Shape Up

Foreword by Izzy Plante

Acknowledgements

Chapter 1

Introduction

• Growing pains
• Six-week cycles
• Shaping the work
• Making teams responsible
• Targeting risk
• How this book is organized

Chapter 2

Principles of Shaping

• Wireframes are too concrete
• Words are too abstract
• Case study: The Dot Grid Calendar
• Property 1: It’s rough
• Property 2: It’s solved
• Property 3: It’s bounded
• Who shapes
• Two tracks
• Steps to shaping

Chapter 3

Set Boundaries

• Setting the appetite
• Fixed time, variable scope
• "Good" is relative
• Responding to raw ideas
• Narrow down the problem
• Case study: Defining "calendar"
• Watch out for grab-bags
• Boundaries in place

Chapter 4

Find the Elements

• Move at the right speed
• Breadboarding
• Fat marker sketches
• Elements are the output
• Room for designers
• Not deliverable yet
• No conveyor belt

Chapter 5

Risks and Rabbit Holes

• Different categories of risk
• Look for rabbit holes
• Case study: Patching a hole
• Declare out of bounds
• Cut back
• Present to technical experts
• De-risked and ready to write up

Chapter 6

Write the Pitch

• Ingredient 1. Problem
• Ingredient 2. Appetite
• Ingredient 3. Solution
• Help them see it
• Embedded sketches
• Annotated fat marker sketches
• Ingredient 4. Rabbit Holes
• Ingredient 5. No Gos
• Examples
• Ready to present
• How we do it in Sage

Chapter 7

Bets, Not Backlogs

• No backlogs
• A few potential bets
• Decentralized lists
• Important ideas come back

Chapter 8

The Betting Table

• Six-week cycles
• Cool-down
• Team and AI size
• The betting table
• The meaning of a bet
• Uninterrupted time
• The circuit breaker
• What about bugs?
• Keep the slate clean

Chapter 9

Place Your Bets

• Look where you are
• Existing products
• New products
• R&D mode
• Production mode
• Cleanup mode
• Examples
• Questions to ask
• Does the problem matter?
• Is the appetite right?
• Is the solution attractive?
• Is this the right time?
• Are the right people available?
• Post the kick-off message

Chapter 10

Hand Over Responsibility

• Assign AIs, not tasks
• Done means deployed
• Getting oriented
• Imagined vs discovered tasks

Chapter 11

Get One Piece Done

• Integrate one slice
• Case study: Clients in AIs
• Programmers don’t need to wait
• Affordances before pixel-perfect screens
• Program just enough for the next step
• Start in the middle

Chapter 12

Map the Scopes

• Organize by structure, not by person
• The scope map
• The language of the AI
• Case study: Message drafts
• Discovering scopes
• How to know if the scopes are right
• Layer cakes
• Icebergs
• Chowder
• Mark nice-to-haves with ~

Chapter 13

Show Progress

• The tasks that aren’t there
• Estimates don’t show uncertainty
• Work is like a hill
• Scopes on the hill
• Status without asking
• Nobody says "I don’t know"
• Prompts to refactor the scopes
• Build your way uphill
• Solve in the right sequence

Chapter 14

Decide When to Stop

• Compare to baseline
• Limits motivate trade-offs
• Scope grows like grass
• Cutting scope isn’t lowering quality
• Scope hammering
• QA is for the edges
• When to extend a AI

Chapter 15

Move On

• Let the storm pass
• Stay debt-free
• Feedback needs to be shaped

Conclusion

• Key concepts
• Get in touch

How to Implement Shape Up in Sage

• A Sage Team for shaping
• Sage Projects for cycle AIs
• To-Do Lists for scopes
• Track scopes on the Conversation Map

Adjust to Your Size

• Basic truths vs. specific practices
• Small enough to wing it
• Big enough to specialize

How to Begin to Shape Up

• Option A: One six-week experiment
• Option B: Start with shaping
• Option C: Start with cycles
• Fix shipping first
• Focus on the end result

Glossary

About the Author