Feature Creep Crunch

What this snack represents

Now with 40% more edge cases than originally estimated. Expands from one checkbox into a full platform appetite.

Plain English: This snack translates a familiar software cost into visible time burn.

Formula: Base estimate + review + rework + context switching

What this teaches: Time Burn, Technical Debt.

Why teams underestimate it

The shelf label works because the cost is usually hidden across clarification, review, context switching, QA, and follow-up decisions. BarkBowl makes those ingredients visible before the work is treated as free.

Time formula

Base estimate + review + rework + context switching

Worked example

One serving of Feature Creep Crunch is currently modeled at 8 hours, or 8 person-hours. Multiply servings when the work repeats, spreads across more people, or needs another review cycle.

Warning label

May contain scope creep and ceremonial overhead.

Evidence needed

Before committing this snack, collect an owner, a reason, a time formula, a rollback or no-op alternative, and a short explanation of what the receipt does and does not prove.

How to reduce the cost

Async Notes Jerky

No-op alternative

If evidence, authority, or capacity is missing, the mature move may be to record a no-op decision and revisit when the missing ingredient exists.

What this teaches

• Time Burn
• Technical Debt

Related lesson

time-burn

Related field report

time-burn

Related glossary terms

Time Burn, Technical Debt

Related blueprint

Decision record starter · Open Blueprint Gallery

Copy decision-record starter

Decision: Consider Feature Creep CrunchTime formula: Base estimate + review + rework + context switchingEvidence: [link or owner]
No-op alternative: [what happens if we do not act]
Boundary: This is a draft for human review, not an approval.
Reviewed at UTC: [timestamp]

Boundary note

This snack produces draft learning and time-burn evidence only; it does not approve fixes, validate credentials, train models, or certify safety.

Dogfooding notes

Exercises product snapshotting, time totals, TTL refresh, telemetry, learning translation, and checkout record creation.