Haandol TL;DR

Recent posts 지난 글

  • Why Jev was created. Jev 가 만들어진 이유.
  • What to Decide Before Running a Software Factory Software Factory를 돌리기 전에 정해야 할 것들
  • 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
  • As Agents Automate Development, Where Do Developers Go Next? Agent로 개발을 자동화한 다음, 개발자는 어디로 갈까
  • Why Did Some Teams Get Up to 10x Faster with the Same AI Tools? — Five Habits of Frontier Development 같은 AI 도구를 썼는데 왜 어떤 팀은 10배까지 빨라졌을까 — Frontier Development의 다섯 습관
  • When Should AI Adoption Be Measured by Business Metrics? — Revisiting AHEAD and LEVER 2/2 AI 도입 성과는 언제 비즈니스 지표로 봐야 할까 — AHEAD·LEVER 다시 보기 2/2
Language
  • Good Code for Agents to Read — Code That Screams Its Business Purpose

    Code that reveals business intent to the next agent

    에이전트가 읽을 좋은 코드 — 비즈니스를 외치는 코드

    Code that reveals business intent to the next agent

    2026.03.13
  • Connecting EncBird's Conversation Data to Review Activities — A GenAI Flywheel

    Reusing conversation data across personalized learning features

    EncBird의 대화 데이터를 복습 기능으로 이어가기 — GenAI 플라이휠

    Reusing conversation data across personalized learning features

    2026.03.12
  • Dystopia If We Reach AGI, Utopia If We Do Not

    Why reaching AGI might be more dystopian than not reaching it

    AGI에 도달하면 디스토피아, 도달하지 못하면 유토피아

    Why reaching AGI might be more dystopian than not reaching it

    2026.03.12
  • Context Engineering — Letting Agents Handle the Work Between Requirements and Code

    Abstracting the work between requirements and code

    컨텍스트 엔지니어링 — 요구사항과 코드 사이를 에이전트에 맡기기

    Abstracting the work between requirements and code

    2026.03.11
  • What to Consider Before Building an MCP Server

    Design tradeoffs to examine before building an MCP server

    MCP 서버 만들기 전에 고민할 내용

    Considerations before developing MCP server

    2026.03.02
  • What to Know Before Buying a LeRobot SO-ARM101 (Physical AI)

    The real cost and setup behind a LeRobot SO-ARM101

    LeRobot SO ARM 101 처음 사기전에 알아두면 좋은 내용 (Physical AI)

    Things to know before buying LeRobot SO ARM 101

    2026.02.05
  • Demystifying Physical AI

    A practical introduction to Physical AI and robot learning

    간단히 설명한 피지컬(Physical) AI

    Demystifying Physical AI

    2026.01.02
  • What to Consider Before Taking MCP Servers to Production

    Preparing MCP-powered agents for production

    MCP 서버 프로덕션에 올리기 전에 고려해야 할 것

    What to prepare for production with an MCP-powered LLM agent

    2025.07.01
  • Why Your Business Should Build Its Own Chatbot Now

    Why your business should start its own GenAI chatbot now

    왜 비즈니스들은 지금 자신만의 챗봇을 만들어야 하는가?

    Why your business should start your own GenAI chatbot now

    2025.06.03
  • RFTCR — A New SDLC Framework for Agent-Driven Software Development

    A five-stage SDLC for turning business requirements into code

    RFTCR - 에이전트 주도 소프트웨어 개발을 위한 새로운 SDLC 프레임워크

    RFTCR - A new SDLC framework, RFTCR, for agentic software development

    2025.05.11
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