, dT_old
+            return dstate_now, dstate_old
         end
 
-        dout_now = copy(dT_now)
-        dout_old = copy(dT_old)
+        dout_now = copy(dstate_now)
+        dout_old = copy(dstate_old)
     end
 end
 
@@ -458,14 +474,14 @@ states_after_smoother, states_before_smoother = integrate(
 
 # Next, we pass all of our states to the AD function to get back to the desired derivative:
 
-dT_now, dT_old = compute_adjoint_values(
+dstate_now, dstate_old = compute_adjoint_values(
     states_before_smoother, states_after_smoother, M, parameters
 )
 
 # And we're done! We were interested in sensitivity to the initial salinity of box 
-# two, which will live in what we've called `dT_old`. Checking this value we see
+# two, which will live in what we've called `dstate_old`. Checking this value we see
 
-@show dT_old[5]
+@show dstate_old[5]
 
 # As it stands this is just a number, but a good check that Enzyme has computed what we want
 # is to approximate the derivative with a Taylor series. Specifically,
@@ -492,26 +508,27 @@ use_to_check = states_after_smoother[M + 1]
 diffs = []
 step_sizes = [1e-1, 1e-2, 1e-3, 1e-4, 1e-5, 1e-6, 1e-7, 1e-8, 1e-9, 1e-10]
 for eps in step_sizes
-    T_new_smoothed = zeros(6)
+    state_new_smoothed = zeros(6)
 
     initial_temperature = [20.0; 1.0; 1.0]
     perturbed_initial_salinity = [35.5; 34.5; 34.5] + [0.0; eps; 0.0]
 
-    T_old = [initial_temperature; perturbed_initial_salinity]
-    T_now = [20.0; 1.0; 1.0; 35.5; 34.5; 34.5]
+    state_old = [initial_temperature; perturbed_initial_salinity]
+    state_now = [20.0; 1.0; 1.0; 35.5; 34.5; 34.5]
 
     for t in 1:M
-        rho = compute_density(T_now, parameters)
+        rho = compute_density(state_now, parameters)
         u = compute_transport(rho, parameters)
-        T_new = compute_update(T_now, T_old, u, parameters, 10 * parameters.day)
+        state_new = compute_update(state_now, state_old, u, parameters, 10 * parameters.day)
 
-        T_new_smoothed[:] = T_now + parameters.rf_coeff * (T_new - 2.0 * T_now + T_old)
+        state_new_smoothed[:] =
+            state_now + parameters.rf_coeff * (state_new - 2.0 * state_now + state_old)
 
-        T_old = T_new_smoothed
-        T_now = T_new
+        state_old = state_new_smoothed
+        state_now = state_new
     end
 
-    push!(diffs, (T_old[1] - use_to_check[1]) / eps)
+    push!(diffs, (state_old[1] - use_to_check[1]) / eps)
 end
 
 # Then checking what we found the derivative to be analytically:
@@ -521,7 +538,7 @@ end
 # which comes very close to our calculated value. We can go further and check the 
 # percent difference to see 
 
-@show abs.(diffs .- dT_old[5]) ./ dT_old[5]
+@show abs.(diffs .- dstate_old[5]) ./ dstate_old[5]
 
 # and we get down to a percent difference on the order of ``1e^{-5}``, showing Enzyme calculated
 # the correct derivative. Success! 
diff --git a/lib/EnzymeCore/ext/AdaptExt.jl b/lib/EnzymeCore/ext/AdaptExt.jl
index e2adabd524..2e7a03cabd 100644
--- a/lib/EnzymeCore/ext/AdaptExt.jl
+++ b/lib/EnzymeCore/ext/AdaptExt.jl
@@ -18,4 +18,4 @@ function Adapt.adapt_structure(to, x::BatchDuplicatedNoNeed)
     return BatchDuplicatedNoNeed(adapt(to, x.val), adapt(to, x.dval))
 end
 
-end
+end
\ No newline at end of file
diff --git a/lib/EnzymeCore/test/runtests.jl b/lib/EnzymeCore/test/runtests.jl
index 749378cde7..cd16b2f8e8 100644
--- a/lib/EnzymeCore/test/runtests.jl
+++ b/lib/EnzymeCore/test/runtests.jl
@@ -25,4 +25,4 @@ y = @view data[3:end]
 @test_throws ErrorException Duplicated(d, y)
 
 @test_throws ErrorException Active(data)
-@test_throws ErrorException Active(d)
+@test_throws ErrorException Active(d)
\ No newline at end of file
diff --git a/src/rules/allocrules.jl b/src/rules/allocrules.jl
index 9b6e2d6ff8..6785d650af 100644
--- a/src/rules/allocrules.jl
+++ b/src/rules/allocrules.jl
@@ -163,4 +163,4 @@ end
             (LLVM.API.LLVMBuilderRef, LLVM.API.LLVMValueRef, LLVM.API.LLVMValueRef)
         )
     )
-end
+end
\ No newline at end of file
diff --git a/src/rules/parallelrules.jl b/src/rules/parallelrules.jl
index da4827c817..12dafed918 100644
--- a/src/rules/parallelrules.jl
+++ b/src/rules/parallelrules.jl
@@ -900,4 +900,4 @@ function wait_rev(B, orig, gutils, tape)
     debug_from_orig!(gutils, cal, orig)
     callconv!(cal, callconv(orig))
     return nothing
-end
+end
\ No newline at end of file
diff --git a/src/typeanalysis.jl b/src/typeanalysis.jl
index 8ad34aadf2..d7050be4d1 100644
--- a/src/typeanalysis.jl
+++ b/src/typeanalysis.jl
@@ -22,4 +22,4 @@ end
 
 # typedef bool (*CustomRuleType)(int /*direction*/, CTypeTree * /*return*/,
 #                                CTypeTree * /*args*/, size_t /*numArgs*/,
-#                                LLVMValueRef)=T
+#                                LLVMValueRef)=T
\ No newline at end of file