Things I build for myself.

I built this because I wanted to actually understand my health data. Not just glance at rings on a watch, but see patterns across every signal I generate over time and measure the real impact of treatment decisions.

It's a Python-based data warehouse that pulls from Apple Watch, iPhone health metrics, sleep tracking, workouts, nutrition logs, and a medical device. Custom importers handle each source, with deduplication logic to resolve the messiness of multi-device recording. Everything lands in a local SQLite database with 18 months of history: 16,000+ daily metrics, 524 nights of sleep staging, and 458 logged workouts.

The analysis side generates reports for HRV, resting heart rate, sleep architecture, SpO2, VO2 max, blood pressure, weight trend, and training load. Sleep stage data is calibrated against a clinical in-lab baseline. The Apple Watch systematically under-reads deep sleep. I measured exactly how far off it is against a polysomnogram and built a correction factor into the database. The milestone system lets me mark treatment start dates and run before/after comparisons on any metric.

I built a professional operations system in Notion around six interconnected databases: Goals, Companies, Projects, People, Action Items, and a Daily Log. Every project ties to a client. Every action item traces back to the log entry that created it. Every win links to the goal it advanced. The schema supports real analysis: which goals have gone untouched for two weeks, which projects are consuming the most days, which people show up most often in high-energy entries.

The system comes alive through the daily log. I built a custom Claude Code skill for daily capture. Give it a freeform brain dump, or a screenshot of the day's calendar, and it turns that into a structured Notion entry: projects touched, goals moved forward, people mentioned, accomplishments, blockers. If something reads as an action item it flags it. If it sounds like a real win it proposes a Big Wins record. Nothing gets created without my confirmation.

Designing this required deep thinking about how professional work is actually structured, what information compounds over time, and how to make daily capture frictionless enough to actually stick. The schema, the relationship model, and the workflow are all decisions I made. Building it is what proved they were right.

This site was designed and built using Claude Code from the ground up. The design system, the component architecture, the typography, the color tokens, the blog infrastructure, the contact form on Cloudflare Pages Functions. All of it was built through an iterative process with an AI development partner, not a template or a theme.

The workflow looks like this: design decisions get captured in a structured memory system of Markdown files that persist across sessions. There's a CLAUDE.md in the project root, a memory index that tracks decisions made about writing style, design direction, and copy rules, and a project file that documents what's been built, what's in progress, and what comes next. When a new session starts, the agent loads that context and continues exactly where the last one left off.

Every visual change is verified in a live browser preview before it's called done. The agent runs the dev server, takes screenshots, inspects the accessibility tree, and confirms the output looks right. It's the closest thing to pair programming I've found, with a partner who reads every file before touching anything and never forgets the project history.