Everything Is an Event

Home Assistant was the gateway drug.

A smart home platform that tracks every light switch, every door sensor, every thermostat change as a timestamped event in a database. The first time you query it — "what was the temperature in the bedroom at 3am last Tuesday?" — something clicks. You've been generating this data for years. The thermostat knew. The motion sensor knew. The data existed. Nobody was asking it questions.

The built-in database is fine for simple things. Did the garage door open today? When did the last motion event fire? But the moment you want to ask harder questions — what's the average bedroom temperature during sleep over the last month, and how does it correlate with how long it takes to fall asleep — the built-in tools fall apart. They weren't designed for time-series analysis. They were designed to show you a graph of the last 24 hours.

That's where the real architecture starts. A time-series database built for exactly this kind of question. Events go in with a timestamp, a source, a type, and a payload. The database handles compression, partitioning, and aggregation natively. You can bucket events by minute, hour, day, or week. You can compute averages, standard deviations, and correlations across months of data in milliseconds. The database doesn't just store events. It makes them queryable at scale.

The shape: everything that happens is an event. If you store it with a timestamp and a source, you can ask questions about your life that you couldn't ask before.

Once that clicks, you start seeing events everywhere. A workout is an event. A medication dose is an event. A conversation with AI is an event. A git commit is an event. A calendar entry, a text message, a change in GPS coordinates — all events. Each one, by itself, is trivial. Together, over time, they're a complete record of what you did, when you did it, and what was happening around you at the time.

The phone becomes a sensor platform. Heart rate, step count, sleep stages, location, screen time — data your phone already collects but doesn't surface in any useful way. An app that reads this data and pushes it to the event database turns your phone into a first-class data source, on par with the thermostat and the motion sensor. Now your health data and your home data live in the same timeline. Now you can ask: on nights when the bedroom was above 74 degrees, how did my resting heart rate compare to nights when it was below 70?

Nobody asks these questions manually. That's the point. The system asks them for you.

The AI layer sits on top of the event database and processes what it finds. Every message to the AI gets enriched with current context — what time it is, whether you're home, what's on the calendar, what the weather is, what the last few events were. The AI doesn't need to ask "how's your day going?" It already knows you've been home since 2pm, took a walk at 4, and have a call in an hour. The event database is why the AI can be proactive without being annoying. It has the data. It doesn't need to interrogate you.

Then there's the consolidation loop. Once a day, in the background, the AI reviews the raw events from the last 24 hours and extracts what matters. A workout that was notably longer than average. A sleep score that's been trending down for a week. A pattern of late-night screen time that correlates with poor mornings. The raw events are too granular for a person to review. The AI distills them into observations, stores those as memories, and carries the patterns forward. Yesterday's events become today's context.

This is what "less asking" means. Every question the AI doesn't have to ask you is a question it can answer from the event stream. What time do you usually wake up? The data knows. Are you exercising more or less than last month? The data knows. Did the change in medication timing affect your sleep? The data knows. The event database replaces self-reporting with measurement, and measurement is more reliable, more consistent, and requires zero effort after the initial setup.

The hard part isn't the technology. Time-series databases exist. Phone health APIs exist. Home automation platforms exist. The hard part is the plumbing — getting all these sources to write to the same timeline in a consistent format. Once the plumbing works, the questions you can ask are limited only by what you chose to track. And the answer to "what should I track?" turns out to be: everything you can. Storage is cheap. The question you wish you could answer next year is the one you forgot to start recording today.