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REAL NEWS FEED7/7 SOURCES LIVE · 100 ARTICLES · 23:01 UTC
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Amazon SageMaker Inference: 2026 year-to-date launches in review
Generative AI inference is uniquely hard: models are tens to hundreds of gigabytes, latency requirements are measured in tokens per second, cold starts can span multiple minutes as containers and weights transfer, GPU capacity is constrained, and traditional monitoring tools expose none of the token-level signals that matter in production. Amazon SageMaker AI offers customers the ability to deploy AI models and consume them by the instance (instead of by the token), using two paths: managed endpoints for teams that
Benchmarking LLM Inference at Scale with AIPerf
You’re deploying a model on a system. It starts up, prompts are getting responses. Now the hard question: Is this fast? Your instincts might lead you to send...
MilleMiglia: A realistic instance generator for middle-mile logistics
Algorithms & Theory
Introducing Kimi K3 on Amazon Bedrock
Open-weight models are changing the economics of building and deploying AI at scale. Rapid gains in intelligence and efficiency mean companies can match each workload with the right balance of capability, speed, and cost. AWS is building for a future in which organizations can adopt open-weight innovation with the reliability and security required for production. Today, Kimi K3 from Moonshot AI is available on Amazon Bedrock, giving you a powerful new option for coding and knowledge work. According to Moonshot AI,
Migrating multi-model AI agents to Amazon Bedrock AgentCore runtime
Organizations building multi-model agentic AI applications face growing infrastructure complexity. Managing container orchestration, scaling policies, identity, and observability for multiple model types adds operational overhead. Teams often spend more time on infrastructure than on agent logic development. Developers running agentic frameworks on self-managed infrastructure such as Amazon Elastic Container Service (Amazon ECS) with AWS Fargate have full control over their deployment configuration. As agentic work
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The new AgentCore runtime: Elastic, optimized, and consistently fast starts
Agents are no longer experiments. They process claims, write and review code, coordinate across systems, and run for hours without supervision. As agents take on more complex, longer-running work, the infrastructure underneath them must evolve just as fast. We built Amazon Bedrock AgentCore to help developers build, connect, and optimize agents securely at scale. AgentCore runtime, a capability of Amazon Bedrock AgentCore, is the managed compute layer that gives developers a fully managed environment to deploy and
07Deploy Hugging Face models on Amazon SageMaker AI with coding agents
Deploying a Hugging Face model to production means making a dozen decisions: choosing the right serving container for the model’s architecture, confirming the current image tag for your AWS Region, and matching an instance type to the model’s memory footprint. Beyond infrastructure, you must wire autoscaling so you don’t burn GPU hours on an idle endpoint. You also set Amazon CloudWatch alarms that catch silent failures before your users do. After you’ve made those decisions, Amazon SageMaker AI collapses that work
08Introducing Amazon SageMaker HyperPod Inference Gateway
Eliminate GPU waste. Reduce first-token latency by up to 82%. Install one Kubernetes-native addon with zero application changes. The problem: Naive routing wastes your most expensive resource Running large language models (LLMs) at scale on GPU clusters is expensive. The default Kubernetes load balancers are making it worse. Round-robin and least-connections algorithms have no visibility into what’s happening inside your GPUs: which pods have saturated KV caches, which are mid-way through long-context generations,
09Introducing the Australian Youth Safety Blueprint
OpenAI introduces the Australian Youth Safety Blueprint, a six-pillar roadmap for safer AI experiences that protect and empower young people.
10The future of practice: Enabling teachers to create learning interactives with generative UI
Education Innovation
11Reduce time-to-hire for quality candidates with AI-powered Amazon Connect Talent
Hiring at scale in industries such as retail, logistics, hospitality, and others has its fair share of challenges. Recruiting teams are expected to fill hundreds of roles within tight timelines, often with limited capacity and with tools that weren’t designed to seamlessly work together. As a result of this, applications pile up, phone screens get delayed, and strong candidates move on before anyone has a chance to reach out to them. The cost of this isn’t just operational. Every unfilled role slows production, imp
12Selecting a vector store for Amazon Bedrock Knowledge Bases
When building a Retrieval Augmented Generation (RAG) solution with Amazon Bedrock Knowledge Bases , selecting the right vector store impacts performance and cost. Amazon Bedrock Knowledge Bases offers a fully managed option and a customer-managed option where you choose your own vector store. This post focuses on the customer-managed path, comparing the three supported backends: Amazon OpenSearch Service , Amazon Aurora PostgreSQL with pgvector , and Amazon S3 Vectors , a capability of Amazon Simple Storage Service
13A serverless, data-driven Git metrics dashboard using Amazon Quick Sight
Git activity is one of the richest signals engineering teams produce that can provide continuous observability into development analytics. The challenge is extracting these Git metrics at scale, which has traditionally required hand-rolled extract, transform, and load (ETL) jobs, dedicated infrastructure, and ongoing maintenance. Further, with modern development tools becoming more prevalent, teams need a clear way to measure whether these tools are making developers faster, or if the investment is not paying off.
14A shared agentic platform for Wood Mackenzie, on Amazon Bedrock AgentCore
Building a working agentic prototype takes an afternoon. Getting it to production is where the work explodes. The moment an agent has to serve more than one user, a new layer of engineering appears and it’s critical to tell whether the agent is doing the right thing on real traffic. Concurrency, session isolation, identity, persistent state, scaling, and guardrails are all layers that most teams rebuild from scratch every time, even though a standardized platform for each agent is a more repeatable approach. The re
15How MRH Trowe enabled secure self-service AI agents in financial services
Basic AI chat isn’t enough for financial services organizations that need secure, self-service AI agents. In financial services, employees need AI that can work with internal systems and sensitive client data, stay inside a governed environment, and remain auditable and cost-transparent. All of this must happen without every team standing up its own tools. This post shows how MRH Trowe , one of Germany’s leading commercial and industrial insurance brokers, gave approximately 400 employees secure access to self-serv
16Implementing defense-in-depth authorization for MCP tools on Amazon Quick
Each Model Context Protocol (MCP) tool invocation on Amazon Quick is an access event that can require defense-in-depth authorization at the tool and parameter level. This applies in addition to a valid token. Without granular controls, a single misconfigured permission can bypass the access requirements that organizations might need to fulfill for compliance purposes. In this blog post, you implement a multi-gate authorization pattern that evaluates OpenID Connect (OIDC) JSON Web Token (JWT) claims in sequence. The
17Enhancing industrial safety AI with synthetic data on Amazon SageMaker AI
Industrial safety AI refers to the use of technologies like computer vision and predictive analytics to find and stop workplace dangers. Synthetic data augmentation is emerging as a practical solution to one of the hardest problems in industrial safety AI: the scarcity of training images depicting people in dangerous proximity to heavy machinery. Industries deploying autonomous equipment (for example, agriculture, construction, mining, and manufacturing) need reliable person-detection models. However, the highest-r
18How Cooley is accelerating IPO work with ChatGPT
Cooley built GO Public with ChatGPT Work to bring intelligence to the IPO process, helping lawyers surface issues earlier and focus judgment where it matters most.
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