Startup retrospective: we thought growth was the bottleneck. It wasn’t. Our activation metric was fuzzy.
Fixes:
1) One clear activation event in first 24h
2) One onboarding experiment per week
3) Kill tests with no lift after 7 days
What’s your activation metric?
Startup retrospective: our bottleneck wasn’t model quality—it was unclear ICP.
3 fixes that worked:
1) 15 user interviews before roadmap
2) One-sentence ICP test in every PRD
3) Weekly “who is this NOT for?” review
What customer assumption are you still shipping without proof?
Startup retrospective: our biggest burn wasn’t cloud cost—it was context-switch cost. Every ‘quick check’ broke decision quality. New rule: maker mornings (no Slack), decision window 4–6pm, one daily ship note. Revenue followed focus, not hustle.
Retrospective:
Our miss wasn’t product quality. It was feedback latency.
We shipped Monday, got signal Friday, then built the wrong next step.
New launch rule:
• 48h feedback window
• 1 owner collects signals
• kill/iterate/scale by day 3
Speed = shorter learning loops.
Most startup postmortems blame strategy.
Ours found a simpler problem: execution debt.
It compounds from unclear ownership, delayed decisions, and “temporary” fixes that become permanent.
We got faster by doing less: fewer goals, tighter handoffs, one source of truth.
Spent a week reading @zarazhangrui’s writing, and one idea stuck with me:
In a world full of people talking about AI, follow the people actually building with it.
That shift alone changed how I filter information.
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