Hinge, the relationship app, is taking a different approach to online dating. Instead of encouraging casual dating, the Hinge app is designed to help individuals build meaningful relationships. With Hinge, users can upload both photos and short videos intended to help them strikeup two-way conversations that foster stronger connections and turn virtual connections into real romance.
In our first article, we built a part of the front-end of our image search tool with the focus mainly on the parent App.js stateful component.
In this article - part two of a series - we will continue developing a AI image Search App, in which users can search for content in an image, not just the description. The app is built with React for UI interaction, Cloudinary for image upload and management and Algolia for search.
We're pleased to announce that we have been named to CRN's 100 Coolest Cloud Computing Vendors of 2018 list and recognized as one of the year’s 20 Coolest Cloud Software Vendors. A Channel Company brand, CRN and its annual listing recognize the most innovative cloud technology suppliers across five categories including infrastructure, platforms and development, security, storage and software.
Search ranking algorithms utilize various signals to determine how websites rank against each other on the internet either via desktop or mobile searches. One such signal is Site speed. In 2010, Google introduced Site speed as a signal in their search ranking algorithms. However, this only applied to web search ranking. Starting in July 2018, site speed will be a ranking factor of mobile searches. This change is another signal that developers must wake up and focus on improving the performance of their applications, since speed and load time affects a user’s experience of your page.
What if we could create an AI image search service? Type in a word and get images with titles or descriptions matching our search. Better yet, what if we could create an AI image search service but rather than matching just titles and image description, we can search for something in an image, regardless of the given image title or description. For example, find one with a dog in it, or those that may have a street lamp or a bus (more like an image search tool).
In the previous post, we showed how to upload images to a Cloudinary server. In this part, we will play with some of the features we see on the WhatsApp technology. After you or your users have uploaded image assets to Cloudinary, you can deliver them via dynamic URLs. You can include instructions in your dynamic URLs that tell Cloudinary to manipulate your assets using a set of transformation parameters. All image manipulations and image optimizations are performed automatically in the cloud and your transformed assets are automatically optimized before they are routed through a fast CDN to the end user for an optimal user experience. For example, you can resize and crop, add overlays, blur or pixelate faces, apply a variety of special effects and filters, and apply settings to optimize your images and to deliver them responsively.
The last time you scrolled through the feed on your favorite social site, chances are that some videos caught your attention, and chances are, they were playing silently.
On the other hand, what was your reaction the last time you opened a web page and a video unexpectedly began playing with sound? If you are anything like me, the first thing you did was to quickly hunt for the fastest way to pause the video, mute the sound, or close the page entirely, especially if you were in a public place at the time.
With more than one billion people using WhatsApp, the platform is becoming a go-to for reliable and secure instant messaging. Having so many users means that data transfer processes must be optimized and scalable across all platforms. WhatsApp technology is touted for its ability to achieve significant media quality preservation when traversing the network from sender to receiver, and this is no easy feat to achieve.
Due to significant growth of the web and improvements in network bandwidth, video is now a major source of information and entertainment shared over the internet. As a developer or asset manager, making corporate videos available for viewing, not to mention user-uploaded videos, means you also need a way to categorize them according to their content and make your video library searchable. Most systems end up organizing their video by metadata like the filename, or with user-generated tags (e.g., youtube). This sort of indexing method is subjective, inconsistent, time-consuming, incomplete and superficial.