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I built a book discovery engine using AI-powered semantic search

Hacker News

I built a book discovery engine using AI-powered semantic search

Hi all, I built a book discovery engine that lets you search by mood, theme, or aesthetic instead of relying on keywords or bestseller lists. Try it at www.zilu.app. The interesting technical aspects: - Uses multi-modal AI (book cover + synopsis embeddings) for search. - Works without predefined genres—users describe a feeling, and AI finds relevant books. - Unlike traditional keyword search, it captures abstract, theme-based queries (e.g., "a surreal, dreamlike novel with a quiet sense of dread"). - Current implementation is cover-based, with text understanding in progress. Right now, it’s an experiment in redefining content discovery beyond books—potentially applicable to movies, art, and other media. Would love to hear thoughts, critiques, and ideas! You can try it out at www.zilu.app.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
77%77% 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.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: user, using · Missing: mac, agents, macos
47%47% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
38%38% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal
32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
19%19% 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.

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