The Auto-DJ Case Files: Building a Self-Learning Music Brain | Coderz Club

The Auto-DJ Case Files: Building a Self-Learning Music Brain From Your YouTube Habit

A 3–5 month field guide for turning a browsing habit into an AI orchestra Prologue: The Case That Started With a Skip Button Every great investigation starts with something small and irritating. Yours starts on a Tuesday night. You're three hours deep into a YouTube rabbit hole — lo-fi beats, a Vietnamese city-pop deep cut, a live session you didn't mean to watch twice — and you think, someone should be paying attention to this. That someone is about to be you. Not as a listener. As a detective. Here's the case file: somewhere in your YouTube history is a pattern. A shape. You skip certain songs after four seconds. You replay others three times in a row without noticing. You always end up on synthwave at 11pm and never before 6pm. Right now, that pattern is invisible — smeared across a history page that YouTube barely lets you search, let alone reason about. Your job over the next few months is to build the instrument that makes it visible: a system that watches what you actually do, figures out what it means, and hands you back playlists that feel like they were made by someone who knows you. Because they were. That someone is a machine you built, trained on a dataset of one: you. This won't be a weekend project. It's closer to building a small orchestra — a browser extension as your field agent, a backend as your evidence locker, a couple of ML models as your forensic lab, and a frontend as the stage where the final performance happens. Every piece depends on every other piece. That's what makes it hard, and it's also what makes it worth doing slowly, in public, with your own hands on every layer. Let's start where every good documentary starts: by following one song from the crime scene to the credits. Chapter 1: The Case of the Repeating Refrain Following one song's journey It's 9:47pm. You click a YouTube video: "Kiss of Life – Midas Touch (Live Performance)." You watch 41 seconds, skip to the chorus, watch another 90 seconds, then close the tab. Here's what needs to happen for that unremarkable moment to eventually shape a playlist called "Monday Chill": The Witness (Browser Extension). A content script sitting quietly inside the YouTube tab notices the video element load. It reads the video ID, title, channel name, and duration from the page. It watches the <video> element's timeupdate, pause, seeked, and ended events like a stenographer taking notes: play at 0:00, seek to 1:32, pause at 3:04, total watch time 41s + 90s = 131s out of 210s (62%). The Tip-Off (Event API). Every so often — batched, not on every tick, because nobody wants to be that extension hammering a server — the extension bundles these observations into a small JSON payload and POSTs it to your backend: {video_id, title, channel, watch_segments, timestamp, session_id}. The Evidence Locker (Database). Your FastAPI backend receives the tip-off, validates it, and writes a raw event row into PostgreSQL. Nothing clever happens yet — this is just chain-of-custody. You never want to lose the raw signal, because every clever thing you do later will be derived from it, and derived things need to be recomputable. The Lab Work (ML Pipelines, offline). On a schedule — say, once a night — a pipeline wakes up, looks for new raw events, and does the actual detective work: Is this even a music video? (classification) What song is this, really? (metadata resolution via title parsing + external lookups) What does it sound/feel like? (audio embedding + mood tagging) How does this fit with everything else you've watched? (behavioral signal: implicit rating from watch %, skip pattern, replay count, time of day) The Case Board (Vector store + relational tables). The song's audio embedding gets written to a vector column (via pgvector), its metadata to a normal relational table, and its behavioral score gets updated incrementally — like a detective updating a suspect's file every time new evidence comes in. The Reveal (Playlist Generator). Periodically, a clustering job looks at the whole case board — all your songs, their embeddings, their mood tags, their time-of-day patterns — and groups them. A cluster of moody, low-energy, evening-heavy tracks with "chill," "lofi," or "acoustic" tags gets surfaced and, because most of its listens happened on Mondays, gets named "Monday Chill." The Broadcast (Frontend + Player). Your React app queries the backend, gets back a list of playlists with their tracks, and renders them. When you hit play, it doesn't awkwardly embed a YouTube iframe — it either streams audio you've legitimately extracted for personal use, or falls back to a controlled YouTube player, and it logs that playback too, feeding the loop again. That's the whole organism, end to end. Six weeks from now this will feel obvious. Right now, notice the shape: observe → transmit → store → understand → organize → present, with a feedback loop closing the circle back to ste