-
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.
-
-
글쓰기의 역설적 규칙들
Seemingly Paradoxical Rules of Writing
With regard to writing, there are many rules and also no rules at all.
-
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.
-
나의 미니멀한 MacBook Pro 설정 가이드
My Minimal MacBook Pro Setup Guide
Setting up my new MacBook Pro from scratch
-
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.
-
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.
-
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
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
작동하고 작동하지 않는 작업별 LLM 평가
Task-Specific LLM Evals that Do & Don't Work
Evals for classification, summarization, translation, copyright regurgitation, and toxicity.
-
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
-
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.
-
언어 모델링 논문 목록 (논문 클럽 시작하기)
Language Modeling Reading List (to Start Your Paper Club)
Some fundamental papers and a one-sentence summary for each; start your own paper club!
-
2023년 연간 회고
2023 Year in Review
An expanded charter, lots of writing and speaking, and finally learning to snowboard.
-
푸시 알림: 무엇을 보내고, 무엇을 피하고, 얼마나 자주 보낼지
Push Notifications: What to Push, What Not to Push, and How Often
Sending helpful & engaging pushes, filtering annoying pushes, and finding the frequency sweet spot.
-
도메인 외 파인튜닝을 통한 환각 탐지 부트스트래핑
Out-of-Domain Finetuning to Bootstrap Hallucination Detection
How to use open-source, permissive-use data and collect less labeled samples for our tasks.
-
2023 AI 엔지니어 서밋 회고
Reflections on AI Engineer Summit 2023
The biggest deployment challenges, backward compatibility, multi-modality, and SF work ethic.
-
AI 엔지니어 2023 기조 연설 - LLM 시스템을 위한 빌딩 블록
AI Engineer 2023 Keynote - Building Blocks for LLM Systems
Evals, retrieval-augmented generation, guardrails, and collecting feedback; all that good stuff.
-
생성형 요약의 평가 및 환각 탐지
Evaluation & Hallucination Detection for Abstractive Summaries
Reference, context, and preference-based metrics, self-consistency, and catching hallucinations.
-
LLM 패턴을 문제에 맞추는 방법
How to Match LLM Patterns to Problems
Distinguishing problems with external vs. internal LLMs, and data vs non-data patterns
-
AI 시스템의 맥락 기반 검색
Contextual Retrieval in AI Systems \ Anthropic
-
효과적인 AI 에이전트 구축
Building Effective AI Agents \ Anthropic
-
Claude 3.5 Sonnet으로 SWE-bench Verified의 기준을 높이다
Raising the bar on SWE-bench Verified with Claude 3.5 Sonnet Jan 06, 2025