The mental model that makes everything else on Zo make sense, and the first three things to do. This is an on-ramp, not documentation. For mechanics, the official docs are the source of truth: https://docs.zocomputer.com/llms.txt
The mental model: your own server, your files, your AI
Most AI products are a chat window attached to someone else's computer. Zo flips that. Zo is your computer: a real, always-on server in the cloud that belongs to you, with an AI that lives on it and works for you.
Three pieces, and the relationships between them are the whole model:
1. Your server. A persistent Linux machine that stays on when you close the tab. It can run programs, host websites, serve APIs, and execute scheduled work at 3am while you sleep. Anything a normal server can do, yours can do.
2. Your files. A real filesystem that persists forever. Notes, code, data, documents. This matters more than it sounds: files are how your AI remembers. A chat conversation evaporates; a file stays. People who get good at Zo quickly learn to ask the AI to write things down: project notes, decisions, instructions for next time. Your file system becomes a long-term memory that every future conversation can draw on.
3. Your AI. An assistant with hands. It does not just answer questions about your server and files; it operates them. It can write and run code, create and edit files, build a website and host it, set up a recurring job, connect to your other accounts. You describe outcomes; it does the work on your machine.
Put together: you own a computer that an AI can operate, and everything you build accumulates there. Day 30 builds on day 1, because it is all still on the machine.
A few words you will see immediately, in one line each (the docs cover all of them properly):
- Skill: a saved procedure your AI can perform on request.
- Automation: work that runs on a schedule without you asking.
- Site: web pages your server hosts for the world.
- Service: a program that runs continuously on your server.
- Persona: a configured version of your AI with its own instructions and access.
When to use which is a judgment call, and we have a whole guide for it: Skill vs Automation vs Site vs Service.
The first three things to do
Do these in order. Each one teaches you a layer of the model by using it.
1. Make it do something real with files
Skip the small talk. Give Zo a job that produces an artifact:
"Create a folder for my [project]. Inside it, write a one-page brief: what it is, who it's for, and the next three steps. Ask me whatever you need first."
Then open the file it made. This teaches the most important lesson on day one: the output of a conversation can be a durable thing on your machine, not just text in a chat. From now on, end work sessions with "write down what we decided in the project folder."
2. Connect one integration you actually use
Pick the one account you touch daily, usually email or calendar, and connect it. Then ask for something useful:
"Look at my calendar for this week and write a plain-English summary of where my time is going."
This teaches the second lesson: Zo is more useful the more of your real life it can reach. You do not need to connect everything on day one. One real integration doing one real job beats ten connected and idle.
3. Schedule one small automation
Take something you would want every day or every week and put it on a schedule:
"Every weekday at 8am my time, check my calendar and email, and send me a short morning brief."
Say "my time" and name your timezone. Scheduling has sharp edges around timezones, and you will care about them eventually: Automations That Fire.
This teaches the third lesson: your computer works when you are not there. That is the moment Zo stops feeling like a chatbot and starts feeling like staff.
Where to go next
- Mechanics, settings, how anything on the platform works: the official Zo docs, starting at https://docs.zocomputer.com/llms.txt
- How to think about building bigger things: The Classified Method
- Stuck: ask Friday, or post in #help-and-support.