Peregrine — Feature Backlog

Unscheduled ideas and deferred features. Roughly grouped by area.

Settings / Data Management

Backup / Restore / Teleport — Settings panel option to export a full config snapshot (user.yaml + all gitignored configs) as a zip, restore from a snapshot, and "teleport" (export + import to a new machine or Docker volume). Useful for migrations, multi-machine setups, and safe wizard testing.
Complete Google Drive integration test() — scripts/integrations/google_drive.py test() currently only checks that the credentials file exists (TODO comment). Implement actual Google Drive API call using google-api-python-client to verify the token works.

Wire real LLM test in Step 5 (Inference) — app/wizard/step_inference.py validates an endpoint_confirmed boolean flag only. Replace with an actual LLM call: submit a minimal prompt to the configured endpoint, show pass/fail, and only set endpoint_confirmed: true on success. Should test whichever backend the user selected (Ollama, vLLM, Anthropic, etc.).

Iterative refinement feedback loop — Apply Workspace cover letter generator: show previous result + a "Feedback / changes requested" text area + "Regenerate" button. Pass previous_result and feedback through generate() in scripts/generate_cover_letter.py to the LLM prompt. Same pattern for resume bullet expansion in the wizard (wizard_generate: expand_bullets). Backend already supports previous_result/feedback in wizard_generate tasks (added to _run_wizard_generate).
Apply Workspace refinement UI ready to wire — Remaining work: add a "Feedback / changes requested" text area and "Regenerate" button in app/pages/4_Apply.py, pass both fields through submit_task → _run_wizard_generate. Backend is complete.

Browser autofill extension — Chrome/Firefox extension that reads job application forms and auto-fills from the user's profile + generated cover letter; syncs submitted applications back into the pipeline automatically. (Phase 2 paid+ feature per business plan.)

Concept — A human-in-the-loop concierge tier where a trained operator submits applications on the user's behalf, powered by AI-generated artifacts (cover letter, company research, survey responses). AI handles ~80% of the work; operator handles form submission, CAPTCHAs, and complex custom questions.
Pricing model — Per-application or bundle pricing rather than flat "X apps/month" — application complexity varies too much for flat pricing to be sustainable.
Operator interface — Thin admin UI (separate from user-facing app) that reads from the same staging.db: shows candidate profile, job listing, generated cover letter, company brief, and a "Mark submitted" button. New job status queued_for_operator to represent the handoff.
Key unlock — Browser autofill extension (above) becomes the operator's primary tool; pre-fills forms from profile + cover letter, operator reviews and submits.
Tier addition — Add "ultra" to TIERS in app/wizard/tiers.py; gate "managed_applications" feature. The existing tier system is designed to accommodate this cleanly.
Quality / trust — Each submission requires explicit per-job user approval before operator acts. Full audit trail (who submitted, when, what was sent). Clear ToS around representation.
Bootstrap strategy — Waitlist + small trusted operator team initially to validate workflow before scaling or automating further. Don't build operator tooling until the manual flow is proven.

Podman support — Update Makefile to auto-detect docker compose vs podman-compose (e.g. COMPOSE ?= $(shell command -v docker 2>/dev/null && echo "docker compose" || echo "podman-compose")). Note in README that rootless Podman requires CDI GPU device spec (nvidia.com/gpu=all) instead of runtime: nvidia in compose.yml.
FastAPI migration path — When concurrent-user scale demands it: port Streamlit pages to FastAPI + React/HTMX, keep scripts/ layer unchanged, replace daemon threads with Celery + Redis. The scripts/ separation already makes this clean.

See also: docs/plans/email-sync-testing-checklist.md for outstanding test coverage items.