/-- Harder inclusion: at a point in `D f K`, the function `f` has a derivative, in `K`. -/
theorem D_subset_differentiable_set {K : Set F} (hK : IsComplete K) :
    D f K ⊆ { x | DifferentiableWithinAt ℝ f (Ici x) x ∧ derivWithin f (Ici x) x ∈ K } := by
  have P : ∀ {n : ℕ}, (0 : ℝ) < (1 / 2) ^ n := fun {n} => pow_pos (by norm_num) n
  intro x hx
  have :
    ∀ e : ℕ, ∃ n : ℕ, ∀ p q, n ≤ p → n ≤ q →
      ∃ L ∈ K, x ∈ A f L ((1 / 2) ^ p) ((1 / 2) ^ e) ∩ A f L ((1 / 2) ^ q) ((1 / 2) ^ e) := by
    intro e
    have := mem_iInter.1 hx e
    rcases mem_iUnion.1 this with ⟨n, hn⟩
    refine' ⟨n, fun p q hp hq => _⟩
    simp only [mem_iInter, ge_iff_le] at hn
    rcases mem_iUnion.1 (hn p hp q hq) with ⟨L, hL⟩
    exact ⟨L, exists_prop.mp <| mem_iUnion.1 hL⟩
  /- Recast the assumptions: for each `e`, there exist `n e` and linear maps `L e p q` in `K`
    such that, for `p, q ≥ n e`, then `f` is well approximated by `L e p q` at scale `2 ^ (-p)` and
    `2 ^ (-q)`, with an error `2 ^ (-e)`. -/
  choose! n L hn using this
  /- All the operators `L e p q` that show up are close to each other. To prove this, we argue
      that `L e p q` is close to `L e p r` (where `r` is large enough), as both approximate `f` at
      scale `2 ^(- p)`. And `L e p r` is close to `L e' p' r` as both approximate `f` at scale
      `2 ^ (- r)`. And `L e' p' r` is close to `L e' p' q'` as both approximate `f` at scale
      `2 ^ (- p')`. -/
  have M :
    ∀ e p q e' p' q',
      n e ≤ p →
        n e ≤ q → n e' ≤ p' → n e' ≤ q' → e ≤ e' → ‖L e p q - L e' p' q'‖ ≤ 12 * (1 / 2) ^ e := by
    intro e p q e' p' q' hp hq hp' hq' he'
    let r := max (n e) (n e')
    have I : ((1 : ℝ) / 2) ^ e' ≤ (1 / 2) ^ e :=
      pow_le_pow_of_le_one (by norm_num) (by norm_num) he'
    have J1 : ‖L e p q - L e p r‖ ≤ 4 * (1 / 2) ^ e := by
      have I1 : x ∈ A f (L e p q) ((1 / 2) ^ p) ((1 / 2) ^ e) := (hn e p q hp hq).2.1
      have I2 : x ∈ A f (L e p r) ((1 / 2) ^ p) ((1 / 2) ^ e) := (hn e p r hp (le_max_left _ _)).2.1
      exact norm_sub_le_of_mem_A P _ I1 I2
    have J2 : ‖L e p r - L e' p' r‖ ≤ 4 * (1 / 2) ^ e := by
      have I1 : x ∈ A f (L e p r) ((1 / 2) ^ r) ((1 / 2) ^ e) := (hn e p r hp (le_max_left _ _)).2.2
      have I2 : x ∈ A f (L e' p' r) ((1 / 2) ^ r) ((1 / 2) ^ e') :=
        (hn e' p' r hp' (le_max_right _ _)).2.2
      exact norm_sub_le_of_mem_A P _ I1 (A_mono _ _ I I2)
    have J3 : ‖L e' p' r - L e' p' q'‖ ≤ 4 * (1 / 2) ^ e := by
      have I1 : x ∈ A f (L e' p' r) ((1 / 2) ^ p') ((1 / 2) ^ e') :=
        (hn e' p' r hp' (le_max_right _ _)).2.1
      have I2 : x ∈ A f (L e' p' q') ((1 / 2) ^ p') ((1 / 2) ^ e') := (hn e' p' q' hp' hq').2.1
      exact norm_sub_le_of_mem_A P _ (A_mono _ _ I I1) (A_mono _ _ I I2)
    calc
      ‖L e p q - L e' p' q'‖ =
          ‖L e p q - L e p r + (L e p r - L e' p' r) + (L e' p' r - L e' p' q')‖ :=
        by congr 1; abel
      _ ≤ ‖L e p q - L e p r‖ + ‖L e p r - L e' p' r‖ + ‖L e' p' r - L e' p' q'‖ :=
        (le_trans (norm_add_le _ _) (add_le_add_right (norm_add_le _ _) _))
      _ ≤ 4 * (1 / 2) ^ e + 4 * (1 / 2) ^ e + 4 * (1 / 2) ^ e := by gcongr
        -- Porting note: proof was `by apply_rules [add_le_add]`
      _ = 12 * (1 / 2) ^ e := by ring

  /- For definiteness, use `L0 e = L e (n e) (n e)`, to have a single sequence. We claim that this
    is a Cauchy sequence. -/
  let L0 : ℕ → F := fun e => L e (n e) (n e)
  have : CauchySeq L0 := by
    rw [Metric.cauchySeq_iff']
    intro ε εpos
    obtain ⟨e, he⟩ : ∃ e : ℕ, (1 / 2) ^ e < ε / 12 :=
      exists_pow_lt_of_lt_one (div_pos εpos (by norm_num)) (by norm_num)
    refine' ⟨e, fun e' he' => _⟩
    rw [dist_comm, dist_eq_norm]
    calc
      ‖L0 e - L0 e'‖ ≤ 12 * (1 / 2) ^ e := M _ _ _ _ _ _ le_rfl le_rfl le_rfl le_rfl he'
      _ < 12 * (ε / 12) := (mul_lt_mul' le_rfl he (le_of_lt P) (by norm_num))
      _ = ε := by field_simp [(by norm_num : (12 : ℝ) ≠ 0)]; ring

  -- As it is Cauchy, the sequence `L0` converges, to a limit `f'` in `K`.
  obtain ⟨f', f'K, hf'⟩ : ∃ f' ∈ K, Tendsto L0 atTop (𝓝 f') :=
    cauchySeq_tendsto_of_isComplete hK (fun e => (hn e (n e) (n e) le_rfl le_rfl).1) this
  have Lf' : ∀ e p, n e ≤ p → ‖L e (n e) p - f'‖ ≤ 12 * (1 / 2) ^ e := by
    intro e p hp
    apply le_of_tendsto (tendsto_const_nhds.sub hf').norm
    rw [eventually_atTop]
    exact ⟨e, fun e' he' => M _ _ _ _ _ _ le_rfl hp le_rfl le_rfl he'⟩
  -- Let us show that `f` has right derivative `f'` at `x`.
  have : HasDerivWithinAt f f' (Ici x) x := by
    simp only [hasDerivWithinAt_iff_isLittleO, isLittleO_iff]
    /- to get an approximation with a precision `ε`, we will replace `f` with `L e (n e) m` for
      some large enough `e` (yielding a small error by uniform approximation). As one can vary `m`,
      this makes it possible to cover all scales, and thus to obtain a good linear approximation in
      the whole interval of length `(1/2)^(n e)`. -/
    intro ε εpos
    obtain ⟨e, he⟩ : ∃ e : ℕ, (1 / 2) ^ e < ε / 16 :=
      exists_pow_lt_of_lt_one (div_pos εpos (by norm_num)) (by norm_num)
    have xmem : x ∈ Ico x (x + (1 / 2) ^ (n e + 1)) := by
      simp only [one_div, left_mem_Ico, lt_add_iff_pos_right, inv_pos, pow_pos, zero_lt_two,
        zero_lt_one]
    filter_upwards [Icc_mem_nhdsWithin_Ici xmem] with y hy
    -- We need to show that `f y - f x - f' (y - x)` is small. For this, we will work at scale
    -- `k` where `k` is chosen with `‖y - x‖ ∼ 2 ^ (-k)`.
    rcases eq_or_lt_of_le hy.1 with (rfl | xy)
    · simp only [sub_self, zero_smul, norm_zero, mul_zero, le_rfl]
    have yzero : 0 < y - x := sub_pos.2 xy
    have y_le : y - x ≤ (1 / 2) ^ (n e + 1) := by linarith [hy.2]
    have yone : y - x ≤ 1 := le_trans y_le (pow_le_one _ (by norm_num) (by norm_num))
    -- define the scale `k`.
    obtain ⟨k, hk, h'k⟩ : ∃ k : ℕ, (1 / 2) ^ (k + 1) < y - x ∧ y - x ≤ (1 / 2) ^ k :=
      exists_nat_pow_near_of_lt_one yzero yone (by norm_num : (0 : ℝ) < 1 / 2)
        (by norm_num : (1 : ℝ) / 2 < 1)
    -- the scale is large enough (as `y - x` is small enough)
    have k_gt : n e < k := by
      have : ((1 : ℝ) / 2) ^ (k + 1) < (1 / 2) ^ (n e + 1) := lt_of_lt_of_le hk y_le
      rw [pow_lt_pow_iff_right_of_lt_one (by norm_num : (0 : ℝ) < 1 / 2) (by norm_num)] at this
      linarith
    set m := k - 1
    have m_ge : n e ≤ m := Nat.le_sub_one_of_lt k_gt
    have km : k = m + 1 := (Nat.succ_pred_eq_of_pos (lt_of_le_of_lt (zero_le _) k_gt)).symm
    rw [km] at hk h'k
    -- `f` is well approximated by `L e (n e) k` at the relevant scale
    -- (in fact, we use `m = k - 1` instead of `k` because of the precise definition of `A`).
    have J : ‖f y - f x - (y - x) • L e (n e) m‖ ≤ 4 * (1 / 2) ^ e * ‖y - x‖ :=
      calc
        ‖f y - f x - (y - x) • L e (n e) m‖ ≤ (1 / 2) ^ e * (1 / 2) ^ m := by
          apply le_of_mem_A (hn e (n e) m le_rfl m_ge).2.2
          · simp only [one_div, inv_pow, left_mem_Icc, le_add_iff_nonneg_right]
            exact div_nonneg (inv_nonneg.2 (pow_nonneg zero_le_two _)) zero_le_two
          · simp only [pow_add, tsub_le_iff_left] at h'k
            simpa only [hy.1, mem_Icc, true_and_iff, one_div, pow_one] using h'k
        _ = 4 * (1 / 2) ^ e * (1 / 2) ^ (m + 2) := by 

Proof state:
F : Type u_1
inst✝¹ : NormedAddCommGroup F
inst✝ : NormedSpace ℝ F
f : ℝ → F
K✝ K : Set F
hK : IsComplete K
P : ∀ {n : ℕ}, 0 < (1 / 2) ^ n
x : ℝ
hx : x ∈ D f K
n : ℕ → ℕ
L : ℕ → ℕ → ℕ → F
hn :
  ∀ (e p q : ℕ),
    n e ≤ p →
      n e ≤ q → L e p q ∈ K ∧ x ∈ A f (L e p q) ((1 / 2) ^ p) ((1 / 2) ^ e) ∩ A f (L e p q) ((1 / 2) ^ q) ((1 / 2) ^ e)
M :
  ∀ (e p q e' p' q' : ℕ), n e ≤ p → n e ≤ q → n e' ≤ p' → n e' ≤ q' → e ≤ e' → ‖L e p q - L e' p' q'‖ ≤ 12 * (1 / 2) ^ e
L0 : ℕ → F := fun e => L e (n e) (n e)
this : CauchySeq L0
f' : F
f'K : f' ∈ K
hf' : Tendsto L0 atTop (𝓝 f')
Lf' : ∀ (e p : ℕ), n e ≤ p → ‖L e (n e) p - f'‖ ≤ 12 * (1 / 2) ^ e
ε : ℝ
εpos : 0 < ε
e : ℕ
he : (1 / 2) ^ e < ε / 16
xmem : x ∈ Ico x (x + (1 / 2) ^ (n e + 1))
y : ℝ
hy : y ∈ Icc x (x + (1 / 2) ^ (n e + 1))
xy : x < y
yzero : 0 < y - x
y_le : y - x ≤ (1 / 2) ^ (n e + 1)
yone : y - x ≤ 1
k : ℕ
k_gt : n e < k
m : ℕ := k - 1
h'k : y - x ≤ (1 / 2) ^ (m + 1)
hk : (1 / 2) ^ (m + 1 + 1) < y - x
m_ge : n e ≤ m
km : k = m + 1
⊢ (1 / 2) ^ e * (1 / 2) ^ m = 4 * (1 / 2) ^ e * (1 / 2) ^ (m + 2)

Next tactic:
field_simp