138 lines
4.5 KiB
Markdown
138 lines
4.5 KiB
Markdown
# Recipe Manager — Project Vision
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**Project Name:** Recipe Manager
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**Owner:** Paul Huliganga
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**Created:** 2026-03-23
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**Status:** Planning → MVP Development
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---
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## Vision
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Build a **self-hosted, privacy-first recipe management app** that Anne and Elizabeth can use to organize, import, and cook from their recipes—replacing CopyMeThat with full data ownership and extensibility.
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### Why Build This?
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1. **Data sovereignty** — We control our data, not a SaaS provider
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2. **Agentic engineering practice** — Perfect scope for autonomous agent-driven development
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3. **Extensibility** — Future AI features (meal planning, cost tracking via Fintrove integration)
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4. **Learning** — Hands-on experience with modern web stack + agent collaboration
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---
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## Success Criteria
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### MVP (v0.1) — "Anne and Elizabeth can use it"
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- ✅ Manual recipe entry (title, ingredients, instructions, source URL, notes)
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- ✅ Recipe viewing/editing/deletion
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- ✅ Basic organization (tags/collections)
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- ✅ Search (text-based)
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- ✅ Cook mode (ingredient checkboxes, step tracking, wake lock)
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- ✅ Self-hosted deployment (Docker Compose on paje.ca)
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### v1.0 — "Feature parity with CopyMeThat"
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- Recipe scraping (browser extension or bookmarklet)
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- Multi-device sync
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- Print styles
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- Scaling (adjust serving sizes)
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- Random recipe suggestion
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### v2.0 — "AI-enhanced"
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- Ingredient substitution suggestions
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- Meal planning
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- Shopping list generation
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- Integration with Fintrove (recipe cost tracking)
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---
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## Constraints
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### Technical
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- **Stack:** Node.js + TypeScript + React + SQLite
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- **Deployment:** Docker Compose, self-hosted on paje.ca
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- **Storage:** Local-first (SQLite), cloud sync optional in v1
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- **Browser support:** Modern evergreen browsers (Chrome, Firefox, Safari, Edge)
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### Development
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- **Agent-driven:** Sub-agents build, test, document autonomously
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- **Version control:** Git with conventional commits
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- **Testing:** Unit tests for core logic, E2E tests for critical flows
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- **Documentation:** Auto-generated architecture decision records (ADRs)
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### Timeline
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- **MVP:** 48-72 hours of agent work (overnight runs)
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- **v1.0:** 1-2 weeks (incremental agent iterations)
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- **v2.0:** TBD (depends on AI integration complexity)
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---
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## Non-Goals (for MVP)
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- ❌ Mobile apps (web-first, PWA later)
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- ❌ User authentication (single household deployment)
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- ❌ Recipe sharing/community features
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- ❌ Video support
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- ❌ Nutrition tracking (maybe v2)
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---
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## Key Decisions
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### Why SQLite?
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- Local-first, zero-config, portable
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- Perfect for single-household deployment
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- Can sync via file replication (Syncthing, Dropbox) if needed
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### Why React?
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- Agent familiarity (Codex knows React cold)
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- Rich ecosystem for forms, UI components
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- Can evolve to Next.js if SSR needed
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### Why Docker Compose?
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- Consistent deployment across machines
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- Easy backup/restore (just copy SQLite file)
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- Can add services incrementally (reverse proxy, backup cron, etc.)
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---
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## Agent Instructions Reference
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See `AGENT_INSTRUCTIONS.md` for how agents should approach development, testing, and documentation.
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---
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## Decisions Made (2026-03-23)
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1. **Domain/URL:** `recipes.paje.ca` ✓
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2. **Initial users:** Whole family (Anne, Elizabeth, Paul, Joseph) ✓
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3. **Data migration:** Export from CopyMeThat if possible (free tier may be limited now)
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4. **First priority after MVP:** Recipe scraping extension (v1.0 milestone)
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---
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## AI Features Vision (v2.0+)
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These are the "smart" features planned for future releases:
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### Cooking Intelligence
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- **Ingredient substitutions** — "Out of buttermilk? Use milk + vinegar"
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- **Meal planning** — Generate weekly menus from your recipe collection
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- **Shopping lists** — Aggregate ingredients, de-duplicate, organize by grocery aisle
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- **Recipe recommendations** — "You haven't made this in 3 months, try it tonight!"
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### Data & Integration
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- **Nutrition estimates** — Calorie/macro breakdown via USDA database
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- **Fintrove integration** — Track recipe costs, generate budget-aware meal plans
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- **Recipe costing** — "This dinner costs $4.50/serving"
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### Advanced Input
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- **Voice input** — Add ingredients/steps via speech-to-text
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- **OCR import** — Scan physical recipe cards or cookbooks
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- **Smart parsing** — AI-enhanced recipe extraction from non-standard sites
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**Why later?** Need MVP data (recipes + usage patterns) to tune AI features effectively. Scraping comes first so Anne & Elizabeth can import their existing collections.
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---
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_This is a living document. Agents should update it as the project evolves._
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