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#SPEED READER DOWNLOAD FULL#

Performing network requests to far fewer third parties (zero, in the common case).

Fetching far fewer images or videos (since images and videos not core to the page’s presentation are never retrieved).

Never fetching or executing script or CSS.

In turn, for documents which SpeedReader determines are readable, the sources of SpeedReader improvements include:

If the classifier has determined that the page is readable, SpeedReader extracts the readable subset of the document before rendering, currently using existing, battle-tested algorithms, and continues rendering the document.

In our tests, it determines whether a page can be displayed in reader mode with 91% accuracy.

A Machine Learning classifier to determine whether there is a readable subset of the initial, fetched page HTML.

SpeedReader achieves performance improvements through a two-step pipeline: The key difference is that SpeedReader operates before page rendering, which allows it to determine which resources are needed for the page’s core content before fetching anything. Our technique provides a user experience similar to existing reader mode tools, but with network, performance and privacy improvements that exceed existing ad and tracking blocking tools, on a significant portion of websites.

#SPEED READER DOWNLOAD FULL#

SpeedReader works on 22% of web pages in general, and a full 46% of pages shared on social media.Ĭurrent reader modes do not save network and memory resources, since reader modes fetch the page’s resources before rendering the “readable” version. During testing, SpeedReader issued 115 fewer requests to third parties, and interacted with 64 fewer trackers on average, on applicable pages. SpeedReader improves user privacy as well, by removing all commonly recognized trackers. On applicable pages, SpeedReader speeds up page loading by 27×, and reduces network use by 84×, compared to regular browsing. SpeedReader automatically detects which pages are suitable, significantly improving the end-user experience – especially on slower mobile connections. For suitable pages, SpeedReader removes all non-essential content before page load, saving time, bandwidth and removing ads and trackers in that SpeedReader goes beyond what Brave does by default in terms of blocking trackers and ads. It delivers huge improvements to not only the aesthetics of the browsing experience, but to browsing performance and privacy as well. SpeedReader is a new approach to reader modes. To address these issues, Brave is developing SpeedReader. Reader modes aim to make the reading experience more pleasant, but do not try to improve page load times or protect users’ privacy, as the entire page is loaded first, before removing the bloat. As such, major browsers include a reader mode, which reduces the visual complexity of web sites by eliminating distracting ads, videos, sponsored content links, and other web elements not essential to the page. Web browsers sometimes need to intervene for users to continue enjoying the web.

Such growth in capability has however resulted in page “bloat,” making pages expensive to load, and bringing along ubiquitous advertising and tracking. The modern web’s progress has led far beyond Hypertext Markup for document discovery, with media-rich experiences and dynamic applications. SpeedReader is useful on 22% of web pages in general, and has even higher applicability for user-shared content (31% on Twitter and 42% on Reddit).Ī New Approach to Reader Modes: Performance and Privacy Gains By removing all non-essential content before a page loads, SpeedReader goes beyond reader mode’s aesthetic improvements and delivers:ġ) radical performance gains (speedups ranging from 20× to 27×, bandwidth savings on the order of 84×, and memory reduction of 2.4×) Ģ) significant privacy benefits, which include a complete elimination of ad and tracking related requests. Summary: SpeedReader is Brave’s new approach to reader mode, the browser tool that makes web pages more pleasant and less complex to read. Ben Livshits (Chief Scientist), as well as by Mohammad Ghasemisharif (Ph.D student in Computer Science at the University of Illinois at Chicago and intern at Brave). This post is an abbreviated version of this report, based on research conducted at Brave by Andrius Aucinas (Performance Researcher), Peter Snyder (Privacy Researcher), and Dr.