stroblmeandClaude Opus 5
45cc7504e1
A `webpush` alert channel, and the PWA it needs to arrive. The payload is encrypted to the subscription (RFC 8291) and the request signed with this installation's own keypair (RFC 8292), both over `http-ece` — `pywebpush` does the same in one call but brings `requests` and `aiohttp` with it, two HTTP stacks beside httpx on a machine that may be a Raspberry Pi. The manifest and the worker are hand-written rather than `vite-plugin-pwa`: there is nothing worth precaching when the page carrying the credential is `no-store`, so the worker handles `push` and `notificationclick` and nothing else. `registration.scope` is the app's root in both places it runs, which is why the payload carries no URL. A run finishing in error is the first event worth waking someone for; `ok` and `cancelled` describe to nothing, so a nightly batch that works stays quiet. The events were already on the bus — only the filter changed. `WEBPUSH_FILE` is a derived path, so the keypair lands on the data volume with the alerts beside it. Off it, a rebuild would silently stop every phone being notified: the key they subscribed against would be gone. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_014EbeFPm6WNC3YD9vrqqT3a
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).