Probabilistic programming is en vogue. It is used to describe complex Bayesian networks, quantum programs, security protocols and biological systems. Programming languages like C, C#, Java, Prolog, Scala, etc. all have their probabilistic version. Key features are random sampling and means to adjust distributions based on obtained information from measurements and system observations. We show some semantic intricacies, argue that termination is more involved than the halting problem, and discuss recursion and run-time analysis.