Goal Learn to diagnose this phase's failures systematically instead of reinstalling packages, changing random configuration, or copying a fix without understanding it.
The debugging loop 1. Reproduce the failure with the smallest reliable action. 2. Capture the exact symptom: command, status code, stack trace, network request, database state, or UI state. 3. Locate the earliest boundary where actual behavior diverges from expected behavior. 4. Form one hypothesis. 5. Change one variable. 6. Re-run the same reproduction. 7. Keep the fix only if the evidence supports the hypothesis.
Boundary walk user action -> cancel/snapshot relevant queries -> optimistic cache write -> server mutation -> rollback on error -> invalidate/reconcile on settle
Start at the visible symptom and walk backward. Do not debug the database when the browser never sent the request. Do not debug React state when the API is returning a deterministic 403. The boundary walk tells you which evidence to collect next.
Failure drill 1 — Optimism without rollback Create or imagine this failure in a safe development environment. Write the expected symptom first. Then list the two or three observations that would distinguish this failure from neighboring causes. Repair it with the smallest change and verify the original reproduction no longer fails.
Failure drill 2 — Temporary IDs colliding with real IDs Create or imagine this failure in a safe development environment. Write the expected symptom first. Then list the two or three observations that would distinguish this failure from neighboring causes. Repair it with the smallest change and verify the original reproduction no longer fails.
Failure drill 3 — Older mutation response overwriting newer user intent Create or imagine this failure in a safe development environment. Write the expected symptom first. Then list the two or three observations that would distinguish this failure from neighboring causes. Repair it with the smallest change and verify the original reproduction no longer fails.
Failure drill 4 — Optimistically updating the wrong filtered list Create or imagine this failure in a safe development environment. Write the expected symptom first. Then list the two or three observations that would distinguish this failure from neighboring causes. Repair it with the smallest change and verify the original reproduction no longer fails.
Do not hide the evidence Avoid immediately clearing caches, deleting lockfiles, resetting the database, disabling security checks, or reinstalling everything. Those actions may erase the evidence that teaches you what was wrong. Use them only after you can explain why they are relevant.
Debugging exit ticket Pick one failure from this workshop and write a short incident note: symptom, scope, root cause, fix, verification, and prevention. This is the same communication pattern used in professional bug reports and production incident follow-ups.
Debugging Workshop 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 “Debugging Workshop” as one observable behavior. Write the success signal before touching code.
Trace the boundary map: user action → snapshot → optimistic cache → server result → commit/rollback. 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: Optimistic task update/create flow with rollback and authoritative server reconciliation.. 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.
onMutate: async (input) => {
await queryClient.cancelQueries({ queryKey: taskKeys.all });
const previous = queryClient.getQueryData(taskKeys.all);
applyOptimisticChange(input);
return { previous };
},
onError: (_err, _input, ctx) => restore(ctx?.previous),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 “Debugging Workshop” to a different TaskFlow resource or input. Write the acceptance criteria first, implement it, and provide the evidence that each criterion is satisfied.
Capture the previous cache state before changing it.
Use the smallest change that demonstrates this lesson. Stop after it works and capture evidence.Temporary IDs colliding with real IDs
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.