As avid consumers of long-form content – blogs, editorial articles, personal websites, book excerpts, short stories, videos and more – we admire its capacity to illuminate and entertain without daunting us. Nonetheless, the long form experience online suffers a few key problems.
First, good content is tough to find on the internet. Few general publications are of uniformly high quality. Finding articles on specific topics is hard (try “environmental economics”). And it’s tough, as an individual, to identify the go-to sources for a given domain.
Second, existing content organization stifles discovery of articles and new interests. Most media websites are organized by subject area: the reader has to know what she is searching for. Further, today we rely heavily on email threads and social media to supply us with a backlog of fresh reading. That’s unfortunate: most threads fizzle and social media is overrun with junk.
Indeed, these problems are especially frustrating because thoughtful content exists in droves on the internet, our inquisitive friends and networks know the best sources in various fields, and it’s human nature to want to share good articles you read (there’s a joy in sharing good arguments, even without explicit incentives – haven’t you done it?).
These frustrations inspired PwrSet, an elite discovery and sharing platform for long-form content. Its decentralization and scalability imply greater potential than any curation service. With it, we intend to solve all the aforementioned problems.
PwrSet empowers its users to unearth top quality content efficiently: all articles are backed by personal endorsements from an elite network, and as the platform grows it will implicitly aggregate a set of “go-to” sources across a wide range of domains. Further, PwrSet organizes content around subsets of people (hence the name: a powerset is the set of all subsets, and our members are powerful thinkers) to facilitate intellectual exploration. Users thus face a diversity of reading options that other users, similar or known to them, have enjoyed – while retaining more classic subject-area type search (so the platform also creates a searchable repository of high-quality long-form content, which doesn’t really exist).
An example: Bob, himself, would never search for anything about Byzantine art history (let alone history). But Jennifer, his brilliant friend who coinhabits one of his subsets, shares and endorses an article about a particular mosaic. Bob sees this, is intrigued enough to click – thanks, in no small part, to Jennifer’s endorsement, and ultimately enjoys the article. As such, Bob i) discovers an article and interest he probably never would have, and ii) guaranteed a minimal reading quality through Jennifer’s endorsement.
At the outset there will be ten subsets (emotional, builder, wanderer, artist, visionary, life-arbitrageur, networker, big spender, polymath, rational) to which we recommend users based on a survey (of actual article choices), and eventually there will be more – custom, thematic, and temporary subsets, etc. Users can also follow other users’ profiles and receive notifications about their sharing activity.
On PwrSet, users share and bookmark the internet without the hassle of email or ever-changing news feeds. Unlike other social media, PwrSet is specifically for disseminating thought-provoking content and so replaces complicated interlocking email groups and (hopefully) avoids the noise of many varieties. Many smart friends tell us that this is the chief purpose of social media for them anyway. Sharing and subsets also foster interactions with people (including new ones) of similar intellectual and reading interests, a prospect we are looking to develop more seriously into a “Person who might be interesting to you” feature and dedicated discussion sessions about articles. You might be able to meet and debate Marek, a software engineer from Prague, about distributive justice!
We see PwrSet’s current incarnation as an experiment. Growing the platform creates an unprecedented dataset of quality, vouched-for content, unique also for capturing network structure and spread. Jointly, these data can help us understand what makes a good article textually and contextually and map out sources on the internet. This, in turn, allows us to automate article-finding and master recommendations, thus extracting value and structure out of the internet’s brilliant chaos.
Anirudha and Adam