My Life Isn't an OS — So I Built One

Another LifeOS?

There are plenty of things called LifeOS now. At the simple end, there's ChatGPT plus scheduled tasks. Then there are Obsidian templates and manually maintained knowledge bases—I wrote about that more old-school approach in an earlier article on knowledge management. Add AI to an existing setup and you get something like Compass. At the other end are larger collections of tools, such as danielmiessler/LifeOS.

None of them quite felt right for me. ChatGPT can remind me to do things, but I don't want my life records living in a chat. Obsidian templates are flexible, but if I want an agent to update a project from my phone or collect my daily to-dos automatically, I still have to build that part myself. And I don't really want to install an entire suite of tools—I don't want to clutter up my machine. :) What I want is something lean, dependable, and easy to change as my habits change.

Say I take a photo of lunch: can I log it right away? If I tell the agent at work, “I finished this task,” can it find that note when it writes my evening review? Can project updates, health logs, and knowledge notes live in their own places instead of piling up in one chat window?

So I built another LifeOS. It's not a one-click product or a deployment guide to follow step by step. I just want to show how I put the pieces together and let the system gradually adapt to my day-to-day life.

What I Want It to Do

Most of what I ask it to do is mundane: log a meal, note some project progress, or record that I finished a task. Those little daytime interactions need to be quick. I don't want the agent reading every document in my vault before it can jot down breakfast. At night, it can go back through the records, write a daily review, and update project notes. I can talk to it from my phone or work computer, while most of my personal data stays on the server at home.

I can also turn saved source material into Wiki notes, then use those notes to check facts when I write. In the morning, the agent picks tasks from my existing projects, considers recent health records when suggesting training or rest, and puts the day's work in TickTick. TickTick is my list for today; the lasting state of a project lives in the Vault.

Here's a part I find especially handy: when I mark today's items in TickTick as done, dropped, or postponed, it notifies the agent to record the change. The evening review can then use those records to update the relevant projects. I don't have to rewrite a project file every time I tap a checkbox during the day.

Automatically summarize each evening to help maintain projects and generate a daily report：

The Big Picture: Two Paths for Data and Work

The diagram looks busy, but day to day there are really two paths:

• During the day: I tell the agent something → it turns that into an event → note.py capture writes it to SQLite → I get a short confirmation.
• When it's time to organize things: the agent reads the relevant records and Vault files → writes a daily review, updates project documents, or turns Raw material into Wiki notes. n8n can trigger some of that work on a schedule or through a webhook.

Why separate them? I want daytime logging to stay light. Anything that requires reading and checking context can wait. SQLite records what happened; the Vault holds the current state of projects and tasks, knowledge notes, and report text. n8n has its own Data Tables for automation data, but those are not the same database as the LifeOS event log.

First, Keep the Network Boundaries Straight

I want to reach my agent from my phone without exposing its port directly to the public internet. I use Tailscale to put my devices on a private network, then Tailscale Serve as the access point. Serve is for devices in the tailnet; it isn't the same thing as Funnel, which publishes a service to the internet. And being on a private network doesn't remove the need for application-level authorization.

Health data takes a different route. I don't want to keep Tailscale running on my phone all the time 🤣, so Caddy on my cloud server provides a public HTTPS endpoint. It checks that an incoming request is authorized before forwarding it to n8n at home. That is separate from the private route I use to access OpenClaw on my phone.

Hiring an Assistant: OpenClaw

I ended up choosing OpenClaw for the agent layer. The number of models it can connect to matters less to me than having a usable chat interface, a separate agent and workspace for LifeOS, and a way for n8n to call on it at set times. Logging one thing doesn't require loading every rule for projects, health, and Wiki notes. When I ask it to write a review or a knowledge note, it can load the relevant skill then.

That's also why “log this” isn't some elaborate agent workflow. The agent understands what I said, calls a write script once, and stops. Chat history helps with conversation, but I want project states, articles, and reports saved as files I can open myself.

On my phone, I access OpenClaw through Tailscale Serve. ChatGPT's custom MCP takes another route, using a separate tunneling tool to call services at home. Both look like talking to an AI, but they don't travel through the same network path.

The Core of LifeOS: Fast and Slow Paths

I split the LifeOS work into a fast path and a heavier one. The fast path is for everyday logging: the agent understands what I said, calls the Python script note.py to write to SQLite, gets an ID back, and replies. I don't let the model construct SQL itself. capture adds a new record; edit --id corrects one I got wrong. If I say “I finished the task,” that's a new completion event, not an edit to an old entry. A simple record usually gets a reply in a few seconds, though the model and network make a difference.

Why not put task status in the database too? Because SQLite is a log of what happened, not a live table of unfinished tasks. Project goals, task lists, and longer-term progress live in Markdown files in the Vault. In the evening, the agent uses the day's records to maintain those files. When it plans my morning, it starts with open tasks in the Vault, then checks recent completion events so it doesn't schedule something I've just finished but haven't yet reflected in the Vault.

The heavier path handles the work that can wait: daily and weekly reviews, project upkeep, saving source material, turning Raw material into Wiki notes, and helping me think through an article. It loads the skills it needs. Logging breakfast doesn't require the entire health-analysis playbook.

The Vault follows the same general ideas as my earlier knowledge-management setup, but with clearer jobs for each directory:

• Raw/ keeps original files and material worth saving, not an AI summary passed off as a source.
• Wiki/ holds reusable knowledge drawn from records and source material, rather than a daily stream of notes.
• Area/ covers long-running parts of life—family, health, work, and growth—and the practices I've developed for them.
• Project/ holds projects with goals and tasks. Their long-term state belongs here, not in a guess based on chat history.
• Reports/ holds daily and weekly reviews. The text lives in files; the database only registers their paths.
• Outputs/ is for things I want to explain to other people, including this article. I use Wiki notes to check what I say, not to assemble an article by pasting them together.

Giving It an Engine: n8n

Once the database and Vault have distinct jobs, there's still a practical question: who tells the agent to plan in the morning and review at night? I use n8n for schedules and webhook workflows. n8n triggers jobs, handles data on the automation side, and calls OpenClaw. The agent interprets records and writes readable documents. I don't treat n8n as the LifeOS source of truth, or the agent as a reliable scheduler.

Take health data. Health Sync on my iPhone can send it through the cloud endpoint to n8n at home. ChatGPT can also call a local MCP tool, log_health, which posts to n8n's health webhook. The workflow writes exercise, sleep, and body metrics to Data Tables and can update them several times a day. In the evening, n8n triggers a review: it pulls that day's output from its database and puts it into a prompt so the “assistant” can write a review alongside the day's captures.

The workflow looks like this, and it can also be triggered by an external webhook. n8n can now help build workflows with AI, which makes getting started easier than it used to be 👍:

Wrapping Up

Look back at the architecture diagram: there's a lot behind it, but using it day to day is simpler. I say something and it gets recorded. In the morning it helps me pick tasks; at night it turns the records into a review. Behind the scenes, Gitea handles version history and syncs automatically to GitHub, a CPA model proxy routes calls and tracks usage, and ChatGPT uses custom MCP to call services at home. I'll leave those details for another time.

This LifeOS is built around my own habits, not as a product anyone can install with one click. But if you're putting together something similar, I hope some of these choices are useful. If you have ideas—or spot something I've got wrong—I'd love to hear them.