93 lines
3.7 KiB
Markdown
93 lines
3.7 KiB
Markdown
# Vynl Feature Roadmap
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Ordered by impact + ease of implementation.
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## Tier 1 — Big Wins, Easy to Build
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### 1. "More Like This" Button
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- Button on every recommendation card
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- Uses that single song as seed for a new discovery
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- Just pre-fills the Discover query with "find songs similar to [artist] - [title]"
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- **Effort: 30 min** — frontend-only, no backend changes
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### 2. "Why Do I Like This?" Reverse Analysis
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- User pastes a song, AI explains what draws them to it
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- Then finds more songs with those same qualities
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- New mode on Discover page or standalone input
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- **Effort: 1 hour** — new prompt template, reuse existing UI
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### 3. Mood Scanner
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- Simple mood sliders: happy/sad, energetic/chill
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- Injects mood context into the recommendation prompt
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- "I'm feeling chill and melancholy — find me music for that"
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- **Effort: 1-2 hours** — UI sliders + prompt modification
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### 4. Share Discoveries
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- "Share" button generates a public link like deepcutsai.com/share/abc123
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- Shows recommendations without requiring login
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- Great for viral growth on social media
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- **Effort: 2-3 hours** — new endpoint + public page
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### 5. "Surprise Me" Mode
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- One button, no input needed
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- AI picks a random obscure angle from user's taste profile
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- "You seem to like songs in D minor with fingerpicked guitar — here's a rabbit hole"
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- **Effort: 1-2 hours** — new discovery mode, creative prompt
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## Tier 2 — Medium Effort, High Value
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### 6. Playlist Generator
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- Generate a full 20-30 song playlist around a theme
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- "Road trip playlist", "Sunday morning cooking", "90s nostalgia"
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- Save as a playlist in the app, export as text
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- **Effort: 3-4 hours** — new endpoint, playlist creation, UI
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### 7. Daily Digest Email
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- Automated daily/weekly email with 5 personalized recommendations
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- Brings users back without them having to open the app
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- Requires Resend integration (already planned)
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- **Effort: 4-5 hours** — Resend setup, email template, Celery scheduled task
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### 8. Artist Deep Dive
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- Click any artist name to get an AI card
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- Shows: why they matter, influences, best album to start with, similar artists
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- Modal or dedicated page
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- **Effort: 3-4 hours** — new endpoint + UI component
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### 9. Song Preview Embeds
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- Inline YouTube Music player in recommendation cards
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- Users can listen without leaving the app
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- YouTube iframe embed with the video ID
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- **Effort: 2-3 hours** — need to fetch YouTube video IDs (ytmusicapi), embed iframe
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### 10. Music Timeline
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- Visual timeline of when recommended/liked artists released music
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- Shows patterns in user's taste ("you love 2008-2012 indie")
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- Uses MusicBrainz release dates
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- **Effort: 4-5 hours** — data fetching + timeline visualization
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## Tier 3 — Bigger Builds, Major Differentiators
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### 11. Collaborative Discovery / Taste Compatibility
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- Two users compare taste profiles
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- Compatibility percentage score
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- Shared "you'd both love this" recommendations
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- **Effort: 6-8 hours** — friend system, profile comparison logic, shared rec generation
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### 12. Vinyl Crate Simulator
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- Gamified discovery: swipe through 50 random albums
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- Tinder-style UI: swipe right to save, left to pass
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- Rapid feedback builds taste profile faster
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- **Effort: 8-10 hours** — swipe UI, album data source, feedback loop
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### 13. Concert Finder
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- After recommending an artist, check if they're touring nearby
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- Link to tickets (Songkick API or Bandsintown API)
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- Potential affiliate revenue
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- **Effort: 6-8 hours** — external API integration, location input, UI
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### 14. "Surprise Me" Advanced — Rabbit Holes
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- Multi-step guided journey: "Start here → then try this → go deeper"
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- Each step builds on the last, taking the user on a curated path
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- **Effort: 8-10 hours** — multi-step UI, state management, chained prompts
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