Many developers hate doing code reviews. Reading foreign code is hard, and suggesting improvements is even harder. Yet a dramatic portion of code review time goes to figuring out the boring details: formatting, naming, microoptimizations and best practices. We believe that all of those can be automated with ML on Code, either learning from a particular project or from all the open source code in the world which is relevant. This talk will be about open source "analyzers" - ML-driven code review agents which deal with the boring but important details.