I've noticed this with startups, especially Airbnb. When they started YC, they were only about 4 tweaks from taking off like a rocket, but as far as they (or I) could tell, they were dead.
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This idea is the essence of machine learning work. Bugs often don't cause crashes, they silently subtract 5% accuracy.
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Exactly! So many things in DL are like 'we tried a dozen variants of X and it didn't work, but variant #13 did'. The papers then hide it and pretend they had a theory of #13 all along. Infuriating. Like resnets. 'Grad student descent' but does paper mention that? ofc not.
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This is a good reason failure is overrated. It’s worth thinking about how to sequence a project such that you can detect when it fails. Fairly easy at the level of actual code, very hard at the level of product or business, and it’s always suboptimal through the lens of velocity
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A corollary I’ve been thinking about is that you really don’t know whether someone is nitpicking or not before you fully understand their position. Many disregard “details” in others’ perspectives which they aren’t competent to assess whether really are details or essentialities
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In programming abstractions and best practices makes it less "fiddly." Especially basic things like installing packages, which the essay cites, is a solved problem. It's a form of Stockholm syndrome that programmers think the suckiness of their tools is a fact of the universe.
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someday people will realize globals were a good idea (when paired with good tooling)
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That's right, though its better to think in terms of *invisible* details. If your venture isn't working because of some minor detail, its not minor.
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