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An open source PDF Reader with annotation, notes, SRS, Linking support

Hacker News

An open source PDF Reader with annotation, notes, SRS, Linking support

A calm, local-first desktop PDF reader designed to make sure you remember what you read. You can link notes across files and group notes/annotations into a larger Markdown note, which can be linked with others. The Releases are only for Windows, but you can use the code to build for any system Tauri Supports (Linux- Debian or macOS). It's fast and small in size and has first-class support for annotations and note-taking. You can check out the code here https://github.com/roshanmishra86/Mereth-Reader Looking forward to your feedback.

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, notes · Missing: agents, agent, cursor
73%73% 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 NewsStrong engagement from HN community · Strong signals: open source, io · Missing: https docs, excited, just released
62%62% 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: supports · Missing: reddit linkedin, podcasting, created
58%58% 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
32%32% 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
28%28% 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
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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