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AIMovieQuotes – Find movie quotes by theme using semantic search

Hacker News

AIMovieQuotes – Find movie quotes by theme using semantic search

I built AIMovieQuotes ( https://www.aimoviequotes.com/ ). It’s a tool to find meaningful quotes from movies by searching with a movie title and a theme (like “love,” “betrayal,” or “courage”). The problem it solves: I often wanted to find a specific quote from a movie that captured a certain feeling, but couldn’t remember the exact words. Traditional keyword searches on existing quote databases are too literal. If you search for “love quote from Inception,” you might get nothing, even if the movie has lines deeply related to the concept of love. How it works (the technical side): Instead of relying on keyword matching, the tool uses an embedding model to convert movie dialogues and the user’s query into high-dimensional vectors. It then performs a semantic similarity search to find the most conceptually relevant lines, even if they don’t contain the exact keyword. The front end is a simple static site built with Next.js, and the search is handled by a serverless function calling a vector database. Why I built it: This was a weekend project to experiment with applying semantic search to a concrete, everyday problem outside of the typical enterprise or coding contexts (like documentation search). It demonstrates how AI can understand the meaning behind a request rather than just matching words. I’d be interested in your feedback, especially on: The accuracy of the quote matches. Ideas for other non-traditional datasets where semantic search could be useful. The overall user experience. Try it out here: https://www.aimoviequotes.com

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
74%74% 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.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
66%66% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: model, user, context · Missing: mac, agents, macos
57%57% 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: exist, existing, ide · Missing: https docs, excited, just released
47%47% 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 · Missing: mobile apps, ios, personal
22%22% 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
11%11% 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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