theorem strong_law_aux1 {c : ℝ} (c_one : 1 < c) {ε : ℝ} (εpos : 0 < ε) : ∀ᵐ ω, ∀ᶠ n : ℕ in atTop,
    |∑ i in range ⌊c ^ n⌋₊, truncation (X i) i ω - 𝔼[∑ i in range ⌊c ^ n⌋₊, truncation (X i) i]| <
    ε * ⌊c ^ n⌋₊ := by
  /- Let `S n = ∑ i in range n, Y i` where `Y i = truncation (X i) i`. We should show that
    `|S k - 𝔼[S k]| / k ≤ ε` along the sequence of powers of `c`. For this, we apply Borel-Cantelli:
    it suffices to show that the converse probabilites are summable. From Chebyshev inequality, this
    will follow from a variance control `∑' Var[S (c^i)] / (c^i)^2 < ∞`. This is checked in `I2`
    using pairwise independence to expand the variance of the sum as the sum of the variances,
    and then a straightforward but tedious computation (essentially boiling down to the fact that
    the sum of `1/(c ^ i)^2` beyong a threshold `j` is comparable to `1/j^2`).
    Note that we have written `c^i` in the above proof sketch, but rigorously one should put integer
    parts everywhere, making things more painful. We write `u i = ⌊c^i⌋₊` for brevity. -/
  have c_pos : 0 < c := zero_lt_one.trans c_one
  have hX : ∀ i, AEStronglyMeasurable (X i) ℙ := fun i =>
    (hident i).symm.aestronglyMeasurable_snd hint.1
  have A : ∀ i, StronglyMeasurable (indicator (Set.Ioc (-i : ℝ) i) id) := fun i =>
    stronglyMeasurable_id.indicator measurableSet_Ioc
  set Y := fun n : ℕ => truncation (X n) n
  set S := fun n => ∑ i in range n, Y i with hS
  let u : ℕ → ℕ := fun n => ⌊c ^ n⌋₊
  have u_mono : Monotone u := fun i j hij => Nat.floor_mono (pow_le_pow_right c_one.le hij)
  have I1 : ∀ K, ∑ j in range K, ((j : ℝ) ^ 2)⁻¹ * Var[Y j] ≤ 2 * 𝔼[X 0] := by
    intro K
    calc
      ∑ j in range K, ((j : ℝ) ^ 2)⁻¹ * Var[Y j] ≤
          ∑ j in range K, ((j : ℝ) ^ 2)⁻¹ * 𝔼[truncation (X 0) j ^ 2] := by
        apply sum_le_sum fun j _ => ?_
        refine' mul_le_mul_of_nonneg_left _ (inv_nonneg.2 (sq_nonneg _))
        rw [(hident j).truncation.variance_eq]
        exact variance_le_expectation_sq (hX 0).truncation
      _ ≤ 2 * 𝔼[X 0] := sum_variance_truncation_le hint (hnonneg 0) K
  let C := c ^ 5 * (c - 1)⁻¹ ^ 3 * (2 * 𝔼[X 0])
  have I2 : ∀ N, ∑ i in range N, ((u i : ℝ) ^ 2)⁻¹ * Var[S (u i)] ≤ C := by
    intro N
    calc
      ∑ i in range N, ((u i : ℝ) ^ 2)⁻¹ * Var[S (u i)] =
          ∑ i in range N, ((u i : ℝ) ^ 2)⁻¹ * ∑ j in range (u i), Var[Y j] := by
        congr 1 with i
        congr 1
        rw [hS, IndepFun.variance_sum]
        · intro j _
          exact (hident j).aestronglyMeasurable_fst.memℒp_truncation
        · intro k _ l _ hkl
          exact (hindep hkl).comp (A k).measurable (A l).measurable
      _ = ∑ j in range (u (N - 1)),
          (∑ i in (range N).filter fun i => j < u i, ((u i : ℝ) ^ 2)⁻¹) * Var[Y j] := by
        

Proof state:
Ω : Type u_1
inst✝¹ : MeasureSpace Ω
inst✝ : IsProbabilityMeasure ℙ
X : ℕ → Ω → ℝ
hint : Integrable (X 0)
hindep : Pairwise fun i j => IndepFun (X i) (X j)
hident : ∀ (i : ℕ), IdentDistrib (X i) (X 0)
hnonneg : ∀ (i : ℕ) (ω : Ω), 0 ≤ X i ω
c : ℝ
c_one : 1 < c
ε : ℝ
εpos : 0 < ε
c_pos : 0 < c
hX : ∀ (i : ℕ), AEStronglyMeasurable (X i) ℙ
A : ∀ (i : ℝ), StronglyMeasurable (indicator (Set.Ioc (-i) i) id)
Y : ℕ → Ω → ℝ := fun n => truncation (X n) ↑n
S : ℕ → Ω → ℝ := fun n => ∑ i in range n, Y i
hS : S = fun n => ∑ i in range n, Y i
u : ℕ → ℕ := fun n => ⌊c ^ n⌋₊
u_mono : Monotone u
I1 : ∀ (K : ℕ), ∑ j in range K, (↑j ^ 2)⁻¹ * variance (Y j) ℙ ≤ 2 * ∫ (a : Ω), X 0 a
C : ℝ := c ^ 5 * (c - 1)⁻¹ ^ 3 * (2 * ∫ (a : Ω), X 0 a)
N : ℕ
⊢ ∑ i in range N, (↑(u i) ^ 2)⁻¹ * ∑ j in range (u i), variance (Y j) ℙ =
    ∑ j in range (u (N - 1)), (∑ i in filter (fun i => j < u i) (range N), (↑(u i) ^ 2)⁻¹) * variance (Y j) ℙ

Next tactic:
simp_rw [mul_sum, sum_mul, sum_sigma']