leaf – one month later: website, releases and lots of improvements

Hacker News

Hi HN, About a month ago, I shared leaf here while it was still in its early stages. Since then, the project has shipped multiple releases, with UX improvements, bug fixes, and a documentation website now available. leaf is a terminal-based Markdown reader focused on a GUI-like experience, with navigation, search, table of contents, clickable links, syntax highlighting, editor integration, LaTeX rendering, Mermaid diagrams, and more. It works on Linux, macOS, Windows, and Termux. GitHub: https://github.com/RivoLink/leaf Thanks to all contributors and everyone who starred the project for their support, and feedback on UX, performance with large files, and missing features is still very welcome.

Visit product View on Hacker NewsMay 30, 2026

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Launch Intel predictions

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

76%76% 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: io · Missing: https docs, excited, just released

59%59% 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: latex · Missing: supports, reddit linkedin, podcasting

59%59% 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

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit

13%13% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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