DocGoblin – A Super Fast Desktop Search Engine for Your PDFs

Hacker News

Hello everyone, I'm happy to show you my project and would love to get some feedback on it! DocGoblin is a desktop search engine designed to handle large collections of PDFs. It indexes your documents and allows you to perform fast and accurate searches for specific terms, phrases, or sections across your entire library in less than a second. For more details on the technologies : * JavaFX for UI * Lucene for indexing and search * PDFium for PDF rendering * Runs on Windows, Mac and Linux * Packaging via jpackage / jlink (distributed as native installers) I also created an open-source example for packaging a java/javaFX app on github : https://github.com/JulienTroudet/easyJavaFXSetup DocGoblin is my first "commercial" and independant project :) I'm really proud of it!

Visit product View on Hacker NewsJul 25, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, open · Missing: agents, macos, agent

74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

72%72% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

63%63% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

59%59% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

32%32% 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 · Missing: web3, chat, crypto

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

Incorrect prediction on native model

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