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원제 — Accessing OpenAI GPT models on Amazon Bedrock with global cross-Region inference in Thailand, Malaysia, Singapore, Indonesia, and Taiwan

“Learn how to run OpenAI GPT-6 Sol, Luna, and Astra on Amazon Bedrock through global cross-Region inference from Singapore, Jaka…”

나온 때 2026-10-12

가져온 때 2026-10-12 23:01 UTC · cd9c7afc09d255a5

뜯은 자리 — 목록뿐 (원문을 못 읽었다)

원제 — Applying technology to understand and treat disease

“In collaboration with scientists and research partners, Google is helping to make disease more detectable, treatable, and preve…”

대상 주장 — @keyframes gemini-draw-border-timeline{0%{--border-draw-percent:0%;--border-mask-color:transparent;--btn-bg-color:var(--btn-bg-start);--gradient-angle:var(--start-angle,145deg)}85%{--bord…

나온 때 2026-10-12

가져온 때 2026-10-12 23:01 UTC · 611b6aeecd885647

뜯은 자리 — 원문 페이지

원제 — Deploy decision models with llama.cpp on Amazon SageMaker AI

“Learn how to deploy decision models with llama.cpp on Amazon SageMaker AI. This technical how-to serves OpenAI-compatible chat …”

대상 주장 — 8xlarge, 32 threads llama-cpp:server-sagemaker-cpu-v1 231/231 179/231 (77.5%) 1.715 seconds 19.291 seconds Qwen3.5-4B Q4KM, ml.

나온 때 2026-10-12

가져온 때 2026-10-12 23:01 UTC · ae2468b8fd36a230

뜯은 자리 — 목록뿐 (원문을 못 읽었다)

원제 — Run multi-node inference workloads using Ray LLM Deep Learning Containers

“Learn how to deploy one large language model across two GPU nodes on Amazon EKS using the multi-node Ray LLM Deep Learning Cont…”

나온 때 2026-10-12

가져온 때 2026-10-12 23:01 UTC · d2840f479e1f9b59

뜯은 자리 — 목록뿐 (원문을 못 읽었다)

원제 — ICYMI: What landed for AI builders in September 2026

“A monthly recap of the latest Amazon Bedrock, Amazon Bedrock AgentCore, and Strands updates from September 2026: broader model …”

대상 주장 — Strands Decider 2B is a small, open source, 2B-parameter decision model that picks between predefined options (instead of generating text), delivering answers in ~115ms locally.

나온 때 2026-10-09

가져온 때 2026-10-09 23:01 UTC · b8dcceb26e0c78ac

뜯은 자리 — 목록뿐 (원문을 못 읽었다)

원제 — How Postman runs Agent Mode for 40 million developers on Amazon Bedrock

“Building an AI agent that works in a demo is a different problem from running one for 40 million developers. Postman and AWS sh…”

대상 주장 — Building an AI agent for a demo and operating one for 40 million developers are different engineering problems.

나온 때 2026-10-09

가져온 때 2026-10-09 23:01 UTC · bb5f27d54530b3f8

뜯은 자리 — 목록뿐 (원문을 못 읽었다)

원제 — Impactful scheduling for GPU clusters

제목과 링크만 주는 소스라 인용할 것이 없다.

대상 주장 — Based on submitted workloads, at any moment in time we have outstanding requests for 2-3x more GPUs than are available.

나온 때 2026-10-09

가져온 때 2026-10-09 23:01 UTC · a45cab2b414bcf3b

뜯은 자리 — 원문 페이지

원제 — Asana cuts model costs 76x in browser tests with GPT-6.1 Sol

“Using GPT-6 Astra in Codex, Asana made its browser agent 76x cheaper and 5x faster in tests to offer customers more capable mod…”

대상 주장 — Using GPT-6 Astra in Codex, Asana made its browser agent 76x cheaper and 5x faster in tests to offer customers more capable models.

나온 때 2026-10-09

가져온 때 2026-10-09 23:01 UTC · 0f1eab57236c05e2

뜯은 자리 — 목록뿐 (원문을 못 읽었다)

원제 — Sophos cuts threat investigation time by 96% with OpenAI Daybreak

“Discover how Sophos uses OpenAI’s Daybreak to cut cyber-threat investigation time by 96% and automate 52% of MDR cases while …”

대상 주장 — Discover how Sophos uses OpenAI’s Daybreak to cut cyber-threat investigation time by 96% and automate 52% of MDR cases while preserving human oversight.

나온 때 2026-10-09

가져온 때 2026-10-09 23:01 UTC · c2bd9140cf451f0f

뜯은 자리 — 목록뿐 (원문을 못 읽었다)

원제 — LegalOn halves Codex costs while maintaining development speed

“LegalOn cut estimated daily Codex costs by 65% while maintaining development speed. It matched Astra, Sol, and Luna to tasks an…”

대상 주장 — LegalOn cut estimated daily Codex costs by 65% while maintaining development speed.

나온 때 2026-10-08

가져온 때 2026-10-09 23:01 UTC · a13e16f89e5be705

뜯은 자리 — 목록뿐 (원문을 못 읽었다)

원제 — Introducing Playground: Create and play custom games

“Playground is a new experimental gaming platform that lets you create, play, and share custom games.”

대상 주장 — @keyframes gemini-draw-border-timeline{0%{--border-draw-percent:0%;--border-mask-color:transparent;--btn-bg-color:var(--btn-bg-start);--gradient-angle:var(--start-angle,145deg)}85%{--bord…

나온 때 2026-10-07

가져온 때 2026-10-09 23:01 UTC · 6f521205a43d3a67

뜯은 자리 — 원문 페이지

원제 — Proaction boosts sales 60% and saves 75+ hours with Codex

“With Codex, GPT-Live-1, and GPT-6 Astra, Proaction builds, operates, and sells modern fleet management faster.”

대상 주장 — With Codex, GPT-Live-1, and GPT-6 Astra, Proaction builds, operates, and sells modern fleet management faster.

나온 때 2026-09-25

가져온 때 2026-09-25 23:01 UTC · 794516609b5bcb0e

뜯은 자리 — 목록뿐 (원문을 못 읽었다)

원제 — Scaling MoE reinforcement learning on Amazon EKS with EFA and DeepEP with 40% more throughput

“Learn how to scale Mixture-of-Experts (MoE) reinforcement learning on Amazon EKS using Elastic Fabric Adapter (EFA) and DeepEP.…”

대상 주장 — Performance and cost optimizations This section covers two key optimizations: using DeepEP to reduce expert-parallel communication overhead over EFA, and using Amazon Elastic Compute Clou…

나온 때 2026-09-25

가져온 때 2026-09-25 23:01 UTC · b9cac234d7f78a36

뜯은 자리 — 목록뿐 (원문을 못 읽었다)

원제 — Accelerate multimodal RL training with SkyRL on Amazon SageMaker HyperPod

“Learn how to run SkyRL, an open-source reinforcement learning framework, on Amazon SageMaker HyperPod to post-train a Qwen3-VL-…”

나온 때 2026-09-25

가져온 때 2026-09-25 23:01 UTC · a4bd252f1f6e25fa

뜯은 자리 — 목록뿐 (원문을 못 읽었다)

원제 — NarrateAI: production-ready LLM quality assurance on Amazon Bedrock

“NarrateAI delivers production-ready LLM quality assurance on Amazon Bedrock. This post details five techniques—adaptive pipel…”

대상 주장 — NarrateAI transforms business intelligence for over 4,000 AWS executive leaders through a two-layer architecture built on Amazon Bedrock AgentCore, a platform to build, connect, and optim…

나온 때 2026-09-25

가져온 때 2026-09-25 23:01 UTC · 4c2843cb765a0255

뜯은 자리 — 목록뿐 (원문을 못 읽었다)

원제 — Deploying real-time personalized speech with Qwen3-TTS on Amazon SageMaker AI

“Deploy the publicly available Qwen3-TTS-12Hz-1.7B-Base text-to-speech model from Amazon SageMaker JumpStart to a fully managed,…”

대상 주장 — g6.4xlarge (1x NVIDIA L4 GPU, 24 GB), which is sufficient for the 1.7B model.

나온 때 2026-09-25

가져온 때 2026-09-25 23:01 UTC · c28aa6c37d511074

뜯은 자리 — 목록뿐 (원문을 못 읽었다)

원제 — How Datacor built self-service rental analytics with Amazon Quick Sight

“Learn how Datacor built a self-service rental analytics experience for gas and welding distributors by embedding Amazon Quick S…”

대상 주장 — An Azure Data Factory pipeline extracts source tables as Apache Parquet files and streams them into Amazon Simple Storage Service (Amazon S3).

나온 때 2026-09-25

가져온 때 2026-09-25 23:01 UTC · 09c0269709886573

뜯은 자리 — 목록뿐 (원문을 못 읽었다)

원제 — Multi-Region training with Amazon SageMaker HyperPod and Qumulo

“Amazon SageMaker HyperPod and Cloud Native Qumulo let you place training compute in one AWS Region while keeping your dataset i…”

대상 주장 — The spoke cluster runs in the US West (Oregon) Region (us-west-2) and reads the data remotely through CDF with 60 ms of network latency.

나온 때 2026-09-25

가져온 때 2026-09-25 23:01 UTC · ad552593d4bb8e33

뜯은 자리 — 목록뿐 (원문을 못 읽었다)

원제 — Improving HCLS AI reasoning with open-source agent skills

“AI agents on foundation models often misapply healthcare and life sciences decision frameworks, citing the right guideline but …”

대상 주장 — The collection covers 38 skills across 11 HCLS domains including genomics, drug discovery, claims operations, and medical imaging.

나온 때 2026-09-16

가져온 때 2026-09-16 23:01 UTC · f38341d7421950db

뜯은 자리 — 목록뿐 (원문을 못 읽었다)

원제 — Fault tolerant distributed training on Amazon EKS using NVRx

“Integrate NVIDIA Resiliency Extension (NVRx) into PyTorch FSDP training on Amazon EKS to overlap checkpoint I/O with training a…”

대상 주장 — Synchronous checkpointing adds a second source of idle time: every save blocks all ranks on I/O, which on the cluster sizes in this post consumed up to 40% of total wall time.

나온 때 2026-09-16

가져온 때 2026-09-16 23:01 UTC · 8f850829b2016926

뜯은 자리 — 목록뿐 (원문을 못 읽었다)

원제 — Our framework for reporting model misalignment

“OpenAI shares a framework for tracking, investigating, and disclosing model misalignment, alongside six reports of unexpected o…”

나온 때 2026-09-16

가져온 때 2026-09-16 23:01 UTC · 827a0c403a3c663f

뜯은 자리 — 목록뿐 (원문을 못 읽었다)

원제 — Helping older adults use AI in everyday life

“OpenAI and AARP are bringing free, hands-on ChatGPT workshops to 1,000 older adults across 10 U.S. cities to build practical AI…”

나온 때 2026-09-16

가져온 때 2026-09-16 23:01 UTC · a8a40dafc20d6b2f

뜯은 자리 — 목록뿐 (원문을 못 읽었다)

원제 — Optimizing agent system prompts with Amazon Bedrock AgentCore

“AgentCore optimization turns production traces into proposed configuration changes, then validates them before promotion. This …”

대상 주장 — A single reflector pass can focus on patterns in the first 5–10 traces it examines and miss failure modes that appear only in a smaller portion of the corpus.

나온 때 2026-09-16

가져온 때 2026-09-16 23:01 UTC · 93c67c600c06037d

뜯은 자리 — 목록뿐 (원문을 못 읽었다)

원제 — Build a serverless PII redaction pipeline with Amazon Bedrock Data Automation

“Learn how to automate end-to-end PII detection and redaction from scanned documents at scale using Amazon Bedrock Data Automati…”

대상 주장 — We tested document samples spanning six document quality levels in our use case, from clean typed forms through low-resolution 100 dots per inch (DPI) scans (Table 2).

나온 때 2026-09-16

가져온 때 2026-09-16 23:01 UTC · 6fc8596fd9d93039

뜯은 자리 — 목록뿐 (원문을 못 읽었다)

원제 — Reimagining advertising with AI

“Explore new AI-powered advertising experiences from OpenAI, including Sponsored Agents, tools for marketers, and integrations w…”

나온 때 2026-09-16

가져온 때 2026-09-16 23:01 UTC · 8c2a426559207a9d

뜯은 자리 — 목록뿐 (원문을 못 읽었다)

원제 — How to connect AI usage to business value

“Learn how ChatGPT Work and Codex analytics help teams understand AI usage and spend, identify training needs, and connect adopt…”

나온 때 2026-09-16

가져온 때 2026-09-16 23:01 UTC · ec2f604ddc5e799a

뜯은 자리 — 목록뿐 (원문을 못 읽었다)

원제 — Take on your most ambitious work with GPT-6 Astra on Amazon Bedrock

“GPT-6 Astra from OpenAI is now generally available on Amazon Bedrock. It brings deeper reasoning and sharper judgment to your m…”

나온 때 2026-09-08

가져온 때 2026-09-08 23:01 UTC · 61555b0baa6fc6fd

뜯은 자리 — 목록뿐 (원문을 못 읽었다)

원제 — Pathway’s brain-inspired architecture development on Amazon SageMaker HyperPod

“Pathway's Baby Dragon Hatchling (BDH) is a brain-inspired, post-transformer architecture that reasons in latent space instead o…”

대상 주장 — A 150M-parameter model, built on Pathway’s BDH architecture, reasons recurrently in latent space, and sets a new state of the art in cost efficiency on ARC-AGI-1.

나온 때 2026-09-08

가져온 때 2026-09-08 23:01 UTC · ac890dcdb67da5cf

뜯은 자리 — 목록뿐 (원문을 못 읽었다)

원제 — Amazon SageMaker Feature Store introduces UpdateRecord for feature-level writes

“Amazon SageMaker Feature Store now supports feature-level writes. With the new UpdateRecord API, you can update one or more fea…”

나온 때 2026-09-08

가져온 때 2026-09-08 23:01 UTC · dfa6dc0f8a44644d

뜯은 자리 — 목록뿐 (원문을 못 읽었다)

원제 — Govern models with MLflow and Amazon SageMaker AI Model Registry sync: Part 2

“Governing models across accounts is the next step after automatic model registration. This post extends managed MLflow and Amaz…”

대상 주장 — Topology 1: Hub-and-spoke central governance For larger organizations with multiple teams, it is common to have multiple separate development accounts with a central governance account.

나온 때 2026-09-08

가져온 때 2026-09-08 23:01 UTC · 83d37ea8c0fb1187

뜯은 자리 — 목록뿐 (원문을 못 읽었다)