diff --git a/docs/src/tutorials/ensemble.md b/docs/src/tutorials/ensemble.md
index 0b7459bf1..712d96841 100644
--- a/docs/src/tutorials/ensemble.md
+++ b/docs/src/tutorials/ensemble.md
@@ -27,14 +27,14 @@ This results is compared to a multistart approach with 4 random initial points:
 
 ```@example ensemble
 x0s = [x0, x0 .+ rand(2), x0 .+ rand(2), x0 .+ rand(2)]
-function prob_func(prob, i, repeat)
-    remake(prob, u0 = x0s[i])
+function prob_func(prob, ctx)
+    remake(prob, u0 = x0s[ctx.sim_id])
 end
 
 ensembleprob = EnsembleProblem(prob; prob_func)
 @time sol = solve(ensembleprob, OptimizationOptimJL.BFGS(),
     EnsembleThreads(), trajectories = 4, maxiters = 5)
-@show findmin(i -> sol[i].objective, 1:4)[1]
+@show minimum(s.objective for s in sol.u)
 ```
 
 With the same number of iterations (5) we get a much lower (1/100th) objective value by using multiple initial points. The initialization strategy used here was a pretty trivial one but approaches based on Quasi-Monte Carlo sampling should be typically more effective.
diff --git a/docs/src/tutorials/remakecomposition.md b/docs/src/tutorials/remakecomposition.md
index bc41b7321..fe9e1784b 100644
--- a/docs/src/tutorials/remakecomposition.md
+++ b/docs/src/tutorials/remakecomposition.md
@@ -46,7 +46,7 @@ res1 = solve(prob, BBO_adaptive_de_rand_1_bin(), maxiters = 4000)
 This is a good start can we converge to the global optimum?
 
 ```@example polyalg
-prob = remake(prob, u0 = res1.minimizer)
+prob = remake(prob, u0 = res1.u)
 res2 = solve(prob, OptimizationLBFGSB.LBFGSB(), maxiters = 100)
 
 @show res2.objective