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Kimi K3 2.8T-A50B: 지금까지 출시된 가장 큰 오픈 모델; Opus 4.8급 성능에 Sonnet 5 가격대
[AINews] Kimi K3 2.8T-A50B: the largest open model ever released; Opus 4.8-class at Sonnet 5 pricing
a great week for open models continues.
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대규모 언어 모델에서의 정렬 허위 행동
Alignment faking in large language models
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대규모 언어 모델의 감정 개념과 그 기능
Emotion concepts and their function in a large language model
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대규모 언어 모델의 창발적 내성 인식
Emergent introspective awareness in large language models
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엔비디아 코스모스 3, 네모트론 3 울트라, RTX 스파크
[AINews] NVIDIA Cosmos 3, Nemotron 3 Ultra, and RTX Spark
Jensen scores a huge win.
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[AINews] Anthropic이 Series H에서 9650억 달러 모금, Opus 4.8 및 Dynamic Workflows/ultracode 출시
[AINews] Anthropic raises $965B Series H, releases Opus 4.8 and Dynamic Workflows/ultracode
Total Anthropic victory!
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Databricks, 엔터프라이즈 에이전트 워크플로우에 GPT-5.5 도입
Databricks brings GPT-5.5 to enterprise agent workflows | OpenAI
Databricks uses GPT-5.5 for enterprise agent workflows after the model set a new state of the art on the OfficeQA Pro benchmark.
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현대 LLM의 어텐션 변형 시각 가이드
A Visual Guide to Attention Variants in Modern LLMs
From MHA and GQA to MLA, sparse attention, and hybrid architectures
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ImportAI 449: LLM이 다른 LLM을 학습시킴; 72B 분산 학습 실행; 컴퓨터 비전은 생성 텍스트보다 더 어렵다
ImportAI 449: LLMs training other LLMs; 72B distributed training run; computer vision is harder than generative text
Will AI cause a political interregnum
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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.
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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.
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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
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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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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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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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Claude 3.5 Sonnet으로 SWE-bench Verified의 기준을 높이다
Claude SWE-Bench Performance
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효과적인 AI 에이전트 구축
Building Effective AI Agents