diff --git a/benchmarks/Project.toml b/benchmarks/Project.toml
index a32aeef..7a322e0 100644
--- a/benchmarks/Project.toml
+++ b/benchmarks/Project.toml
@@ -1,8 +1,6 @@
 [deps]
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 MCMCChains = "c7f686f2-ff18-58e9-bc7b-31028e88f75d"
-MappedArrays = "dbb5928d-eab1-5f90-85c2-b9b0edb7c900"
-Optim = "429524aa-4258-5aef-a3af-852621145aeb"
 Pathfinder = "b1d3bc72-d0e7-4279-b92f-7fa5d6d2d454"
 ProgressMeter = "92933f4c-e287-5a05-a399-4b506db050ca"
 Revise = "295af30f-e4ad-537b-8983-00126c2a3abe"
diff --git a/benchmarks/bouncy.jl b/benchmarks/bouncy.jl
index 53b41d2..9aed9cb 100644
--- a/benchmarks/bouncy.jl
+++ b/benchmarks/bouncy.jl
@@ -14,56 +14,76 @@ using ForwardDiff
 using ForwardDiff: Dual
 using Pathfinder
 using Pathfinder.PDMats
+using MCMCChains
+using TupleVectors: chainvec
+using Tilde.MeasureTheory: transform
 
 Random.seed!(1)
 
+function make_grads(post)
+    as_post = as(post)
+    d = TV.dimension(as_post)
+    tr(θ) = transform(as_post, θ)
+    obj(θ) = -Tilde.unsafe_logdensityof(post, tr(θ))
+    ℓ(θ) = -obj(θ)
+    @inline function dneglogp(t, x, v, args...) # two directional derivatives
+        f(t) = obj(x + t * v)
+        u = ForwardDiff.derivative(f, Dual{:hSrkahPmmC}(0.0, 1.0))
+        u.value, u.partials[]
+    end
+
+    gconfig = ForwardDiff.GradientConfig(obj, rand(d), ForwardDiff.Chunk{d}())
+    function ∇neglogp!(y, t, x, args...)
+        ForwardDiff.gradient!(y, obj, x, gconfig)
+        y
+    end
+    ℓ, dneglogp, ∇neglogp!
+end
+
+# ↑ general purpose
+############################################################
+# ↓ problem-specific
+
 # read data
 function readlrdata()
     fname = joinpath("lr.data")
     z = readdlm(fname)
-    A = z[:, 1:end-1]
+    A = z[:, 1:(end-1)]
     A = [ones(size(A, 1)) A]
     y = z[:, end] .- 1
     return A, y
 end
-A, y = readlrdata();
-At = collect(A');
 
 model_lr = @model (At, y, σ) begin
     d, n = size(At)
     θ ~ Normal(σ = σ)^d
     for j in 1:n
-        logitp = dot(view(At, :, j), θ)
+        logitp = view(At, :, j)' * θ
         y[j] ~ Bernoulli(logitp = logitp)
     end
 end
+
+# Define model arguments
+A, y = readlrdata();
+At = collect(A');
 σ = 100.0
 
-function make_grads(model_lr, At, y, σ)
-    post = model_lr(At, y, σ) | (; y)
-    as_post = as(post)
-    obj(θ) = -Tilde.unsafe_logdensityof(post, transform(as_post, θ))
-    ℓ(θ) = -obj(θ)
-    @inline function dneglogp(t, x, v) # two directional derivatives
-        f(t) = obj(x + t * v)
-        u = ForwardDiff.derivative(f, Dual{:hSrkahPmmC}(0.0, 1.0))
-        u.value, u.partials[]
-    end
+# Represent the posterior
+post = model_lr(At, y, σ) | (; y)
 
-    gconfig = ForwardDiff.GradientConfig(obj, rand(25), ForwardDiff.Chunk{25}())
-    function ∇neglogp!(y, t, x)
-        ForwardDiff.gradient!(y, obj, x, gconfig)
-        return
-    end
-    post, ℓ, dneglogp, ∇neglogp!
-end
+d = TV.dimension(as(post))
+
+
+ℓ, dneglogp, ∇neglogp! = make_grads(post)
 
-post, ℓ, dneglogp, ∇neglogp! = make_grads(model_lr, At, y, σ)
-# Try things out
-dneglogp(2.4, randn(25), randn(25));
-∇neglogp!(randn(25), 2.1, randn(25));
+# Make sure gradients are working
+let
+    @show dneglogp(2.4, randn(d), randn(d))
+    y = Vector{Float64}(undef, d)
+    @show ∇neglogp!(y, 2.1, randn(d))
+    nothing
+end
 
-d = 25 # number of parameters 
 t0 = 0.0;
 x0 = zeros(d); # starting point sampler
 # estimated posterior mean (n=100000, 797s)
@@ -129,8 +149,7 @@ sampler = ZZB.NotFactSampler(
     ),
 );
 
-using TupleVectors: chainvec
-using Tilde.MeasureTheory: transform
+# @time first(Iterators.drop(tvs,1000))
 
 function collect_sampler(t, sampler, n; progress = true, progress_stops = 20)
     if progress
@@ -166,7 +185,6 @@ elapsed_time = @elapsed @time begin
     bps_samples, info = collect_sampler(as(post), sampler, n; progress = false)
 end
 
-using MCMCChains
 bps_chain = MCMCChains.Chains(bps_samples.θ);
 bps_chain = setinfo(bps_chain, (; start_time = 0.0, stop_time = elapsed_time));