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Maas Lalani
@maaslalani
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Torontomaaslalani.comJoined December 2014

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🔭 New This Week: The Project Overview page gives you a single place to view all activity across a project. - Track how many tests you've conducted - Check team activity like notes and session invites - Jump to new sessions, heatmaps, and testers
Gif of overview page expanding. Shows graph of tests over time, links to team activity, and links to sessions, heatmaps, and testers.
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Why is all this useful? Using computer vision to recognize products allows buyers to get information quickly and enables retail staff to surface meaningful information even if it's their first day on the job. [5/6]
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The identification is done by taking a photo or video and sending it to our ML server. By offloading the processing to the cloud, we can power sophisticated ML models on less powerful devices (like AR glasses). The first example in this thread is running in a web browser! [3/6]
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Building a machine learning model to recognize a product requires hundreds of images. We needed a shortcut. We discovered we could use a realistic 3D model of the product to generate thousands of images from different angles and lighting conditions. [2/6]
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Check out Hawkeye's first case study on Urban Outfitters 🛍 and learn how conducting eye tracking tests can result in better user experiences 👀
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We're excited to introduce our new blog series Looking Deeper! In each post, we'll conduct an eye tracking study of a different product and report back our findings. To kick things off, we asked twenty UCSB students to shop the Urban Outfitters website 👀 blog.usehawkeye.com/looking-deeper
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