> We can argue about how to version APIs and how a service is such RESTful and such not RESTful. We can mull over pettiest of things such as semicolon or the gender of a pronoun and let insanely clever people leave our community. We can exchange the worst of words over "females in the industry" while we more or less are saying the same thing, Too much drama.
> But soon this will be no good. Not good enough. We got to grow up and go back to school, relearn all about Maths, statistics, and generally scientific reasoning.
The programming community is large and diverse. We can do all of these things, including the rigorous (what the author seems to call "scientific" [1]) ones and the UI/marketing ones too. I think this diversity is a strength, as long as managers and entrepreneurs find and retain the skill-sets necessary for their domains.
[1] I prefer to use "science" to mean falsifiability, preferably with strong experimental designs. Much of what the author talks about is mathematical rigor (from computer science), which is also important, but not experimental science. See also: https://en.wikipedia.org/wiki/Philosophy_of_science#Defining...
> We can argue about how to version APIs and how a service is such RESTful and such not RESTful. We can mull over pettiest of things such as semicolon or the gender of a pronoun and let insanely clever people leave our community. We can exchange the worst of words over "females in the industry" while we more or less are saying the same thing, Too much drama.
> But soon this will be no good. Not good enough. We got to grow up and go back to school, relearn all about Maths, statistics, and generally scientific reasoning.
The programming community is large and diverse. We can do all of these things, including the rigorous (what the author seems to call "scientific" [1]) ones and the UI/marketing ones too. I think this diversity is a strength, as long as managers and entrepreneurs find and retain the skill-sets necessary for their domains.
[1] I prefer to use "science" to mean falsifiability, preferably with strong experimental designs. Much of what the author talks about is mathematical rigor (from computer science), which is also important, but not experimental science. See also: https://en.wikipedia.org/wiki/Philosophy_of_science#Defining...