Here’s a tutorial that walks you through a simple scenario of setting up a free Shutterstock account and writing a Node.JS application to use the computer vision (CV) features of the Shutterstock API.
You’ll use the API to search for images and then upload pictures of your own to use with reverse image search.
Finally, you’ll use CV keyword suggestion to identify objects in photos.
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We use some powerful search technology to get customers to the media that they want, and a lot of that runs under the covers.
However, we’re now making some of our computer vision-based search tools available to all users of our API so you can try them out with no commitment and make use of them in your applications.
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Now that we offer API-specific subscriptions, you can get straightforward access to our media library from almost any programming language or client.
These subscriptions include API access, so you can get started using our media in your applications in just a few minutes.
There’s even a free option if you want to try things out.
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Shutterstock Editor is the easy design app for creating professional-looking content for social media, presentations and more. Image filtering is a crucial feature for any image-based design tool, and Shutterstock Editor provides robust support for it. This post outlines how we provide performant filtering within the constraints of various browser and hardware limitations.
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Today the Shutterstock Developer Platform team is excited to release an updated JavaScript SDK for Shutterstock’s API.
The SDK includes methods for all endpoints in the API, so you can do anything with the SDK that you can do with the API by itself, like searching and downloading media.
We’ve published it as an NPM package, so you can install it in your JavaScript, ECMAScript, TypeScript, and Node.JS projects.
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Estimating work accurately is probably one of the most challenging skills for software engineers to master and perform through their careers. As such, how agile engineering teams assign points to their sprint backlogs is a subject of ongoing debate that traces its roots all the way back to the beginnings of Scrum.
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The business world has been buzzing from the phrase “Big Data” for years. Marketing around the term makes it seem like a magical phrase that can solve any problem just as long as we have enough data. However, even though we can store petabytes of data, it does us little good if we don’t have some way to let that data tell us a story and allow us to ask questions of that story. This is the benefit of data visualization. Below are three essential techniques for getting the most from your data.
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One of my earliest memories at Shutterstock is sitting with 60 people crammed into a 20-person meeting room and one developer presenting what they released that sprint. I’ve forgotten what that demo was about now, but clearly recall that everyone applauded at the end. In that moment, the unmistakable enthusiasm, congeniality, and love of building sold me on how cool Shutterstock was.
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I work on the Search team here at Shutterstock. This team is responsible for building all the user experiences related to search—most notably, the image/video search results page, the asset details page (which you see when you click an image on the search results page), and so on. One of the advantages of our new experience, from an engineering perspective, is that it’s all written with React, which allows us to rapidly iterate and easily share common pieces of functionality across the whole site.
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We recently open-sourced an extension of Apache’s TinkerPop JavaScript driver compatible with Amazon’s Identity and Access Management (IAM) database authentication.
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