Verification actually run

• Backend: 96/96 tests passing (cd apps/backend && .venv/bin/pytest) — SM-2 scheduler edge cases, badge thresholds, full auth/group/word/room/review/ mnemonic/settings/admin flows, cross-user permission checks, cascade deletes. Also boot-tested with a real uvicorn process and curl, not just TestClient.
• Frontend: lints clean (eslint), type-checks and builds clean (tsc -b && vite build), 16/16 unit tests passing (vitest run).
• Ollama suggestions checked live against a real daemon running llama3.2, via uvicorn + curl rather than mocks. All three documented states were observed end to end: ok with generated text, disabled with no AI settings present, and unavailable with the provider pointed at a port nothing is listening on. See docs/local-ai-ollama.md.
• The Ollama walkthrough was followed literally from a clean shell — fresh virtualenv, pip install, .env, boot, first suggestion — and took well under a minute, comfortably inside the 10-minute target. Installing Ollama and running ollama pull llama3.2 are excluded from that figure: the model download is several GB and dominated entirely by your connection. The MnemoLab suggestion UI itself is covered by unit tests; it has not been click-tested in a browser.
• The web app's Docker Compose quick start has been click-tested end to end: register, complete onboarding, create a group, add words with translations and mnemonics, run a forced-recall review session, create a Mind Palace room, and place a word on its canvas. The screenshots in the root README were captured from that session, not staged.
• Three real bugs were caught and fixed by the test suite along the way: an SM-2 interval that could overflow on long correct streaks, a naive/aware datetime mismatch against SQLite, and a SQLAlchemy identity-map staleness bug where placements/attempts added mid-request didn't show up in the response.

Known gaps

• Alembic manages schema changes. Run cd apps/backend && alembic upgrade head before a direct local server start; the Docker backend runs this automatically.
• No refresh-token rotation — a single 7-day access token. Fine for an MVP, not for a production launch.
• Blog/About marketing pages from the templates aren't built — the landing page is real; a full blog would need a content backend, which felt out of scope for the app itself.
• MnemoLab image generation is intentionally not implemented — that needs real credentials and an infrastructure decision for you to make, not something to fake. AI mnemonic suggestions are implemented and opt-in via Ollama.
• Scheduled notification delivery is implemented: a durable job dispatches due reminders, claims each occurrence so two instances cannot deliver it twice, and writes a desktop notification the shell polls for. What is still missing is transport — push and email have no provider and only write to the log, and no desktop toast has been observed on a real machine. So a reminder fires and is recorded; whether anyone sees it depends on where they are running the app.
• Ollama suggestions have been verified with the backend run directly on the host. Reaching a host-installed Ollama daemon from inside the Docker containers has not been tested, and http://localhost:11434 will not resolve to the host from within a container — see docs/local-ai-ollama.md.

See docs/internal/repo-audit.md and docs/internal/evidence-gaps.md for a broader, per-surface evidence review beyond this page's backend/frontend/AI scope.