/-- If a sequence of functions real or complex functions are eventually differentiable on a
neighborhood of `x`, they are Cauchy _at_ `x`, and their derivatives
are a uniform Cauchy sequence in a neighborhood of `x`, then the functions form a uniform Cauchy
sequence in a neighborhood of `x`. -/
theorem uniformCauchySeqOnFilter_of_fderiv (hf' : UniformCauchySeqOnFilter f' l (𝓝 x))
    (hf : ∀ᶠ n : ι × E in l ×ˢ 𝓝 x, HasFDerivAt (f n.1) (f' n.1 n.2) n.2)
    (hfg : Cauchy (map (fun n => f n x) l)) : UniformCauchySeqOnFilter f l (𝓝 x) := by
  letI : NormedSpace ℝ E := NormedSpace.restrictScalars ℝ 𝕜 _
  rw [SeminormedAddGroup.uniformCauchySeqOnFilter_iff_tendstoUniformlyOnFilter_zero] at hf' ⊢
  suffices
    TendstoUniformlyOnFilter (fun (n : ι × ι) (z : E) => f n.1 z - f n.2 z - (f n.1 x - f n.2 x)) 0
        (l ×ˢ l) (𝓝 x) ∧
      TendstoUniformlyOnFilter (fun (n : ι × ι) (_ : E) => f n.1 x - f n.2 x) 0 (l ×ˢ l) (𝓝 x) by
    have := this.1.add this.2
    rw [add_zero] at this
    exact this.congr (by simp)
  constructor
  · -- This inequality follows from the mean value theorem. To apply it, we will need to shrink our
    -- neighborhood to small enough ball
    rw [Metric.tendstoUniformlyOnFilter_iff] at hf' ⊢
    intro ε hε
    have := (tendsto_swap4_prod.eventually (hf.prod_mk hf)).diag_of_prod_right
    obtain ⟨a, b, c, d, e⟩ := eventually_prod_iff.1 ((hf' ε hε).and this)
    obtain ⟨R, hR, hR'⟩ := Metric.nhds_basis_ball.eventually_iff.mp d
    let r := min 1 R
    have hr : 0 < r := by simp [hR]
    have hr' : ∀ ⦃y : E⦄, y ∈ Metric.ball x r → c y := fun y hy =>
      hR' (lt_of_lt_of_le (Metric.mem_ball.mp hy) (min_le_right _ _))
    have hxy : ∀ y : E, y ∈ Metric.ball x r → ‖y - x‖ < 1 := by
      intro y hy
      rw [Metric.mem_ball, dist_eq_norm] at hy
      exact lt_of_lt_of_le hy (min_le_left _ _)
    have hxyε : ∀ y : E, y ∈ Metric.ball x r → ε * ‖y - x‖ < ε := by
      intro y hy
      exact (mul_lt_iff_lt_one_right hε.lt).mpr (hxy y hy)
    -- With a small ball in hand, apply the mean value theorem
    refine'
      eventually_prod_iff.mpr
        ⟨_, b, fun e : E => Metric.ball x r e,
          eventually_mem_set.mpr (Metric.nhds_basis_ball.mem_of_mem hr), fun {n} hn {y} hy => _⟩
    simp only [Pi.zero_apply, dist_zero_left] at e ⊢
    refine' lt_of_le_of_lt _ (hxyε y hy)
    exact
      Convex.norm_image_sub_le_of_norm_hasFDerivWithin_le
        (fun y hy => ((e hn (hr' hy)).2.1.sub (e hn (hr' hy)).2.2).hasFDerivWithinAt)
        (fun y hy => (e hn (hr' hy)).1.le) (convex_ball x r) (Metric.mem_ball_self hr) hy
  · -- This is just `hfg` run through `eventually_prod_iff`
    

Proof state:
case right
ι : Type u_1
l : Filter ι
E : Type u_2
inst✝⁴ : NormedAddCommGroup E
𝕜 : Type u_3
inst✝³ : IsROrC 𝕜
inst✝² : NormedSpace 𝕜 E
G : Type u_4
inst✝¹ : NormedAddCommGroup G
inst✝ : NormedSpace 𝕜 G
f : ι → E → G
g : E → G
f' : ι → E → E →L[𝕜] G
g' : E → E →L[𝕜] G
x : E
hf' : TendstoUniformlyOnFilter (fun n z => f' n.1 z - f' n.2 z) 0 (l ×ˢ l) (𝓝 x)
hf : ∀ᶠ (n : ι × E) in l ×ˢ 𝓝 x, HasFDerivAt (f n.1) (f' n.1 n.2) n.2
hfg : Cauchy (map (fun n => f n x) l)
this : NormedSpace ℝ E := NormedSpace.restrictScalars ℝ 𝕜 E
⊢ TendstoUniformlyOnFilter (fun n x_1 => f n.1 x - f n.2 x) 0 (l ×ˢ l) (𝓝 x)

Next tactic:
refine' Metric.tendstoUniformlyOnFilter_iff.mpr fun ε hε => _