This is part three of a series on display consistency in embedded systems. The first two parts were technical. This one is about why the technical parts worked. The picture: ATtiny85 thermometer. Neural network inference. QUAD7SHIFT display. Built from datasheets. He had datasheets. No Stack Overflow. No libraries to install. No AI to generate boilerplate. No tutorials that abstracted away the in
[05] When to Pull the Trigger on FIRE — Monte Carlo Says You're Already Free This is Part 5 of a 6-part series: Building Investment Systems with Python "You need 25x your annual expenses." That's the standard FIRE rule. For ¥9.6M annual expenses, that's ¥240M. Most people see that number and think: "I'll never get there." But the 25x rule assumes a fixed 4% withdrawal rate, zero income, zero ada
[04] The 90/10 Portfolio — Dividend Core + Growth Satellite with a Live Simulator This is Part 4 of a 6-part series: Building Investment Systems with Python In the manifesto, I described a 90/10 portfolio philosophy: 90% in dividend-growing core positions, 10% in a deep-value satellite aiming for 3-5x. Today we build both sides — the dividend snowball model for the core, and a live interactive s
[03] Designing a Personal Commitment Line — Two Loans, One Defense System This is Part 3 of a 6-part series: Building Investment Systems with Python Every major corporation maintains a revolving credit facility — a pre-arranged borrowing line they can draw from instantly during a crisis. They pay a commitment fee for the privilege of having this standby capacity, even when they don't use it. The
[02] Stress Testing Your Life — What Happens at -30%, -50%, -60%? This is Part 2 of a 6-part series: Building Investment Systems with Python After the 2008 financial crisis, regulators required banks to run stress tests — hypothetical scenarios where markets crash 30%, 40%, 60% — and prove they could survive. Your personal balance sheet faces the same risks. If you hold a securities-backed loan,