"Bayesian" is an overloaded term. There's Bayes' theorem/rule, which basically everyone agrees with, since it's a theorem that's very simple to prove with a few high school math operations.
Then there is the philosophical Bayesian interpretation of probability, that claims that probabilities are fundamentally about our own mental state of belief, as opposed to frequencies at the limit of infinite repetition of some experiment.
Then there is the Bayesian methods of statistics / machine learning etc, which are about handling parameters as random variables and the observed data as fixed, as opposed to assuming that there's one fixed parameter (without a distribution to talk about) and the data should be modeled as random (from an oversimplified bird's eye view). And it was also oversold as a miracle cure for all our problems: for some time, before the deep learning era, you just had to have "Bayesian" in your ML paper title to make it sexy and interesting.
Then there is the online Bayesian rationalist community, where Bayes is used to explain the meaning of life, the universe, it's the great grand explanation of everything, a self help tool, the key to seeing the light, a semi-religious experience, the way to enlightenment (they even call it the Way, capitalized - I guess a Buddhist reference?). As if being Bayesian was this secret club, that sets you apart from average people, a symbol of belonging to the in-group etc. [1]
It's important to keep these apart.
[1] For example: https://youtu.be/NEqHML98RgU?t=73 (it's explicitly not about the math but about self-help and intuition to benefit our lives etc...)
Then there is the philosophical Bayesian interpretation of probability, that claims that probabilities are fundamentally about our own mental state of belief, as opposed to frequencies at the limit of infinite repetition of some experiment.
Then there is the Bayesian methods of statistics / machine learning etc, which are about handling parameters as random variables and the observed data as fixed, as opposed to assuming that there's one fixed parameter (without a distribution to talk about) and the data should be modeled as random (from an oversimplified bird's eye view). And it was also oversold as a miracle cure for all our problems: for some time, before the deep learning era, you just had to have "Bayesian" in your ML paper title to make it sexy and interesting.
Then there is the online Bayesian rationalist community, where Bayes is used to explain the meaning of life, the universe, it's the great grand explanation of everything, a self help tool, the key to seeing the light, a semi-religious experience, the way to enlightenment (they even call it the Way, capitalized - I guess a Buddhist reference?). As if being Bayesian was this secret club, that sets you apart from average people, a symbol of belonging to the in-group etc. [1]
It's important to keep these apart.
[1] For example: https://youtu.be/NEqHML98RgU?t=73 (it's explicitly not about the math but about self-help and intuition to benefit our lives etc...)