2014 - 2018: Rabbit - Watch Together
#Audio & Video Technology #Online Streaming #Chat #Entertainment #Social #Cloud Computing
Rabbit is the way to watch anything with anyone. Discover, share and watch content anywhere with your friends. React and respond in-the-moment or when you have time. Even if you’re in a different city, another country, or on the other side of the world, Rabbit allows you to keep in touch while watching your favorite shows together.
Rabbit launched to users in 2014 and today there are 3.6 million monthly unique viewers around the world. Rabbit users are some of the most active on any web or mobile app: they watch for an average of 12.5 hours a month, and the most active users watch for 28.5 hours a month. The huge majority of Rabbit users watch on mobile and are under the age of 30 years old.
Public watch party groups round shared interests
Public or Private watch party events
Discovery / Recommended Content
Business Strategy (Together with a leadership team of 4)
Product Lead for Web, iOS and Android teams (15+ engineers)
Designer (UX and UI) for web, iOS and Android apps
Managed QA team
Named by Forbes “The World’s Top 10 Most Innovative Companies of 2015 in Video”.
2014: JETPAC Spotter (DEMO APP)
This app was created to demo our Deep Belief Object Recognition technology, run directly on a phone for the first time.
Imagine all photos tagged automatically, the ability to search the world by knowing what is in the world's shared photos, and robots that can see like humans.
We trained our Deep Belief Convoluted Neural Network on a million photos, and like a brain, it learned concepts of textures, shapes and patterns, and combining those to recognize most common objects, including breeds of dogs and types of plants.
Basic Design (UI and UX) for app, App Store and marketing assets
Image Tagging for the data set
JETPAC GOT ACQUIRED BY GOOGLE IN AUGUST 2014.
Our technology is now used in Google Image Search, Google Photos and TensorFlow.
2013: Jetpac City guides
– From idea to acquisition
#AI #Image Processing #Machine Learning #Geotagging #Travel #Guide #Photos
About Jetpac City Guides:
In only six months our small team designed, developed and launched the Jetpac City Guides iOS app, built on millions and millions of geolocated publicly shared Instagram photos that we had analyzed and indexed using image processing, machine learning and later AI / deep learning.
Designer (UX and UI)
We received the Webby Award for best mobile app in 2014 and got featured on iTunes home page “Best New App”.
2011-2013: Jetpac travel guide App For ipad
#Machine Learning #Image Processing #Geotagging #Social #Travel #iPad #Cloud Computing
At Jetpac we took billions of public photos shared on Facebook and indexed them by location to create the basis for our photo centric travel guide specifically designed for the iPad retina screen.
Most of the 100 billion photos on Facebook were un-located with no explicit geo-tag, but we were able to locate them using geo-parsing technology we pioneered, using the text – captions, album names, etc.
We then selected the best travel photos with machine learning. To do so, the team first started by rating some 2 million of the photos manually, which then allowed them to train algorithms to look for the signals that correlate to good travel photos.
By signing in with your Facebook account users could browse all their friends's best travel photos sorted by location so everyone had their own very personal travel guide.
The Jetpac iPad app for iPad was featured on the Apple.com homepage, on nationwide App Store billboards and in the March 2013 newsletter. The app became the highest rated travel app in the iTunes App Store at the time.
I attended a 3 month long intense UX Design bootcamp in San Francisco in Summer 2014, and here are some of the projects I worked on.
Airbnb is not streamlined like a hotel chain and thankfully so, but that also means that every Airbnb host is different, every listing description is different and as a traveler you never know what to expect. That is exactly we love Airbnb, but at the same time, I've found a lot of unnecessary inconsistency that is not of the charming kind, but only leads to bad reviews and a trip that was less than perfect.
The result of my research and design is a new app experience for guests who have booked a trip on Airbnb which provides guests with a place in the app for more relevant information regarding their booking and an option to receive time relevant information about their stay. The result is that guests and hosts can expect a more consistent and less confusing experience.
Many apps have what I call "buried treasures" - features that could be really powerful but they're just obscured by layers of usability issues and extraneous complications. I think the Shared Photo Stream feature in iOS is one of those features.
I personally really enjoy using the shared Photo Stream feature to share my photos with friends and family without having to post them publicly. I also like the social and collaborative element of contributing my photos to shared photo streams with friends after a trip or a party.
To see how I could improve the shared Photo Stream experience so more iPhone photographers could discover and find the benefits of using the shared Photo Stream feature I did a usability test and then did a re-design based on my findings.
Rethinking engagement for the fastest-growing news sharing platform in Tech
As a MVP with a limited feature set, the original Quibb for iOS was able to provide a basic mobile experience but lacked an underlying design structure to drive user and business goals. Without key features found on their web platform and limited avenues for user interaction, Quibb needed to reimagine their mobile experience to increase engagement and build community on the app.
See the full project on my design partner's website