Want to know what other Internet users are currently reading?

Then I propose to try the extension for Google Chrome, which allows you to fulfill this desire. It is called the “ Eye of the People ” and, according to the idea, it should show the most interesting of the currently readable users of the network.
In short, the essence of his work in the following. The extension tracks the user's browser activity, logs information about where and when he “went” and periodically sends data to the server. The server in real time integrates the received information, forms and maintains the constantly changing rating of popular content. This rating is returned to all users of the extension in the form of a short list of links. In fact, there is an exchange of visited pages on the Internet, which allows to find out what is interesting for other people.
The advantages of such a way to share visited resources include the following.

• Efficiency. Everything happens almost in real time. Your visit to a web page may affect your current ranking in 10-15 minutes.
• Objectivity. The assessment of the attractiveness of the content is made by the system, excluding the user's subjective desires.
• Ease. The user himself does not need to do absolutely nothing special actions. Just get the result.

There are still potential advantages that may appear in the future, as well as certain disadvantages, but about them below.
And before you try to install an extension, or decide that you do not need it, let me tell you a little about why it is safe, how it works, and why it was actually created.

The idea behind this system is quite simple. Therefore, when it came to my mind, I was sure that something like this probably already exists. However, after searching, I did not find anything close and decided to try to do it myself. Most likely just looking bad. Therefore, if someone shares a link to something similar, look with interest. In any case, the possible development of this system, which I have in my head, certainly differs from anything that exists and about this also slightly below.

Security

Probably one of the first fair questions that may arise is the following. But is it possible that this extension will take and begin to share confidential information that may be on the pages viewed by the user? I'll try to answer.
First, the extension does not send any content from the content viewed by the user. Only URLs of the visited pages are sent to the server, accompanied by additional information about the user's behavior. At the moment, such consists solely of the time spent on the relevant resource. You can verify this by looking at the background.html file. Even if the extension wanted to do this, it would not be able to, because it does not contain content scripts, without having the appropriate permissions. That is why during the installation of the extension you will not see a warning that it “can access your personal data on all websites”.
Secondly, any address, before getting into the rating, is tested by the server for accessibility. Therefore, if the page is protected from free access, then even its address will definitely remain “between us”.
Third, the ranking algorithm is designed to minimize the likelihood of “random” pages appearing in the ranking. There are those that are formally open, but you would not want them to appear before the eyes of a wide range of viewers. The main thing that hinders the possibility of such an event is the decisive advantage of those pages that were visited by many different people.
However. At the time of this writing, the extension has not yet been published in the public domain. The purpose of this story is just to attract a significant number of users immediately after its publication, providing him with a minimally meaningful performance. Therefore, one of you will be among the very first real users. The relative weight of user links, when their number is small, is understandably quite high. To mitigate the effect of distortions associated with the features of the initial period of use, I “tightened” some parameters governing the likelihood of “highlighting” user links on the one hand, and on the other, so that it was not boring, I added the possibility of the appearance of semi-random links that are not related to user activity. That they can be observed in the list of the first time (example in the picture). I made the list for the time being shorter. When the unlimited number of users is typed, I will disconnect this external source, and the list will become longer.
Despite all these circumstances, if you want an absolute guarantee of the non-appearance of certain pages in the list, then at first you can open them in incognito mode (in this mode, extensions are blocked by default).

How it works

A little more about how the system works.
The basis of assessing the popularity of a resource is based on two characteristics: the number of users who visited it during a certain time elapsed from the current moment, as well as indicators of individual interest shown to it by each user. The interest rate is calculated as a non-linear function of the time spent browsing a specific page. Considering only time is pretty rough. But for the first version, I think it is quite acceptable. In the future, this can be improved if we consider the additional characteristics of the behavior, as well as the features of the page itself.
The extension measures the times for each web page and periodically sends the accumulated data to the server, receiving in response a current list of rankings. It is clear that the data transmitted will affect the rating at best to the next request. The interval between requests is now typically five minutes, but this value is variable, which can vary under certain circumstances.
Server data received from the user adds to his individual "piggy bank". With a certain periodicity, the server “prints” piggy banks of active users, calculates indicators of interests, leads to its own time scale and integrates data from different users, turning them into estimates of the popularity of each available page for each predetermined point in time. With another certain periodicity, the server builds a rating. For this, an array of accumulated “instant” popularity ratings is analyzed to a certain depth into the past. In the process of analysis, these ratings are summarized in the final. This takes into account the number of users who visited the page, as well as the relevance of each “instant” rating (more recent - more relevant). The existing list of pages is sorted, cut off, checked, filtered and becomes current. View depth is a variable value depending on current user activity. The range is approximately from a couple of hours to half an hour.
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Development

I hope that even in this form, as now, this system will be interesting to someone.
But for me, its creation is only a small step, which may allow to come closer to the realization of an idea that really interests me.
Many of us read the news every day, looking for something fresh and interesting for us, based on our personal preferences. To do this, we use the search, rss-readers, ribbons of friends, just run through your favorite sites, and also use a bunch of different ways. The stream of fresh content today is rather big and sometimes it takes a lot of time to filter it. Yes, there are many ways to customize this thread, based on their preferences, but this does not always work, in itself, still requiring time and effort.
In thinking about this problem, I somehow got the idea of ​​a system that would study the behavior of a person on the network and, on the basis of the data obtained, would study for their own interests. Based on these interests, such a system will automatically build and modify, if necessary, a set of personal filters that it will apply to the stream of constantly flowing fresh content. And the user will be offered already “selective” content, without requiring any special actions from him.
We now return to our system with a browser extension. In the course of her work, she constantly has a “hot” list of visited web pages. We can say that the set of elements of this list specifies a coordinate system in a multidimensional space, and the values ​​of the corresponding indicators of interest - the position of a point associated with a particular user in this space. If it turns out to select groups of points close to each other, at the same time removed from the rest, we will get a division of users into groups with similar in some sense interests. Then, if we build independent ratings within the selected user groups, we will get a lot of lists focused on certain interests of people. And these lists will be determined not by some artificial categories such as “sport” or “politics”, but by more natural partitions, which, one may hope, will produce a result that is relevant to the interests of users.
Then you can connect the analysis directly to the page content. If you learn to classify content, it will become real to solve the problem of connecting the entire information flow, including that part of it that is not covered by users of the system.
Of course, one should not take the given model with users-points in multidimensional space too literally. This is an illustration of the possibility of an approach, and the implementation may be slightly different. Since this is still in my thoughts, I will not go into further details. Specificity should be based on research data, which I hope to get through the development of the current implementation of the system.
It is possible that the results of the work of this very current implementation will be commonplace and not interesting for you, as for the user of the extension. But maybe understanding the idea will push to still shelter the green eye for some time (by the way, if you right-click on the extension button, then there is an option to “Hide the button”).

About the disadvantages

The main disadvantage today is the lack of users. It is clear that the results of such a system will not be very reliable. For example, since this publication appeared on Habré, then in the beginning the generated rating will turn out to be a kind of rating of articles from Habr. It is hoped that this will correct itself in time.
It is also obvious that since now the system does not preliminarily classify URLs coming to it, the top lines of the rating will rather quickly be occupied by large services like GMail or the main pages of popular resources (for example, habrahabr again). I think that it will be necessary to filter out these cases gradually and purposefully, having organized, perhaps, a separate rating. But for now, I want to look at what happens with you.
It is clear that according to the idea I want to see in the generated list something interesting for us from what we may have missed for some reason. There is no guarantee that the proposed method will meet these expectations. And I propose not to consider this system as an alternative to anything, but only as another way to look at the flow of incoming information.
I have no idea how many people want to install an extension, what the load will be and how it will be distributed. Of course, I tested the system, but it was an “artificial” mode. Therefore, I apologize in advance for possible “misunderstandings” if something goes wrong.
Now, before publishing the text, I can publish the extension. And I wish all readers good luck in the upcoming new year!