React Flow's selection changes had nowhere to go: the canvas passed
edges but no onEdgesChange, so no edge ever carried .selected and both
the selected-edge stroke and its rest colour were unreachable. The edges
are derived from the bindings, so the selected ids are held on their own
and marked on after the layout has had the array — a rebuild cannot drop
the selection, and selecting cannot make the graph lay itself out again.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01SKL7sUgNWhukDEz95vSMQv
The brain now opens sorted (the snapshot carries emit counts, and values
always carried timestamps), the hosted demo is the range-aware dashboard
that was missing, and the demo is hosted-only — so a server-side route
seeding one per signed-in user is not wanted rather than not done.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01HTsT1isxUjw5gtkJk8WhuA
`scripts/seed_demo.py` wipes and recreates one persistent demo — `home`,
`home_history` and `pv_model`, plus a `demo` dashboard carrying all fifteen
widget types across three sections. Operational script for the hosted
instance only: `make seed-hosted-demo`, with `API_URL` selecting which one.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01HTsT1isxUjw5gtkJk8WhuA
The socket belonged to whichever hook instance ran its effect first. Passive
effects run children before parents, so on Home that was the brain graph rather
than the shell: navigating to a sibling route unmounted the graph, which closed
the socket, while the shell kept the reference count above zero. From there the
page was deaf for the rest of its life, with nothing left to reconnect it.
A module-level connection with a real refcount replaces it — connect on the
first subscriber, disconnect on the last — and the hook is a thin subscription
with the same signature, correct under StrictMode's mount/unmount/mount.
A 1008 now reconnects instead of returning silently: the token is read afresh
per attempt, and three consecutive rejections fall through to the caller's auth
handler so a revoked session surfaces rather than spins.
The snapshot's emit counts are read into a store of their own, apart from the
live count, so a graph that connects into a busy engine is drawn as busy without
every neuron claiming it just fired. The neuron and edge pulses now key off a
change seen while they were mounted, so returning to Home no longer replays
every emission of the session.
Home gets a live indicator for the case none of this can fix: quiet while the
socket is up, and named in words when it is down, since HTTP polling keeps the
rest of the page looking current.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01HTsT1isxUjw5gtkJk8WhuA
The ring hung off the node itself, and `inset` on an absolute child
resolves against the padding box — so on the brain's thick status ring it
landed inside the outer edge and painted over the status the ring carries.
Both node shapes now hang it off a border-less wrapper, where the same
offsets clear the rim whatever border the node draws (measured: 4px on all
four sides, on a 1px card border and a 7px neuron ring alike).
The shape is a conic-gradient arc masked to the padding band, running one
lap in `--duration-pulse` and going out on the way past.
Note: BrainNode.tsx also carries an unrelated in-flight change from a
concurrent session (the `seen` ref gating the pulse on a real emit); the
two could not be separated in one file.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
The brain graph counts node_executed events client-side and the websocket is
torn down on every shell change, so anything a flow published during the
navigation gap was lost. The bus now keeps a session tally per qualified node
and the snapshot hands it back, letting a reconnecting client catch up.
Both the route and the tunnel connector build that snapshot from one helper
so the portal cannot drift from the direct connection.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01HTsT1isxUjw5gtkJk8WhuA
The schema enumerates every endpoint an installation serves, including the
paths HTTP trigger nodes mount at runtime, and api.fluksio.com was serving it
- along with /docs - to anyone who asked. It now follows ENVIRONMENT, which is
the rule the portal's backend already used. The generated client is built from
a local run, so nothing in the workflow depends on the deployed host exposing it.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01XtBzdDyLsmDaF1W7DLYtYM
The four widget types added this round had renderers but no way to configure
them. The panel now offers a bar's nested reading, unit and range (no step —
that is a slider's), the forecast's item count on the agenda's field, a chart's
y axis title beside its y range, and the icon's mapping editor: a value, a
glyph and a colour per row, first match wins, with a fallback glyph below.
Adds tests/widgets.spec.ts, which asserts each of them on /view: the nested bar
inside its outer fill, the glyph following the message, five forecast columns
fading outwards, a clock that reads the wall without being flagged unbound,
a segmented control and a latching button reading back what they published,
and an unbound tile that says so instead of taking the page down. The axis
title is drawn into uPlot's canvas, so it is checked by panel round-trip.
mobile.spec.ts grows a bar and a forecast so the width check covers them.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01HTsT1isxUjw5gtkJk8WhuA
A port with `stream` set publishes repeatedly during one execution, and
until now nothing on the canvas said so. `deriveEdges` carries the flag
from the producing `MessageSpec` onto the edge, `LiveEdge` turns it into
an `edge-stream` class, and the class draws a dashed stroke whose offset
marches toward the target. The pulse still lands on top: both animations
share one declaration when a value arrives on a streaming edge.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01HTsT1isxUjw5gtkJk8WhuA
The editor's mount-time fitView animated from React Flow's default viewport,
which read as the graph swiping in from the corner on every open. The first
fit is instant now, later ones stay animated, and a scaleIn wrapper gives the
same entrance the brain view has — with a re-measure on completion so the
handle bounds are not stored mid-scale.
Home only renders the brain band once some flow has nodes, so a fresh install
no longer reserves a screenful of empty space above the flows card.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01HTsT1isxUjw5gtkJk8WhuA
The ring share drops from the wordmark's 0.231 to 0.18 so the disc reads as a
dot at 32px, and both the border and the gap are rounded where they are applied
— a fractional border snaps per edge and left the disc off centre. Connections
and their port dots go up with it, pulse width included, so an arriving message
never draws its edge thinner than it rests.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01HTsT1isxUjw5gtkJk8WhuA
Plumbing only: the widget-type literal and its dtype table on both sides,
the regenerated client, a curated lucide map and four stubs the renderers
are wired to. Also a latching switch and a segmented dropdown, both a
`style` on the control that already publishes and reads back, plus the
option editor a dropdown never had.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01HTsT1isxUjw5gtkJk8WhuA
The "running" tile counted paused and invalid flows as running, so it read
"5/5" beside "1 flow(s) cannot run". Its note is now additive rather than a
precedence chain, so a quarantine no longer hides the invalid count.
Adds dur() beside si(): a ms reading picks its own unit, so a slow run reads
"1.24 s" instead of "1.2k ms".
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01HTsT1isxUjw5gtkJk8WhuA
CLOUD_CONFIG_FILE defaulted to a path relative to the working directory, so the
enrolment — this installation's credential and the portal key it pinned — sat
inside the container and was lost on every rebuild, silently detaching a
connected installation. It belongs beside the OAuth key and the flow secrets,
for exactly the same reason.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01XtBzdDyLsmDaF1W7DLYtYM
The app supports Python 3.10 (requires-python, and the base image is
python:3.10); datetime.UTC only exists from 3.11. Every other module already
uses timezone.utc — this was the one file out of step, and it only passed
locally because the dev venv is 3.13. The container would not import at all.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01XtBzdDyLsmDaF1W7DLYtYM
A superuser-only tab that redeems a claim code and shows the link's state. It
names the account a remote session acts as, because that is the thing being
granted and the person granting it should see it spelled out.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01XtBzdDyLsmDaF1W7DLYtYM
The same bundle is served by a portal under /i/{id}, so it reads its API base,
credential and router basepath from an injected config instead of the build-time
URL and localStorage. A normal installation finds no config and behaves exactly
as before; the credential deliberately never touches localStorage, since two
installations open in one browser share an origin and would overwrite each
other's session.
The websocket URL was resolving an absolute path against the base, which
discards the base's own path — harmless until the base gained one, then it
aimed the socket at the wrong host entirely.
Connection state gets a store of its own, apart from the engine's: the proxy's
503 carries {offline, last_seen}, which raises a banner naming when the
installation was last heard from and turns a failed mutation into 'not
delivered' rather than a generic error. The screen keeps its last data
underneath, since stale readings with a timestamp beat a blank page. A
reconnecting socket invalidates every query, because whatever happened while it
was down was missed.
Verified in a browser against a real hub and installation: the full UI loads
through the tunnel with no console errors, and killing the installation raises
the banner within a poll.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01XtBzdDyLsmDaF1W7DLYtYM
An installation can be enrolled with a portal by redeeming a claim code, after
which it holds one authenticated websocket open and answers proxied API calls
over it. Requests are dispatched into this process's own ASGI app, so the HTTP
trigger routes flows install at runtime are visible to it, and the live flow
stream is bridged straight off the event bus.
decode_token grows the third branch its docstring anticipated: tokens signed by
the enrolled portal resolve to the local account that performed the enrolment,
verified against a JWKS pinned at that moment. With no enrolment the branch
raises immediately, so an offline installation is unchanged and untouched.
Disconnecting deletes one file, which is the entire local revocation.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01XtBzdDyLsmDaF1W7DLYtYM
A record or an artifact reference was serialised into the port row, and the
panel widened until the type selects and the buttons beside them were pushed
off its edge — a checkpoint reference is 130 characters of digest, and none of
them are what you want while wiring a flow. What shows now is what the value
*is*: 'artifact · weights.json · 60B', 'record · 3 fields'. A chevron unfolds
the whole of it, wrapped, inside the panel it belongs to.
A scalar still reads as itself, and scrolls its own overflow into view when it
is longer than the room it was given. That behaviour already existed inside
the edge inspector; it moves to Common/Marquee so the panel can have it too,
and the inspector drops its own copy of the raw-JSON block along with it.
The rows are smaller for it: the type select finally fits the word 'artifact',
and a port nothing has come through on says so with a dash rather than a
sentence — nine ports of 'nothing has come through yet' is a panel of prose
about the absence of values.
The e2e check asserts both halves: that the summary is what appears, and that
the panel is still exactly 400px with the value unfolded.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01AD8SfVhzXBG2nAfFcVh3iD
make seed-demo builds demo_training — prepare on the engine, a GPU-bound
train, evaluate back here — and a panel that draws the loss curve while the
training is still going. It is the session's whole argument in one flow: batch
runs with parameters and a result, a generator yielding on a declared port
rather than logging, fluksio.emit from inside a callback, artifacts carrying
the dataset and the weights between machines, and a sweep whose configs are
isolated from each other. The train node prefers its label rather than
requiring it, so it runs before a GPU box exists and says which machine and
which numeric backend it actually used.
Building it turned up two real bugs. A run waited for a worker its flow only
*preferred*, because required_labels ignored device_policy — so the example
hung on a label it did not need. And a run's seed never reached the flow, so
sweeping over seeds ran the same experiment N times; it now fills an input of
that name when the flow declares one, which is what the field looked like it
did all along.
Pressing Run on a batch flow now submits a run rather than taking the old
non-durable path — that button is the first thing anyone evaluating will press,
and it was quietly doing something else.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01AD8SfVhzXBG2nAfFcVh3iD
The first cut had node code call fluksio.log_metric, which was a second,
undeclared way for data to leave a node: invisible to validation, absent from
the canvas, and stored where the graph could not see it. That is precisely the
MLflow discrepancy this framework exists to avoid, so it is gone.
A node that produces values over time is a generator. Every yield is a dict
keyed by output port, published the instant it happens — same port, same type
check, same place on the canvas as any other value — and what it returns is
its result. A port doing this declares stream: true, and a run keeps every
number one takes, so experiment tracking is a consequence of the graph rather
than an API beside it: a chart binds to a training curve the way it binds to a
temperature. fluksio.emit writes the same ports imperatively, for where a
yield cannot reach — inside a training framework's callback.
In a live flow an emission also wakes what is downstream, as a subscriber
publishing does; in a run it does not, because a run's graph is scheduled once
and mid-node cascades would leave 'finished' with nothing to mean. The
enqueued item carries no payload: the value is already in state, and one
carrying it would re-apply an old emission after the node returned.
Verified on the stack: 30 loss values arrived live on the flow socket during a
run, attributed to the node that produced them, and the same node run on the
remote worker streamed its curve back across the socket.
Also caches remote compile results per worker, so attaching a GPU box does not
put a network round trip in every rebuild.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01AD8SfVhzXBG2nAfFcVh3iD
It expects worker_main.py beside it, which is true on a worker host and not in
a clone — so trying a remote worker started with copying files around. It now
looks one directory over as well.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01AD8SfVhzXBG2nAfFcVh3iD
The milestone is measured on being lighter than Kedro, so make bench-startup
measures it rather than asserting it: 61 ms from submit to result against
1110 ms for kedro run on a pipeline that does the same nothing. The difference
is not orchestration, it is that nothing is booted per run — on a 510-config
sweep that is about nine minutes of pure startup that never happens.
docs/flows/runs.md is the guide: batch flows, sweeps, reporting from inside a
node, artifacts, and the two sanctioned patterns for objects that cannot be
serialized — keep them in one node, or cross at a checkpoint.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01AD8SfVhzXBG2nAfFcVh3iD
The engine runs where the automations are and the GPU is somewhere else,
usually behind a different network — so the worker connects out and the engine
answers over the socket it was given. Nothing has to expose Redis, and the
same connection works through the tunnel the hosted access will use.
What travels is the protocol the local pool already speaks, so a node cannot
tell which kind of worker it is on. A node declares device: gpu and
device_policy, the label is resolved per call (a worker attaching later needs
no rebuild), and a run whose labels nothing carries waits in the queue saying
what it waits for rather than failing — submit from the couch, the GPU box
picks it up when it is switched on.
Two things had to move with it. Compiling now happens on the machine that will
run the node: a node importing torch is correct on the GPU box and a missing
module on the engine, so checking it here failed nodes that were fine. And the
artifact endpoint accepts a worker's own credential, because storing a
checkpoint is exactly what that credential is for — and only that.
Verified against the real split: the training ran on this host (its checkpoint
names the machine and a numpy the engine does not have), streamed 40 metric
points back mid-run, and the evaluate node read the checkpoint on the engine.
Cancel kills the remote training; pulling the worker fails the run in six
seconds instead of waiting out its ten-minute timeout.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01AD8SfVhzXBG2nAfFcVh3iD
A checkpoint is not a message. DType.ARTIFACT carries a reference — digest,
size, media type, name — so everything on the wire stays JSON and thirty
megabytes never sit in Redis, which answers the vision's open binary-payload
question by narrowing it: inline codecs would only serve payloads too small to
be worth a round trip, and nothing asks for that.
The store is content-addressed rather than per-run, for three reasons that all
pay later: a sweep whose fifty configs share one preprocessed input stores it
once, a reference stays valid however it is passed around because it names
content instead of a location, and the digest is what a stage cache will
compare — so building it in now is what keeps that from being a change to the
message contract.
Node code calls fluksio.save_artifact/load_artifact and cannot tell whether it
is writing the engine's own directory or putting bytes over HTTP, which is
what will let the same flow run on a remote worker unchanged.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01AD8SfVhzXBG2nAfFcVh3iD
A training loop has numbers worth keeping thousands of steps before it has a
result. Node code now imports fluksio and calls log_metric/progress, which
sends a line back without ending the call; the engine writes those to
run_metric in batches from the run's own driver rather than folding them off
the event bus, which drops what it cannot keep up with.
Two things fall out. Each event resets the worker deadline, so a node's
timeout measures silence rather than duration — which is what lets a two-hour
training keep a liveness contract instead of racing it. And the worker pool's
_running is now keyed by (run, node), so cancelling one config of a sweep
kills that training and leaves the rest alone.
Fixes a latent framing bug: read_line returned whatever a read had taken,
which was fine while a worker only ever sent one line per request and
unparseable as soon as it sent several. It now keeps the remainder.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01AD8SfVhzXBG2nAfFcVh3iD
A cascade has no end worth recording; a run does. Parameters go in, the graph
executes until it drains, and the result is kept — which is what an ML
experiment is and what a CI-style job is, so both are one entity.
Each run gets a state backend namespaced to itself, so two runs of one flow
cannot overwrite each other's messages; that is a constructor argument rather
than a change to the pipeline, because every key the engine keeps already goes
through the state backend. Its record is written by the driver thread rather
than folded off the event bus, which drops what it cannot keep up with. Its
own Redis stream wakes an engine up, and from the claim onwards the database
row is the truth: redelivering hours of training because an acknowledgement
was late is not recovery, so a stale lease is what marks a run whose engine
died.
Flows gain mode: batch, which are built and validated but never activated, and
nodes gain a device label for the worker that must run them.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01AD8SfVhzXBG2nAfFcVh3iD
Two things the first pass got wrong.
The Changes and "Given up on" lists had no cap, so they ran as long as the
audit trail did — and worse, a truncating flex row still offers its whole
unwrapped line as a min-content contribution, so the card sized itself to the
longest entry and took the page sideways with it. Both now use the same
capped, scrolling box the runs and failures lists already had, which fixes the
length and the width together.
Running downwards, a rank of eight nodes was two thousand pixels across
because a node box is landscape: siblings cost a rank four times as much
across as they do down it. A rank wider than two now wraps onto the ranks
below, settling over a few passes since wrapping one rank pushes what was
under it up into the room that freed. The value chip on an edge no longer
reserves its width there either — that width is spent across the screen rather
than along the flow, and the rank gap it would widen is already wider than the
chip is tall. The same flow that laid out 2040x216 is now 803x722; a chain and
a diamond are unchanged, and so is every desktop layout.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01VDSXaRhvqHYNevgDGmNAto
The canvas lays itself out: a layered graph, left to right on a desktop and
top to bottom on a phone, with room reserved for the value each edge carries.
Nodes cannot be dragged and `NodeDef.position` is gone from the document —
a graph nobody can arrange is one worth keeping small, which is what keeps
flows atomic. Endpoints join the same layout, so their lanes and the
localStorage that remembered where they were dragged go too.
Mobile, per the new Responsive section of DESIGN-GUIDELINES.md: the dock caps
its width and wraps instead of running off the screen, the dashboard stacks
into one column rather than shrinking a wall panel to a fifth of its size, and
Home stops widening its grid track past the viewport. A Playwright project at
a phone's width fails the build when a screen no longer fits.
Along the way: publish is the checkmark that was already there rather than a
button that appears and disappears, with discard beside it on both the flow
and the dashboard; the brain reveals a neuron's name on the first tap; and the
port sparklines get room to breathe.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01VDSXaRhvqHYNevgDGmNAto
A chart is drawn in buckets, and nothing it can show changes until the
bucket it is drawing closes — so the resolution sets the refresh rather
than a flat five-second floor. A week at quarter-hour buckets now asks
four times an hour instead of sixty, for the same picture. Leaving the
field empty follows the window; a slower rate is still honoured.
`make seed-example` builds the thing to evaluate it with: a flow that
logs a temperature to InfluxDB, a flow that answers a chart's request by
turning the window into Flux and the rows back into a series, and a
dashboard holding the chart. The reading flow declares the request as an
input with a starting value, which is how a flow says a value reaches it
from a panel rather than from a node upstream.
Axis labels keep enough decimals to stay distinct — `si` rounds to three
figures, so every tick of a chart living inside one degree read "19".
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Also the one the session settled by hand: database nodes stay transport
and credentials, and the Python nodes either side do the query building
and the answer shaping. That is what a series read mode inside the node
would have prevented, so the read-mode item goes with it.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
The frame centres a single reading, which is right for a gauge and wrong
for a list.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
stroblmeandClaude Opus 5