For two weeks last July, I cocooned myself in a hotel in Portland, OR, living and breathing probabilistic programming as a “student” in the probabilistic programming summer school run by DARPA. The school is part of the broader DARPA program on Probabilistic Programming for Advanced Machine Learning (PPAML), which has resulted in a great infusion of energy (and funding) into the probabilistic programming space. Last year was the inaugural one for the summer school, one that is meant to introduce and disseminate the languages and tools being developed to the broader scientific and technology communities. The school was graciously hosted by Galois Inc., which did a terrific job of organizing the event. Thankfully, they’re hosting the summer school again this year (there’s still time to apply!), which made me think that now is a good time to reflect on last year’s program and provide a snapshot of the state of the field. I will also take some liberty in prognosticating on the future of this space. Note that I am by no means a probabilistic programming expert, merely a curious outsider with a problem or two to solve.