At our weekly MCMC meeting, Goodman reminded us of the Leslie Greengard (NYU) philosophy that any high-performing numerical method simply must be complicated. No simple ten-line algorithms can solve a large class of problems fast and in a wide range of cases. That's a bit of a cartoon, but you catch my meaning. Along those lines, we discussed heterogeneous approaches to MCMC, to work towards black-box solutions that would work without tuning or optimization or specialized initialization or long execution times on large classes of problems with challenging probability functions. In particular we looked at problems with our ensemble sampling methods, and ideas for methods that combine classification (of regions of parameter space) with sampling.