diff --git a/docs/Project.toml b/docs/Project.toml
index 3ee0316c59..2eaca463c1 100644
--- a/docs/Project.toml
+++ b/docs/Project.toml
@@ -97,8 +97,8 @@ OptimizationNLopt = "0.3"
 OptimizationOptimJL = "0.4"
 OptimizationOptimisers = "0.3"
 OptimizationPolyalgorithms = "0.3"
-OrdinaryDiffEq = "6"
-OrdinaryDiffEqRosenbrock = "1"
+OrdinaryDiffEq = "6, 7"
+OrdinaryDiffEqRosenbrock = "1, 2"
 Plots = "1"
 SciMLExpectations = "2"
 SciMLSensitivity = "7"
diff --git a/docs/src/showcase/massively_parallel_gpu.md b/docs/src/showcase/massively_parallel_gpu.md
index 4d3ebb97d9..e645cf3c15 100644
--- a/docs/src/showcase/massively_parallel_gpu.md
+++ b/docs/src/showcase/massively_parallel_gpu.md
@@ -83,7 +83,7 @@ Now, from this problem, we build an `EnsembleProblem` as per the DifferentialEqu
 specification. A `prob_func` jiggles the parameters and we solve 10_000 trajectories:
 
 ```@example diffeqgpu
-prob_func = (prob, i, repeat) -> ODE.remake(prob, p = (StaticArrays.@SVector rand(Float32, 3)) .* p)
+prob_func = (prob, ctx) -> ODE.remake(prob, p = (StaticArrays.@SVector rand(Float32, 3)) .* p)
 monteprob = DiffEqGPU.EnsembleProblem(prob, prob_func = prob_func, safetycopy = false)
 sol = ODE.solve(monteprob, ODE.Tsit5(), DiffEqGPU.EnsembleThreads(), trajectories = 10_000, saveat = 1.0f0)
 ```
diff --git a/docs/src/showcase/ode_types.md b/docs/src/showcase/ode_types.md
index 428f3d28c2..9fa02e7201 100644
--- a/docs/src/showcase/ode_types.md
+++ b/docs/src/showcase/ode_types.md
@@ -312,7 +312,7 @@ numerical solution:
 
 ```@example odetypes
 println("Quantity of carbon-14 after ", sol.t[11], " years:")
-println("Numerical: ", sol[11])
+println("Numerical: ", sol.u[11])
 println("Analytic:  ", u[11])
 ```
 
diff --git a/docs/src/showcase/optimization_under_uncertainty.md b/docs/src/showcase/optimization_under_uncertainty.md
index bfad4e7568..f433d650d1 100644
--- a/docs/src/showcase/optimization_under_uncertainty.md
+++ b/docs/src/showcase/optimization_under_uncertainty.md
@@ -69,7 +69,7 @@ import Distributions
 cor_dist = Distributions.truncated(Distributions.Normal(0.9, 0.02), 0.9 - 3 * 0.02, 1.0)
 trajectories = 100
 
-prob_func(prob, i, repeat) = DE.remake(prob, p = [p[1], rand(cor_dist)])
+prob_func(prob, ctx) = DE.remake(prob, p = [p[1], rand(cor_dist)])
 ensemble_prob = DE.EnsembleProblem(prob, prob_func = prob_func)
 ensemblesol = DE.solve(ensemble_prob, DE.Tsit5(), DE.EnsembleThreads(), trajectories = trajectories,
     callback = cbs)