Terminal Reader
md file.md delivers clean, consistent output with table handling and pager-friendly rendering.
MD Star
MD Star
MD Star is a document-native toolchain for engineers. One Rust parser core drives both the terminal reader and the desktop editor — markdown behavior stays consistent everywhere.
A compact, high-signal surface for writing and reviewing technical docs at speed.
md file.md delivers clean, consistent output with table handling and pager-friendly rendering.
Autosave, outline navigation, file association, and instant visual confirmation in a native GPUI window.
One semantic Rust pipeline powers both interfaces — edge cases are fixed once, consistently everywhere.
Diagram blocks render across both interfaces without custom user transforms or extra tooling.
Move through linked documents quickly with document-centric traversal patterns and backlink awareness.
Roadmap includes YAML, JSON, and XML readers with the same ergonomics as the Markdown pipeline.
A single semantic parser with dedicated render layers for each interface.
mdstar-corerender-terminalrender-htmlmdmdstar-appThe parser lives in mdstar-core and emits a typed AST. Neither render layer re-parses — they consume the same tree, so edge cases are fixed once.
mdstar-render-terminal walks the AST and emits ANSI escape sequences. Tables, code fences, and Mermaid labels are all handled at this layer.
mdstar-render-html feeds the GPUI-powered app. Outline navigation, autosave, and live preview all derive from the same parsed document model.
Clone, build, and choose terminal or desktop mode.
$ git clone \ https://github.com/danieldear/mdstar $ cd mdstar
$ cargo build \ --release \ -p mdstar-app
# terminal reader $ ./target/release/md README.md # desktop app $ cargo run -p mdstar-app \ -- --app
Near-term priorities to harden the stack for broader contribution.
Add YAML, JSON, and XML readers with quick section jumping and the same ergonomics as the Markdown pipeline.
Improve backlinks, traversal speed, and multi-file movement across linked document graphs.
Stabilize crate naming, packaging, and release automation for a smooth contributor onboarding flow.