1. The Core: Word Counting as Our “Steps”

1. Automated Word Counting
   • What: Every time a user reads in our app (or listens to an audiobook), we record the exact number of words completed. This is more precise than pages, more consistent across formats, and better aligned with our “reading fitness” narrative.
   • Why: Counting words is like counting steps. It’s the atomic unit that forms the backbone of our entire reading-tracker concept. If you imagine a fitness device that can only measure time spent jogging but not the distance, it’s missing a key layer of data. Words are our “distance.”
   • Implementation Detail: For ebooks, we can map the user’s current location in the text to a word count. For audiobooks, we can approximate the words by correlating timestamps with the known word count of that section, or by receiving structured text from publishers.
   • Validation: This solves a major user question: “How much did I read, really?” They know “2,000 words read” as tangibly as “2,000 steps walked.”
2. Session Summaries with Word Count
   • What: After finishing or pausing a reading session, show a recap: total words read, average words/minute, total session time.
   • Why: This summary provides immediate feedback. We anchor on words as the “distance.” If Strava runners enjoy seeing “4 miles at 9:30 pace,” then readers will enjoy “2,300 words at 200 wpm.”
   • Logic for Sequencing: Must be in place early because everything else (leaderboards, feed, profiles) depends on that word count data.

2. Leaderboards for Small Networks

1. Weekly Leaderboard (Words Read)
   • What: A leaderboard resetting every Monday, showing how many words each person in your circle has read this week. This might be labeled “Weekly Reading Challenge” or “Words This Week.”
   • Why: The ability to see how you stack up against friends (or coworkers) is deeply motivating. Think of it as “XP for reading.” We want to keep it simple: top 5–10 people in your circle with word counts.
   • Small Networks: We don’t need a giant social graph. Even 3–5 friends reading together can create enough excitement. The user sees that a friend is at 12,000 words on a Tuesday, so they try to catch up.
   • Logic for Sequencing: Introduce after we’re reliably tracking word counts. The feed remains the day-to-day “micro” feedback; the leaderboard is the “macro” feedback on how you stack up over the week.
2. Profile-based Access
   • What: The user sees each person’s profile from the leaderboard: “Jenny read 18,000 words.” Tapping on Jenny’s name shows her reading stats.
   • Why: Tying the leaderboard to the user’s profile fosters accountability. People can peek in, see how many words Jenny read historically, how many reviews she’s done.
3. Strict Privacy Settings
   • What: Users can opt into or out of the leaderboard. If someone wants to read privately, that’s fine. If they want to only see the scoreboard but not appear on it, that’s also an option.
   • Why: Some readers dislike competition. A forced leaderboard might push them away. We make it optional, but heavily encourage it for small friend groups.

3. Minimal Social Mechanic: Feed + Leaderboard

1. Reading Session Feed (Word-Based)
   • What: A simple chronological feed with entries like “Mia read 1,400 words in Atomic Habits” or “Alan read 2,800 words in Dune.”
   • Why: This is the day-to-day scoreboard. The leaderboard captures cumulative progress, while the feed captures snapshots of activity. The synergy between the two fosters short-term and long-term motivation.
   • Small-Network Focus: Since people might only have 2 or 3 reading friends, that’s enough to keep the feed lively. At scale, we can implement friend groups or filters, but let’s start small.
2. Light Engagement Actions
   • What: “Cheer” or “Thumbs up” for each session (similar to “kudos” on Strava). Possibly short text comments.
   • Why: These micro-rewards matter. The user sees immediate social affirmation for reading, hooking them into the daily habit.
   • Logic: Keep it minimal. We don’t want a lengthy comment thread. This is a quick, positive nudge.

4. Profiles with a “Top 4” Display

1. Basic Profile Screen
   • What: A user’s profile page featuring their username, total words read, a reading pace metric (words/min average), perhaps the last few books read.
   • Why: People want to see each other’s “reading identity.” It’s the reading version of a runner’s trophy case.
   • Top 4 Books or Favorites
     • A small curated set of “favorite books” or “top 4 recent reads,” akin to Letterboxd’s “Favorite Four Films.”
     • Show these in a neat, visual row. The user can pick which four they want displayed.
     • This accomplishes two goals: (1) Show a personal reading style at a glance. (2) Encourage “book pride,” which helps fill the profile with interesting details.
2. Review & Progress Snapshots
   • What: Under the “top 4,” a user might show some stats: total books finished this year, total words read this month, or “Currently Reading: The Name of the Wind.”
   • Why: This is a minimal “shelf” system without being full-blown shelf mania. It gives a quick sense of user activity—similar to a fitness profile showing “logged 5 workouts this week.”
   • Logic for Sequencing: We want it in place so the user’s identity in the leaderboard or feed links to something interesting.

5. Invite Flows and Tiny Friend Graph

1. Invite a Friend to Compare Word Counts
   • What: A frictionless invite link that you can send via text or email. Once your friend joins, you automatically follow each other.
   • Why: We want to replicate the “closed-loop invitation” that Strava or Slack does. They make it effortless to bring a buddy. Since we only need 2–3 reading friends to make leaderboards meaningful, let’s optimize for that.
   • Logic: The user sees the leaderboard or feed and is prompted: “Invite a friend to join and see your reading stats.” That’s your single best user acquisition flow in early days.
2. Limited Search / Friend Management
   • What: We let users find each other by username, email, or phone contact sync.
   • Why: Overbuilding an elaborate friend-finder or suggestions algorithm is out of scope for now. We keep it simple. We don’t need mass social at this stage; we need real-world reading buddies.