Presence and Ephemeral Collaboration Volume 07 - Advanced Application Architecture
Presence is not Task persistence Presence answers questions like “Who is viewing this board?” or “Who is online?” It should not represent Task status, approvals, payroll totals, inventory quantities, or other durable business facts.
Possible future use channel .on('presence', { event: 'sync' }, () => { const viewers = channel.presenceState() // Render "3 people viewing this board" }) .subscribe(async status => { if (status === 'SUBSCRIBED') { await channel.track({ userId, displayName, viewing: 'tasks-board', onlineAt: new Date().toISOString(), }) } })
Good presence payloads • display name/avatar reference
• current collaborative surface
• cursor/selection metadata if needed
• ephemeral online timestamp
Bad presence payloads • access token
• sensitive HR/payroll data
• database truth that must survive disconnects
• large user profile objects
TaskFlow decision Phase 26 teaches Presence but does not ship it as a required TaskFlow feature. We only add it later if collaboration UX needs it.
Presence and Ephemeral Collaboration matters because it changes a real boundary in TaskFlow. The goal is to understand the inputs, owner, observable output, and failure modes before adding more abstraction.
Restate the goal of “Presence and Ephemeral Collaboration” as one observable behavior. Write the success signal before touching code.
Trace the boundary map: database change → event stream → subscription → cache reconciliation → UI. Mark the one boundary this lesson changes.
Inspect the current repository state and record the exact files, route, table, or runtime involved. Avoid creating an abstraction before you can name the existing owner.
Implement the smallest vertical slice. Add one coherent change, run the narrowest verification command, and read the output before continuing.
Connect the slice to TaskFlow’s phase artifact: Realtime task updates that merge into TanStack Query state with scoped subscriptions and reconnect behavior.. Verify behavior from the outside as a user or API client would.
Create one controlled failure that is safe to reproduce. Predict the symptom first, capture evidence, locate the earliest broken boundary, then fix only that cause.
Close the reference and rebuild or explain the core change from memory. Record what you would monitor or test before trusting the same change in production.
channel.on("postgres_changes", filter, payload => {
queryClient.invalidateQueries({ queryKey: taskKeys.all });
});Do not copy this as a finished answer. Predict what each boundary does, type the smallest useful slice, run it, then compare the result with your prediction.
Return to the nearest observable boundary: input, network, process log, database, or rendered UI. Compare actual evidence with the predicted path.
Change one variable at a time and reproduce the original failure after the fix; otherwise you have correlation, not diagnosis.
Without copying the final source, create a second small scenario that applies “Presence and Ephemeral Collaboration” to a different TaskFlow resource or input. Write the acceptance criteria first, implement it, and provide the evidence that each criterion is satisfied.
Deduplicate events that echo the current user's own mutation.
Use the smallest change that demonstrates this lesson. Stop after it works and capture evidence.Subscriptions never cleaned up
Predict the symptom before causing the failure. Then locate the earliest boundary where reality diverges from expectation.Why does this responsibility live here, what input does it trust, what output proves success, and what would go wrong if another layer owned it?
If your explanation is vague, repeat the boundary trace before moving on.Close the reference. Reproduce the core idea from memory or explain the exact request/data flow aloud. Then check your diff or notes for accidental shortcuts that bypass validation, authorization, error handling, or verification earned in earlier phases.