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The ever-increasing need for random numbers is clear in many areas of computer science, from neural networks to optimization. As such, most common programming language provide easy access to Pseudorandom Number Generators. However, these generators are not all made equal, and empirical verification has previously shown some to be flawed in key ways. Because of the constant changes in programming languages, we perform the same empirical benchmarking using large batteries of statistcal tests on a wide array of PRNGs, and identify that while some languages have improved significantly over the years, there are still cases where the default PRNG fails to deliver sufficiently random results.