Biblos – Semantic Search the Church Fathers

Hacker News

I'm pleased to present an update to Biblos, a semantic search tool designed for biblical research. This release incorporates instructor-large embeddings to enhance the precision of verse retrieval. Introducing some key features including historical church writings and commentaries alongside the main biblical corpus. Available for use at https://biblos.app/ Technical Insights: - Utilizes Chroma for vector search, now powered by instructor-large embeddings for improved semantic accuracy. - Features Anthropic's Claude LLM model to generate summaries that provide context and clarity for search results. - Developed with a Retrieval Augmented Generation (RAG) architecture, the app offers a streamlined user experience through a Streamlit Web UI, all orchestrated with Python.

Visit product View on Hacker NewsDec 7, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, model, user · Missing: mac, agents, macos

85%85% 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.

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

57%57% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

56%56% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io, including · Missing: https docs, excited, just released

55%55% 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

33%33% 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

8%8% 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.

Incorrect prediction on native model

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