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Nudge one weight, watch your backlog reshuffle

Every roadmap meeting treats the ranked list as objective. Score 8 feature candidates, turn one weight dial from 1× to 1.5×, and watch feature #1 fall — the spreadsheet was making choices all along.

Same cards, three frameworks — three different lists.
#1Onboarding checklist100
#2In-app notifications76
#3Dark mode60
#4Bulk CSV export26
#5AI activity summaries23
#6Mobile offline mode18
#7Calendar sync15
#8Custom roles & permissions11

Score is indexed to the current #1 (=100). Arrows show movement vs the neutral-weights baseline. Click a row to re-score it yourself.

Ranking volatility — positions that change per +10% weight nudge
Reach
0 of 8
Impact
0 of 8
Confidence
0 of 8
Effort
0 of 8
Ranking currently matches the neutral-weights baselineSeeded scores are illustrative for a generic product — the mechanic is the point.
The Demystify reveal

The weight dials turn the invisible act of picking weights into a visible lever — proof the ranked list was never as objective as the spreadsheet made it look.

What is RICE scoring?

RICE ranks feature ideas by Reach × Impact × Confidence ÷ Effort: how many people it touches, how much it moves them, how sure you are, and what it costs to build. It turns a prioritization argument into a number — which is exactly why the number deserves scrutiny.

If RICE is a formula, how can the ranking be biased?

Every input is a human judgment, and so is how much each factor counts. Weighting impact a little higher or effort a little lower is invisible in a spreadsheet but can swap #1 and #6 — the sim makes that one dial turn visible.

What is stakeholder gaming in prioritization?

It's when someone nudges a single input — usually confidence, because it's the softest — to push their pet feature up the list. The sim's sneaky-stakeholder mode lets you accept or reject exactly that override and watch what it does to the ranking.

This playable is part of the registry: its tool page

Next: see if the model even fits

Can You Actually Run This? shows the GPU memory bill for any open model line by line — the same X-ray habit, applied to hardware.