Winnow efficiently trains and operates any number of unique Bayesian (Naive Bayes) classifiers on large sets of content. It has very high performance and works with very small training and unbalanced training sets. It has been used to power an innovative Web feed reader that uses smart tags, which learn and find the content you want to see, from more sources than you can follow with traditional feed readers. It works particularly well with Ruby and Ruby on Rails.
nyu is a combination of modern academic approaches to parsing formal grammars from PEGs and expression grammars that represents the new state of the art in parser generators. nyu grammars are written in a powerful language based on PEGs (parsing expression grammars) but with modifications to allow both the AST and the parser to be specified intuitively in a single grammar. nyu outputs parsers that take advantage of the chilon::parser meta-programming library for C++. The generated parsers are almost as concise and readable as the input grammars, yet perform as well as hand-written C code. nyu ASTs are built using tuples, variant types, and lists, and allow self referential parsers and AST nodes to be manipulated. Advanced features such as hashed containers and grammar inheritance are also possible and well tested. nyu is currently powerful enough to deal with complex grammars and bootstraps its own parser.