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Biblos – Semantic Bible Embedded Vector Search and Claude LLM

Hacker News

Biblos – Semantic Bible Embedded Vector Search and Claude LLM

Introducing Biblos, a simple tool for semantic search and summarization of Bible passages. Leveraging Chroma for vector search with BAAI BGE embeddings, semantically find related verses across the Bible. The tool employs Anthropic's Claude LLM model for generating high-quality summaries of retrieved passages, contextualizing your search topic. Built on a Retrieval Augmented Generation (RAG) architecture, the app implements a simple Streamlit Web UI using Python. Deployed using render.com, the app is available at https://biblos.app Note: Search by just topic/keywords, e.g. "Kingdom of Heaven", for broader results!

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Actual performance

136points
84comments
Made the leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, model, context · 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.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
61%61% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
40%40% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
36%36% 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
13%13% 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
4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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