I always use Juno which has special rendering and looks great. But trying out different versions of Julia, I saw the REPL...
julia> using DifferentialEquations
julia> f(t,u) = 1.01*u
f (generic function with 1 method)
julia> u0=1/2
0.5
julia> tspan = (0.0,1.0)
(0.0, 1.0)
julia> prob = ODEProblem(f,u0,tspan)
DiffEqBase.ODEProblem{Float64,Float64,false,#f,Void,UniformScaling{Int64}}(f, 0.5, (0.0, 1.0), nothing, UniformScaling{Int64}
1*I)
julia> sol = solve(prob,Tsit5(),reltol=1e-8,abstol=1e-8)
DiffEqBase.ODESolution{Float64,1,Array{Float64,1},Void,Void,Array{Float64,1},Array{Array{Float64,1},1},DiffEqBase.ODEProblem{Float64,Float64,false,#f,Void,UniformScaling{Int64}},OrdinaryDiffEq.Tsit5,OrdinaryDiffEq.InterpolationData{#f,Array{Float64,1},Array{Float64,1},Array{Array{Float64,1},1},OrdinaryDiffEq.Tsit5ConstantCache{Float64,Float64}}}([0.5, 0.506305, 0.52193, 0.543053, 0.569506, 0.602174, 0.641202, 0.687147, 0.740325, 0.801221, 0.870275, 0.948019, 1.03501, 1.1319, 1.23937, 1.3582, 1.3728], nothing, nothing, [0.0, 0.0124078, 0.0425009, 0.0817804, 0.128873, 0.184097, 0.246274, 0.314792, 0.388595, 0.46686, 0.548714, 0.633432, 0.720359, 0.808954, 0.898761, 0.989412, 1.0], Array{Float64,1}[[Inf], [0.505, 0.506019, 0.507074, 0.510728, 0.51124, 0.511368, 0.511368], [0.511368, 0.513871, 0.516476, 0.52555, 0.526831, 0.527151, 0.52715], [0.52715, 0.530517, 0.534033, 0.546313, 0.548052, 0.548487, 0.548483], [0.548483, 0.552683, 0.557081, 0.572476, 0.574663, 0.57521, 0.575201], [0.575201, 0.580367, 0.585789, 0.60482, 0.607532, 0.608211, 0.608196], [0.608196, 0.614345, 0.620814, 0.643571, 0.646824, 0.647638, 0.647614], [0.647614, 0.65483, 0.662437, 0.689249, 0.693091, 0.694054, 0.694018], [0.694018, 0.702347, 0.711143, 0.742198, 0.746659, 0.747777, 0.747728], [0.747728, 0.757244, 0.767308, 0.802891, 0.808014, 0.809296, 0.809233], [0.809233, 0.820004, 0.831408, 0.871778, 0.877599, 0.879057, 0.878977], [0.878977, 0.891086, 0.903919, 0.949387, 0.955951, 0.957596, 0.957499], [0.957499, 0.971034, 0.985387, 1.03628, 1.04364, 1.04548, 1.04536], [1.04536, 1.06042, 1.0764, 1.1331, 1.1413, 1.14335, 1.14322], [1.14322, 1.15991, 1.17763, 1.24053, 1.24963, 1.25191, 1.25176], [1.25176, 1.27021, 1.2898, 1.35936, 1.36943, 1.37195, 1.37178], [1.37178, 1.37414, 1.37659, 1.38505, 1.38623, 1.38653, 1.38653]], DiffEqBase.ODEProblem{Float64,Float64,false,#f,Void,UniformScaling{Int64}}(f, 0.5, (0.0, 1.0), nothing, UniformScaling{Int64}
1*I), OrdinaryDiffEq.Tsit5(), OrdinaryDiffEq.InterpolationData, true, 0, :Success)
julia> using RecursiveArrayTools
julia> VectorOfArray(sol.u)
17-element Array{Float64,1}:
0.5
0.506305
0.52193
0.543053
0.569506
0.602174
0.641202
0.687147
0.740325
0.801221
0.870275
0.948019
1.03501
1.1319
1.23937
1.3582
1.3728
julia> DiffEqArray(sol.t,sol.u)
17-element Array{Float64,1}:
0.0
0.0124078
0.0425009
0.0817804
0.128873
0.184097
0.246274
0.314792
0.388595
0.46686
0.548714
0.633432
0.720359
0.808954
0.898761
0.989412
1.0