diff --git a/src/TinyHugeNumbers.jl b/src/TinyHugeNumbers.jl
index 023f516..5418ae0 100644
--- a/src/TinyHugeNumbers.jl
+++ b/src/TinyHugeNumbers.jl
@@ -124,8 +124,11 @@ const huge = HugeNumber()
 # but it cannot be instantiated, this might be useful in situations like `clamp(value, tiny, huge)`
 # in this case Julia attempts first to promote `tiny` and `huge` to the same type and then
 # uses the result to promote `value` to the resulting type. However, there is no "common" type for
-# both `tiny` and `huge` so we introduce a special structure that will accomodate that
+# both `tiny` and `huge` (except for the `Union` but we can't use it either since it introduces ambiguities) 
+# so we introduce a special structure that will accomodate that
 # see also: https://github.com/ReactiveBayes/TinyHugeNumbers.jl/issues/3
+# note: as a result, we cannot store `tiny` and `huge` in the same container (e.g. `Array`), 
+# but `[ 1.0, tiny, huge ]` will work just fine
 struct PromoteTinyOrHuge 
     PromoteTinyOrHuge() = error("Cannot instantiate an internal structure for promotion.")
 end
diff --git a/test/runtests.jl b/test/runtests.jl
index eede59a..3a08cb3 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -1,6 +1,6 @@
 using TinyHugeNumbers, Aqua, Test
 
-Aqua.test_all(TinyHugeNumbers, deps_compat = (; check_extras = false, check_weakdeps = true))
+Aqua.test_all(TinyHugeNumbers, deps_compat=(; check_extras=false, check_weakdeps=true))
 
 @testset "TinyHugeNumbers.jl" begin
     import TinyHugeNumbers: TinyNumber, HugeNumber
@@ -75,9 +75,6 @@ Aqua.test_all(TinyHugeNumbers, deps_compat = (; check_extras = false, check_weak
         @test @inferred(promote_type(T, HugeNumber, TinyNumber)) == T
     end
 
-    @test_throws "Cannot convert `tiny` to `huge`" [tiny, huge]
-    @test_throws "Cannot convert `huge` to `tiny`" [huge, tiny]
-
     for a in (1, 1.0, 0, 0.0, 1.0f0, 0.0f0, Int32(0), Int32(1), big"1", big"1.0", big"0", big"0.0")
         T = typeof(a)
         for v in Real[tiny, huge]
@@ -115,20 +112,40 @@ end
 
 @testset "ForwardDiff.jl compatibility" begin
     import ForwardDiff
-    
+
     f(x) = clamp(x, tiny, huge)
 
     @test @inferred(ForwardDiff.derivative(f, 1.0)) === 1.0
     @test @inferred(ForwardDiff.derivative(f, 2.0)) === 1.0
     @test @inferred(ForwardDiff.derivative(f, 0.0)) === 0.0
-    @test @inferred(ForwardDiff.derivative(f, huge+1.0)) === 0.0
-    @test @inferred(ForwardDiff.derivative(f, tiny-1.0)) === 0.0
+    @test @inferred(ForwardDiff.derivative(f, huge + 1.0)) === 0.0
+    @test @inferred(ForwardDiff.derivative(f, tiny - 1.0)) === 0.0
 
     g(x) = clamp(x^2, tiny, huge)
 
     @test @inferred(ForwardDiff.derivative(g, 1.0)) === 2.0
     @test @inferred(ForwardDiff.derivative(g, 2.0)) === 4.0
     @test @inferred(ForwardDiff.derivative(g, 0.0)) === 0.0
-    @test @inferred(ForwardDiff.derivative(g, huge+1.0)) === 0.0
-    @test @inferred(ForwardDiff.derivative(g, tiny-1.0)) === 2(tiny-1.0)
+    @test @inferred(ForwardDiff.derivative(g, huge + 1.0)) === 0.0
+    @test @inferred(ForwardDiff.derivative(g, tiny - 1.0)) === 2(tiny - 1.0)
+end
+
+@testset "Storing `tiny` and `huge` in arrays" begin
+    @static if VERSION >= v"1.10"
+        @test_throws "Cannot convert `tiny` to `huge`" [tiny, huge]
+        @test_throws "Cannot convert `huge` to `tiny`" [huge, tiny]
+    else
+        @test_throws ErrorException [tiny, huge]
+        @test_throws ErrorException [huge, tiny]
+    end
+
+    for a in (1.0, 0.0, 1.0f0, 0.0f0, big"1.0", big"0.0")
+        @test [a, tiny, huge] == [a, tiny(a), huge(a)]
+        @test [tiny, a, huge] == [tiny(a), a, huge(a)]
+        @test [tiny, huge, a] == [tiny(a), huge(a), a]
+
+        @test [a, huge, tiny] == [a, huge(a), tiny(a)]
+        @test [huge, a, tiny] == [huge(a), a, tiny(a)]
+        @test [huge, tiny, a] == [huge(a), tiny(a), a]
+    end
 end