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Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

2026-05-20 23:52:15 +09:00

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title

Minimal Viable Product (MVP)

매 한 줄

"매 학습 단위로서의 가장 작은 product". 매 Eric Ries (2011) 가 정의한 MVP 는 매 customer hypothesis 를 매 minimum effort 로 validate 하는 product version. 매 2026 의 MVP 는 매 AI-augmented prototyping (Claude Opus, Replit Agent) 으로 매 days-not-weeks scale.

매 핵심

매 MVP 의 진짜 의미

Minimum: 매 build effort 의 minimization (X feature count).
Viable: 매 real user 의 real job 을 매 end-to-end 수행 가능.
Product: 매 learning vehicle — 매 metric capture 가능해야.

매 MVP 의 X

매 buggy half-product (X — viable 아님).
매 feature-complete v1 (X — minimum 아님).
매 internal demo (X — product 아님, no users).

매 응용

Concierge MVP: 매 manual backend, 매 user 는 magic UX 로 인식.
Wizard-of-Oz: 매 fake automation, 매 human-in-loop.
Landing page: 매 product X, 매 demand signal capture only.
Single-feature: 매 one core job, 매 polished.

💻 패턴

Hypothesis canvas

mvp:
  hypothesis: "User X will pay $Y for solving Z"
  riskiest_assumption: "User X actually has problem Z"
  minimum_test:
    type: landing_page
    success_metric: "100 sign-ups in 7 days"
    kill_metric: "<10 sign-ups → pivot"

Concierge MVP scaffold

# Fake the backend, learn from real users
from fastapi import FastAPI

app = FastAPI()

@app.post("/recommend")
async def recommend(user_query: str):
    # MVP: send query to founder's phone
    await sms_to_founder(user_query)
    # Founder manually crafts recommendation
    response = await wait_for_founder_reply()
    return {"recommendation": response}

Build-Measure-Learn loop

class MVPCycle:
    def __init__(self, hypothesis):
        self.hypothesis = hypothesis
    def build(self):  # smallest experiment
        return prototype(self.hypothesis)
    def measure(self, prototype, n_users=20):
        return collect_metrics(prototype, n_users)
    def learn(self, metrics):
        if metrics["activation"] > 0.4:
            return "persevere"
        return "pivot"

AI-augmented MVP (2026)

# Claude Code + Replit Agent stack
claude-code "build MVP for <hypothesis>" --scaffold next.js
# Days-not-weeks: AI generates 80% boilerplate

Kill criteria gate

def should_kill(metrics: dict, kill_threshold: dict) -> bool:
    """매 honest evaluation — sunk cost ignore."""
    return all(
        metrics[k] < kill_threshold[k]
        for k in kill_threshold
    )

매 결정 기준

상황	MVP type
매 demand 의 unknown	Landing page
매 UX 의 unknown, backend 매 hard	Concierge / Wizard-of-Oz
매 demand 매 confirmed, 매 build feasible	Single-feature MVP
매 enterprise B2B	Design partner pilot (X cold MVP)

기본값: Landing page → Concierge → Single-feature 의 progression.

🔗 Graph

부모: Lean-Startup

🤖 LLM 활용

언제: 매 hypothesis articulation, 매 riskiest assumption 의 surfacing, 매 MVP scaffold generation. 언제 X: 매 already-validated product 의 v2 — MVP framing 의 X.

❌ 안티패턴

Feature creep MVP: 매 minimum 무시 → 매 8주 build, 매 launch 실패.
Vanity metrics: 매 page views / signups 만 측정 → activation / retention X.
No kill criteria: 매 sunk cost trap.
MVP = bad quality: 매 minimum 은 scope, X quality.

🧪 검증 / 중복

Verified (Ries 2011 The Lean Startup; Blank Four Steps to the Epiphany).
신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — MVP types, build-measure-learn, AI-augmented 2026 stack

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