Series
Building Software in the AI Era
Master the tools, habits, and operating models needed to ship high-quality software in a day-to-day workflow shaped by LLMs, moving past outdated sprints and velocity metrics.
8 parts81 minStarted Apr 2026
The curriculum
8 parts, read in order.
- 1
Why Sprints Are Broken
Technical debt is inevitable. What matters is whether you manage it deliberately. A practical framework for prioritisation and stakeholder communication9 min read - 2
Shape Up: A Practical Introduction
A practical introduction to Shape Up - the planning methodology built on appetite over estimation, fixed time with flexible scope, and small autonomous teams.10 min read - 3
Writing Pitches That Work
How to write Shape Up pitches that give engineering teams real clarity - problem definition, appetite, solution shaping, rabbit holes, and no-gos explained.10 min read - 4
Spec Driven Development With LLMs
How to write specifications that produce useful LLM output, covering interface definitions, edge cases, and why the spec is your highest-leverage artifact.10 min read - 5
Reviewing AI Generated Work
How to review AI-generated code effectively, what failure modes to watch for, and how to maintain quality standards as code volume increases.11 min read - 6
Redefining Engineering Roles in the AI Era
AI makes implementation cheaper and judgment more valuable. Learn how engineering roles, hiring, and team design are changing.10 min read - 7
Managing Expectations in the AI Era
How to have honest conversations with stakeholders about AI productivity - what actually changes, what doesn't, and how to set realistic expectations.11 min read - 8
Running A Better Table
How to run a Shape Up betting table - the planning meeting where pitches get selected and cycles begin or quietly collapse.10 min read
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