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새로운 AI 기반의 Google Finance가 유럽으로 확대되고 있습니다
The new AI-powered Google Finance is expanding to Europe.
A screenshot of the AI-powered experience on Google Finance.
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딥 에이전트, LangSmith, 병렬 처리를 사용한 회사 실사 에이전트 구축
https://www.langchain.com/blog/building-a-company-due-diligence-agent-with-deep-agents-langsmith-and-parallel
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창의적인 거장들이 AI로 중소기업 광고를 만들 때 나타나는 결과 보기
See what happens when creative legends use AI to make ads for small businesses.
black and white card with headshots of susan credle, jayonta jenkins and tiffany rolfe
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vLLM V0에서 V1로: 강화학습에서 수정보다 정확성을 먼저
vLLM V0 to V1: Correctness Before Corrections in RL
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검색에서 바로 해볼 수 있는 5가지 가드닝 팁
5 gardening tips you can try right in Search
An abstract background featuring soft, stippled illustrations of flowers and a butterfly in a bright palette of blue, green, and red. In the center of the image is a white circle c…
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AlphaEvolve: Gemini 기반 코딩 에이전트로 다양한 분야의 영향력 확장 – Google DeepMind
AlphaEvolve: Gemini-powered coding agent scaling impact across fields â Google DeepMind
Explore how AlphaEvolve's Gemini-powered algorithms are driving impact across business, infrastructure, and science.
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에이전트 관찰성: 학습을 강화하기 위한 피드백의 필요성
https://www.langchain.com/blog/agent-observability-needs-feedback-to-power-learning
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오픈 ASR 리더보드에 벤치마킹 조작 방지 기능 추가
Adding Benchmaxxer Repellant to the Open ASR Leaderboard
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구글이 XPRIZE, Range Media Partners와 함께 350만 달러 규모의 'Future Vision' 영화 경진대회 개최
Google is partnering with XPRIZE and Range Media Partners on the $3.5 million Future Vision film competition.
<img src="https://storage.googleapis.com/gweb-uniblog-publish-prod/images/futurevisionxprize_social.max-600x600.format-webp.webp" />Google is partnering with XPRIZE and Range Media…
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Open SWE: 내부 코딩 에이전트를 위한 오픈소스 프레임워크
Open SWE: An Open-Source Framework for Internal Coding Agents
Built on Deep Agents and LangGraph, Open SWE provides the core architectural components for internal coding agents.
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오픈 모델이 임계점을 돌파했다
Open Models have crossed a threshold
Open models like GLM-5 and MiniMax M2.7 now match closed frontier models on core agent tasks — file operations, tool use, and instruction following — at a fraction of the cost and …
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Import AI 455: AI 시스템들이 자신들을 스스로 구축하기 시작할 것이다
Import AI 455: AI systems are about to start building themselves.
The first step towards recursive self improvement
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AI와 함께 일하고 성과를 복합하는 방법
How to Work and Compound with AI
Context as infra, taste as config, verification for autonomy, scale via delegation, closing the loop.
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Interrupt 2026: 엔터프라이즈 규모의 에이전트
Previewing Interrupt 2026: Agents at Enterprise Scale
This year, we're doing it again. Interrupt 2026 is May 13–14 at The Midway in San Francisco, and the lineup, the format, and the scale have all leveled up.
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메모리 검색 개선: New Computer가 LangSmith로 50% 높은 회상률을 달성한 방법
Improving Memory Retrieval: How New Computer achieved 50% higher recall with LangSmith
New Computer used LangSmith to improve their memory retrieval system, achieving 50% higher recall by tracking regressions in comparison view and adjusting conversation prompts acco…
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AI 에이전트란 무엇인가?
What is an AI agent?
Introducing a new series of musings on AI agents, called "In the Loop".
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LLM 판사를 인간 선호도에 정렬하기
Aligning LLM-as-a-Judge with Human Preferences
Deep dive into self-improving evaluators in LangSmith, motivated by the rise of LLM-as-a-Judge evaluators plus research on few-shot learning and aligning human preferences.
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LangSmith를 이용한 회귀 테스트
Regression Testing with LangSmith
Evaluate and iterate on LLM applications with confidence using LangSmith's regression testing. Compare experiments, track performance, and identify changes.
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Azure 마켓플레이스에서 LangSmith이 거래 가능한 상품으로 출시 발표
Announcing LangSmith is now a transactable offering in the Azure Marketplace
LangSmith is now available in Azure Marketplace. Deploy the DevOps platform for LLM apps in your Azure VPC with full data control and MACC credit support.
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평가 주도 개발을 통한 LLM 신뢰성의 반복적 향상
Iterating Towards LLM Reliability with Evaluation Driven Development
Dosu uses evaluation driven development and LangSmith to build reliable LLM products at scale, monitor production performance, and iterate with confidence.
