Quoth – Semantic search for quotes using pgvector and OpenAI embeddings

Hacker News

I built Quoth ( https://quoth.app ) to solve a personal problem: finding quotes by meaning rather than exact words. You can search for "adventure" and discover quotes about exploration you've never seen before. The project uses Next.js 15 with App Router, TypeScript, Prisma 6, PostgreSQL with pgvector for semantic search, OpenAI embeddings for quote vectors, Dynamic OG image generation for sharing. It was built in ~3 weeks using AI-assisted development (Claude/Copilot). Features include semantic search across ~1000 curated quotes, community submissions with verification system, daily newsletter, social sharing with custom typography. This is a passion project with no business model. I'm particularly interested in feedback on the search relevance and any ideas for improving the curation process. Source isn't public yet but considering it. Happy to discuss the AI-assisted development workflow that made this feasible as a solo side project.

Visit product View on Hacker NewsJun 30, 2025

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

64%64% 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: claude, model, new · Missing: mac, agents, macos

60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

58%58% 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, 000, io · Missing: https docs, excited, just released

42%42% 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: personal · Missing: mobile apps, ios, entrepreneurs

37%37% 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

14%14% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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