stroblmeandClaude Opus 5
bdad6d7fc2
The site read as a design journal: rationale paragraphs, hedges
("deliberately", "on purpose", "genuinely"), meta-commentary about the docs
themselves, and one em-dash every ten lines carrying an aside.
Roughly twenty rationale blocks are gone or reduced to what a reader needs
in order to use the thing. Em-dashes go from 507 to 135, and what is left is
structural rather than prose: list and definition separators, table cells,
and four inside code blocks that quote what the CLI actually prints.
Also: api.example.com becomes api.fluksio.com (the emails stay, since
bootstrap.py really defaults to admin@example.com and RFC 2606 reserves it);
the mqtt table gains the two settings it had drifted behind on and inject's
wording matches the engine; llms.txt lists the two connector pages that were
in the nav but not in it; and the two device/device_policy notes now agree.
Builds clean under `zensical build --strict`.
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
Setup differs depending on what you are here for. Pick the path that matches.
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.