The author seems to imply that frequentists are not just wrong but borderline malicious – they use all sorts of ad hoc procedures, have no theoretical justifica

The author seems to imply that frequentists are not just wrong but borderline malicious – they use all sorts of ad hoc procedures, have no theoretical justification for anything they're doing, and so on and so on. But in fact, there is an extensive body of theory about when you can "throw away information" (cf. minimally sufficient statistics), the difference between P(D|H) and P(H|D) (cf. likelihood theory) and so on. His undergraduate textbook might not talk about all that, but a graduate textbook sure would -- it's certainly not lore full of implicit assumptions that everyone's forgotten. A much better explanation of what is happening to statistics is this: flipping P(D|H) to P(H|D) used to be really hard, so we came up with all sorts of tricks to either approximate it or to get by without having to bother about P(H|D) at all, things like p-values. Now there's computers and really good sampling algorithms so we don't have to use the approximations anymore. But some people still prefer the approximations because they're used to them. There. No incompetence or malice involved. Other than that, if you ignore the tone of the article, it's an insightful read if you're new to Bayesian statistics and want to understand what's the point.