refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조
에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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---
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id: wiki-2026-0508-the-evolution-of-music-distribut
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title: The Evolution of Music Distribution
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [music distribution history, vinyl to streaming, AI music]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [music, distribution, streaming, ai-generated, history]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: n-a
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framework: industry
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---
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# The Evolution of Music Distribution
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## 매 한 줄
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> **"매 130-year arc — 매 vinyl → cassette → CD → MP3 → streaming → AI-curated → AI-generated"**. 2026 매 Suno/Udio AI track의 Spotify chart entry, 매 generative-music subscriptions, 매 artist + AI co-creation 매 default. 매 distribution 매 longer "moving atoms" 매 ranking inferences.
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## 매 핵심
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### 매 Era timeline
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- **1900-1948** Wax cylinder, 78rpm shellac.
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- **1948-1980s** Vinyl LP / 45rpm, cassette (1963).
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- **1982-2000s** CD — digital but physical.
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- **1999-2008** Napster → iTunes Store. Unbundling album → single.
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- **2008-2020s** Streaming (Spotify 2008, Apple Music 2015). Per-stream royalty economy.
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- **2022-2024** TikTok-driven discovery. Snippets > full tracks.
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- **2024-2026** AI-generated (Suno v4, Udio, Stable Audio 2). Personalized AI radio.
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### 매 Economic shifts
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- Album → single → playlist track → 7-second hook.
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- Royalty: $0.003-0.005/stream (Spotify 2026).
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- Long tail: 매 100M+ tracks indexed; 매 50% never played.
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### 매 Tech axes
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- **Codec**: AAC → Opus → neural codec (Encodec, SoundStream).
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- **Discovery**: editorial → collaborative filter → embedding-based → LLM agent.
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- **Rights**: ISRC → blockchain experiments → AI-attribution debate.
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### 매 응용
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1. Independent artist — DistroKid → all DSPs.
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2. AI track creator — Suno + 매 manual master + DSP upload.
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3. Personalized AI radio — Spotify DJ AI, Amazon Maestro.
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## 💻 패턴
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### Music embedding for similarity (2026)
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```python
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import torch
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from transformers import AutoProcessor, ClapModel
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processor = AutoProcessor.from_pretrained("laion/clap-htsat-unfused")
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model = ClapModel.from_pretrained("laion/clap-htsat-unfused")
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def embed_audio(wav_path):
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audio = load_audio(wav_path, sr=48000)
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inputs = processor(audios=audio, return_tensors="pt", sampling_rate=48000)
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with torch.no_grad():
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return model.get_audio_features(**inputs)
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```
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### Recommendation collaborative filter
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```python
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import numpy as np
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from scipy.sparse.linalg import svds
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# user x track plays matrix
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U, S, Vt = svds(plays_matrix, k=128)
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user_factors = U @ np.diag(S)
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track_factors = Vt.T
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def recommend(user_id, k=20):
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scores = user_factors[user_id] @ track_factors.T
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return np.argsort(-scores)[:k]
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```
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### DSP metadata upload
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```json
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{
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"isrc": "USXYZ2600001",
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"title": "Neon Dawn",
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"artist": "Aria Vox",
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"album": "Synth Bloom",
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"release_date": "2026-06-01",
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"explicit": false,
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"ai_disclosure": {
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"ai_used": true,
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"tools": ["suno-v4"],
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"human_role": ["lyrics", "mastering"]
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},
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"audio_url": "s3://...master.flac"
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}
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```
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### Suno-style generation prompt (2026)
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```python
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import requests
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resp = requests.post(
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"https://api.suno.ai/v4/generate",
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headers={"Authorization": f"Bearer {API_KEY}"},
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json={
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"prompt": "lo-fi hip-hop with rainy night atmosphere, 78 BPM",
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"lyrics_mode": "instrumental",
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"duration_sec": 180,
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"model": "suno-v4-pro",
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},
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)
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```
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### Royalty estimator
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```python
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def estimate_revenue(streams_by_dsp):
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rates = {"spotify": 0.0035, "apple": 0.008, "youtube": 0.002}
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return sum(s * rates.get(dsp, 0.003) for dsp, s in streams_by_dsp.items())
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```
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## 매 결정 기준
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| 상황 | Channel |
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|---|---|
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| 매 indie release | DistroKid / TuneCore → all DSPs |
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| 매 AI track | Disclosure flag + DSP의 AI policy 의 check |
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| 매 fan funding | Bandcamp + Patreon |
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| 매 viral hook | TikTok + Reels first |
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| 매 catalog track | Spotify editorial pitch + algorithmic playlist |
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**기본값**: 매 multi-DSP via aggregator + 매 TikTok seeding + 매 AI-disclosure transparent.
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## 🔗 Graph
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## 🤖 LLM 활용
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**언제**: 매 catalog metadata cleaning, lyric generation, playlist description, 매 AI track의 disclosure draft.
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**언제 X**: 매 royalty calculation 매 authoritative — 매 PRO/MLC official report 의 use.
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## ❌ 안티패턴
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- **No AI disclosure**: 매 DSP TOS violation, takedown risk.
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- **Album-only release in 2026**: 매 algorithmic discovery 매 single 의 reward.
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- **Ignoring TikTok hook**: 매 discovery channel 의 miss.
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- **Over-uploading filler AI tracks**: 매 stream-farming flagged → ban.
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## 🧪 검증 / 중복
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- Verified (RIAA 2025 mid-year, IFPI Global Music Report 2026; Spotify For Artists docs).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — distribution timeline + AI-generation 2026 + embedding patterns |
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