-
Import AI 441: 내 에이전트는 작동 중이야. 너의는?
Import AI 441: My agents are working. Are yours?
Plus: Corrupting AI systems with a poison fountain
-
D4RT: 통합 고속 4D 장면 재구성 및 추적 – Google DeepMind
D4RT: Unified, Fast 4D Scene Reconstruction & Tracking â Google DeepMind
D4RT: Unified, efficient 4D reconstruction and tracking up to 300x faster than prior methods.
-
Veo 3.1 재료에서 비디오로: 더 나은 일관성, 창의성, 제어
Veo 3.1 Ingredients to Video: More consistency, creativity and control
Our latest Veo update generates lively, dynamic clips that feel natural and engaging — and supports vertical video generation.
-
임포트 AI 440: 레드퀸 AI; AI 규제 AI; O-링 자동화
Import AI 440: Red queen AI; AI regulating AI; o-ring automation
How many of your are LLMs?
-
2025년 LLM의 현황: 진전, 문제, 그리고 예측
The State Of LLMs 2025: Progress, Problems, and Predictions
A 2025 review of large language models, from DeepSeek R1 and RLVR to inference-time scaling, benchmarks, architectures, and predictions for 2026.
-
LLM 연구논문: 2025년 목록 (7월~12월)
LLM Research Papers: The 2025 List (July to December)
In June, I shared a bonus article with my curated and bookmarked research paper lists to the paid subscribers who make this Substack possible.
-
2025 연간 회고
2025 Year in Review
An eventful year of progress in health and career, while making time for travel and reflection.
-
DeepSeek V3에서 V3.2로: 아키텍처, 희소 주의, 강화학습 업데이트
From DeepSeek V3 to V3.2: Architecture, Sparse Attention, and RL Updates
Understanding How DeepSeek's Flagship Open-Weight Models Evolved
-
세 가지 간단한 단계로 제품 평가하기
Product Evals in Three Simple Steps
Label some data, align LLM-evaluators, and run the eval harness with each change.
-
표준 LLM을 넘어서 - Sebastian Raschka 박사
Beyond Standard LLMs - by Sebastian Raschka, PhD
Linear Attention Hybrids, Text Diffusion, Code World Models, and Small Recursive Transformers
-
새로운 Principal 기술 IC들을 위한 조언: 나에게 쓰는 노트
Advice for New Principal Tech ICs (i.e., Notes to Myself)
Based on what I've learned from role models and mentors in Amazon
-
LLM 평가의 4가지 주요 접근법 이해하기 (기초부터)
Understanding the 4 Main Approaches to LLM Evaluation (From Scratch)
Multiple-Choice Benchmarks, Verifiers, Leaderboards, and LLM Judges with Code Examples
-
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.
-
Qwen3을 처음부터 이해하고 구현하기
Understanding and Implementing Qwen3 From Scratch
A Detailed Look at One of the Leading Open-Source LLMs
-
GPT-2에서 gpt-oss로: 아키텍처 발전 분석
From GPT-2 to gpt-oss: Analyzing the Architectural Advances
And How They Stack Up Against Qwen3
-
주요 LLM 아키텍처 비교
The Big LLM Architecture Comparison
From DeepSeek-V3 to Kimi K2: A Look At Modern LLM Architecture Design
-
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
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
LLM 추론을 위한 강화학습의 현황
The State of Reinforcement Learning for LLM Reasoning
Understanding GRPO and New Insights from Reasoning Model Papers
-
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.
-
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…
-
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.
-
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.
-
LLM 추론 모델 인퍼런스의 현황
The State of LLM Reasoning Model Inference
Inference-Time Compute Scaling Methods to Improve Reasoning Models
