Fuzzing is the art of applying random data in the hopes of finding inputs to programs that cause unexpected behaviors. Traditionally, this has taken a deterministic approach which is akin to something to do with monkeys, typewriters, and Shakespear. Enter american fuzzy lop (afl), not just a weird looking bunny [1], but actually a new generation fuzzer from Google that takes into account code coverage. This talk will give a brief overview of afl-fuzz, show off its features, and gives an example of things I have found, and how much bounty has been made. Comparisons to other fuzzers will be made.