I would be very happy to watch a Korean drama where a poorly-fitting Stan program induces a love triangle emphasized through lots of freeze frames and bumpy music. Unfortunately there are higher priorities to foster a strong local community! Keep in mind that these are my opinions, based on my experience, and they clash with many others here.
The exact priorities will depend slightly on the initial audience that you want to target. For example, are you interested in recruiting active Stan users? Are you interested in recruiting potential Stan users with statistics backgrounds? Are you interested in recruiting potential Stan users with applied backgrounds? From your post it sounds like the latter so I’ll continue with that assumption.
Building statistical analyses that respects and exploits the domain expertise in applied problems is hard. It requires significant statistics training and time to dedicate to each analysis. The rewards are powerful, robust inferences and decisions but that reward is often so far into the future that people’s more immediate incentives lead them to other avenues, such as machine learning.
In order to build a sustainable community you have to reach out to those who appreciate this difficulty and are willing to put in the necessary effort. Typically these are people who have already been burned by the fragility of popular approaches that promise less work, and are desperate for better methodologies and tools. These people are scattered across applied fields, both in academia and industry, and hence hard to find!
Consequently, trying to reach out to the masses, especially by sugar-coating the realistic efforts required in of statistical modeling, is pretty ineffective and typically attracts people who quickly get frustrated with the amount of necessary work and leave. That constant churn makes it difficult to sustain the early community.
Instead I recommend making it easy for the right people to find you and then being ready for when they do.
For the former you’ll want an easily searched webpage where you collect all of your resources and help guide interested people through them. Complementary social media accounts are especially useful for broadcasting the availability of these resources and events – even if they have a small audience initially that audience can grow steadily over time and allow to reach much larger audience that you would have otherwise. If you host events then try to make the schedule as consistent as possible and, as Bob said, as open to the general public as possible. Reduce the barrier to entry into your community without making any unrealistic promises!
As for the resources themselves, I strong recommend providing as comprehensive a coverage as possible. This might include, for example, links to good introductions to calculus (especially intuitive discussions of differentiation and integration) and linear algebra which are important prerequisites for any discussion of probability and statistics. From there you can build up resources that covering probability theory, Bayesian inference, Bayesian computation, modeling building, and then modeling techniques.
One of the challenges in building up these resources is that many of the available references use difficult vocabulary or cover only parts of the necessary material. There is no great reference that covers everything, and even combining multiple textbooks can leave some holes. My best attempts at filling these holes is available at https://betanalpha.github.io/writing/, but inevitably you’ll find more holes so you will want to take advantage of the more global Stan community to ask questions and potentially develop your own material.
I know this all sounds daunting but, to abuse a cliche, building a community is not a destination but rather a journey. It helps to start small with reading or study groups or simply open discussions to gauge the interests of the initial community and focus your initial efforts of translating or developing resources for those interests and then slowly expanding as your community grows.
Good luck!