stroblmeandClaude Opus 5
11e032386b
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
73 lines
2.6 KiB
Markdown
73 lines
2.6 KiB
Markdown
# Pick your starting point
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People arrive at Fluksio from two directions, and the honest answer to "how do
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I set this up?" is different for each — not just in the commands, but in how
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much of an afternoon it is reasonable to spend.
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Pick the one that sounds like you. Everything past this section is the same for
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both.
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<div class="fluksio-lanes" markdown>
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<div class="fluksio-lane fluksio-lane--science" markdown>
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### Data science
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*"I have a training script. I want to stop losing track of what I ran."*
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One `pip install`, one command, and you are writing Python again. No Docker, no
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database, no ports to open. Flows are files, runs are rows, and the metrics are
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just the numbers your loop already produces.
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[Set up for experiments →](data-science.md)
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</div>
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<div class="fluksio-lane fluksio-lane--facility" markdown>
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### Facility automation
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*"I have a homelab and a pile of sensors. I want them to do something."*
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A stack you bring up once and leave running: the engine, a broker, a
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time-series database, dashboards, alerting. Most of the work happens in the
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browser, and it is worth doing properly because you will live in it.
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[Set up a homelab instance →](facility-automation.md)
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</div>
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</div>
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## Not sure?
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Some rough tells:
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| **The flow** | starts, finishes, has a result | never ends |
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| **You mostly** | write Python | wire nodes in the browser |
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| **Time to first result** | a few minutes | an afternoon |
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| **Runs on** | your laptop, or a login node | a box in a cupboard |
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| **Data lives in** | SQLite beside the flows | InfluxDB, usually |
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| **The thing you look at** | run history and loss curves | a dashboard, maybe on a wall |
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If both describe you — a lab with instruments to drive *and* models to
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fit — start with the data-science path. It is the smaller installation, and it
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grows into the other one without being reinstalled: the same engine, the same
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flows, just more of them running all the time.
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## What is the same either way
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Whichever door you came in:
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- **Flows are files in a git repository.** Every save is a commit. You can read
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the history with ordinary git, and you can copy a flow between installations
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by copying a directory.
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- **Editing is separate from running.** You edit a draft; the engine keeps
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running what was published until you publish.
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- **Nodes are typed.** A port declares what it carries, and a mismatch is
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caught at edit time rather than at three in the morning.
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- **Everything the browser does is an API call.** The dashboard is a client of
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the same REST API you can script against.
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