IntelliQ – AI-powered quizzes with multiplayer and doc uploads

Hacker News

Hey HN I’m 1/3 of the team behind IntelliQ – an AI-powered quiz platform designed for curious minds. You can: - Create your own quizzes or generate them with AI - Play solo or with friends in real-time multiplayer - Upload PDFs to turn them into quizzes - Bookmark and organize quiz collections - Try randomized surprise quizzes We built it using: Supabase (auth, db, vector search), OpenAI, Hono, Cloudflare, Next.js, Shadcn/ui, and Upstash Redis. We’ve unlocked full access for HN here: https://app.intelliq.dev/hn ( signup required to track quiz progress ) We’d love your thoughts on: - How smooth multiplayer feels - The doc-to-quiz flow - What features or quiz types we should build next Thanks for checking it out

Visit product View on Hacker NewsApr 22, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: openai, using, open · Missing: mac, agents, macos

83%83% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

33%33% 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

17%17% 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

5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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