Stan reference manual: the Bible of the Stan modelling language. There is a 90% probability it has a solution to your problem (if only you could find where).
Stan forums: the official Stan discourse. Active and friendly community with relatively fast response times.
Stan case studies: many helpful and detailed demonstrations of Stan use cases. Be sure to look at the robust workflow examples for PyStan, RStan, and cmdStanpy.
Stan prior recommendations: recommendations for priors from Stan team. Always being updated (for better or worse).
Stan example models: a veritable treasure trove of prewritten Stan models across a wide array of statistical problems.
ShinyStan: helpful visualization tools compatible with RStan.
ArviZ: helpful visualization tools compatible with PyStan.
Stan for cognitive science: aims to centralize all types of work in cognitive science that use the software Stan. Has some useful tutorials and links to papers using Stan.
rlssm: a Python package by Laura Fontanesi for fitting reinforcement learning (RL) models, sequential sampling models (DDM, RDM, LBA, ALBA, and ARDM), and combinations of the two, using Bayesian parameter estimation.
Bayesian Inference and more foundational knowledge
More of Michael Betancourt’s blog: lots of useful posts from intro to probability theory, to modelling, and some others..