Wikipedia is one of the most widely used websites globally. We built a simple extension to that displays similar pages at the top of every Wikipedia page!
A technique we use to visualise how Lateral recommendations would look and work on a website is to create a Chrome extension that inserts the recommendations at load time. This is useful because: No access is required to the websites source files The extension shares assets with the page, so matching styling is easy It allows […]
Give me five is an open source Chrome extension that allows you to recommend the content you push to Lateral based on the content of the page you’re currently visiting. It’s the same code base that the NewsBot Chrome extension is built upon. The screencast shows the extension in action: You can find the source code […]
Creating a similar content recommender for all the abstracts of this year’s EMNLP 2015 Conference in Lisbon using Lateral’s machine learning API.
A while back we partnered up with Blockspring to enable anyone to use our API without needing to write any code. They’ve created an awesome solution that allows you to make use of a range of great APIs using only a spreadsheet. This enables you to bring data into your spreadsheet, run text-analysis and much more. […]
Which public companies work with solar technology, or are similar to Tesla? Find out quickly, using the Lateral API, serving publicly accessible data from Bloomberg.
We are extremely excited to introduce you to NewsBot, the fastest way to find related articles and to stay up-to-date on the news that matters to you!
Having recently released our TED talks demo we felt another interesting application would be the thoughts of one person. No one fits that description more than Maria Popova’s excellent collection of ideas on her Brain Pickings site. With thoughts on music to philosophy we felt it would be an excellent exploration of how our technology represents thoughts.
By ignoring citation graphs and keywords, you can discover papers and researchers you never knew existed. Check it out here (on arXiv papers = ML, CS, math & physics).
TED is an awesome platform for ideas, so we thought an interesting experiment would be having our API provide recommendations based on the talks’ transcripts.