diff --git a/docs/src/tutorials/dae.md b/docs/src/tutorials/dae.md
index dcbe7df6a..56287cb02 100644
--- a/docs/src/tutorials/dae.md
+++ b/docs/src/tutorials/dae.md
@@ -12,7 +12,7 @@ Currently, we recommend using the semi-implicit `EK1` algorithm.
 
 First, define the DAE (here the ROBER problem) as an ODE problem with singular mass matrix:
 ```@example dae
-using ProbNumDiffEq, Plots, LinearAlgebra, OrdinaryDiffEq, ModelingToolkit
+using ProbNumDiffEq, Plots, LinearAlgebra, OrdinaryDiffEq, ModelingToolkit, LinearAlgebra
 
 function rober(du, u, p, t)
     y₁, y₂, y₃ = u
diff --git a/docs/src/tutorials/dynamical_odes.md b/docs/src/tutorials/dynamical_odes.md
index 2204f70e8..caebc979a 100644
--- a/docs/src/tutorials/dynamical_odes.md
+++ b/docs/src/tutorials/dynamical_odes.md
@@ -89,6 +89,8 @@ The probabilistic numerical solvers from ProbNumDiffEq.jl have the same internal
 As a result, we can use the `EK1` both for first and second order ODEs, but it automatically specializes on the latter to provide a __2x performance boost__:
 
 ```
+julia> using BenchmarkTools
+
 julia> @btime solve(prob, EK1(order=3), adaptive=false, dt=1e-2);
   766.312 ms (400362 allocations: 173.38 MiB)