LifeOS

Some lives don't fit a productivity app. Running multiple venues as the GM, sharing custody of a nine-year-old, holding a body-recomposition program together on an irregular shift schedule, and building software on the side — that's not a to-do list problem. It's an optimization problem across domains that normally don't talk to each other: sleep, training, nutrition, schedule, communication.

LifeOS treats those domains as one system. Data flows in from each — recovery from a wearable, nutrition from voice logs, training from quick session entries, mail from several inboxes — and an intelligence layer reads across all of it to produce a small number of genuinely useful decisions each day, in a voice-first interface that fits a life spent in motion.

• A morning brief that reads overnight recovery against the day's real demands and adjusts what to prioritize — not a calendar readout, an interpretation
• Training logging that takes one spoken sentence and handles progressive overload decisions, gated on recovery and nutrition so it never pushes weight at the wrong time
• A weekly review that finds the cross-domain pattern worth seeing — the link between heavy work weeks and stalled progress — and turns it into next week's plan
• Email triage across four addresses that handles the routine locally and reserves the intelligence layer for what actually needs judgment
• A priority hierarchy underneath all of it, so when a kid gets sick, the whole system reshapes around what matters

This is the most ambitious system in the portfolio — and the architecture is where the discipline shows. Every recommendation is gated on real signals, every correlation is held to a pattern-not-a-datapoint standard, and the user can see and edit everything the system believes about them. It's a serious answer to a hard question: what would software actually look like if it were built around one real life?