An in-depth analysis of why certain types of tests break more frequently than others, along with suggestions for creating more robust pipeline tests.
Read it!"Despite this admonition, people are overconfident in claiming correlations to support favored causal interpretations and are surprised by the results of randomized experiments, suggesting that they are biased & systematically underestimate the prevalence of confounds / common-causation."
Read it!"Developers that can write code but can’t wrangle dependencies or reason about code quality or maintenance will suffer. On the other hand, experienced developers will take on some of their work (as part of being more productive), and less experienced developers will use AI to deploy work more on par with their work."
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