Feedly Labs has been a really interesting experience for us because it has helped us get a deeper understanding of who the Feedly community is and how we can better serve you going forward.
One of the insights we learned last fall is that the community seems to care deeply about typography.
Based on that insight, we funded a project focused on giving you more control over fonts and font size through a close partnership with Monotype (one of the best foundries in the world).
Today, we are excited to announce the fruits of that project – which will be available on the Web today and on Mobile, next week.
Open Dyslexic Experiment
Dyslexia is also very close to our heart. People with dyslexia have normal intelligence and vision but might have difficulty reading due to problems identifying speech sounds and learning how they relate to letters and words (decoding).
Some fonts have been emerging which are designed around the common symptoms of dyslexia. We decided as part of the premium fonts project to add support for Open Dyslexic and see if switching to that font can help with the decoding or not. If you are suffering from Dyslexia and want to provide us feedback on how we could help make Feedly better, please join the Feedly Lab.
Google Noto and support for more languages
Last but not least, we are have added support for the Google Noto, which is a beautiful font which works well across lots of languages.
If you are consuming lots of international content and need a font preference that works across lots of languages, it might be a very good choice.
Getting started with Fonts
On mobile, you can use the Aa menu which is available in the article viewer to change your font settings (and theme). On the web, you can go to your account settings > appearance.
It is frustrating to be skimming through your feeds and run into duplicate articles.
This happens for example when you have overlapping keyword alerts where two different keywords exist in the same article. It also happens when some sources publish the same articles into different RSS feeds. Finally, it happens a lot when a company issues a press release and other sources publish that press release with some minor changes.
Giving you the tools and control to tune your feeds is something we care passionately about. Today, we are excited to announce the beta release of a new Leo skill called Deduplication.
What is Deduplication?
This skill helps Leo detect that multiple articles are near exact duplicates of each other and cut that noise from your feeds. On the Web version of Feedly, you will see a small notification at the bottom right of your screen each time Leo removes duplicate from your feeds.
Which language does Deduplication work on?
The Leo deduplication skill works across all languages?
Which Feedly Plan does this skill require?
Because processing duplicates at scale is expensive, this skill will be initially rolled out as part of the Feedly Teams plan.
If you are part of Feedly Teams, there is a preference knob in the Leo settings page to disable this skill.
Beyond near exact duplicates
The deduplication skill is focusing on near exact duplicates. These are articles which have 85% or more overlap. We are working on a different skill called Business Events for articles which are reporting on the same event but with different content. In the case of business events, the content will be grouped instead of being removed.
Are you passionate about the Web, reading, and NLP? Are you curious about how machine learning can help process, filter, and prioritize information more effectively?
The goal of this tutorial is to show you how to leverage the content of your Feedly feeds and boards to run machine learning and NLP experiments using the Feedly API and Colaboratory.
To make things concrete, we are going to create a simple KNN classifier that takes as input the content of The Verge and Engadget, and a board with 50 positive AI articles. Using these inputs, the classifier reliably learns to classify AI and non-AI articles from those sources.
The Colaboratory Cloud Notebook we created has all the building blocks you need to create and run this AI experiment. All in a browser. All this within 20 minutes.
Once you are done with this first experiment, you will also have an example you can easily adapt to your own feeds, boards, and machine learning models!