When you share your carefully-curated content on social media, you want to ensure it reaches as much of your audience as possible. One important part of this process is picking an amazing image to include in that post. That’s why we created the Social Media Image Recommender, an algorithm which looks through your article’s text and images, and tells you which picture you should use when sharing your post on Facebook, Twitter, LinkedIn, or any other social network. As a bonus, it also smart-resizes your image to the correct size for social sharing, keeping the most important elements in the frame and discarding unimportant background, thanks to the magic of deep learning and machine vision.
Want to jump right in and try it out yourself? Check out our Image Recommender Demo! Like it? Sign up and integrate the Image Recommender Algorithm into your digital workflow to process thousands of articles every hour, and supercharge your content publishing pipeline!
Your website publishes thousands of articles each day. Your writers create stories, embed images, and tag them for SEO purposes. It’s your job to share them out on social media… but you’re struggling to keep up with the volume.
After coming up with a snappy tagline, you still have to select the best image and crop it to different sizes for Facebook, Twitter, LinkedIn, and all the other networks. Using a batch image-cropper might remove something important from the photo — like Elon Musk’s face, or half of the car being featured — so you put in a lot of time cropping and resizing by hand.
What if you had an automated way of handling the image picking and cropping process? Well, there’s now an algorithm for that. Today we’ll talk about how we’ve managed to bring together many different algorithms into a single ensemble that can intelligently select, crop, and score images for social media sharing.
Modern cyber attacks, such as Botnets and Ransomware, are becoming increasingly dependent on (seemingly) randomly generated domain names. Those domains are used as a way to establish Command & Control with their owners, which is a technique called Domain Fluxing. The recent WannaCry ransomware was famously stopped simply by registering one of those domain names.
The ability to quickly classify a domain name as *safe* or *malicious* is a critical task in the cybersecurity world. It can help alert security experts of any suspicious activity or even block that activity. Such a system will have two requirements:
- Needs to be accurate, you don’t want to block your users from accessing safe websites
- Needs to be scalable, able to handle thousands of transactions per second
There are plenty of approaches to this problem, especially in the academic world (S. Yadav – 2010, J. Munro – 2013). The fine folks at H2O.ai also have an excellent code sample we found here. This blog post will briefly describe how H2O’s implementation works and how you can deploy and scale it on Algorithmia. Read More…
Traveling Salesman is one of the classic NP-Hard problems: finding the optimal solution can take a long time, but there are some great shortcuts available which come close! Algorithmia now brings you a fast, near-optimal way to find the fastest route through multiple cities, thanks to the power of Genetic Algorithms and easily-accessible APIs. Read More…
Great algorithm descriptions really help developers to understand your algorithms, and help them become successful quickly. Today, we’re happy to announce that we’ve shipped several improvements aimed at helping you write great descriptions, with ready-to-edit sections aimed at helping developers quickly comprehend your algorithm’s purpose and usage.
Algorithm descriptions are based on Markdown and we’ve now shipped a Markdown editor that will let you easily edit your descriptions. The editor is based on CommonMark standard and also includes support for GFM tables.