Saber is a production-grade, cross-platform Flutter application for handwritten note-taking. It is a monolith app with a few standalone packages: a custom vector-ink canvas engine (pen, pencil, highlighter, shape pen, laser pointer), page-based editor, image/PDF/SVG insertion, Quill text overlays, background patterns, shape recognition, and undo/redo. The main entry point is lib/main.dart, which wires routing, i18n, Sentry consent, sharing intents, and background sync. Notes are stored as versioned binary .sbn2 files via the self-contained packages/sbn library, with legacy .sbn support and migration logic. The application persists notes through a file manager responsible for CRUD, import/export, recently-used tracking, and directory watching. Nextcloud sync is implemented over WebDAV with client-side end-to-end encryption: file paths are hex-encrypted, content is encrypted, and conflicts are resolved using last-modified timestamps and a best-file strategy. Settings are managed through typed stow preferences and a large settings surface covering theme, writing tools, editor behavior, performance, and error reporting. Platform runners exist for Android, iOS, macOS, Windows, and Linux, with distribution metadata for Play Store, F-Droid, Flathub, AppImage, Snap, and Windows installers. The codebase is well-tested with unit, widget, golden, and format-compatibility tests, plus CI workflows for all major platforms. It is not a data/research repository; there are no notebooks, ML pipelines, or analytical datasets. Technical drift is moderate: the interactive canvas is a maintained fork of Flutter's InteractiveViewer, some dependencies are pinned or patched for F-Droid builds, and generated i18n files plus legacy format migration code require ongoing upkeep. Overall, this is a mature, actively maintained application with several cleanly separated components that can be reused in other Flutter projects.
Handwrite is a CLI application that converts a scanned handwriting sample into a TrueType font. The pipeline consists of three stages: SHEETtoPNG uses OpenCV to detect and extract individual character images from a grid-based form; PNGtoSVG uses Potrace to convert the PNG images to SVG vector outlines; and SVGtoTTF uses FontForge to compile the SVGs into a font file with configurable metrics, kerning, and bearings. The architecture is a simple sequential pipeline orchestrated by the CLI entry point. External dependencies include OpenCV, Pillow, Potrace, and FontForge. The codebase is well-structured with unit tests, CI, and modern Python packaging (uv, Python 3.14). The character detection module in `sheettopng.py` demonstrates reusable grid-based contour extraction logic.