stroblmeandClaude Opus 5
d01a8dad37
Follows the portal: the noun is "instance" everywhere the app says it — UI strings, CLI output, error details, docs and comments. The wire keys (`instance_id`, `instance_token`) and the hub route this calls move with it. An existing cloud.json is adopted rather than refused: without the key alias the dataclass fails to parse, which the caller swallows and reads as "never enrolled" instead of "reconnect". `instance_key` on a node type becomes `target_key`. It means the outside thing a node points at, which is a different sense of the word, and keeping both would put two meanings of "instance" in one codebase. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_015YrQnKV3bnQd4K342y8tKj
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 instance, 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 instances
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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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