KoboHighlights – Extract and Display Highlights from Kobo Database

Hacker News

Hey everyone! I created KoboHighlights to easily extract and display highlights from my KoboReader.sqlite file. Although I'm not a professional developer, I realized that others might find it useful too, so I decided to share it with the community. What It Does: - Extracts highlights from KoboReader.sqlite file - Displays highlights in a user-friendly interface - Allows sending highlights to Notion (for a single book or for all) - Supports saving highlights to Local Storage for offline access - Supports downloading as TXT, MD and HTML - Multilingual (supports English and Turkish for now) I built this project with my limited coding skills, so if you have any feature requests or notice any issues, please let me know. I'll do my best to improve the project based on your feedback. Source: https://github.com/TaylanTatli/KoboHighlights

Visit product View on Hacker NewsOct 13, 2024

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1points

Did not reach leaderboard

Launch Intel predictions

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

81%81% 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.

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

69%69% 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 · Missing: https docs, excited, just released

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

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Strong signals: friendly, interface · Missing: plus, platform, intuitive

55%55% 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

38%38% 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

15%15% 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.

Incorrect prediction on native model

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