Qw

Qwiz – Building a daily global quiz app with synchronised leaderboards

Hacker News

Qwiz – Building a daily global quiz app with synchronised leaderboards

Ello HN! I just shipped Qwiz after a few weeks of solo development. It's a daily trivia game where everyone worldwide gets the same 10 questions each day and competes on a global leaderboard. Interesting technical challenges I solved: Global Synchronisation: Ensuring all players worldwide get questions at exactly the same time across timezones. Used CloudKit with scheduled functions and local timezone calculations. Question Management: Built a content pipeline to manage 15k+ questions across 10 categories with difficulty balancing, duplicate detection, and fact-checking workflows. Real-time Leaderboards: Handling concurrent score submissions from thousands of users while maintaining accuracy. Implemented optimistic updates with conflict resolution. Analytics Engine: Custom system tracking performance across categories, improvement over time, and global statistics without compromising privacy. Performance: Keeping the app snappy while handling complex scoring algorithms, offline capabilities, and seamless syncing when back online. What I learned: Global time coordination is MUCH harder than I expected. Trivia question quality control and making sure its balanced and fair is surprisingly complex Users care more about consistent daily habits than feature complexity CloudKit's eventual consistency model required creative workarounds Tech stack: SwiftUI, CloudKit, custom backend for question management, Core Data for offline support. The app has 8 additional game modes (word puzzles, math challenges, etc.) but the daily global challenge is the core hook. Would love feedback from the community! Happy to answer questions about any of the technical implementation. LINK - https://apps.apple.com/gb/app/qwiz-daily-quiz-trivia-fun/id6...

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Actual performance

1points
Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
93%93% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: model, apple, apps · Missing: mac, agents, macos
52%52% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps, users · Missing: mobile apps, ios, personal
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
26%26% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, pipe, io · Missing: https docs, excited, just released
17%17% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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