But seriously, you can have the most pathological prior distribution, so you can then stick with it forever. (Let's say your prior predestines you to always find that whatever the new piece of data you get is so unlikely that it's more likely that it's an error/conspiracy than a real piece of data that you have to do belief update on.)
So, instead of coming up with a significance level, you have to estimate the chance of observing a null result, which determines how much new data moves your posterior distribution. The "advantage" of the Bayesian approach is that - in theory - you can incorporate every tiny little bit of data into your model (distribution). The disadvantage is, that it's very susceptible to various biases (through a biased prior).
I think it’s the opposite. Your confidence intervals are only true if your distribution is true. But how can it be that perfect? At least Bayesians adjust their knowledge like scientists.
I still find Bayes to be more grounded and less “pie in the sky” than frequentists.