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Gemini Document Processor – Generate Th Summaries from PDF/ePub with AI

Hacker News

Gemini Document Processor – Generate Th Summaries from PDF/ePub with AI

Hello HN! I'd like to share Gemini Document Processor, an open-source tool I've developed. This tool uses Google's Gemini AI (their latest API) to create high-quality Thai language summaries from PDF and EPUB files. Key features include: - Support for both PDF and EPUB files - Intelligent chunking for efficient Gemini API processing - Automatic image extraction from documents - Direct integration with Obsidian (export directly to vault) - Smart retry system when errors occur (switches models/increases timeouts) - Real-time progress tracking via web interface I built this tool because I needed to read many English documents and wanted detailed summaries in Thai. If you frequently read long documents or want to build a knowledge base from multiple sources, this tool could save you significant time. The output is a well-formatted Markdown file with images and metadata, ideal for storing in Obsidian, Notion, or other PKM systems. Try it by cloning the repo and running it with Python (requires a Google Gemini API key). Feedback, suggestions, and contributions are very welcome!

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Indie HackersFits the IH revenue-focused audience · Strong signals: gemini · Missing: supports, reddit linkedin, podcasting
95%95% 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: model, google, models · Missing: mac, agents, macos
85%85% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: interface, efficient · Missing: plus, platform, intuitive
48%48% 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, io · Missing: https docs, excited, just released
46%46% 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: google · Missing: mobile apps, ios, personal
29%29% 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
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: smart · 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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