June 2013 - October 2014

ReadingPack enabled you to follow other people's reading lists and recommend great articles you recently read. Like Pocket, but social.

I built ReadingPack from the ground up and worked on it full-time (bootstrapped). I did everything from product design, frontend, backend, marketing, and support. ReadingPack started as a Chrome extension but later on, I added other tools such as mobile apps, more extensions (FireFox, Opera, and Safari), and integrations (e.g. IFTTT integration, Spritz, etc).

After almost 2 years and hundreds of weekly active users, I ran out of money and decided to stop working on ReadingPack.

ReadingPack on Lifehacker: ReadingPack Organizes Articles to Read, Helps You Discover New Ones.

  • Onboarding

    As a new user, it was critical to make sure you will complete the following stages in order to fully understand and get value from ReadingPack.

    • Install a browser extension: Until a later stage, the main way to use ReadingPack (to save and recommend articles) was through a browser extension (Chrome, FireFox, Safari, and Opera). Therefore, the most important thing was to make sure the user installs it.

    • Follow some people: The real value of ReadingPack came only if you followed other people. During the onboarding, ReadingPack asked the user to follow at least 3 people from the community (with an option to filter people by a topic of interest).

    • Recommend an article: Once a user followed some people, ReadingPack automatically created a personalized feed, and asked her to recommend an article. This stage was useful because (1) it helped to let the user try a core feature of the product really fast since the signup, and (2) once a user recommended an article, her profile wasn't empty anymore. Empty profiles are not fun.

  • Feed

    The feed was the main place to discover new articles to read, based on recommendations from people the user follows on ReadingPack.

    I built an algorithm especially for the feed so the first articles were the most recommended by people the user follows, considering (1) how many people recommended them, and (2) when the user saw them for the first time (if an article appeared a week ago and the user expressed lack of interest, the importance of that article will decrease even if a lot of people recommended it).