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Product Mindset for Engineers in 30 Days
27 lessons published · new lesson added daily.
1
Day 1
Why Your Design Assumptions Reflect You, Not Your Users
2
Day 2
Measuring Whether a Coding Agent Actually Made an Engineer Faster
3
Day 3
How to keep an AI agent from shipping the wrong thing
4
Day 4
Why an MVP is a bet you should write down
5
Day 5
Why a rising acceptance rate didn't mean our coding agent worked
6
Day 6
Why acceptance rate is a misleading AI metric
7
Day 7
How to spot A/B tests that can't actually fail
8
Day 8
What a customer's spreadsheet taught me about my AI feature
9
Day 9
Why usage dashboards can hide whether a feature helps
10
Day 10
How Roadmap Dates Turn Into Promises You Didn't Make
11
Day 11
Why RICE's Effort number stops measuring risk when AI writes the code
12
Day 12
How coding agents build the wrong feature by treating the goal as settled
13
Day 13
Why agents repeat the same bug without a learning loop
14
Day 14
Why action logs can't show why an agent chose the wrong tool
15
Day 15
Why funnel analytics can't tell you a step shouldn't exist
16
Day 16
Why owning the 'why' not the 'how' protects your job from agents
17
Day 17
Churn metrics
18
Day 18
Activation metrics
19
Day 19
Codebase first-run experience
20
Day 20
Value-based pricing
21
Day 21
Value propositions for coding agents
22
Day 22
Feature flag rollouts
23
Day 23
Cold dogfooding
24
Day 24
The "so that" clause
25
Day 25
Acceptance criteria for coding agents
26
Day 26
Scope cutting with coding agents
27
Day 27
Tech debt expiry dates