Why ShowZ
Most show apps either browse well or discuss well. ShowZ is designed to do both by combining discovery rails, rich show detail, and room-based chat in one flow.
ShowZ is a native SwiftUI iOS app for TV discovery and discussion, built to combine fast browsing with meaningful show conversation.
Most show apps either browse well or discuss well. ShowZ is designed to do both by combining discovery rails, rich show detail, and room-based chat in one flow.
Select a version, then scroll horizontally through screenshots from that release.
Version 1
Introduced For You, Trending, Popular, show detail, and foundational search navigation.
Version 2
Improved recommendation logic, refined search, added Ask AI, and launched the new chat system.
Version 3
Grouped liked shows into a dedicated state, improved recommendations, and polished chat visuals.
Added For You personalization for TV shows.
Added Trending to surface the most trending TV shows.
Added Popular to surface the most popular TV shows.
Added expanded show details with deeper television show context.
Added search navigation to find and browse different TV shows.
Tied liked shows into personalization so likes directly shape the feed.
Minor enhancements to improve trending accuracy.
Minor enhancements to improve popular-lane accuracy.
Show detail fixes and improvements.
Search fixes and additions.
Visual design adjusted to better fit the iOS keyboard.
Added different AI options to ask more about TV shows.
Chat overhaul created a dedicated space and design for user conversations.
Added clickable chat options users can select.
This section keeps all TV shows you liked in one place.
Recommended TV shows are based on liked titles; this beta feature is still being tuned for accuracy.
Design tweaks were made to the chat experience for better clarity.
This section hosts the product demo video for reviewers. The player activates automatically when the media file is present.
Demo Video Coming Soon
A guided walkthrough of discovery lanes, show detail flow, and room-based chat will be published here.
Deeper product context plus implementation details for evaluating architecture decisions, data strategy, and local run setup.