Open-source, citations-first RAG search for Epstein Files

Hacker News

I built an open-source RAG app for searching the Epstein files (36,000+ documents). Every answer links to the exact source chunk. Click to verify. No black-box AI. Try it at https://epfiles.ai (10 free messages, then use your own xAI key) Stack: Next.js + FastAPI + ChromaDB + xAI (Grok). What's included: - Pre-built ChromaDB (auto-downloads on first run) - Document chunks (~190MB, separate download) - Scripts to regenerate embeddings with your own model - One-command Docker setup Limitations: - LLM can still hallucinate, citations let you verify fast - Fixed corpus (House Oversight release only) - Requires API keys: OpenAI (embeddings), xAI (generation) GitHub: https://github.com/benbaessler/epfiles

Visit product View on Hacker NewsDec 29, 2025

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Indie HackersIH features products with proven revenue · Strong signals: para · Missing: supports, reddit linkedin, podcasting

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Hacker NewsMay not resonate with HN audience · Strong signals: 000, io · Missing: https docs, excited, just released

43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

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Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

17%17% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

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