What is new in Leo 0.6?

We pushed Leo 0.5 to a limited beta in early March and collected lots of interesting feedback. The team is listening and crunching through all that feedback and adapting Leo to improve UI/UX as well as the relevance of the underlying machine learning models.

Here is a summary of the changes we are pushing out today as part of Leo 0.6 Beta

Smart Topics

One of the feedback we collected was that the difference between mentions and topics was not clear. So in 0.6, we merged these two concepts into a single one we call Smart Topics. Just search what you want to prioritize and Leo will start analyzing the content of your feeds and prioritize the articles which are a match.

Search for companies, products, people and topics in a unified experience

Level of Aboutness

Sometimes you are interested in a company, product, or topic and you want to see every article mentioning that topic. Sometimes, for more popular topics, you are only interested in reading an article if the article is truly about that topic or company.

Leo 0.6 exposes a “level of aboutness” knob that gives you more control over the model so that you can cut out low salience matches.

Tune the aboutness parameter of each layer

For example, if you are interested in NLP or BERT, you can train Leo to only prioritize research articles that are prominently about those topics (as opposed to articles which only briefly touch on those topics).

This is a particularly powerful feature when combined with Google News Keyword alerts.

Global Priorities

Some Leo 0.5 beta customers mentioned that it was critical for them to be able to define priorities that span across multiple feeds. For example, you might be doing research about Stablecoin and want to prioritize that topic across both your Tech feed, your Business feed, or all your personal or team feeds.

In Leo 0.6, the priority designer allows you to pick “All Team Feeds” or “All Personal Feeds” as the scope of the priority.

Create a priority that spans across all your team feeds

This change reduces the total number of priorities you need to create and manage when researching topic and trends across multiple of your feeds.

Quick Access

Some users mentioned that they would like to be able to navigate their content by priority. If you are interested in a specific topic like Docker, it makes sense to be able to quickly see if there are new Docker related articles in your Feedly and easily access those articles.

In Leo 0.6, we added a new Priorities section to the left navigation bar that surfaces all your priorities and gives you quick access to all the article Leo has flagged as important.

Quick Access to all the NLP article prioritized by Leo

We added two settings in the Leo settings to let you personalize this feature. You can decide if you want to see priorities in your left navigation. If you want to see all the priorities or all the global ones (default). If you want to see all the priorities or only the ones which hav unread articles.

Inlined Entities

Your interests and priorities are continuously evolving. Often, you discover a new company, product, or topic while reading an article and you want to be able to teach Leo about it.

In Leo 0.6, the most prominent topics mentioned in an article are highlighted so that you can quickly prioritize them (or mute them)

Inlined Entities allow for quick prioritization of new topics

As part of Leo’s Cyber Security skill, you will also see highlights of CVE entities. More to come soon.

Like for the Quick Access feature, there is a Leo setting that allow you to turn off Inlined Entities if that is your preference.

Like Board Improvements

The ML team is spending time understanding how you are engaging with your priority feeds (which articles are saved to a board, which articles are being Less Like This’ed) and tuning the underlying ML models to improve accuracy. You should expect to see the quality of your priority feeds improve over the next 8 weeks.

Power Search

A lot of Feedly Pro and Feedly Teams customer rely on power search to find specific articles in their feeds and boards. In Leo 0.6, we are expanding power search and let you search with your priority feeds.

Search for BERT within the NLP priority

For teams using Leo to discover and track trends, opportunities, and trends across industries, the combination of Leo priorities and Power search is a powerful way to quick find the most crucial information

Thank you!

We want to thank all the beta customers who have been working very closely with us over the last few weeks (and sometimes months). We are very grateful for your time and precious feedback. This open collaboration is not only powerful and efficient but it is also very fun. We look forward to the next 3 months!

Edwin, Remi, and Victoria

Love reading? Love the Web? Join the Leo Beta Program

Introduction to Leo 0.5

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, trends, or mentions.

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.

Join leo beta

Here is a quick overview of the Leo 0.5 Beta feature set.

New Priority Tab

If you are part of the Leo 0.5 Beta Program, each of your feeds has now 2 tabs.

The All Tab includes all the articles published by the sources you follow.

The new Priority Tab includes the subset of articles flagged by Leo as important – based on the priorities you defined for your Leo.

Three Core Prioritization Skills: Mentions, Topics, and Like Board

Leo 0.5 ships with three core skills: mentions, topics, and like-board. Each of these skills allow you to prioritize articles differently.

Leo 0.5 ships with three core prioritization skills

The mentions skill allows you to prioritize articles based on mentions of people, company or keywords which are important to you.

Ask Leo to prioritize articles mentioning JP Morgan

For example, you can ask Leo to prioritize all the articles that mention “JP Morgan”

The topic skill allow you to prioritize articles which are about a specific topic you are interested in.

Ask Leo to prioritize articles about quantum computing

For example, you can ask Leo to analyze your tech feed and prioritize articles which are about artificial intelligence, quantum computing, or gaming.

Leo ships with one thousand pre-trained topics. If the topic you are interested in is part of that list, the topic skill is a powerful tool to let you focus your feed on what really matters to you.

Sometimes, the topic you are interested in a very niche. This is where the Like Board skill is very useful and powerful.

Prioritize articles similar to the ones saved in your Smart Venue board

For example, if you are in the Sports industry, you might be interested in the emerging Smart Venue trend. Leo does not know out of the box about Smart Venue but if you can create a board and save 30-50 articles about Smart Venue, you can use the Like Board skill to teach your Leo a new personalized topic and ask Leo to prioritize future articles which are similar to the ones you save in that board.

Once you have defined the priorities of your Leo, he will continuously read your feed and flag articles which are aligned with those priorities.

The Like Board is particularly powerful because the more articles you save to that board, the more accurate Leo’s recommendation will become.

Finally, you can easily define more sophisticated priorities by combining multiple skills/layers.

Combine multiple layers

Feedback Loop Via Less Like This

When Leo makes a back prioritization, you have the control to provide him feedback via the Less Like This button.

Provide Leo feedback via Less Like This

There are 5 different classes of feedback you can offer to your Leo:

The “Not About” feedback allows you to teach Leo that it matched the wrong keyword or topic. For example, you were interested in ICO (Initial Coin Offering) and Leo detected ICO (Internet Commissioner Office).The “duplicated article” feedback allow you to flag articles which are on topic but you have already read about via a different sourceThe “I’m not interested in” feedback allow you to flag class of articles you are not interested about. For example, you might not be interested in market research type articles. If you can flag 10-20 articles as I am not interested in market research, Leo is going to learn and start prioritizing fewer market research articles.Sometimes (specially for keyword alerts), you might get articles from sources you do not care about. The ‘mute domain’ feedback allows you to train your Leo to mute articles from those domains.Finally, sometimes, the reason is more complex. The ‘Something else’ feedback offers you an easy way out.


We also heard from a lot of users that duplicate articles are a big source of noise and echo in their feeds. If you are tired of seeing the same article or press release being pushed across multiple sources, Leo 0.5 near exact deduplication is here to help.

A sign at the bottom right shows the count of duplicates which are automatically removed

Leo continuously monitors your feeds and when he detects duplicates, it automatically clean up your feeds so that you only get one copy of the article or press release.

This is particularly useful if you follow a lot of Google Keyword Alerts or if you follow source that cross post content.

Control and Transparency

A very important aspect of the Leo promise is that it is a fun, non-black-box AI you fully control and can easily collaborate with.

Transparency via clear explanations

Transparent because each time Leo makes a prioritization, he will explain why the article was prioritized and give you the opportunity to refine that prioritization.

Full control

Control because you explicitly define all the priorities of your Leo and you can at anytime go in the Train Leo section and remove or refine a priority. No black box. No lag.

Goodbye Information Overload

Leo 0.1 Alpha customers saw 40-70% noise reduction on their feeds. More targeted feeds mean that you can save time while reducing the risk of missing important articles, or being the last to know about an important risk or market opportunity.

We look forward to seeing how your will be training your Leo!

join leo beta

-Edwin, Remi, and Victoria

New AI-Driven Discovery Experience

We love the Web because it is an open and distributed network that offers everyone the freedom and control to publish and follow what matters to them.
We also love the web because it has enabled a new generation of content creators (Ben Thompson, Bruce Schneier, Tina Eisenberg, Seth Godin, Maria Popova, etc.). Those independent thinkers continuously explore the edge of the known and share insightful and inspiring ideas with their communities.
Connecting people to the best sources for the topics that matter to them has been core to our mission since the very start of Feedly.
But discovery is a hard problem. The web is organic, a reflection of the global community’s changing needs and priorities. There are millions of sources across thousands of topics and we all have a different appetite when it comes to feeding our minds.

About twelve months ago, we created a machine learning team to see if the latest progress in deep learning and natural language processing could help us crack this nut.
Today, we are excited to give you a preview of the result of that work with the release of the new discovery experience in the Feedly Lab app (Experience 06).
Two thousand topics
The first discovery challenge is to create a taxonomy of topics.
You can think of Feedly as a rich graph of people, topics, and sources. To build the right taxonomy, we started with the raw data on all of Feedly’s sources. We had to create a model to clean, enrich, and organize that data into a hierarchy of topics. Learn more about the data science behind this.
The result is a rich, interconnected network of two thousand English topics. And it’s mapped well with how people expect to explore and read on the Web.
Some topics are broad: tech, security, design, marketing. Some are very niche: augmented reality, malware, typography, or SEO.
On the discovery homepage, we showcase thirty topics based on popular industries, trends, skills, or passions. You can access all of the topics in Feedly via the search box.

The fifty most interesting sources
The second discovery challenge is to find the fifty most interesting sources someone researching any topic might want to follow.
Ranking sources is hard because not all sources are equal. In tech as an example, you have mainstream publications like The Verge or TechCrunch, expert voices like Ben Thompson, and lots of B-list noisy sources which don’t add much value.
In addition, for niche topics like virtual reality, some sources are specific to VR while others cover a range of related topics.
To solve this challenge, we created a model which looks at sources through three different lenses:

follower count
relevance (how focused is the source on the given topic)
engagement (a proxy for quality and attention)

The outcome is new search result cards. You can explore the fifty most interesting sources for a given topic and sort them using the lens that is most important to you.

One of the benefits of the new topic model is that the 2,000 topics are organized in a hierarchy. This makes it easy for you to zoom in or out and explore many different neighborhoods of the Web.

For example, from the cybersecurity topic, you can jump to a list of related topics that let you dig deeper into malware, forensics, or privacy.

One more thing…
We have done a lot of research over the last four years to understand how people discover new sources. One insight we learned is that people often co-read certain sources. For example, if you are interested in art, design, and pop culture and you follow Fubiz, there is a high chance that you also follow Designboom.
With that in mind, we spent some time creating a model that learns what sources are often co-read. The idea is that a user could enter a source that they love and discover another source they could pair it with.
You can learn more about the machine learning model (we call it feed2vec) powering this experience through the article Paul published here.
As a user, you can access this feature by searching in the discover page for a source you love to read. The result will be a list of sources which are often co-read with that source.

Thank you!
I would like to thank Paul, Michelle, Mathieu, and Aymeric for the great research work they did to take this project from zero to one. People who have tried to tackle discovery know that it is a very hard challenge and the results of this project have been very impressive.
We would also like to thank the community for participating in the Battle of the Sources experiment. Your input was key in helping us learn how to model the source ranking. We are going to continue to invest in discovery and we look forward to continuing to collaborate with you.
We would also like to thank Dan Newman, Daron Brewood, Enrico, Joey, Lior, Paul Adams, Ryan Murphy, and Joseph Thornley from the Lab for reviewing an earlier version of this article.