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AI Engineer 2025 - LLM 기술로 추천 시스템과 검색 개선
AI Engineer 2025 - Improving RecSys & Search with LLM techniques
Recsys & search are converging with LLMs via semantic IDs, data augmentation, and unified foundation models.
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뛰어난 리더십: 자질, 행동, 그리고 스타일
Exceptional Leadership: Some Qualities, Behaviors, and Styles
What makes a good leader? What do good leaders do? And commando, soldier, and police leadership.
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바닥부터 배우는 LLM 코딩: 완전한 강의
Coding LLMs from the Ground Up: A Complete Course
Why build LLMs from scratch? It's probably the best and most efficient way to learn how LLMs really work. Plus, many readers have told me they had a lot of fun doing it.
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MCP, Q, tmux를 이용한 일일 뉴스 요약 뉴스 에이전트 구축
Building News Agents for Daily News Recaps with MCP, Q, and tmux
Learning to automate simple agentic workflows with Amazon Q CLI, Anthropic MCP, and tmux.
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LLM-as-Judge는 제품을 구하지 못합니다—프로세스 개선이 핵심입니다
An LLM-as-Judge Won't Save The Product—Fixing Your Process Will
Applying the scientific method, building via eval-driven development, and monitoring AI output.
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LLM 추론을 위한 강화학습의 현황
The State of Reinforcement Learning for LLM Reasoning
Understanding GRPO and New Insights from Reasoning Model Papers
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내 글쓰기 과정에 대한 자주 묻는 질문
Frequently Asked Questions about My Writing Process
How I started, why I write, who I write for, how I write, and more.
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처음부터 시작하는 추론: 1장
First Look at Reasoning From Scratch: Chapter 1
Welcome to the next stage of large language models (LLMs): reasoning. LLMs have transformed how we process and generate text, but their success has been largely driven by statistic…
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NVIDIA GTC 2025 - LLM 기반 애플리케이션 구축
NVIDIA GTC 2025 - Building LLM-Powered Applications
Chip Huyen and I share what we've learned, best practices, and insights at NVIDIA GTC 2025.
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LLM 시대의 추천 시스템 및 검색 개선
Improving Recommendation Systems & Search in the Age of LLMs
Model architectures, data generation, training paradigms, and unified frameworks inspired by LLMs.
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LLM 추론 모델 인퍼런스의 현황
The State of LLM Reasoning Model Inference
Inference-Time Compute Scaling Methods to Improve Reasoning Models
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AI 읽기 클럽 구축: 기능과 배경 이야기
Building AI Reading Club: Features & Behind the Scenes
Exploring how an AI-powered reading experience could look like.
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글쓰기의 역설적 규칙들
Seemingly Paradoxical Rules of Writing
With regard to writing, there are many rules and also no rules at all.
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주간 논문 클럽을 운영하는 방법 (그리고 학습 커뮤니티 구축하기)
How to Run a Weekly Paper Club (and Build a Learning Community)
Benefits of running a weekly paper club, how to start one, and how to read and facilitate papers.
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나의 미니멀한 MacBook Pro 설정 가이드
My Minimal MacBook Pro Setup Guide
Setting up my new MacBook Pro from scratch
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ML 시스템 구축, 확장, 실행 등의 39가지 교훈
39 Lessons on Building ML Systems, Scaling, Execution, and More
ML systems, production & scaling, execution & collaboration, building for users, conference etiquette.
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AlignEval: 평가를 쉽고 재미있으며 자동화되게 만드는 앱 구축하기
AlignEval: Building an App to Make Evals Easy, Fun, and Automated
Look at and label your data, build and evaluate your LLM-evaluator, and optimize it against your labels.
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Weights & Biases LLM 평가기 해커톤 - 해커톤 심사위원
Weights & Biases LLM-Evaluator Hackathon - Hackathon Judge
Being a human judge at the Weights & Biases LLM-as-a-Judge Hackathon
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다양한 웹 프레임워크를 사용하여 같은 앱 만들기
Building the Same App Using Various Web Frameworks
FastAPI, FastHTML, Next.js, SvelteKit, and thoughts on how coding assistants influence builders' choices.
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LLM 평가자의 효율성 평가 (LLM-as-Judge)
Evaluating the Effectiveness of LLM-Evaluators (aka LLM-as-Judge)
Use cases, techniques, alignment, finetuning, and critiques against LLM-evaluators.
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ML/AI 엔지니어 면접 및 채용하는 방법
How to Interview and Hire ML/AI Engineers
What to interview for, how to structure the phone screen, interview loop, and debrief, and a few tips.
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AI 엔지니어 2024 키노트 - LLM 1년간의 경험과 배움
AI Engineer 2024 Keynote - What We Learned from a Year of LLMs
Special double-feature closing keynote from the 6 authors of the hit O'Reilly article on Applied LLMs.
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Netflix PRS 2024 - 추천 경험에 LLM 적용
Netflix PRS 2024 - Applying LLMs to Recommendation Experiences
Challenges and lessons from deploying LLM experiences: evals, scalability, guardrails.
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프롬프트 기초 및 효과적으로 적용하는 방법
Prompting Fundamentals and How to Apply them Effectively
Structured input/output, prefilling, n-shots prompting, chain-of-thought, reducing hallucinations, etc.
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LLM으로 1년간 구축하며 배운 것들
What We've Learned From A Year of Building with LLMs
From the tactical nuts & bolts to the operational day-to-day to the long-term business strategy.
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원숭이 마음을 길들이는 AI 코치 만들기
Building an AI Coach to Help Tame My Monkey Mind
Building an AI coach with speech-to-text, text-to-speech, an LLM, and a virtual number.
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작동하고 작동하지 않는 작업별 LLM 평가
Task-Specific LLM Evals that Do & Don't Work
Evals for classification, summarization, translation, copyright regurgitation, and toxicity.
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머신러닝 모델을 단위 테스트에서 모킹하지 마세요
Don't Mock Machine Learning Models In Unit Tests
How unit testing machine learning code differs from typical software practices
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파인튜닝을 위한 합성 데이터 생성 및 활용 방법
How to Generate and Use Synthetic Data for Finetuning
Overcoming the bottleneck of human annotations in instruction-tuning, preference-tuning, and pretraining.