I built a free tool to clean up and export your Kindle highlights

Hacker News

Hey HN, Last weekend I built a small web app called ClipVault to solve a personal itch — cleaning up my Kindle highlights from the My Clippings.txt file. Nothing fancy, straight to the point. The app runs locally in your browser (nothing gets uploaded), and lets you: - Import your Kindle’s My Clippings.txt via drag & drop - Easily remove duplicates, headers, and unwanted clutter - View your highlights sorted by book - Export them to Obsidian (Markdown), Evernote (.enex), or CSV It's completely free, private, and requires no login. Just open the page and get to work. I’m not trying to monetize this or anything — I just figured others might find it useful too. If you use Obsidian, Evernote, or just like keeping your reading notes tidy, I’d love to know what you think. Please let me know if you I missed anything or you encounter issues while trying it. Cheers

Visit product View on Hacker NewsJul 28, 2025

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

79%79% 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 NewsMay not resonate with HN audience · Missing: https docs, excited, just released

40%40% 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

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

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

29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

22%22% 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

20%20% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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