Parse.ly is a rich, adaptive web application that discovers your unique interests to filter and prioritize content from countless news and blog sources on the web. This talk will introduce Parse.ly with a quick demo and then delve right into how the Parse.ly engineering team makes use of the Solr open source search engine. This will include discussion of initial design mistakes that were later revised and "real world issues" that were overcome in scaling a system that currently processes millions of articles per week. Finally, we will discuss the existing Solr and Python landscape, and how we at Parse.ly aim to help the Solr community with the open source release of high-quality, Pythonic components for doing common Solr tasks.
25. Twitter @amontalenti Product Twitter @parse_ly Website http://parse.ly Team Blog http://blog.cogtree.com Sign up now! It’s Free! Promo Code SLIDES Andrew Montalenti [email_address]
Notas do Editor
What if a system could deliver you only the most relevant content, prioritized to what’s important to you?
You tell us what you’re into – type in your interests and weight them according to your preferences. We deliver you a filtered, prioritize news feed with content gathered from over 120,000 online sources: mainstream news, blogs, social media channels. Most important items at the top. Waste less time skimming headlines, more time on relevant news items.
A comprehensive news filtering system that can will monitor, filter and prioritize news items for employees and allow them to delegate and action those items? (Parse.ly Premium) A Content Personalization API , that can recommend relevant online content to users with very little a priori information? (Parse.ly API)
![[object Object],Andrew Montalenti Co-Founder & Technology Lead [email_address]](https://image.slidesharecdn.com/parselysolrslideshare-091029232112-phpapp02/85/Parsely-Inside-a-modern-RIA-built-with-Solr-1-320.jpg)
