This talk will present research into type inferencing in Python. The main goal of this work is to extract a performance gain from type-specific opcodes added to Python's VM. The type information needed to use these new opcodes during code generation comes about from a simple, localized type inferencing algorithm. The design of this algorithm required that no changed be made to the semantics of Python. Because of this requirement the type inference algorithm is hindered by Python's dynamic nature. This talk will discuss what in Python's semantics makes type inferencing difficult, the algorithm used, the added opcodes, and the results of adding the new opcodes.