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I think this is awesome! However for me then most interesting thing is the tech behind it. Having worked in the same office as the team behind this and been to

I think this is awesome! However for me then most interesting thing is the tech behind it. Having worked in the same office as the team behind this and been to a few talks that Tom's given, here are a few things that HN might find interesting. * Built by the same guy behind BayesHive: https://bayeshive.com/ https://bayeshive.com/ * An example of probabilistic functional programming in the wild. (This let's you build programs where the variables are probability distributions.) * Good example of something built in Haskell that really speaks to the strenghts of the language! Maybe Tom could give us a brief synopsis of how this was built and how it works?

glutamate 12y ago
Thanks Ben. On the programming side: * 100% Haskell. Even our post-install script in the packages we build for the server are in Haskell. * Use Spock and postgresql-simple * 95% of functionality is in a library which is written in Nice Haskell - e.g. all functions are total, type safety where it makes sense. * Library consists of data types, pure functions, and functions that live in a DB monad. All code is decoupled from the request-response server implementation. * Front-end in AngularJS On the statistics/learning side: * We apply Bayes' theorem every time you press that green "Next" button. There is a statistical model for your choices based on your ultimate preferences, which we estimate. * Lots of tricks to bring this inference down to 50ms on a modest CPU and to maximise information yield from user interaction. More examples of this choice paradigm coming in the next few weeks.