stroblmeandClaude Opus 5
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It described the wrong object. A dashboard is a document that may hang on a
hallway tablet and in a desk browser at the same time, and only one of those
has fingers on it — so the flag moves off `DashboardDef.settings` and onto
`PanelDef` as a plain bool, ticked in the Panels dialog. `useCanvasRoot` takes
it as an argument rather than reading the document, and `/panel/{id}` is the
only surface with a panel to ask.
Dropping the message binding with it is deliberate: nothing drove it, and a
flow deciding whether a screen has fingers on it was never the point. A stored
`settings.touch` is inert rather than migrated, which `_check_settings`
skipping unknown names already guaranteed.
The rail was the other half. It had no touch behaviour at all and its 40px
buttons met neither branch of the 44/32 rule. `[data-touch] .dui-rail{-item}`
in `ui/core/core.css` spends the padding and the gap on the buttons instead,
so they reach the 44px target and the rail comes out taller at exactly the
same width — `RAIL_INSET` never moves, and the arrangement under it does not
either.
Also closes the panels-dialog icon gap: `DashboardSummary` carries the `icon`
now, so the dialog draws each assigned dashboard's rail glyph beside its
checkbox. `initials()` went from three identical copies in the looks to one in
`Dashboard/icons.ts`, so the dialog and the rail fall back the same way.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Va7ExQDtuwKN7kNpHhWWNQ
Fluksio
Fluksio is a node-based automation software that brings trust and reliability to your flow. It just works and looks good. Get started by running
pip install fluksio
fluksio serve
and you're ready to go.
For data science
You can turn your existing data science project into a flow by decorating your functions with @node ...
# myresearch/train.py
import fluksio
from fluksio import Port, node
@node(
requires=["dataset", Port("lr", "float")],
provides=[Port("loss", "float", stream=True), Port("weights", "artifact")],
device="gpu", device_policy="prefer",
)
def fit(dataset, lr, epochs=25):
for epoch in range(epochs):
loss = step(...)
yield {"loss": loss} # published as it happens, kept as a series
return {"weights": fluksio.save_artifact("weights.pt")}
... and passing them to a Flow:
# myresearch/pipeline.py
from fluksio import Flow, Port
from myresearch.data import prepare
from myresearch.evaluate import evaluate
from myresearch.train import fit
train = Flow("train", nodes=[prepare, fit, evaluate],
inputs=[Port("lr", "float", initial=0.01)], outputs=["score"])
Fluksio will automatically infer the order of nodes based on the inputs and outputs you defined. When everything is set, you can launch your first run as follows:
fluksio run train --lr 0.05 --wait
Checkout our documentation for more infos.
Some other features
- Flows: typed messages between nodes, wired by name, edited on a canvas or declared in code. Every change is a commit in a git repository you own.
- Runs: an experiment and a CI-style job are the same entity. Parameters, seed, result, per-node timings, artifacts and the commit it ran at.
- Dashboards: charts and controls bound to the same messages the flows carry, with no separate metrics pipeline.
- Remote workers:
pip install fluksio-workeron the GPU box; it dials out over one websocket, so nothing there has to be reachable.
Fluksio can also be used for facility automation. Visit us on fluksio.com or go straight to our documentation.
License
Copyright (C) 2026 Melvin Strobl - GNU Affero General Public License v3.0 or later. Running a modified version over a network obliges you to offer its users the corresponding source (AGPL §13).