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
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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 instance, 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 instances 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.