... Or, Lessons Learned While Using Ruby's MP System to Model a 2,500 Year-Old, Dead Language During LSRC III's Reject Conf, I began a project to model the linguistic behavior of verbs in Classical Latin. Owing to the irregularity of human communication, modeling the provision of unambiguous answers (return values) to ambiguously asked things (flexible / incomplete method calls) might have required hundreds, if not thousands, of method definitions or static values entered in a database. But what if heuristics could be given to a Ruby class such that it "thought" as language learners are taught to think? What if it could be taught to be flexible in respecting the ambiguous calls given and to still give precise, correct answers back - as human language learners are capable of doing? By adopting this design paradigm, code could become leaner and more reflective of human cognitive process. Thankfully for Rubyists, this is not a dream, this is reality. Our programs can operate more intelligently, more heuristically, and more insightfully. We can save ourselves days of development time by integrating this next tier of metaprogramming patterns I propose to demonstrate. While I will demonstrate these patterns by modeling linguistics based on the [LatinVerb library](https://github.com/sgharms/LatinVerb), these techniques have wider application across problem domains.