Airbnb Engineering » Tech Talks http://nerds.airbnb.com Nerds Tue, 29 Mar 2016 17:30:33 +0000 en-US hourly 1 http://wordpress.org/?v=435 Mobile Infrastructure http://nerds.airbnb.com/mobile-infrastructure/ http://nerds.airbnb.com/mobile-infrastructure/#comments Mon, 14 Dec 2015 21:26:26 +0000 http://nerds.airbnb.com/?p=176595377 Building a Continuous testing environment for Android Performance Performance improvements in your application shouldn’t be put off until the last minute! But sadly, performance profiling & tooling is still pretty manual and archaic. In this talk, Colt McAnlis will walk through how to build a automated perf-testing environment for your code. Then, you can run […]

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Building a Continuous testing environment for Android Performance

Performance improvements in your application shouldn’t be put off until the last minute! But sadly, performance profiling & tooling is still pretty manual and archaic. In this talk, Colt McAnlis will walk through how to build a automated perf-testing environment for your code. Then, you can run tests daily, weekly, or when your co-worker checks in their code (you know the one.. they like to use ENUMs everywhere….)

Colt McAnlis is a Developer Advocate at Google focusing on Performance & Compression; Before that, he was a graphics programmer in the games industry working at BlizzardMicrosoft (Ensemble), and Petroglyph. He’s been an Adjunct Professor at SMU Guildhall, a UDACITY instructor (twice), and a Book Author. When he’s not working with developers, Colt spends his time preparing for an invasion of giant ants from outer space.

You can follow him on G+TwitterGithubLinkedin, or his Blog

Deep Link Dispatch on Android

Deep links are great opportunities to engage users by linking them to deeper content within an application. While Android provides mechanisms to handle deep links, it’s not perfect and leaves opportunity to improve how deep links are managed. We’ll present our tool, Deep Link Dispatch, and demonstrate how Airbnb handles deep links in a structured and convenient way.

Christian Deonier is a member of the Airbnb Android team focused on foundation for features and architecture of the application. He is also the co-creator of DeepLinkDispatch, an Android library for deep links. Before joining Airbnb, he focused on making mobile applications on both iOS and Android, and also worked at Oracle. In his free time, he races cars and, true to Airbnb, travels frequently.

Felipe Lima is a Brazilian Software Engineer at Airbnb working on the Android team, focused on its infrastructure, developer productivity and Open Source tools. Before joining Airbnb, Felipe worked at We Heart It.

Localization on iOS

Airbnb is an international company that aims to bring a local experience to all of our users. Presenting information in a way that is appropriate for the user’s locale is crucial. In this talk, we will share some of the engineering challenges involved in delivering truly local experiences on iOS, and how we’ve solved them here at Airbnb.

Youssef Francis is a Software Engineer on the Airbnb iOS team, focused on improving the search, discovery and booking experience on mobile. Before joining Airbnb, he founded a small startup dedicated to building usability enhancements for jailbroken iOS devices, and has been a member of the iOS jailbreak community since 2007. He likes to play board games and solve puzzles.

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How Technology and Engineers Can Impact Social Change http://nerds.airbnb.com/impacting-social-change/ http://nerds.airbnb.com/impacting-social-change/#comments Wed, 18 Nov 2015 23:55:32 +0000 http://nerds.airbnb.com/?p=176595344 At the OpenAir 2015 conference, the panel discussion “How Tech Can Reach Underserved Communities” explored how technology can create positive social change—and how engineers in particular can make a difference. The panelists: Alanna Scott, engineer, Airbnb Grace Garey, co-founder, Watsi Raquel Romano, software engineer, Google.org Moderator: Mario Lugay, impact advisor, Kapor Center for Social Impact […]

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At the OpenAir 2015 conference, the panel discussion “How Tech Can Reach Underserved Communities” explored how technology can create positive social change—and how engineers in particular can make a difference.

The panelists:

Alanna Scott, engineer, Airbnb
Grace Garey, co-founder, Watsi
Raquel Romano, software engineer, Google.org
Moderator: Mario Lugay, impact advisor, Kapor Center for Social Impact

Some highlights (edited for brevity or clarity):

Examples of projects to reach underserved communities (3:11 in the video)

Romano said she had been working with a Google.org group focused on crisis response and reaching people before, during, and after a natural disaster. For example, the team developed data feeds that would provide warnings about impending local floods or hurricanes in relevant search results for Google users.

Scott said Airbnb started a Disaster Response Tool three years ago in the wake of Hurricane Sandy. “We were inspired by a host (in the area where the storm hit) who started opening up her home to people who had been displaced. We wanted to build something to support what she was doing and enable the rest of our host community to participate as well.”

Scott: “The Disaster Response Tool was built as a side project. But now we can activate the tool within minutes for a specific location or area that has been hit by a natural disaster. Hosts can list their space for free and we wave all of our fees and create a way for displaced people in that area to find a place to stay.”

Garey: “Watsi is entirely a social impact organization. We let people directly fund healthcare for people all around the world, and 100 percent of donations go to the patient. Technology seemed to be the answer we needed to focus on. We saw people using technology like Airbnb to bust open narrow channels to allow person-to-person interaction and create new ways to solve a problem. So we decided to do the same thing to tackle healthcare in a new way.”

How technology can make a difference (16:13)

Scott: “In the case of a natural disaster, people don’t always have reliable Internet access, or they might not have much battery left on their phone. So we’ve been thinking about how those people can use Airbnb when they are facing technical limitations.”

Helping people in disaster-hit areas may require “using old technology” rather than the latest tech, Scott continued. For example, SMS messaging often continues to work after a disaster when phone calls, email and online access can be difficult, so Airbnb has been exploring ways for users to book or accept reservations through SMS.

Romano: “We’re working on an initiative at Google.org to see how technology can help people with disabilities live more independently. What if we could recognize and translate sign language? What if we could analyze content in video and provide natural language descriptions of it?” Another area of investigation is mobility, in which “eye trackers connect to a communication device, so you can communicate with the world by typing with your eyes.”

How engineers can contribute to social change (20:46)

Scott: “We have a woman user in Florence who donates 50 percent of her Airbnb earnings to a community art project. Another user donates 10 percent of his earnings, and he and his guest decide together which local organization to contribute to. So my advice is to look at how your users are already helping other people with your product, then figure out how to scale it and open it up to your whole community.”

Romano recommended marrying your passion for technology with social issues you care about, because the two are “an amazing combination.” Find others with shared passions by asking around. “Talk to people about what they’re working on and tell them what you’re interested in.”

Romano added that “it’s really hard when you’re trying to prioritize and focus to create space and resources to work on (social impact projects). What works is when people just start doing things (for social impact) without asking for permission. You get other passionate people together and come up with a proof of concept and you can start seeing how it could be better if you had a product manager, user experience person, and multiple engineers working on it.”

Garey added that in 10 to 15 years, the areas of engineering and social change will blur. “So don’t feel like you have to make a choice between working at a company with a product that’s creating value and making a lot of money vs. doing something that’s good for the world. You can do well and do good at the same time.”

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How Facebook Focuses on the Most Important Element of an App http://nerds.airbnb.com/facebook-react-native/ http://nerds.airbnb.com/facebook-react-native/#comments Mon, 31 Aug 2015 21:48:49 +0000 http://nerds.airbnb.com/?p=176595210 Facebook’s React Native iOS framework helps the social network keep its focus on the most important element of a mobile app: the user interface. The user interface should inform every decision made when building a client application for the Web or a mobile platform, said Adam Wolff in his OpenAir 2015 presentation. Wolff is engineering […]

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Facebook’s React Native iOS framework helps the social network keep its focus on the most important element of a mobile app: the user interface.

The user interface should inform every decision made when building a client application for the Web or a mobile platform, said Adam Wolff in his OpenAir 2015 presentation. Wolff is engineering director of product infrastructure at Facebook.

Wolff said that while this goal sounds obvious, many ideas about building applications are a holdover from a time when software applications didn’t put the user experience first. “We need to turn that around and build applications starting with the user interface,” he said.

React Native is a library Facebook developed for writing user interfaces for mobile devices; it’s currently limited to iOS. React Native is designed to enable the building of app experiences on native platforms using a consistent developer experience based on JavaScript and React, Facebook’s JavaScript library for building user interfaces. Facebook has open-sourced both React Native and React on GitHub.

Wolff showed examples of how Facebook used its React Native framework to display user profile pictures (go to 3:18 in the video to hear more) and comments (9:02 in the video).

React Native first showed up in Facebook’s iOS Groups application, Wolff said. Facebook used a conventional UI development kit to build some parts of the app and React Native to build others. “I challenge you to figure out which is which,” he told the audience. (18:32 in the video.)

Facebook also built its Ads Manager iOS app with React Native. A Web team built the app “without having to learn IOS,” Wolff said. He added that this enabled ad developers to write the Ads Manager app instead of people who are simply iOS or Android developers.

The same team that built the Ads Manager iOS app also developed the Android version using Facebook’s React Native for Android framework, Wolff noted. Over 85 percent of the code in the Android version of the app is identical to the code in the JavaScript version. “Even though we made it really easy to change the user interface based on the platform, we ended up reusing a bunch of the code anyway, and the team was able to ship this app in a couple of months,” Wolff said.

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Developer Infrastructure with Airbnb and Bundler http://nerds.airbnb.com/developer-infrastructure-with-airbnb-and-bundler/ http://nerds.airbnb.com/developer-infrastructure-with-airbnb-and-bundler/#comments Thu, 20 Aug 2015 21:59:47 +0000 http://nerds.airbnb.com/?p=176595159 How does Bundler work, anyway? Bundler has turned out to be a super-useful tool for installing and managing dependencies, but many Rubyists don’t really have a handle on why it exists or how, exactly, it works. This talk aims to explain the huge mess that existed before Bundler, and then talk about the solutions to […]

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How does Bundler work, anyway?

Bundler has turned out to be a super-useful tool for installing and managing dependencies, but many Rubyists don’t really have a handle on why it exists or how, exactly, it works. This talk aims to explain the huge mess that existed before Bundler, and then talk about the solutions to those problems that Bundler provides. The talk won’t spend a lot of time on code slides, but will include a fairly detailed explanation of Ruby’s require system, Rubygems, gem dependencies, dependency graph resolution, and how Bundler interacts with them. At the end, the talk will cover the Bundler “superpowers”, allowing rapid development and deployment in ways that were highly impractical before Bundler existed.

André Arko
André thinks Ruby is pretty neat. He leads the Bundler team, co-authored the third edition of The Ruby Way, and runs Ruby Together, the Ruby trade association. At his day job, he provides expert development, architecture, and teaching through Cloud City Development in San Francisco.

Automate the Boring Parts: Web Apps to Ship Web Apps

Delays, bureaucracy, angry release engineers: turning working software into production systems can be a drag. What if you could write software to automate away the pain, and have intelligent systems ship your code for you when it’s ready? Learn how Airbnb replaced manual release processes with code to make shipping smooth, fast, and simple.

Matt Baker
Matt is currently a software engineer at Airbnb on the Developer Infrastructure team, and was previously a game developer and computer graphics researcher. His work spans high-performance distributed systems and low-level rendering techniques. Having grown up across six states and two countries, he enjoys reading, ‘riting, ‘rithmetic, and road trips.

Igor Serebryany

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Airbnb Price Tips Powered By Aerosolve Machine Learning http://nerds.airbnb.com/airbnb-machine-learning/ http://nerds.airbnb.com/airbnb-machine-learning/#comments Wed, 19 Aug 2015 20:11:58 +0000 http://nerds.airbnb.com/?p=176595153 Matching buyers with sellers depends a lot on price. Price too high and buyers are turned off. Price too low and the seller loses out. To increase the odds of matching buyers and sellers, Airbnb’s team of data scientists and engineers have worked hard to develop pricing technology, said Bar Ifrach, Airbnb data scientist, during […]

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Matching buyers with sellers depends a lot on price. Price too high and buyers are turned off. Price too low and the seller loses out.

To increase the odds of matching buyers and sellers, Airbnb’s team of data scientists and engineers have worked hard to develop pricing technology, said Bar Ifrach, Airbnb data scientist, during a recent OpenAir 2015 talk.

“We’re trying to empower our hosts with tools to price their listings and get bookings seamlessly and effectively, so we have more hosts and stays on Airbnb and more matches on the platform,” Ifrach said.

Airbnb displays a host’s calendar. Days shown in white are available to book. Gray indicates days already booked, in the past, or that are days the host doesn’t want to book.

At the bottom of days in white, the host sees a color bar, which indicates how likely the host is to get a booking for that day at a given price. Green indicates a high likelihood. Yellow means there’s a medium chance of booking at the current price; red suggests a low probability of booking.

The technology behind this is divided into two parts, Ifrach said: Modeling and Aerosolve.

With modeling, Airbnb is “trying to predict for every day of year, for every listing, what will be the likelihood of getting a booking for any possible price,” Ifrach explained. “Then we can find a price that works best.” Airbnb modeling is doing this “on a huge, global scale” by looking at millions of derived features and over 5 billion training points, he said.

Three main considerations go into the pricing model:

  • Demand, or the impact of seasonality or special events (such as Austin’s SXSW conference) on an area. Airbnb’s model translates demand features into pricing predictions.
  • A listing’s location, such as the market, neighborhood, or street block. Example: San Francisco has many distinct neighborhoods that appeal to different crowds, and “we need to account for that” in pricing, said Ifrach. This is accomplished through grids and k-d trees.
  • A listing’s type and quality. Airbnb’s pricing model is “unique,” Ifrach said, because it incorporates such factors as a property’s size and specific qualities.

As an example, he compared a houseboat situated across from the Eiffel Tower in Paris and a private room with a view of the tower. To determine pricing for each, Airbnb’s model takes into account the differences in the two listing types (houseboat vs. a room), amenities (such as air conditioning), and other qualities. To quantify a listing’s quality, Airbnb’s modeling uses Dirac and Cubic splines to capture the effect of guest reviews.

Airbnb Hector Yee

The second part of Airbnb’s dynamic pricing technology is Aerosolve, “machine learning for humans,” said Airbnb engineer Hector Yee, who took the stage after Ifrach.

Airbnb introduced Aerosolve at OpenAir 2015 as an open-source machine learning library available on GitHub. Aerosolve is designed to interpret complex data sets so that humans can easily understand them. While Airbnb’s modeling suggests pricing to hosts, Aerosolve’s goal is to add context to the pricing recommendation so the host understands it. “You have to understand why the model makes it decisions. Maybe the host’s pricing is too high because they have no reviews or because it’s low season,” Yee explained.

The blog post “Aerosolve: Machine learning for humans” offers details about how Aerosolve works and how developers can use it.

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Building Periscope for Android http://nerds.airbnb.com/building-periscope-for-android/ http://nerds.airbnb.com/building-periscope-for-android/#comments Thu, 13 Aug 2015 18:56:16 +0000 http://nerds.airbnb.com/?p=176595128 Yesterday, Periscope announced that it reached 10 Million Periscope accounts. At OpenAir, Sara Haider detailed how she and her colleagues, faced multiple challenges in developing an Android version of the live video streaming app. There are two video livestreaming protocols, Haider explained: Real-Time Messaging Protocol (RTMP), which is suitable for low latency and real-time interaction […]

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Yesterday, Periscope announced that it reached 10 Million Periscope accounts. At OpenAir, Sara Haider detailed how she and her colleagues,
faced multiple challenges in developing an Android version of the live video streaming app.

There are two video livestreaming protocols, Haider explained: Real-Time Messaging Protocol (RTMP), which is suitable for low latency and real-time interaction among a few participants but isn’t widely supported; and HTTP Live Streaming (HLS), which sits atop the HTTP layer. HLS offers much higher latency, is ideal for non-interactive broadcasters, and is widely supported.
Low latency for Periscope is a must, however, so both protocols were needed to deliver the user experience on Android.

For playback, Periscope used ExoPlayer, Android’s application-level media player, which began supporting HLS earlier this year but lacks RTMP support. “So we built from scratch a completely custom RTMP broadcasting and playback stack for Periscope on Android,” Haider said.

Because RTMP isn’t widely scalable, Periscope relies on HLS to support scale. For example, when a Periscope video stream chatroom gains a lot of participants, Periscope limits the number of people who can comment to keep things moving smoothly for everyone. Those users get an HLS video stream that keeps them in sync while also enabling Periscope to scale the broadcast “to thousands of users,” Haider said. (Jump to 5:19 in the video to hear more.)

The chats accompanying a video feed are handled on their own Real-Time Chat (RTC) channel and come in instantly, though the video feed has variable latency. Also, Android clocks are “completely unreliable,” Haider said, with variations of as much as 15 seconds behind or ahead. To compensate for those variations, Periscope uses the Network Time Protocol (NTP).

The Periscope Android team consisted, at various times, of three app engineers, one video engineer, and one designer. The “bootstrapped stack” the team built consists of open-source libraries Retrofit, OkHttp, EventBus, Glide, and Spongy Castle. They used the standard tools DexGuard, Crashlytics (Twitter’s crash reporting system), Localytics, Android Studio, and Genymotion. As for application libraries, Periscope used PubNub for its chatroom channel in addition to the ExoPlayer media player. (Go to 14:21 in the video to hear more.)

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How Marketplace Search Differs from Traditional Search http://nerds.airbnb.com/how-marketplace-search-differs-from-traditional-search/ http://nerds.airbnb.com/how-marketplace-search-differs-from-traditional-search/#comments Tue, 04 Aug 2015 17:33:58 +0000 http://nerds.airbnb.com/?p=176595123 A marketplace search is when you go to a specific website, such as Airbnb or Amazon, to hunt for a particular product, service or, in Airbnb’s case, property. As Airbnb engineering manager Surabhi Gupta explained in her recent OpenAir 2015 talk, there are three main reasons why marketplace search is more challenging for engineers than […]

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A marketplace search is when you go to a specific website, such as Airbnb or Amazon, to hunt for a particular product, service or, in Airbnb’s case, property. As Airbnb engineering manager Surabhi Gupta explained in her recent OpenAir 2015 talk, there are three main reasons why marketplace search is more challenging for engineers than traditional search (such as on Google or Bing):

Surabhi Gupta OpenAir 2

1. Conversion takes more than a click.

With traditional search, a user types a keyword or phrase, reviews the results, then clicks on the most relevant result. With a marketplace search on Airbnb, other steps sometimes must happen before a user ‘converts’ (makes a booking).

For example, a user may identify a listing she likes but wants to ask the host questions. The host can respond to the questions and accept the reservation; answer the questions but reject the booking; or not get the chance to answer the questions, and the guest moves on to another listing.

Airbnb’s search ranking uses machine learning to try and predict a final booking outcome, in order to help the guest easily find the best listing. To accomplish this, the Airbnb search team modeled the five intermediate states it cares most about: Impression (the displayed search results); Clicks (did the user like a result enough to click on it?); Accept (the host accepts the guest’s reservation); Reject (the dates don’t work out); and Booking.

Each state is given a score based on past user actions. By serving up search results influenced by how likely the user is to book the properties displayed, Airbnb has achieved “a huge booking gain,” Gupta said.

2. Decision making requires a lot of context.

Because Airbnb properties are unique, users need a lot of context to decide whether a listing is right for them. Airbnb’s engineering team has experimented with different ways of presenting information to users. For example, because location and price are the two most important requirements, Airbnb has given users information about neighborhood characteristics displayed on a city map as well as price histograms.

3. The supply is perishable.

Airbnb listings are “perishable” because they are unique properties whose availability comes and goes. To prevent users from reading about a property only to discover it’s not available when they want it, Airbnb built a real-time information infrastructure using MySQL databases, a centralized index, and Ruby on Rails. (Jump to 15:53 in the video for more details.)

Going forward, Airbnb wants to make it easier for users to pick up exactly where they left off when returning to the site; add more personalization options; and obtain a deeper understanding of its listings in order to give guests “the best possible experience” when matching them with hosts.

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Netflix Algorithms Are Key to the ‘Future of Internet Television’ http://nerds.airbnb.com/netflix-algorithms-are-key-to-the-future-of-internet-television/ http://nerds.airbnb.com/netflix-algorithms-are-key-to-the-future-of-internet-television/#comments Fri, 24 Jul 2015 00:02:05 +0000 http://nerds.airbnb.com/?p=176595119 When Netflix’s 60 million subscribers log in to the streaming video service, their home page is populated with TV show and movie recommendations. The recommendations are key to Netflix’s success, as they drive two out of every three hours of video streamed.   The user’s home page “is where all our algorithms for recommending TV […]

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When Netflix’s 60 million subscribers log in to the streaming video service, their home page is populated with TV show and movie recommendations. The recommendations are key to Netflix’s success, as they drive two out of every three hours of video streamed.

 

The user’s home page “is where all our algorithms for recommending TV shows and movies come together,” said Carlos Gomez Uribe, Netflix VP of Innovation. In his recent OpenAir 2015 talk, Gomez Uribe said over 100 engineers are focused on developing algorithms to help Netflix meet its business goal of “inventing the future of Internet television.”

 

One Netflix algorithm organizes the entire video catalog in a personalized way for users. Another looks for similarities between all content Netflix offers. A master algorithm “looks at all the other algorithms to decide which videos make it onto a user’s home page,” said Gomez Uribe.

 

Keyword searches drive 20 percent of video streaming hours, so Netflix’s search algorithm is tied into its recommendations and other algorithms. When users search for a title Netflix doesn’t have, the search results will display recommendations for similar shows. “We try to recommend movies related to a search, even though it’s not exactly what you wanted. All this requires a large number of algorithms,” Gomez Uribe said.

 

Personalization is important because it’s more likely to drive higher engagement with Netflix content vs. simply showing a user what’s popular. When Netflix organizes videos by popularity on user home pages, the “take rate” (the percentage of suggested videos that are actually watched) is “OK,” Gomez Uribe said. “But when we personalize recommendations, the take rate goes way up.” (Go to 5:00 in the video to hear more.)

 

Algorithms also help Netflix perform long-term A/B testing on its user interface, providing alternate ways to organize and display recommendations to users, said Gomez Uribe. In turn, the A/B testing can help Netflix measure subscriber cancellation rates more effectively. Cancellations are an easier metric to track than new member sign-ups because the latter are often fueled by word of mouth—which is notoriously difficult to track.

 

A/B testing has enabled Netflix to “stand our ground” on occasion, Gomez Uribe added. In 2011, Netflix.com unveiled a new user interface. A/B test results influenced the design, as the data showed that the new look-and-feel decreased cancellations and increased hours streamed.

 

Netflix was “so proud” of the new interface that it ran a blog post about it, “New Look and Feel for the Netflix Website” (June 8, 2011). But in short order, Netflix received a considerable number of snarky comments about the new look. Wrote one displeased subscriber: “Please inform your employers that a drunken dyslexic monkey would be a more acceptable design lead for your web concepts.”

 

Data from the A/B tests told Netflix that, despite the snark, “the majority of users were better off” with the new interface. And so, rather than rolling back to old the UI, Netflix moved forward with the new one, continuing to fine-tune it along the way. (Discussion begins around 10:40 in the video.)

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Recap of OpenAir http://nerds.airbnb.com/recap-of-openair/ http://nerds.airbnb.com/recap-of-openair/#comments Mon, 06 Jul 2015 16:04:20 +0000 http://nerds.airbnb.com/?p=176595098 Three weeks ago we hosted OpenAir 2015, our second technology conference. We had an amazing turnout of bright minds from across the industry, more than doubling attendance from 2014. A new generation of companies are emerging whose customers aren’t judging them by their apps and websites but on the experiences and content the products connect […]

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Three weeks ago we hosted OpenAir 2015, our second technology conference. We had an amazing turnout of bright minds from across the industry, more than doubling attendance from 2014.

A new generation of companies are emerging whose customers aren’t judging them by their apps and websites but on the experiences and content the products connect them with. With that in mind the theme for OpenAir 2015 was scaling human connection and we focused on online to offline and the better matching that enables it.

Throughout the day we learned how Instagram helps their users discover new content that inspires them; how Stripe helps people transact across borders, how LinkedIn used data to power their social network, how Periscope came to life on Android, and of course, how Airbnb helps turns strangers into friends.

Behind all of these challenges there are central concepts that we as a tech industry need to understand better – trust, personalization and the data that enables both.

With that in mind, Airbnb open-sourced two new tools for wrangling data. The first is called Airflow which is a sophisticated tool to programmatically author, schedule and monitor data pipelines. People in the industry will know this work as ETL engineering. The second was Aerosolve. Aerosolve is a machine learning package for Apache Spark. It’s designed to combine high capacity to learn with an accessible workflow that encourages iteration and deep understanding of underlying patterns on a human level. Since we launched these tools they have gotten over 2000 stars on GitHub – we can’t wait to see how people use and contribute to them.

We also announced a new tool for our hosts called Price Tips, which is powered by Aerosolve. Price Tips creates ongoing tips for our hosts on how to price their listing, not just for one day, but for each day of the year. This pricing is fully dynamic — it takes into account demand, location, travel trends, amenities, type of home and much more. There are hundreds of signals that go into the model to produce each price tip. We believe that better pricing will be a great way to further empower our hosts to meet their personal goals through hosting.

Finally we closed out the opening keynote morning with the launch of our brand new Gift Cards website. Now anyone in the US can give their family, friends, colleagues, frenemies, whomever, the gift of travel on Airbnb. And for those lucky folks in the audience, we gave everyone a $100 gift card.

We will be following up with more videos from the event, so keep your eyes on this space.

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Android Frameworks from Airbnb and Square http://nerds.airbnb.com/android-frameworks-from-airbnb-and-square/ http://nerds.airbnb.com/android-frameworks-from-airbnb-and-square/#comments Thu, 16 Apr 2015 19:09:01 +0000 http://nerds.airbnb.com/?p=176594918 We wrapped up the month of March with with the theme of android development. It was exciting to host Pierre-Yves Ricau from Square and feature Airbnb engineers, Eric Petzel and Nick Adams. Mapstraction Many mobile applications today require some form of an interactive map as part of their user interface. Google provides a native package […]

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We wrapped up the month of March with with the theme of android development. It was exciting to host Pierre-Yves Ricau from Square and feature Airbnb engineers, Eric Petzel and Nick Adams.

Mapstraction

Many mobile applications today require some form of an interactive map as part of their user interface. Google provides a native package and experience with Google Play Services to satisfy this need, but the questions remains of how one creates interactive maps for devices without Play Services.

In some countries, the majority of devices are sold without Google Play Services. Device manufacturers who ship their devices without Play Services are continuing to gain popularity worldwide. We have built a package that solves these problems, letting developers remain agnostic of manufacturer while providing a consistent map experience to users.

Speaker Bio

Eric Petzel: Software Engineer on the Android team at Airbnb where he focuses on building features for our hosts and guests, as well as tools to share with the Android community. Previously to Airbnb, he worked at Skype on their Android client

Nick Adams: Software Engineer on the Android team at Airbnb. He focuses on building features that improve the quality of the app and expanding to new form factors. Before Airbnb he was a student at the University of British Columbia in Canada.

Crash Fast: Square’s approach to Android crashes

The Square Register Android app has few crashes. Getting there requires a systematic approach: coding defensively, gathering information, measuring impact and improving architecture.

This talk presents our concrete steps towards lowering the crash rate, from the general philosophy to the tools we use, together with real crash examples.

Speaker Bio

Pierre-Yves Ricau: Android baker at Square. I started having fun with Java & Android as a consultant in Paris, then joined a startup in Barcelona and finally joined Square in San Francisco to work with some of the best engineers in the world. I like good wine & low entropy code.

The post Android Frameworks from Airbnb and Square appeared first on Airbnb Engineering.

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