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Rulibre – A lightweight TUI replacement for Calibre

Hacker News

Rulibre – A lightweight TUI replacement for Calibre

I got tired of Calibre's heavy interface when all I needed was browsing my library and sending books to my e-reader. So I built Rulibre, a terminal UI that works directly with your existing Calibre library structure, no migration needed. Features: - Browse books sorted by author/title with a detail panel (metadata parsed from Calibre's .opf files) - Instant search/filter across author, title, and format - Send books to Kindle/Kobo with automatic EPUB→KEPUB conversion for Kobo - Convert between formats using kepubify or Calibre's ebook-convert - Mouse and keyboard support It's written in Rust with ratatui. Install with `cargo install rulibre`.

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: using · Missing: mac, agents, macos
62%62% 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 · Missing: supports, reddit linkedin, podcasting
58%58% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, io · Missing: https docs, excited, just released
51%51% 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 · Missing: mobile apps, ios, personal
47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: interface · Missing: plus, platform, intuitive
41%41% predicted probability of success on AppSumo, 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
4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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