I am testing one narrow product decision for Rankiwiki, a public community ranking site.
On an early ranking page, one or two familiar options can absorb nearly all attention. A newcomer then faces two jobs: understand the current distribution and decide whether a missing option is worth adding.
I am considering a lightweight discovery prompt that exposes the spread—not just the #1 item—and then asks, “Is the option you expected missing?” The concrete success event would be a meaningful second-item view or a new relevant item, not raw vote count.
The limitation matters: Rankiwiki is public today. It does not have private classroom spaces, teacher controls, or student-safety safeguards, so I would not position it as classroom-ready. Safe examples are books, activities, project themes, arguments, or product ideas—never identifiable children.
For a public ranking MVP, which default would you test first?
I build Rankiwiki and am looking for product feedback, not votes:
https://rankiwiki.com/?utm_source=indiehackers&utm_medium=community&utm_campaign=public_rankings&utm_content=20260824_distribution_before_winner
Disclosure: drafted with AI assistance and checked against the public product on 2026-08-24 KST.
The interesting part is that you’re measuring discovery rather than raw engagement. A ranking page can show a winner while still hiding whether users understand the wider option set, so testing second-item views makes the product question much sharper.