Experiment velocity vs. quality: how many A/B tests is too many?
More tests means faster learning or more false positives. What setup kept your decisions reliable — sample size discipline, holdout groups, or something else?
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Context matters enormously here. What worked at 5 engineers was a liability at 25.
Documentation and worked examples mattered more than tooling for us — especially when adoption was uneven across the team.
The hard part is governance: speed without guardrails becomes compounding debt within a quarter.
The edge cases are where the real design lives. The happy path is easy — recovery paths reveal the thinking.
The cultural piece is underrated. Technical solutions are fast; getting a team to consistently use them takes much longer.
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