Quickstart
From signing in to your first running notebook.
This gets you through your first real piece of work: sign in, create a project, and have the agent build and run a notebook. A few minutes, start to finish.
Sign in
Go to app.clusy.io. There's no password. You get a one-time code by email, or you sign in with Google or GitHub. If it's your first time, you'll go through a short setup (what you're here for, a theme, an optional profile) and land in your first project. You can skip it.
Create a project
A project is one body of work, like a dataset you're exploring or a model you're building. From the dashboard, describe what you want to do in the launcher, or hit New project and name it. You can drop a dataset straight onto the launcher too, and it comes along into the new project.
The dashboard: a launcher asking "What do you want to build?" with starter chips, above your recent projects
You don't need to set up compute first. New projects run on CPU, which is free and fine for loading data, cleaning, plotting, and most classical ML. Attach a GPU later, per cell, when something needs it.
Say what you want
You start in chat, before there's a notebook. It's usually easier to describe what you want than to set up the first cell yourself.
If you have a dataset of your own, upload it first so the agent can read it. If you don't, here's one you can paste in as-is:
Load the penguins dataset from
https://raw.githubusercontent.com/mwaskom/seaborn-data/master/penguins.csv, show me the shape and column types, flag any columns with missing values, and plot body mass by species.
The more specific you are, the less back-and-forth.
Watch the notebook build
The agent reads the request, sketches a plan, and starts writing cells. Once there's something to run, the notebook canvas opens on the right. You'll see each cell appear, run, and produce output.
The first cell is the slow one. Running it has to start a sandbox for the project, which takes a few seconds on CPU. Everything after that runs immediately, so if you're watching a spinner on your very first cell, that's what it's doing.
At any point you can click into a cell and edit it, run it again, or write a new cell by hand. The agent picks up from wherever you leave off.
Steer it
Read what came back and react in plain language:
- "The date column is a string, parse it."
- "Plot the missing-value counts as a bar chart."
- "Split into train and test and fit a logistic regression baseline."
Each message is another turn. The agent edits or adds cells and reruns what it needs to.
Try a second idea without losing the first
When you hit a fork (say you want a random forest alongside that logistic baseline), don't overwrite. Branch from the cell where the two diverge. Clusy keeps your loaded data and fitted objects in the new branch, so nothing recomputes, and you can switch between branches to compare.
Keep your work
When you've got something worth keeping, you can commit it to GitHub, share a read-only link, or publish to Hugging Face or Kaggle.