stroblmeandClaude Opus 5
d12c81c8a0
A zensical site under docs/, served by a new `docs` compose service behind Traefik, built with --strict in CI. Same pattern the sibling n3xd workspace uses. Getting started splits the way the landing page does — one path is `pip install fluksio` and a training script, the other is a Docker stack and an afternoon in the browser — because the two audiences will not spend the same amount of time. Everything after that is shared: the concepts, the web interface (app and portal), the CLI and the API, and a reference for node types, payload types and configuration. The three flow guides move here from the docs submodule rather than being copied, so there is one version of them. Styling mirrors DESIGN-GUIDELINES.md: the app's token palette remapped onto Material's variables in both schemes, Inter, the 16px panel radius, and the one terracotta accent spent on the facility lane of the audience split. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01M7Xv3cJEW5c8AXxn2hoojV
2.6 KiB
Pick your starting point
People arrive at Fluksio from two directions, and the honest answer to "how do I set this up?" is different for each — not just in the commands, but in how much of an afternoon it is reasonable to spend.
Pick the one that sounds like you. Everything past this section is the same for both.
Data science
"I have a training script. I want to stop losing track of what I ran."
One pip install, one command, and you are writing Python again. No Docker, no
database, no ports to open. Flows are files, runs are rows, and the metrics are
just the numbers your loop already produces.
Facility automation
"I have a homelab and a pile of sensors. I want them to do something."
A stack you bring up once and leave running: the engine, a broker, a time-series database, dashboards, alerting. Most of the work happens in the browser, and it is worth doing properly because you will live in it.
Not sure?
Some rough tells:
| Data science | Facility automation | |
|---|---|---|
| The flow | starts, finishes, has a result | never ends |
| You mostly | write Python | wire nodes in the browser |
| Time to first result | a few minutes | an afternoon |
| Runs on | your laptop, or a login node | a box in a cupboard |
| Data lives in | SQLite beside the flows | InfluxDB, usually |
| The thing you look at | run history and loss curves | a dashboard, maybe on a wall |
If both describe you — a lab with instruments to drive and models to fit — start with the data-science path. It is the smaller installation, and it grows into the other one without being reinstalled: the same engine, the same flows, just more of them running all the time.
What is the same either way
Whichever door you came in:
- Flows are files in a git repository. Every save is a commit. You can read the history with ordinary git, and you can copy a flow between installations by copying a directory.
- Editing is separate from running. You edit a draft; the engine keeps running what was published until you publish.
- Nodes are typed. A port declares what it carries, and a mismatch is caught at edit time rather than at three in the morning.
- Everything the browser does is an API call. The dashboard is a client of the same REST API you can script against.