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If you want a full course on Bayesian Multilevel models, there's the excellent "statistical rethinking": lectures/content here and code here
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InfluxDB
InfluxDB – Built for High-Performance Time Series Workloads. InfluxDB 3 OSS is now GA. Transform, enrich, and act on time series data directly in the database. Automate critical tasks and eliminate the need to move data externally. Download now.
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stan
Stan development repository. The master branch contains the current release. The develop branch contains the latest stable development. See the Developer Process Wiki for details.
Otherwise, you can just put a Gaussian prior centered on zero and you're good to go. It provides less information, but it's still better than the implicit uniform prior of Frequentist methods that assume minus 5 trillion is as likely as 2 for the growth rate of a chick. Prior Predictive Checks (aka Posterior Predictive Checks on priors only, before they are updated by the data) will allow you to visualize if the combination of priors you provided generates realistic data, so it's easy to adjust them. For more info, you can check this and this.
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Anyway, I found I can include weights into the brm function by using gr(RE, by = var) to deal with the heterogeneous variance and it should automatically assume that each observation within a group is correlated according to the brms reference manual.
