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BLOG is a powerful language to express models with an unknown number of objects and identity uncertainty. Current inference engines for BLOG are either too slow or require users to write a model-specific proposal distribution. We describe here, ongoing work to design a new, fast, generic inference engine for BLOG called blogc. The new implementation uses Gibbs sampling for finite-valued variables and performs an analysis of the model to generate customized sampling code in C. We describe our algorithms and methods in the context of various commonly used models and demonstrate significant performance improvement.