2nd/10_Wiki/Topics/Other/Big-Picture.md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-big-picture	Big Picture	10_Wiki/Topics	verified	self	Big Picture Thinking System-Level View Holistic View	none	A	0.9	applied	meta systems-thinking architecture decision-making		2026-05-10	pending	language framework python general

Big Picture

매 한 줄

"매 zoom out before you zoom in". Big Picture thinking 매 system-level perspective 의 prioritization — local optimization 매 global suboptimum 의 lead 가능. 2026 LLM 시대 매 context window 1M+ tokens 매 entire codebase 의 single prompt 의 fit 가능 — Big Picture 매 finally tractable computationally.

매 핵심

매 Levels of abstraction

• L0 (atom): single function, single line.
• L1 (module): file, class, single concern.
• L2 (subsystem): service, package, bounded context.
• L3 (system): full application, deployment topology.
• L4 (ecosystem): organization, market, regulation.
• 매 mistake: L0 의 stuck — never L3 까지 zoom out.

매 When to zoom out

• 매 stuck 30+ min 의 single bug → L2 의 zoom out.
• 매 architectural decision → L3 mandatory.
• 매 hiring / team structure → L4.
• 매 PR review → L1 + L2 mix.

매 응용

1. Architecture review (data flow diagram).
2. Incident postmortem (5 whys → systemic cause).
3. Roadmap planning (quarter-level priorities).
4. Code review (cross-cutting concerns).

💻 패턴

Pattern 1: Context map (L3 view)

# Visualize bounded contexts (DDD-style)
contexts = {
    "auth": {"depends_on": [], "exposes": ["user_id", "session"]},
    "billing": {"depends_on": ["auth"], "exposes": ["invoice", "subscription"]},
    "notification": {"depends_on": ["auth", "billing"], "exposes": []},
}

def find_critical_path(contexts):
    """매 highest fan-in 의 service 의 SPOF candidate."""
    fan_in = {ctx: 0 for ctx in contexts}
    for ctx, info in contexts.items():
        for dep in info["depends_on"]:
            fan_in[dep] += 1
    return sorted(fan_in.items(), key=lambda x: -x[1])

Pattern 2: Zoom-out checklist

ZOOM_OUT_QUESTIONS = [
    "Who else is affected by this change?",
    "What breaks if this fails at 3am?",
    "Is this the right problem to solve right now?",
    "What does success look like in 6 months?",
    "Who owns this when I leave?",
]

def review_pr(pr_diff: str) -> list[str]:
    return [q for q in ZOOM_OUT_QUESTIONS if not answered_in(pr_diff, q)]

Pattern 3: Pre-mortem (L4 thinking)

def premortem(project: str) -> dict:
    """매 launch 전 의 'imagine it failed' exercise."""
    return {
        "tech_failure": "What technical assumption was wrong?",
        "market_failure": "Why did users not adopt?",
        "team_failure": "What organizational dynamic killed it?",
        "regulation": "What law/policy blocked it?",
    }

Pattern 4: Dependency graph (L2 → L3)

import networkx as nx

def build_dep_graph(modules: dict[str, list[str]]) -> nx.DiGraph:
    g = nx.DiGraph()
    for mod, deps in modules.items():
        for d in deps:
            g.add_edge(mod, d)
    cycles = list(nx.simple_cycles(g))
    if cycles:
        print(f"매 architecture smell: {len(cycles)} cycles detected")
    return g

Pattern 5: LLM-assisted big picture (2026)

from anthropic import Anthropic

client = Anthropic()

def architecture_summary(repo_dump: str) -> str:
    """매 1M context 의 entire repo 의 fit — 2026 standard."""
    msg = client.messages.create(
        model="claude-opus-4-7-1m",
        max_tokens=4000,
        messages=[{
            "role": "user",
            "content": f"""다음 repo 의 architecture 를 L3 perspective 의 summarize.
Identify: (1) bounded contexts, (2) critical path, (3) tech debt hotspots.

{repo_dump}"""
        }],
    )
    return msg.content[0].text

Pattern 6: Tradeoff matrix

def tradeoff_matrix(options: list[str], criteria: list[str], scores: dict) -> str:
    rows = []
    for opt in options:
        row = [opt] + [str(scores[(opt, c)]) for c in criteria]
        rows.append(" | ".join(row))
    return "\n".join(rows)

# Usage
options = ["monolith", "microservices", "modular monolith"]
criteria = ["dev_speed", "ops_cost", "scalability", "team_autonomy"]

매 결정 기준

상황	Approach
Bug fix < 1h	L0/L1 만 — zoom out 의 X.
Recurring bug	L2 zoom out — systemic cause.
New feature	L2 + L3 — fit 의 architecture.
Postmortem	L3 + L4 mandatory.
Quarterly planning	L4 only.

기본값: 매 task 의 start 의 30 sec 의 L3 sketch — bounded contexts, data flow, failure modes.

🔗 Graph

• 부모: Systems_Thinking · Architecture
• 응용: Architecture-Review · Postmortem
• Adjacent: Bounded-Context · Domain-Driven-Design

🤖 LLM 활용

언제: Architecture review, repo onboarding, postmortem synthesis, roadmap drafting. 매 1M context 의 entire codebase 의 fit 가능 — 매 truly novel 2026 capability. 언제 X: Tactical bug fix (L0/L1), perf tuning of single function. 매 LLM 매 generic advice 의 emit — local context 의 lose.

❌ 안티패턴

• Premature zoom-out: 매 every bug 의 L4 의 escalate — 매 paralysis.
• Ivory tower architecture: L3 만 — implementation reality 의 ignore.
• Big-picture-only PR review: 매 nitpick 의 miss.
• Solo big-picture: 매 architect 매 single person — bus factor 1.

🧪 검증 / 중복

• Verified: Donella Meadows "Thinking in Systems" (2008), Eric Evans "DDD" (2003), Nicole Forsgren "Accelerate" (2018).
• 신뢰도 A.
• 중복: Systems_Thinking 매 strict superset — Big Picture 매 daily-practice variant 의 framing.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — full content with L0-L4 levels, zoom-out patterns, LLM 1M context architecture summary