MusicWander reads your Last.fm signals and leans underground to surface artists you actually want to hear next.
A flexible scoring layer uses your listening signals and feedback to shape the queue. Balances similarity with freshness, responds to likes and skips, and adapts as your taste shifts.
Pushes lesser-known artists forward so the queue feels fresh, not chart-driven. Surfaces overlooked artists and keeps mainstream density low.
See how your taste moves session to session. Captures mood, energy, and era. Shows how niche your queue is and updates with every session.
Analyze your listening history and discover artists that match your established taste patterns.
Begin with any song and branch out through similarity, tags, and related artists. Works without a profile.
Handpick a few tracks and map a precise mood. Create private mood cards with shareable links.
Recommendations shift with your listening habits. Late-night versus daytime mood shifts with weekly rhythm awareness.
Down-weights tracks you already know too well. Prefers less-heard tracks and avoids recent repeats.
Exclude moods you're not feeling and steer the queue in real time. Tag-based exclusions that persist per session.
Auto-refill and diversity controls keep things varied. Artist variety limits and remembers recent skips.
Your data never leaves your browser. No accounts, no tracking, no cloud storage. Local-only session storage with optional export.
Parallel requests and smart caching keep discovery quick and responsive. Background caching with lightweight interface.
Install on any device. Works offline with cached data. Mobile-first responsive design with keyboard shortcuts.