# Vynl Feature Roadmap

Ordered by impact + ease of implementation.

## Tier 1 — Big Wins, Easy to Build

### 1. "More Like This" Button
- Button on every recommendation card
- Uses that single song as seed for a new discovery
- Just pre-fills the Discover query with "find songs similar to [artist] - [title]"
- **Effort: 30 min** — frontend-only, no backend changes

### 2. "Why Do I Like This?" Reverse Analysis
- User pastes a song, AI explains what draws them to it
- Then finds more songs with those same qualities
- New mode on Discover page or standalone input
- **Effort: 1 hour** — new prompt template, reuse existing UI

### 3. Mood Scanner
- Simple mood sliders: happy/sad, energetic/chill
- Injects mood context into the recommendation prompt
- "I'm feeling chill and melancholy — find me music for that"
- **Effort: 1-2 hours** — UI sliders + prompt modification

### 4. Share Discoveries
- "Share" button generates a public link like deepcutsai.com/share/abc123
- Shows recommendations without requiring login
- Great for viral growth on social media
- **Effort: 2-3 hours** — new endpoint + public page

### 5. "Surprise Me" Mode
- One button, no input needed
- AI picks a random obscure angle from user's taste profile
- "You seem to like songs in D minor with fingerpicked guitar — here's a rabbit hole"
- **Effort: 1-2 hours** — new discovery mode, creative prompt

## Tier 2 — Medium Effort, High Value

### 6. Playlist Generator
- Generate a full 20-30 song playlist around a theme
- "Road trip playlist", "Sunday morning cooking", "90s nostalgia"
- Save as a playlist in the app, export as text
- **Effort: 3-4 hours** — new endpoint, playlist creation, UI

### 7. Daily Digest Email
- Automated daily/weekly email with 5 personalized recommendations
- Brings users back without them having to open the app
- Requires Resend integration (already planned)
- **Effort: 4-5 hours** — Resend setup, email template, Celery scheduled task

### 8. Artist Deep Dive
- Click any artist name to get an AI card
- Shows: why they matter, influences, best album to start with, similar artists
- Modal or dedicated page
- **Effort: 3-4 hours** — new endpoint + UI component

### 9. Song Preview Embeds
- Inline YouTube Music player in recommendation cards
- Users can listen without leaving the app
- YouTube iframe embed with the video ID
- **Effort: 2-3 hours** — need to fetch YouTube video IDs (ytmusicapi), embed iframe

### 10. Music Timeline
- Visual timeline of when recommended/liked artists released music
- Shows patterns in user's taste ("you love 2008-2012 indie")
- Uses MusicBrainz release dates
- **Effort: 4-5 hours** — data fetching + timeline visualization

## Tier 3 — Bigger Builds, Major Differentiators

### 11. Collaborative Discovery / Taste Compatibility
- Two users compare taste profiles
- Compatibility percentage score
- Shared "you'd both love this" recommendations
- **Effort: 6-8 hours** — friend system, profile comparison logic, shared rec generation

### 12. Vinyl Crate Simulator
- Gamified discovery: swipe through 50 random albums
- Tinder-style UI: swipe right to save, left to pass
- Rapid feedback builds taste profile faster
- **Effort: 8-10 hours** — swipe UI, album data source, feedback loop

### 13. Concert Finder
- After recommending an artist, check if they're touring nearby
- Link to tickets (Songkick API or Bandsintown API)
- Potential affiliate revenue
- **Effort: 6-8 hours** — external API integration, location input, UI

### 14. "Surprise Me" Advanced — Rabbit Holes
- Multi-step guided journey: "Start here → then try this → go deeper"
- Each step builds on the last, taking the user on a curated path
- **Effort: 8-10 hours** — multi-step UI, state management, chained prompts