Granted I don't have much experience with Stan, but from what little I've poked around RStan, the workflow left something to be desired. Setting up a C++ environment, and then embedding Stan code (which appears to be a C++ DSL of sorts) as strings inside your R code seems... unpleasant.
Perhaps if MCMC.jl matures, and if it can offer performance competitive with BUGS, JAGS, Stan, etc., I could see Julia's statistical fortunes rise along with Bayesian methods generally. I'm working on a PhD in a discipline that is just now beginning to dip its toes in Bayesian waters. I get the feeling that the adoption surge is yet to come.
Interesting that you're implementing your own MCMC methods mainly. As part of my coursework I did a little bit of that, but perpetually felt like I wasn't smart enough to anything sophisticated--either in terms of exotic sampling methods, or working with very complex posterior distributions. It may just be my frequentist R toolbox experience messing with me, but I know I feel more comfortable with the safety blanket of a framework.