stroblmeandClaude Opus 5
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`make test-frontend` built PLAYWRIGHT_BASE_URL from DOMAIN in .env, which in a checkout configured for a deployment is that deployment's domain — so the suite that creates and deletes flows, dashboards and users was pointed at app.fluksio.com, held local only by --add-host and tests/guard.ts. The hostname now comes off the running stack (the frontend container's own Traefik rule), so a name no local container answers to cannot be reached at all, and the local targets default to *.localhost instead of reading .env. Target-specific on purpose: an exported DOMAIN outranks --env-file in compose interpolation and would put the production targets on localhost. `rebuild-frontend` replaces the raw compose line CLAUDE.md spelled out, taking the same domain so the baked VITE_API_URL cannot disagree with what Traefik serves. Also: a coverage HTML report that cannot be written no longer fails test-backend after a green suite, and both artifact actions in playwright.yml drop to @v3, which is the only version without the github.com-only guard that failed every run. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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).