Pedagogy: add step-size + stability slider to 50_ode/10_Forward_Euler with detailed explanations

Add an ipywidgets step-size slider to `50_ode/10_Forward_Euler.ipynb` showing convergence as `h` decreases — and, importantly, **divergence / instability** as `h` exceeds the stability boundary for stiff or oscillatory ODEs.

## Code scope

- Slider for `h` (e.g. log-scale 0.001 → 1.0).
- Plot: numerical solution overlaid with analytic / reference solution.
- Side: error vs reference; ideally a log-error subplot showing O(h) convergence in the stable regime.
- Demo on at least one stiff/oscillatory ODE so the *stability* boundary is visible — the trajectory blows up or rings, not just gets less accurate.

## Explanation scope (the deliverable)

Forward Euler is in-curriculum but the *intuition* for stability is conceptually deep. Author:

- **Step size vs accuracy** — why halving `h` halves the error (linear convergence), with one paragraph on truncation error vs roundoff.
- **Step size vs stability** — different concept from accuracy. For `y' = λy` with `Re(λ) < 0`, Forward Euler is stable only when `|1 + hλ| ≤ 1` — a finite disk in the complex plane. Outside the disk, the numerical solution grows even though the true solution decays.
- **Why this matters** — stiff ODEs (large `|λ|`) force tiny `h` for stability even when accuracy doesn't demand it. Motivates implicit methods (lead-in to Heun `20`, RK `30`, and `solve_ivp` `11`).
- **What the slider should reveal** — find the `h` at which the trajectory transitions from convergent to oscillatory to divergent.

## Why this framing

The slider is mechanical; the *teaching value* is in the stability story. Without that explanation the widget is just "smaller h = better answer", which misses the point of why we cover Forward Euler before the more sophisticated methods.