diff --git a/README.md b/README.md
index dc3d31b5..9c52315f 100644
--- a/README.md
+++ b/README.md
@@ -17,10 +17,10 @@ If you are interested in using AdvancedHMC.jl through a probabilistic programmin
 - We presented a poster for AdvancedHMC.jl at [StanCon 2019](https://mc-stan.org/events/stancon2019Cambridge/) in Cambridge, UK. ([pdf](https://github.com/TuringLang/AdvancedHMC.jl/files/3730367/StanCon-AHMC.pdf))
 
 **API CHANGES**
-- [v0.5.0] **Breaking!** Convinience constructors for common samplers changed to:
-  - `HMC(init_ϵ::Float64=init_ϵ, n_leapfrog::Int=n_leapfrog)`
-  - `NUTS(n_adapts::Int=n_adapts, δ::Float64=δ)` 
-  - `HMCDA(n_adapts::Int=n_adapts, δ::Float64=δ, λ::Float64=λ)`
+- [v0.5.0] **Breaking!** Convenience constructors for common samplers changed to:
+  - `HMC(init_ϵ, n_leapfrog)`
+  - `NUTS(n_adapts, target_acceptance)` 
+  - `HMCDA(n_adapts, target_acceptance, integration_time)`
 - [v0.2.22] Three functions are renamed.
   - `Preconditioner(metric::AbstractMetric)` -> `MassMatrixAdaptor(metric)` and 
   - `NesterovDualAveraging(δ, integrator::AbstractIntegrator)` -> `StepSizeAdaptor(δ, integrator)`
@@ -77,15 +77,15 @@ n_samples, n_adapts = 2_000, 1_000
 metric = DiagEuclideanMetric(D)
 hamiltonian = Hamiltonian(metric, ℓπ, ForwardDiff)
 
-# Define a leapfrog solver, with initial step size chosen heuristically
+# Define a leapfrog solver, with the initial step size chosen heuristically
 initial_ϵ = find_good_stepsize(hamiltonian, initial_θ)
 integrator = Leapfrog(initial_ϵ)
 
-# Define an HMC sampler, with the following components
+# Define an HMC sampler with the following components
 #   - multinomial sampling scheme,
 #   - generalised No-U-Turn criteria, and
 #   - windowed adaption for step-size and diagonal mass matrix
-proposal = NUTS{MultinomialTS, GeneralisedNoUTurn}(integrator)
+proposal = HMCKernel(Trajectory{MultinomialTS}(integrator, GeneralisedNoUTurn()))
 adaptor = StanHMCAdaptor(MassMatrixAdaptor(metric), StepSizeAdaptor(0.8, integrator))
 
 # Run the sampler to draw samples from the specified Gaussian, where
diff --git a/src/abstractmcmc.jl b/src/abstractmcmc.jl
index 31bdf999..8e0956c5 100644
--- a/src/abstractmcmc.jl
+++ b/src/abstractmcmc.jl
@@ -289,16 +289,15 @@ end
 make_integrator(spl::HMCSampler, ϵ::Real) = spl.κ.τ.integrator
 make_integrator(spl::AbstractHMCSampler, ϵ::Real) = make_integrator(spl.integrator, ϵ)
 make_integrator(i::AbstractIntegrator, ϵ::Real) = i
-make_integrator(i::Type{<:AbstractIntegrator}, ϵ::Real) = i
 make_integrator(i::Symbol, ϵ::Real) = make_integrator(Val(i), ϵ)
-make_integrator(i...) = error("Integrator $(typeof(i)) not supported.")
+make_integrator(@nospecialize(i), ::Real) = error("Integrator $i not supported.")
 make_integrator(i::Val{:leapfrog}, ϵ::Real) = Leapfrog(ϵ)
 make_integrator(i::Val{:jitteredleapfrog}, ϵ::Real) = JitteredLeapfrog(ϵ)
 make_integrator(i::Val{:temperedleapfrog}, ϵ::Real) = TemperedLeapfrog(ϵ)
 
 #########
 
-make_metric(i...) = error("Metric $(typeof(i)) not supported.")
+make_metric(@nospecialize(i), T::Type, d::Int) = error("Metric $(typeof(i)) not supported.")
 make_metric(i::Symbol, T::Type, d::Int) = make_metric(Val(i), T, d)
 make_metric(i::AbstractMetric, T::Type, d::Int) = i
 make_metric(i::Type{AbstractMetric}, T::Type, d::Int) = i