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Recent posts 지난 글

  • Why Enterprise AX Needs Reusable Solutions and Business Models 엔터프라이즈 AX에 솔루션 기반 접근과 업무 모델이 필요한 이유
  • Eliminating Idle Server Costs for Side Projects with AWS Serverless AWS 서버리스로 사이드 프로젝트의 상시 서버 비용 없애기
  • Bottlenecks After AI Writes the Code — Anthropic's Review and Source Control Practices AI 코드가 늘어난 뒤의 병목 — Anthropic의 리뷰와 소스 관리 정리
  • What makes Jev different? — Replacing the head and RLCD Jev의 차이는 어디서 올까 — 헤드 교체와 RLCD
  • How a Software Factory Can Improve Itself — Evals, KPIs, and Tension Metrics Software Factory가 스스로 개선하려면 — Eval과 KPI, 텐션 메트릭
  • How Do You Evaluate AI That Remembers Users? — Memory and Conversation in EncBird 사용자를 기억하는 AI는 어떻게 평가할까 — EncBird의 메모리와 대화 평가
  • As Agents Get Stronger, Where Will Business Moats Move? 에이전트가 강해질수록, 서비스의 해자는 어디로 옮겨갈까
  • My Thoughts on AWS AI-DLC — The Direction I Believe Agentic Development Should Take AWS AI-DLC에 대한 내 생각 — 내가 믿는 에이전틱 개발의 방향
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  • When Should AI Adoption Be Measured by Business Metrics? — Revisiting AHEAD and LEVER 2/2

    Measuring harness learning before business value

    AI 도입 성과는 언제 비즈니스 지표로 봐야 할까 — AHEAD·LEVER 다시 보기 2/2

    Measuring harness learning before business value

    2026.08.18
  • Why AI Code Review Feels Harder — Context Familiarity and Cognitive Debt

    Why compressed understanding makes review the bottleneck

    AI 코드 리뷰가 버거운 이유 — 컨텍스트에 익숙해질 시간과 인지부채

    Why compressed understanding makes review the bottleneck

    2026.08.18
  • Did AI Coding Tools Actually Cut Development Cost? — Understanding CTS-SW

    Measuring AI by software delivery cost

    AI 코딩 도구가 정말 개발 비용을 줄였을까 — CTS-SW 이해하기

    Measuring AI by software delivery cost

    2026.08.14
  • Why Separate PRDs, ADRs, and Code? — Reading One Abstraction Level at a Time

    Keep intent independent of implementation details

    PRD·ADR·코드를 왜 나눠야 할까 — 추상화 계층 하나만 읽고 판단하기

    Keep intent independent of implementation details

    2026.07.25
  • Why Avoiding Failure Comes First

    Lowering the cost of learning from failed experiments

    망하지 않는 게 먼저인 이유

    Lowering the cost of learning from failed experiments

    2026.06.27
  • How I Built the EncBird Harness Layer by Layer — Harness Engineering in Practice

    How harness engineering actually works in practice, layer by layer

    EncBird에 하네스를 한 겹씩 씌워온 과정 — 실전 하네스 엔지니어링

    How harness engineering actually works in practice, layer by layer

    2026.06.16
  • Why AI Adoption Should Not Start with Token Savings — The 3S Stages 1/2

    Match AI investment to each adoption stage

    AI 도입은 왜 토큰 절감부터 시작하면 안 될까 — 3S로 단계 나누기 1/2

    Matching AI investment to each adoption stage

    2026.06.15
  • A Lens for Interpreting Phenomena—and Agentic Engineering

    Interpreting agentic engineering through the removal of human intervention

    현상을 해석하는 렌즈, 그리고 에이전틱 엔지니어링

    Interpreting agentic engineering through the removal of human intervention

    2026.06.12
  • The Next Evolution of Agents Will Come from Smarter Tools

    Moving domain reasoning from the orchestrator into specialized tools

    에이전트의 다음 진화는 똑똑한 도구에서 온다

    Moving domain reasoning from the orchestrator into specialized tools

    2026.05.27
  • Agentic Engineering and Transitional Technologies

    HITL removal is the direction, and coexistence-oriented tools are transitional

    에이전틱 엔지니어링과 과도기적 기술들

    HITL removal is the direction, and coexistence-oriented tools are transitional

    2026.05.11
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