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[AIP] NoteTrace v1.0.0 released: First Stable Release, Wear OS App, Offline Everywhere
(lemmy.world)
NoteTrace is a self-hosted alternative to Google Keep, Evernote and Apple Notes: notes, checklists and reminders in a card grid, on your own server. AGPL-3.0, a single Docker container, a web app that works offline, a native Android app, and a Wear OS app. No telemetry, no cloud. This is the first stable release.
Part of the TraceApps family: NutriTrace (nutrition), CookTrace (recipes / pantry / shopping), LiftTrace (strength / lifting).
What it does
- Notes and checklists. Pins, colors, nested labels, archive, trash, version history, and full-text search that also finds words in voice transcripts and in photos.
- Reminders and Tasks. Repeating reminders that keep their local time, exact alarms on Android, and one view of everything that's due.
- Offline everywhere. The installed web app keeps editing with no connection and syncs later; Android and the watch work offline too.
- Wear OS. Tick off lists, read notes, see what's due, and speak a note ("call the plumber tomorrow at nine" sets the reminder), with a tile and a watch face complication.
- Voice notes and drawings. Record with the screen off and get a transcript you can tap to jump to.
- Bring your notes with you. Import from Google Keep, Evernote, Memos, Blinko and Markdown vaults; export everything as Markdown with images.
- Optional AI. Tidy up, summarise, or turn a note into a checklist, with the provider of your choice. Off unless you set it up.
Links
- Repo: https://github.com/TraceApps/notetrace
- Docs: https://traceapps.github.io/docs/notetrace/
- Release notes + signed APKs (phone and watch): https://github.com/TraceApps/notetrace/releases/tag/v1.0.0
- Docker:
ghcr.io/traceapps/notetrace:latest(mirror:traceapps/notetrace:latest)
AI Disclosure
Per Rule 7 / [AIP] disclosure requirements AI was used during development as a coding assistant. Level per category:
- Design (architecture, system design): Hint: I make the architectural calls; AI suggests trade-offs and edge cases I might have missed.
- Implementation (production code): Pair: roughly 50/50. AI drafts, I review, adjust, test on real hardware, and only commit what I've verified. Every commit is manually reviewed before it goes to my dev repo.
- Testing (writing tests, test plans, QA): Assisted: real-device testing is manual (I test on my own PC and mobile devices before every release). AI helps draft test plans and think through edge cases.
- Documentation (docs, comments, README, CHANGELOG): Pair: release notes and changelog entries are drafted with AI then edited for tone; comments and code docs are mostly Pair as well.
- Review (code review, PR feedback): Assisted: I'm the reviewer; AI helps with security sweeps, audit passes on complex changes, and consistency checks.
- Deployment (CI/CD config): Hint: Docker/GitHub Actions/release pipeline is largely conventional; AI-suggested improvements only.
Good eye, but I find that part of the rule to be rather odd. It's like saying you can't seed your own Blu-ray rip before seeding someone else's Blu-ray rip first, when it's actually the spreading of the movie itself that everyone wants. At least in my mind.
Is there a good rationale for this part of the rule?