Coding for the Mayor: How I used AI to solve a 1,000-guest "Diplomatic" Crisis
When my wife and I married last year, it was a reputational challenge. As a high-profile lawyer/influencer, her 1,000+ guest list included judges and the Mayor. I couldn’t send generic cards, so I built a Python engine with dynamic text placement and a retry-logic dispatch via GitHub Pages. That wedding was my first test of zero-failure engineering—the same principle now powering my FastAPI Observability Engine.
- The "Rugged" Financial Orchestration Platform
I’m building AnchorFlow to solve the high-stakes logistics of the informal economy. It’s a financial system engineered for environments where bandwidth is low but failure isn't an option. By applying the same "zero-failure" logic used in my wedding engine and AlertEngine, I'm creating a platform that turns passive monitoring into active recovery for real-time mobile money and fintech transactions.
- I built my own observability engine because the alternatives cost more than my server
Building AnchorFlow, a payment orchestration platform on mobile money rails in Zimbabwe, I needed monitoring that acted — not just reported. Datadog was $400/month. Prometheus took days. Neither told me what to do when something broke. So I built fastapi-alertengine: one middleware, embedded directly into FastAPI, that learns your baseline, detects degradation early, and suggests authorized recovery actions in seconds. Zero SaaS bill. Zero vendor lock-in.