Approximating the Heaviside step function & its derivative with sigmoids Top: Smooth approximations to H(x) = 0 (x < 0), 1 (x > 0) using sigmoid(x; σ) = 1 / (1 + e^{-σ x}) for σ = 1 (orange), 2 (green), 4 (red), 8 (purple). Larger σ → sharper step. Bottom: Their derivatives d/dx [sigmoid] = σ ⋅ sigmoid(x; σ) ⋅ (1 − sigmoid(x; σ)) which converge to the Dirac delta δ(x) as σ → ∞. This Enables gradient-based optimization in neural networks and physics simulations where the true step function is non-differentiable.

H.E. @justinsuntron joined @TheBonnieChang on the Nakamoto Stage at Bitcoin Asia to discuss institutional adoption, global capital flows, and Bitcoin’s growing role across the financial system. 🇭🇰