Small amjad masad Amjad Masad on

As the complexity of the apps we're building grows it becomes harder and harder to debug. Beyond your typical console.log and breakpoint insertion debugging Amjad Masad (Software Engineer, Facebook) discusses some of the lesser known JS debugging techniques that has helped him debug some of the more elusive bugs he’s seen while working on Facebook.


This talk was given at the NYCHTML5 meetup at Condé Nast.

Small enrico teotti Enrico Teotti on

Enrico Teotti (Senior Software Developer, XO Group, Inc.) walks us through how he recently leveraged Rails engines to separate the public from the administration portion of a web application and deploy them to different servers leveraging feature flags.


The talk was hosted by NYC.rb and given at Pivotal Labs.


Enrico Teotti is a Senior Software Developer at XO Group, Inc.

Founded by husband and wife team David Liu and Carley Roney, our executive team combines a powerhouse of industry experience with a serious passion for our brands. Plus, they actually have fun doing it! To complement our lifestage brands, we’ve launched many unique services: e-commerce, which is focused on personalized products for weddings; personal website builders; and local mom social networks. Whatever our audience needs, we either direct them to the solution or develop one for them.

Placeholder Leif Walsh on

Leif Walsh (Developer, Tokutek) gives a presentation on the paper The LCA Problem Revisited by Michael A. Bender and Martin Farach-Colton. The lowest common ancestor problem was first stated in 1973 and it took 11 years before an optimal solution was discovered, and another 16 before an understandable and implementable solution with the same bounds was presented. This deceptively simple problem comes together in the end and uses techniques that are powerful in plenty of other places.


This talk was given at the Papers We Love meetup in NYC at Viggle, Inc.



Leif Walsh is a Developer at Tokutek

Founded in 2006, Tokutek is a performance database company that delivers Big Data capabilities to leading open source data management platforms. 100x performance breakthroughs in computer science don’t happen every day. However, at Tokutek, our technology is exactly that revolutionary, resulting in the single biggest improvement to database performance in over thirty years.

Placeholder Chris Wiggins on

Nearly all fields have been or are being transformed by the availability of copious data and the tools to learn from them. Dr. Chris Wiggins (Chief Data Scientist, New York Times) will talk about using machine learning and large data in both academia and in business. He shares some ways re-framing domain questions as machine learning tasks has opened up new avenues for understanding both in academic research and in real-world applications.


This talk was given at iHeartRadio and hosted by the New York Open Statistical Programming Meetup.


Small self square Alex Yankov on

Alexander Yankov of Handybook talks about IRuby Notebook with Rails. It is an interactive Ruby environment powered by irb or pry that combines execution of code with inline text, graphics, and other rich features.


The talk was hosted by NYC.rb and given at Pivotal Labs.



Alex Yankov is an Engineer at Handybook

Handybook is the quickest, most reliable way to book household service providers. Within a couple of minutes, you can book a pre-approved cleaner or handyman for any time you'd like. All you have to do is tell us what you need to get done, where you live, and what time works for you, and we do the rest!

Small jeff picard Jeffrey Picard on

Understanding the billions of data points we ingest each month is no easy task. Through the development of models that allow us to do so, we’ve noticed some commonalities in the process of converting raw data to real-world understanding. Although you can get pretty good results with simple models and algorithms, digging beyond the obvious abstractions and using more sophisticated methods requires a lot of effort. In school we often learn different techniques and algorithms in isolation, with neatly fitted input sets, and study their properties. In the real world, however, especially the world of location data, we often need to combine these approaches in novel ways in order to yield usable results.

In this article we look at the process of understanding the importance of different locations as they relate to consumers, which takes us from simple joins in Hadoop to a sophisticated time-series algorithm called Viterbi.



Jeffrey Picard is a Software Engineer at PlaceIQ

PlaceIQ is able to take large amounts of often unstructured, unrelated, location based data such as photos, place data, event data, digital and social data (and much, much more) and – through a series of processes of data cleansing, normalization, analysis, and machine learning – extract patterns, trends, intelligence and context from the data.

Small weschow profile Wes Chow on

Problem: Chartbeat generates random unique user IDs in the browser when a new reader visits a customers' sites. The original 2 line random user ID function used would generate over 4.8 trillion trillion (yes, that’s 1 trillion squared) different unique IDs, but in practice we were seeing laughably high collision rates. To add to the challenge of fixing this issue, our solution had to run in all browsers, take up minimal code, and work with zero calls to a server.

Wes Chow (CTO, Chartbeat) describes the experiences in solving this problem as well as the mathematical basics of hash functions and pseudo-randomness.


This talk was given at the Full-Stack Engineering Meetup hosted by Gilt.

If you have questions to Wes, you can now submit them in the comments below the post.



Wes Chow is a CTO at Chartbeat

Chartbeat gives you the real-time data you need to take action and meet the challenges of the social web. Through our dashboards, browser overlay Heads Up Display, and APIs, you'll get live stats about your your site's visitor behavior - from engagement metrics, to traffic stats, to geographic data and everything in between. Our real-time insights let you act on every opportunity to build your audience. Our backend is mostly built in Python, using MySQL and MongoDB as data stores – everything hosted in Amazon AWS. We're constantly on the lookout for talented, awesome, and energetic folks to join our Chartteam in our growing-every-second Manhattan office. See all of our current nerdtastic Chartteam members at

Small brennan moore Brennan Moore on

Artsy transitioned from a monolithic Ruby on Rails stack to a distributed system with Node.js apps that share code and rendering between the server and browser and have open sourced their learnings into a boilerplate project called Ezel.

Artsy’s Developer Craig Spaeth and Director of Web Engineering Brennan Moore cover how the transition was managed as well as dive into some code showing how Ezel and these new isomorphic apps work.


This talk was hosted at the nodejs meetup at Shutterstock.

A couple days ago Artsy open sourced the frontend. More info here:
If you want to ask Brennan a question, you can submit it in the comments below the post.



Brennan Moore is a Director of Web Engineering at Artsy

Art meets Science. At its core, Artsy is a technology company. In addition to the engineering team, both the CEO and COO have technical backgrounds (Computer Science from Princeton, and Mathematics from Columbia). Our ultimate goal is to create Artificial Intelligence with the qualities of the world's most omniscient art advisor. If Skynet ends up winning, at least it will have good taste :). We are solving very hard problems. To make art historically relevant recommendations, you can't rely on collaborative filtering alone. We do real-time search in 800 dimensional space (one dimension for every art historical category). We are also working on semantic search, fuzzy full-text search, visual similarity search, and high-resolution image tiling and security among others. We write our own open-source projects and contribute them back to the community.

Small prasanna swaminathan Prasanna Swaminathan on

As online advertising has grown from an experiment on a marketer’s checklist to a critical tool in the proverbial toolbox, so has the demand for actionable metrics of performance.

At first, measuring engagement was straightforward. A site serves a user an ad (delivered by an unbiased third-party, the ad server), and a user clicks on that ad to go to whatever page the marketer desired. Ad servers then collect the number of clicks and impressions, which serves two primary purposes. The first is that marketers use these numbers to draw insights into how their campaigns are performing. The second is that marketers pay their advertising partners based on things like number of clicks.

Soon, marketers clamored to gain deeper insights. Technology vendors introduced cookies to attribute actions on the site, such as a product purchase or online signup, called a “conversion,” to an ad impression or click. It’s this process — attributing actions on a site to ad impressions and clicks — where things get tricky, and which this blog post will attempt to explain.



Prasanna Swaminathan is a Manager of Platform Integrations at MediaMath

MediaMath is a digital marketing technology company dedicated to reengineering modern marketing to offer transformative results based on tangible goals. Its math-driven Marketing Operating System, TerminalOne™, brings together digital media and data into a powerful and flexible solution that simplifies planning, execution, optimization and analysis of both direct response and branding campaigns.

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