Skip to main content

Mixbase – Proto Product Vision

Summary​

Mixbase began as a simple bookmarking tool for DJ mixes across platforms—a way to save your favorite sets from YouTube, SoundCloud, Spotify, and more.

But music listening evolves—and so does Mixbase. It’s becoming something bigger:

  • A personal & social graph of music moments
  • A tool for discovering sonic connections (“I know that sound!”)
  • A system for mapping cultural lineage of tracks, samples, and quotes
  • A memory anchor for life moments tied to music

Think of it as IMDb for DJ sets—but also a diary of your music-driven life.

Why It Matters (Emotional Hook)​

1. Discovery Is Joy​

That feeling: “Omg Rob & Chris… they’re amazing… wait, Rob is Rob Mayth—my favorite artist from back in 2011!” or “This sound… I know this… that’s from the video game with the blue car and racing stripes… oh wow, it’s Ridge Racer 4!”

Mixbase exists to capture those discoveries and make them permanent:

  • The timestamp that hit you
  • The sample origin you hunted down
  • The memory you tied to that sound

2. The Hunt Creates Fans​

DJs don’t annotate every set—and they shouldn’t. The process of figuring it out yourself—the detective work—is what bonds you to the music. Mixbase embraces that fan process: it gives you a space to log, connect, and share your own discoveries.

3. Your Contributions Matter​

Every timestamp, every quote ID, every sample you confirm:

  • Makes the cultural map richer for everyone
  • Builds your reputation as a music archaeologist
  • Shows your personal journey as a listener-turned-contributor

Core Features​

1. Cross-Platform Bookmarks​

  • Save links to mixes from any platform (YouTube, SoundCloud, Mixcloud, Spotify).
  • Jump to the exact timestamp that matters.
  • Never lose track of a favorite moment—even if the mix moves or disappears.

2. Timestamp Annotations​

  • Add notes at precise moments:

    • Track drop IDs (even if unofficial)
    • Sample sources (“this is from California (1993)”)
    • Emotional tags (“perfect for late-night work”)

  • Include context:

    • Public layer: shared facts, useful to the community
    • Private layer: your personal memory (“This was playing when I met <friend>”).

3. Discovery Through Connection​

  • Turn “flashes of recognition” into graph links:

    • “This vocal is in 4 other sets.”
    • “This break is from a 90s trance classic.”

  • Explore sonic relationships:

    • Public graph links to tracks, remixes, and sample origins
    • Private notes about when and where you noticed them.

4. Confirmation Layer & Link Strength​

  • Public annotations are confirmable by others.

  • Each confirmation strengthens edge weight:

    • Single claim → light connection
    • Multiple confirmations → heavy, trusted connection

  • Confidence levels:

    • Unconfirmed: only one user said it
    • Community confirmed: multiple confirmations
    • Official: future option for artist/platform verification

  • Result: a trust-weighted cultural map of music relationships.

5. Sonic Types (Beyond Genre)​

  • Traditional genres = too rigid (“Trance, Hardstyle, Happy Hardcore”).

  • Mixbase introduces Sonic Types:

    • Treble-heavy high-BPM leads (Handz Up core)
    • Cathedral-style psy builds
    • Opera-trance vocals (Lux Tua vibe)

  • Users can coin new micro-genres (“Watery”)—and LLMs suggest patterns based on track features.

  • Build a taste fingerprint beyond traditional labels.

6. Track & Sample Graph​

  • Graph nodes & edges capture music DNA:

    • Nodes: Mix, Track, Sample, Quote, Annotation, User, External Media, Alias Name, Sonic Tag

    • Edges:

      • (Mix) CONTAINS (Track)
      • (Moment) LINKS_TO (Sample)
      • (Sample) ORIGINATES_FROM (Media Source)
      • (Track) HAS_ALIAS (Alias)
      • (Moment) TAGGED_AS (Sonic Type)
      • (User) CONTRIBUTED (Moment|Alias|Tag)

  • Edge weights grow with confirmations (trust score).

  • Enables deep queries like:

    • “Show all confirmed samples from California (1993) used in Astral Projection remixes I’ve bookmarked.”
    • “Show every ‘Handz Up core’ tag across psytrance mixes.”

7. Fan Aliases & Misheard Lyrics​

  • Capture inside jokes and fan-culture naming:

    • “Nordfold – Pathways → The Bear” (misheard lyric)

  • Fans vote on whether they hear it too.

  • Alias adoption tracked over time—some even become dominant names.

  • At certain timestamps, link out to:

    • Producer breakdown videos (“How I made this lead sound”)
    • Cultural context (“This quote originates from…”)

  • Mixbase becomes a portal for learning & context, not just bookmarking.

9. Life Moments Mode​

  • Memory anchoring:

    • “This was playing the night I met <friend>.”

  • Private by default; toggle public if you choose.

  • Build a timeline of your life soundtracked by music.

Why a Graph?​

Mixbase isn’t just a playlist—it’s a knowledge graph:

  • Connections: samples, quotes, remixes, influences
  • Context: who played what, where, and when
  • Weight: how strongly each link is confirmed
  • Personal layer: how you felt or where you were when you heard it

Tech Approach​

  • Relational Layer (Postgres + SQLC): CRUD for mixes, moments, users.
  • Graph Layer (Neo4j or Postgres recursive CTE): exploratory queries & relationship maps.
  • LLM Integration: auto-suggest tags & alias detection.
  • Frontend: graph exploration (Reagraph?).

Fan Journey (Example)​

  1. You save a set: “Hernán Cattáneo – Resident 728”.

  2. You bookmark a timestamp: 25:17 → unknown vocal sample.

  3. You chase it down, find it’s from a 1993 movie.

  4. Another user confirms → connection weight grows.

  5. You notice the same vocal sample in a 2017 psytrance set → cross-link moments.

  6. Your profile now shows:

    • Timestamp contributions
    • Sample discoveries
    • New genre tag: “Opera Trance”.

End Result​

Your music fandom becomes visible and impactful—a map of your taste, discoveries, and contributions to music culture.

Next Steps​

  • Wireframe UI for:

    • Dual-layer annotations (public + private)
    • Confirmation layer & edge strength visualization

  • Graph schema prototype with weighted edges

  • Beta launch targeting heavy-listener power users