AI & 미래
Letting AI Speed Up the Search, Not the Judgment
A practical reflection on using AI to broaden investigation while keeping source checking, trade-off decisions, and accountability with people.
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AI & 미래
AI 이해하기 — 컨텍스트 엔지니어링부터 신뢰할 수 있는 AI 제품 구축까지.
AI에 대한 실용적이고 개발자 중심의 글: AI 도구에서 훌륭한 결과를 얻는 방법, 언어 모델의 실제 작동 방식 이해, 신뢰할 수 있는 AI 기반 제품 구축. LLM, 에이전트, 컨텍스트 엔지니어링의 빠르게 변화하는 환경을 탐색하는 엔지니어를 위해 작성되었습니다.
AI & 미래
Asking AI to Name Its Assumptions
A practical reflection on making AI-assisted work safer by asking the model to expose assumptions before accepting its answer.
AI & 미래
Treating AI Suggestions as Draft Evidence
A practical reflection on using AI output as a starting point for investigation, not as a finished answer that replaces verification.
AI & 미래
Keeping AI Changes Small Enough to Review
A practical reflection on using AI safely by keeping generated changes narrow, inspectable, and tied to evidence.
AI & 미래
Letting AI Draft the Boring First Pass
A practical reflection on using AI for the plain first draft, while keeping human judgment focused on evidence, shape, and responsibility.
AI & 미래
Asking AI to Show Its Working Context
A practical reflection on asking AI systems to expose their working context so engineers can verify evidence, assumptions, and gaps.
AI & 미래
Letting AI Draft While Humans Keep the Map
A practical reflection on using AI to draft faster while humans keep the system map: constraints, sources, ownership, and verification paths.
AI & 미래
What AI Cannot Know From the Prompt Alone
A practical reflection on why prompts are not enough: AI needs source context, runtime evidence, domain constraints, and human judgment to become useful engineering help.
AI & 미래
Using AI Without Outsourcing Judgment
A practical reflection on using AI as a thinking partner while keeping human judgment anchored in source code, tests, logs, and product context.
AI & 미래
AI Tools for Reading a Codebase
A practical guide to using AI tools while reading a codebase: where summaries help, where they mislead, and how engineers can combine AI assistance with source-level verification.
AI & 미래
From Prototype Demo to Production Behavior
A grounded AI product note on the gap between an impressive prototype demo and the production behavior users can safely depend on.
AI & 미래
Human Feedback Loops for AI Work
A grounded look at AI work habits: why useful AI workflows need human feedback loops, evidence checks, test results, and team memory instead of one-off prompt outputs.
AI & 미래
The Cost of Trusting a Fluent AI Answer
A grounded look at fluent AI answers: why confident language can feel like evidence, where that creates risk, and how teams can keep AI useful by checking claims against sources, tests, and real context.
AI & 미래
Vector Databases Explained
A beginner-friendly explanation of vector databases: how embeddings represent meaning, why similarity search helps AI products, and what teams need to handle around chunking, metadata, freshness, evaluation, and cost.
AI & 미래
AI in Cybersecurity
A practical reflection on AI in cybersecurity: how AI helps detection, triage, secure coding, and incident response while creating new risks around automation, privacy, prompt injection, and false confidence.
AI & 미래
Reducing Hallucinations in LLMs
A practical guide to reducing LLM hallucinations with clearer tasks, bounded context, retrieval, citations, verification, refusals, evaluation sets, and product design that makes uncertainty visible.
AI & 미래
Fine-Tuning vs. Prompting vs. RAG
A practical comparison of prompting, RAG, and fine-tuning for AI products: what each approach changes, when it helps, where it fails, and how teams can choose the smallest reliable intervention.
AI & 미래
Ethical Considerations in AI Products
A practical article on product ethics for AI teams: consent, data boundaries, bias and fairness, human review, explanations, risk controls, and incentives that shape whether AI features remain useful and responsible.
AI & 미래
Retrieval-Augmented Generation (RAG) Basics
A beginner-friendly explanation of Retrieval-Augmented Generation: how documents are split into chunks, turned into embeddings, found with vector search, and used to ground AI answers with citations, evaluation, and clear failure checks.
AI & 미래
The Context Window Bottleneck
A practical look at context window limits as one of the quiet bottlenecks in AI workflows. The article explains why long chats, big codebases, stale summaries, and missing working memory can make AI assistance drift, and how teams can work around the limit without turning every task into a document dump.
AI & 미래
When an AI Answer Needs a Test
A calm explainer on treating AI answers as claims that need proportionate verification. The article shows how engineers can keep responsibility by testing AI output against evidence, context, and real system behavior before acting on it.
AI & 미래
Khi AI chạm vào việc hằng ngày, ý tưởng bắt đầu nở ra
Khi AI đủ thực tế để bước vào những việc nhỏ trong văn phòng, ý tưởng không còn nằm ở các bản demo xa xôi. Chúng xuất hiện ngay trong onboarding, hỗ trợ khách hàng, đối chiếu tài chính, chuẩn bị họp, đọc tin tức, kiểm tra tài liệu và rất nhiều workflow lặp lại khác. Bài viết là một góc nhìn bình tĩnh về cách nhận ra các điểm nghẽn ấy, cải tiến từng chút và giữ con người ở phần phán đoán quan trọng.
AI & 미래
Cognitive Debt in AI-Assisted Development: When Code Grows Faster Than Understanding
AI coding tools can make a team produce code faster than it can understand, review, debug, and safely operate that code. This article explains cognitive debt, how it differs from technical debt and intent debt, why agentic coding makes it more visible, and the practical controls teams can use: smaller batches, better PR rationale, human-owned invariants, disposable prototypes, and AI used to repay understanding instead of only generating more work.
AI & 미래
Beyond the Hype: What LLMs Actually Understand
LLMs can feel like they understand us because they handle patterns, context, and language-shaped reasoning well. This explainer separates useful machine understanding from human understanding so teams can verify, constrain, and use AI with better judgment.
AI & 미래
AI Literacy: From Prompting to Orchestrating Agents
Using AI more often does not automatically mean working at a higher level. This guide maps three levels of AI literacy with a visual ladder, concrete tool examples, workflow keys, and a path toward deeper practical guides for each level.
AI & 미래
Your Fear of AI Is the Size of Your Ambition: Why It’s Time to Aim Dramatically Bigger
Two people can look at the same AI breakthrough and feel opposite things — terror or fuel — and the difference reveals something uncomfortable: our fear of the future is roughly the size of our ambition. If your plan is to keep doing exactly what you do, a machine that does it cheaper is frightening. If your plan is to build something dramatically bigger, it’s the best news you’ve ever gotten. This is a clear, energising case for retiring “don’t boil the ocean,” trading the 1.05x present for the 10x future, and why — through ephemeralization and the Jevons paradox — raising your ambitions tends to create more, not less.
AI & 미래
From SDLC to ADLC: How AI Agents Are Rewriting the Software Development Lifecycle
Software development is going through its biggest shift since the graphical interface. The phases engineers once ran by hand — planning, coding, testing, deployment — are increasingly executed by AI agents working in parallel. This is a clear, visual guide to the move from the classic SDLC to the emerging ADLC (Agentic Development Life Cycle): the two lifecycles drawn side by side, the six concrete shifts happening right now, what the early numbers suggest, what it actually means for an engineer’s day-to-day, and five low-risk best practices to start moving faster — without handing over the judgment that still has to be yours.
AI & 미래
AI Agent, 과장 없이 설명하기: 정의, 작동 원리, 그리고 활용법
어느 날 갑자기 모두가 'AI agent'를 얘기하기 시작했습니다. 질문에 답하는 것을 넘어 직접 일을 처리하는 소프트웨어 말이죠. 하지만 agent란 정확히 무엇이고, 우리가 이미 쓰는 chatbot과 어떻게 다르며, 어디서 진짜 도움이 되고 어디서 조용히 문제를 만드는 걸까요? 이 글은 기술 지식 없이도 이해할 수 있는 친절한 안내서입니다. 모든 agent가 실행하는 단순한 루프, 구성하는 다섯 가지 요소, co-pilot에서 auto-pilot까지의 자율성 단계, 직장과 일상의 실질적인 사례, 주의해야 할 실패 유형, 그리고 이번 주부터 유용한 agent를 실제로 써볼 수 있는 방법까지 담았습니다.
AI & 미래
Prompt에서 Workflow로: 매주 몇 시간을 절약하는 AI Workflow 설계하기
대부분의 사람들은 AI를 한 번에 하나씩 질문하며 — 그 가치의 대부분을 놓칩니다. 진짜 레버리지는 영리한 prompt가 아닙니다. 재사용 가능한 AI workflow, 즉 한 번 설계해 영원히 재사용하는 작은 단계들의 연결 고리입니다. 반복되는 두 시간짜리 잡무를 2분 검토로 바꿔주죠. 기술 지식이 없어도 이해할 수 있는 실용적인 안내서입니다: prompt와 workflow의 차이, 모든 workflow가 공유하는 간단한 구조, 직장과 일상에서 바로 훔쳐 쓸 수 있는 workflow, 수동에서 완전 자동까지의 세 단계, 나만의 workflow를 설계하는 6단계 방법, 그리고 실제로 절약하는 시간을 증명하는 간단한 공식.
AI & 미래
2026년 주목할 AI 트렌드 — 그리고 실질적 가치로 바꾸는 방법
매주 '이것이 모든 것을 바꾼다'는 AI 헤드라인이 쏟아지고, 신호와 소음을 구분하려 애쓰는 것은 지칩니다. 이 글은 2026년에 진짜 중요한 AI 트렌드를 차분하게 살펴봅니다 — 에이전틱 AI, 멀티모달, 이미 쓰는 앱에 내장된 AI, 온디바이스 지능, 그리고 인간의 강점으로 떠오르는 판단과 검증 — 거기에 대부분의 글이 건너뛰는 부분을 함께 담았습니다: 어떤 트렌드든 업무와 생활에서 실제 가치로 바꾸는 실용적인 프레임워크. 과대 광고 필터, 가치 사다리, 간단한 가치 방정식, 실제 사례, 30일 계획까지 — 뉴스를 쫓는 것을 멈추고 이점을 복리로 쌓기 시작할 수 있습니다.
AI & 미래
AI Is Rewiring Every Software Role: How Thinking Changes for BA, PO, PM, Dev, QA and Beyond
AI isn't just a faster tool bolted onto how we build software — it's quietly moving the center of gravity of every role. The work that used to define a BA, PO, PM, developer, or QA engineer is being automated from the bottom up, and what remains is a different job built on judgment, taste, and verification. This is a clear-eyed, role-by-role look at what's fading, what's rising, and what to watch out for as teams move from AI-assisted to AI-first — so you can see what may be lost, what's added, and how to stay genuinely valuable in a changed era.
AI & 미래
컨텍스트 엔지니어링: AI를 잘 쓰는 사람과 그렇지 않은 사람의 차이
대부분의 AI 결과가 실망스러운 것은 모델이 나쁜 게 아니라 컨텍스트가 빈약하기 때문입니다. 컨텍스트 엔지니어링에 대한 실용적인 가이드 — 모델 윈도우에 무엇이 있는지, 훌륭한 컨텍스트의 다섯 기둥, 흔한 안티패턴, 그리고 일관되게 더 나은 결과를 내는 재사용 가능한 템플릿.
AI & 미래
Generative AI in Daily Life and Work: A Practical Catalog of What You Can Use Today
Generative AI has quietly moved from headline to household tool — yet most people still use it for little more than the occasional question. This is a hands-on catalog of the most common, genuinely useful applications across work, learning, creativity, and everyday life, organized so you can find at least one thing to try today. No hype, no jargon — just real use cases you can apply immediately, plus the one skill that makes all of them work and the limits you should never ignore.
AI & 미래
Prompt Engineering 은 결국 커뮤니케이션입니다
좋은 prompt 는 마법 문장이 아닙니다. context, 의도, constraints, example, feedback 을 명확히 전하는 커뮤니케이션입니다.
AI & 미래
AI in QA: Automated Test Generation
A practical look at AI-assisted QA and automated test generation: where AI helps with breadth, edge cases, fixtures, and test skeletons, and why human judgment still owns risk, truth, and trust.
AI & 미래
Running Local LLMs for Privacy
A practical explanation of local LLMs for privacy-sensitive work: what improves when prompts and documents stay on owned machines, what quality and operations costs remain, and how teams can adopt local models responsibly.
AI & 미래
AI Agents: The Next Frontier
A practical look at AI agents beyond the hype: what changes when models can use tools, follow workflows, remember context, ask for approval, and produce auditable work, plus where human judgment still matters.
AI & 미래
Data Privacy in the Era of AI
A practical article on data privacy in AI work: how prompts, documents, logs, retrieval, training, and evaluation create new data paths, and how teams can use AI while keeping consent, minimization, access, and retention clear.
AI & 미래
Multimodal AI: Text, Vision, and Audio
A practical introduction to multimodal AI: how text, images, audio, and video change product workflows, what becomes possible, and why evaluation, privacy, accessibility, and human review matter more as inputs become richer.
AI & 미래
AI for Code Refactoring
A practical reflection on using AI for code refactoring: where it helps with mapping, options, tests, and mechanical changes, and why engineers still need boundaries, evidence, and verification before trusting the result.
AI & 미래
Evaluating LLM Performance
A practical guide to evaluating LLM performance with task-specific datasets, rubrics, human review, regression checks, latency, cost, safety, and production feedback instead of relying on impressive demos.