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
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.
Some fonts are free and they are available in the free Feedly Basic Plan. Some fonts are premium and they are part of the Feedly Pro and Feedly Team plans.
We love that the idea for this feature emerged from the Feedly Lab. If you love the Web and love reading and what to provide feedback and share ideas with the team, please join the Feedly Lab.
-The Feedly Team
It is frustrating to be skimming through your feeds and run into
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!
Passionate about the Web, NLP and Machine Learning? Join the Feedly Lab on Slack and connect with the Feedly machine learning team!
Sometimes you want to follow high volume publications like The Verge, NY Times, or VentureBeat because you trust them but you are only interested in narrower topics like #AI, #MixedReality, or #Blockchain.
Reducing noise and information overload is a problem we care passionately about. We have been working over the last 12 months on a new feature called Leo. You can think of Leo as your non-black-box research assistant – an easy-to-control AI tool which helps you reduce noise in your feeds and never miss important articles.
At its core, Leo is composed of a set of NLP and ML skills that allow you to train him to read, understand, and prioritize the content of your feeds. One of the first skills we are building is Topic Classification.
What is Topic Classification?
This skill helps Leo classify articles across 1,000 common topics. Thanks to its topic classification skill, Leo will be able to determine that the article above is about the #retail and #china topics.
Why is this skill useful?
Some sources produce interesting and high-quality content across lots of different topics. Following those sources can result in a large number of articles being added to your feeds, some of which about topics you do not care about. Let’s say that you like Techcrunch, Forbes, and Wired. Following those three sources means that you have to crunch through a thousand articles per week. Some of them on topics you do not care about. With the Leo topic skill, you can train Leo to prioritize articles about #ai across those sources and get the ten articles which are the most relevant to you without having to skim through a large list of articles.
How is Leo learning?
We feed Leo with thousands of articles about each of the thousands of topics that seem to be the most relevant to the Feedly community and generate a terminology and topic model that best captures the nuances across these topics.
Leo then processes all the articles in your feeds and classifies them based on the topic model.
Which languages does Leo understand?
Right now our dataset is English only so Leo only classified articles written in English.
How can you participate?
Leo is in its infancy (Leo 0.1). We are going to need six to nine month of training and quality improvement for Leo to grow and become useful. As part of our Lab effort (more open and transparent development process), we are inviting the Feedly community to contribute to Leo’s training.
You might see at the bottom of some article, a Leo prompt asking you to train Leo about the topics associated with the article you just read. You can in a couple of clicks pick the topics which are relevant and the topics which are wrong.
If you are not interested in participating in the Leo program and think that the footer is a waste of space, there is a preference knob to turn it off in the Leo settings page.
We want to thank Quentin Lhoest for doing the preliminary ML research behind this Leo skill!
□情報提供：ＫＷＡ 村中勝二 氏