diff --git a/Project.toml b/Project.toml
index 6c7f1d1..a6c625f 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,7 +1,7 @@
 name = "ProximalCore"
 uuid = "dc4f5ac2-75d1-4f31-931e-60435d74994b"
 authors = ["Lorenzo Stella <lorenzostella@gmail.com>"]
-version = "0.1.2"
+version = "0.2.0"
 
 [deps]
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
diff --git a/src/ProximalCore.jl b/src/ProximalCore.jl
index 0bc6855..728e3d3 100644
--- a/src/ProximalCore.jl
+++ b/src/ProximalCore.jl
@@ -8,36 +8,6 @@ is_convex(::T) where T = is_convex(T)
 is_generalized_quadratic(::Type) = false
 is_generalized_quadratic(::T) where T = is_generalized_quadratic(T)
 
-"""
-    gradient!(y, f, x)
-
-In-place gradient (and value) of `f` at `x`.
-
-The gradient is written to the (pre-allocated) array `y`, which should have the same shape/size as `x`.
-
-Returns the value `f` at `x`.
-
-See also: [`gradient`](@ref).
-"""
-gradient!
-
-"""
-    gradient(f, x)
-
-Gradient (and value) of `f` at `x`.
-
-Return a tuple `(y, fx)` consisting of
-- `y`: the gradient of `f` at `x`
-- `fx`: the value of `f` at `x`
-
-See also: [`gradient!`](@ref).
-"""
-function gradient(f, x)
-    y = similar(x)
-    fx = gradient!(y, f, x)
-    return y, fx
-end
-
 """
     prox!(y, f, x, gamma=1)
 
@@ -83,11 +53,6 @@ struct Zero end
 
 (::Zero)(x) = real(eltype(x))(0)
 
-function gradient!(y, f::Zero, x)
-    y .= eltype(x)(0)
-    return f(x)
-end
-
 function prox!(y, ::Zero, x, gamma)
     y .= x
     return real(eltype(y))(0)
diff --git a/test/runtests.jl b/test/runtests.jl
index dab2832..ff08292 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -2,8 +2,8 @@ using Test
 using Aqua
 using LinearAlgebra
 using ProximalCore
-using ProximalCore: prox, gradient, convex_conjugate
-using ProximalCore: Zero, IndZero
+using ProximalCore: prox
+using ProximalCore: Zero, IndZero, convex_conjugate
 import ProximalCore: prox!, is_convex, is_generalized_quadratic
 
 @testset "Aqua" begin
@@ -36,10 +36,10 @@ end
         @test is_generalized_quadratic(Zero())
 
         for T in [Float32, Float64]
-            @test let x = T[1.0, 2.0, 3.0]
-                prox(Zero(), x, T(42)) == (x, T(0))
-                gradient(Zero(), x) == (T[0, 0, 0], T(0))
-            end
+            x = T[1.0, 2.0, 3.0]
+            @test Zero()(x) == T(0)
+            @test prox(Zero(), x, T(42)) == (x, T(0))
+            @test prox(Zero(), x, ) == (x, T(0))
         end
         
     end
@@ -57,9 +57,20 @@ end
             @test IndZero()(x) == T(Inf)
             @test IndZero()(T[0, 0, 0]) == T(0)
             @test prox(IndZero(), x, T(42)) == (T[0, 0, 0], T(0))
+            @test prox(IndZero(), x) == (T[0, 0, 0], T(0))
         end
 
     end
+
+    @testset "Others" begin
+
+        @inferred (f -> Val(is_convex(f)))(42)
+        @inferred (f -> Val(is_generalized_quadratic(f)))(42)
+
+        @test !is_convex(42)
+        @test !is_generalized_quadratic(42)
+
+    end
     
     @testset "Conjugation" begin