Parse.ly Blog

In an excellent article discussing some software engineers’ transitions from working on Wall Street to working on startups, our very own Parse.ly CTO, Andrew Montalenti, is profiled.

You can imagine the surprise when we discovered the article as the top choice in our Parse.ly team account today (see above!).  How very meta.

A relevant quote:

[…] soon the work grew redundant, Mr. Montalenti said, and the problems he was asked to solve as part of his day-to-day responsibilities started to seem technically uninteresting. Like many other creatively inclined, intellectually ambitious programmers who took high-paying jobs on Wall Street after college, Mr. Montalenti found himself disillusioned and restless.

Then, in March of last year, he did something very few people in his predicament have the guts to do: He quit his job and founded a company of his own with one of his best friends.

“I’d just like to be able to point to at least one thing after 15 years of working as a software engineer and say, ‘I built that thing,’” said Mr. Montalenti, who, at 26, is now happily running Parse.ly, a Web-based recommendation service.

Click here to read more from Observer.com.

Should be a good time.  See http://www.meetup.com/Hoboken-Tech-Meetup/ to join the meetup!

Tomorrow Parse.ly will be presenting at the NY Tech Meetup.  We’re part of the “university demo” segment, though we’re not actually university students anymore (if only!).  This is a particularly good time to for us to talk to the New York Tech community.  We have a few upcoming product offerings for developers, publishers, and individuals that we’re super excited about.

Primarily, our presentation will be about our API launch and what developers can do with Parse.ly’s personalized recommendation technology.  Developers of news/blog content mashups or online content sites can use our technology to offer Amazon/Netflix-style recommendations to their users.  Here is what the Parse.ly API does for you:

1) parses and cleans RSS/Atom feeds and other content sources in near-real-time, via an integration with PubSubHubbub (PuSH) technology

2) builds a full-text index of your content, as well as personalized “resonance profiles” for different users that can be trained and queried

3) delivers personalized recommendations (Amazon/Netflix-style) of content to users, that can be listed, searched, and filtered

Our whole value proposition is that, yes, you could build algorithms to do personalized recommendations yourself and in-house, but it’s hard. There’s a lot of infrastructure that goes along with it. You or your engineering team will spend months — not days — getting it right. So, why not just plug into our nice API instead?

Our API is a standard HTTP/JSON RESTful API, and we already have a Python binding, with more bindings on the way.  We also have an overview of our algorithms online in our developer docs.

We know there are lots of awesome ways our developer community can leverage this technology.  However, we want to break the ice, so here are a few ideas to get the creative juices flowing:

• an iPad application that creates an elegant, full-screen experience for browsing news content from across the web.  Think Pandora.com, but for news stories and other content that can be read online.
• an iPhone / Blackberry application for getting personalized content recommendations on the go.
• an Adobe Air or other desktop technology application that delivers content recommendations as desktop notifications integrated into the user’s operating system.
• Blogging platform plugins (e.g. Wordpress) that assist with content writing and editing based on current blog posts, future blog post drafts, and user interaction with existing content.
• Custom publishing applications that can produce beautiful, printable flowed layouts of online content, powered by personalized recommendations.
• Personalized versions of any of your favorite content sites on the web; for example, personalized versions of TechMeme, TweetMeme, TechCrunch, or other aggregators.

We’ll be collecting e-mail addresses and info for developers that want an API key to play with our tech.  We’ll automatically add interested people to the Parsely API Developer Google Group.  Interesting ideas and discussions should emerge there. Then, within the next couple weeks, we’ll send out API keys to those who signed up.

At the meetup we’ll also discuss our re-vamped Consumer Beta that will launch within the coming months.  When we launched our private beta last August, we wanted to release a minimal, productive reading interface that allows users to interact with as much content as they wished.  Since then, we’ve been curating feature requests and usage to plan for the next release.  We have excellent ideas about how to make the our web application the best reading interface on the web.  Expect to hear more about it soon.

Finally, Parse.ly is partnering with a number of high-traffic, original content sites on the web through our Parse.ly Publisher Platform, aka P3.  Within the next couple months, you’ll see Parse.ly powering content personalization features ranging from personalized e-mail solutions, to widgets, to full-on Netflix-style experiences.  We’ll be rolling this out with some top online publishers, and will let you know once these are live!

Mike Singleton of FourSquare recently wrote a blog post entitled, “Algorithms as a Service”:

I think there’s a market opportunity to crease an AAS (algorithms as a service) company which provides simple APIs to implementations of common algorithms… Algorithms as a service would give you development efficiency, problem scalability (access to CPU farms), and confidence in the results.

Andrew chimed in with this:

I think what you’ve identified is that some APIs are about getting data into and out of an existing system that sort of lives on its own — e.g., Twitter’s, FourSquare’s, Flickr’s.

Then, other APIs are about abstracting certain problems and simplifying them to a simple API call. These are “algorithms as a service”.

So, in this category I put things like OpenCalais.com (entity extraction algorithms) and SimpleGeo.com (geolocation algorithms). I also put my own startup, Parse.ly, in this category; see http://parse.ly/p3 and http://parse.ly/api. For Parse.ly, what we’re doing is simplifying the following painful steps:

1) parsing and cleaning RSS/Atom feeds and other content sources in near-real-time
2) building personalized “resonance profiles” for different users that can be trained and queried
3) delivering personalized recommendations (Amazon/Netflix-style) of content to users, that can be listed, searched, and filtered

Our whole value proposition is that, yes, you could build algorithms to do personalized recommendations yourself and in-house, but it’s hard. There’s a lot of infrastructure that goes along with it. Your engineering team will spend months — not days — getting it right. So, why not just plug into our nice API instead?

I don’t think it needs a new name — it’s just an evolution of APIs and SaaS given the growing needs of developers to build more complex, dynamic applications and their increasing willingness to license best-of-breed 3rd-party platforms to do so.

January was an exciting month for Parse.ly. At the end of 2009, we were heads-down, polishing our own “algorithms-as-a-service” offering. We aligned our development around a public launch of it at the SIIA Information Industry Summit in NYC, where we were invited to present. Sachin gave a great presentation; here’s what one blogger had to say about it:

Parse.ly, a semantic tool that recommends content, steers users towards content towards personalization and recommendation through their licensed content. When and how [do] personalization really happen? […] Parse.ly collects a little personal interest information from users, “listens” to their content habits and provides recommendations that can be embedded in any number of content applications. Market segmentation data and other demographics fall out of this information naturally. Parse.ly is available to publishers now for integration via their new P3 platform.

At the same time as launching the Parse.ly Publisher Platform (P3), we also put online our API docs and made it possible for you get an API key. Then, we started conversations with some great brands in online / digital publishing (household names, even) about using our platform. These conversations have been going really well — almost too well! These companies know how much more valuable their online properties would be if they were built around engaging, personalized recommendations in the Amazon/Netflix style. And they have a lot of ideas about how to use the data and recommendations P3 will give them. We’ve already started to mock up new user interfaces for our API to make the integration with publishers as smooth as possible.

We’re excited for this new direction for Parse.ly. We agree with Mike that there are opportunities all around us to simplify algorithmically-tough problems to simple and highly-usable APIs. This will not only make web developers more productive, but it will also make the websites we use daily more useful and powerful!

Our good friends at HiiDef just launched a new app that has been in beta for awhile, Flavors.me. This is an excellent tool that has a great, simple, and usable design.

What’s the value preposition of Flavors.me? It’s to unify your various “online identities” into a single, dynamic, automatically-updated, and elegant website.

From the article:

Flavors.me lets you take all that information and put it together in a single website to serve as your “online identity”. All your publicly shared information, aggregated in one place, and displayed beautifully. […] It’s this kind of simplicity, design sense, and user-centric approach that makes me love the web as a place to develop, deploy, and use software.

Check out Andrew’s full review over at his blog.

Sorry for the lack of posts recently, but we’ve been busy changing and improving Parse.ly for the better!

We did, though, get picked up by a couple popular blogs in the past few weeks. Here a few snippets from both ReadWriteWeb and ZDNet.

Bloggers, muckrakers and news fanatics, lend me your ears. It’s entirely possible that we’ve discovered one of the best approaches to media monitoring since RSS itself. My mother always said, “You’ll never get what you want unless you ask.” But with adaptive feed application Parse.ly, that simply isn’t true. Rather than forcing us to abandon our overflowing feed readers, Parse.ly records our preferences and learns to work with us.

ReadWriteWeb 11/11/09

I haven’t figured out a way to manage Google Reader. I tried using Fever, but it doesn’t find news that matters to me… and it cost $30.  Techmeme is my home page, but I think it needs an upgrade.  I would like a feed reader that saves favorite feeds for me, and finds other content that is similar and interesting.

A new product called Parse.ly caught my eye that makes content discovery a painless process.

ZDNet - The Web Life 11/24/09

Check out our press page for more articles written about Parse.ly.  We’ll update you soon about what we have in store for the future!

Hi Parse.ly fans.  Andrew here.  I just wanted to let you know that I presented Parse.ly at the NYC Search & Discovery Meetup on Thurs, Oct. 29.  The meetup is organized by Otis Gospodnetic (blog), who is one of the authors of Lucene in Action and the author of the forthcoming Solr in Action book.  It was graciously hosted at kgbweb (thanks for making that happen, Joe West!).

We make heavy use of Lucene and Solr on Parse.ly, so it was exciting to get an opportunity to present to a community of fellow technologists building systems with these excellent technologies.

Here is the abstract from the talk:

Parse.ly: Inside a modern RIA built with Solr
Andrew Montalenti
—-
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.