What is RICE prioritization?

RICE is a prioritization framework that scores features by Reach (users impacted), Impact (effect per user), Confidence (certainty of estimates), and Effort (time to build). Dividing R × I × C by E gives a comparable score across features.

How do you measure product-market fit?

Product-market fit can be measured across eight dimensions: user pull, retention, NPS, growth source, sales friction, revenue willingness, core loop strength, and market feedback. A PMF scorecard like Duku's aggregates these into a 0–24 score to guide where a startup sits on the PMF spectrum.

What makes a startup ready for zero-to-one?

Zero-to-one readiness means having a validated problem with real user pull, a credible founding team, a testable go-to-market thesis, and enough runway to reach the first milestone. Duku's quiz scores this across four dimensions.

What is sprint capacity planning?

Sprint capacity planning calculates the real engineering capacity for a sprint by accounting for each team member's availability and focus percentage, producing effective developer-days rather than raw headcount estimates.

What does a web3 project need before launching a token?

Before a token launch, a web3 project should have: a battle-tested smart contract audit, a clear tokenomics model with stress-tested emissions, an engaged community ready to amplify, demonstrated user demand, a go-to-market and liquidity plan, and a credible team known in the ecosystem.

Free Product Tools for Early-Stage Founders

RICE scores features by Reach, Impact, Confidence, and Effort - giving you a ranked priority list based on data, not opinion. Add your features below and get an instant ranking.

Type a feature, assign a priority, and add it to the board. Move items between categories at any time to clarify what's in and out of scope.

Must Have

Nothing here yet

Should Have

Nothing here yet

Could Have

Nothing here yet

Won't Have

Nothing here yet

Zero to One

Six questions that reveal whether you're ready to go from idea to product - and where the gaps are that will slow you down.

1. Do you have a clear understanding of who your target user is?

2. Can you describe your product's primary value proposition in one sentence?

3. Has your team validated at least one core assumption with real users?

4. Does your team have engineering capacity to build in the next 30 days?

5. Is your team aligned on what to build next?

6. Do you have metrics or KPIs defined to measure product success?

Enter your team's availability and focus time to calculate effective sprint capacity. The recommended commitment leaves a buffer for bugs, reviews, and the unexpected - adjust the buffer % to your team's reality.

Sprint length (working days)

Buffer (%)

Rate each signal honestly - this is for you, not your investors. Based on the Sean Ellis 40% framework plus key retention and growth indicators. Answer all 8 to see your score.

PMF confidence0 / 24

1. If your product disappeared tomorrow, how would your most active users feel?

2. How is retention looking after the first 30 days?

3. Are users recommending your product without being asked?

4. How frequently do users engage with your core feature?

5. Can your best users articulate the problem you solve - in their own words?

6. What's the signal on willingness to pay?

7. How do users respond when you push back or remove a feature?

8. What does organic growth look like over the last 60 days?

Rate each signal honestly. Seven factors that determine whether your protocol is ready to launch - covers token utility, community strength, audit status, on-chain PMF, GTM, team credibility, and tokenomics. Answer all 7 to see your score.

Launch readiness0 / 21

1. How well-defined is your token utility or value accrual mechanism?

2. Do you have a core community that would advocate for the launch?

3. Is your on-chain architecture audited or ready for audit?

4. How strong is the product-market fit signal with your target on-chain users?

5. Do you have a clear go-to-market plan for community + ecosystem distribution?

6. Is your team's web3 credibility established in your target community?

7. How robust is your token/product economics model?

Product frameworks for early-stage teams