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의미 ID를 활용한 제어 가능 추천을 위한 LLM-RecSys 하이브리드 훈련
Training an LLM-RecSys Hybrid for Steerable Recs with Semantic IDs
An LLM that can converse in English & item IDs, and make recommendations w/o retrieval or tools.
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Qwen3을 처음부터 이해하고 구현하기
Understanding and Implementing Qwen3 From Scratch
A Detailed Look at One of the Leading Open-Source LLMs
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GPT-2에서 gpt-oss로: 아키텍처 발전 분석
From GPT-2 to gpt-oss: Analyzing the Architectural Advances
And How They Stack Up Against Qwen3
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주요 LLM 아키텍처 비교
The Big LLM Architecture Comparison
From DeepSeek-V3 to Kimi K2: A Look At Modern LLM Architecture Design
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LLM 연구 논문: 2025년 목록 (1월~6월)
LLM Research Papers: The 2025 List (January to June)
A topic-organized collection of 200+ LLM research papers from 2025
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긴 맥락 질의응답 시스템 평가
Evaluating Long-Context Question & Answer Systems
Evaluation metrics, how to build eval datasets, eval methodology, and a review of several benchmarks.
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LLM의 KV 캐시 이해와 처음부터 구현하기
Understanding and Coding the KV Cache in LLMs from Scratch
KV caches are one of the most critical techniques for efficient inference in LLMs in production.
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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.