Tag Archives: news

Happy 20th Birthday, Amazon EC2

Post Syndicated from Channy Yun (윤석찬) original https://aws.amazon.com/blogs/aws/happy-20th-birthday-amazon-ec2/

Twenty years ago today, Jeff Barr wrote a blog post that launched the Amazon EC2 Beta. That single post introduced resizable Linux virtual servers in the cloud, billed by the hour, with one instance type (m1.small) in one Region (US East). It was minimal yet useful, and it changed how the world thinks about computing infrastructure.

In 2021, Jeff covered the fifteen years of EC2 with the backstory and memorable EC2 launches. Over the last five years, AWS has continued to push the boundaries of what cloud computing can deliver, building custom silicon for general-purpose and AI workloads and expanding EC2 into new form factors and deployment models that our customers in 2006 could not have imagined.

The 20 years in brief
In his 15th anniversary post, Jeff chose important milestones of EC2 that established the foundational building blocks that customers still rely on today. Amazon Elastic Block Store (2008) provided persistent block storage. Elastic Load Balancing, Auto Scaling, and Amazon CloudWatch (2009) made applications scalable and highly available. Amazon Virtual Private Cloud (2009) gave customers logically isolated networks. AWS Nitro System (2017) enabled faster innovation and enhanced security. AWS Graviton processors (2018) were designed for cost-sensitive scale-out workloads.

Over 20 years, EC2 grew from one to over 1,200 instance types to meet customer needs across general-purpose, compute-, memory-, and storage-optimized, accelerated computing, and high-performance computing families. These instances expanded from one AWS Region to 39 Regions globally. AWS also extended EC2 beyond the Region boundary with AWS Outposts (2018) running EC2 instances locally, AWS Local Zones (2019) place globally, and AWS Wavelength (2019) inside global 5G telecommunications carrier networks.

While I hate to play favorites, I want to choose some of my favorite EC2 launches of the past five years:

  • AWS Inferentia for ML inference at scale (2019): We introduced purpose-built ML inference instances (inf1) with AWS Inferentia chips. Amazon EC2 Inf2 instances became generally available in April 2023 for large-scale generative AI inference workloads. Together with the Inferentia family, AWS Trainium instances now give customers a full stack of AWS-designed silicon optimized for every phase of the AI lifecycle both inference and training.
  • EC2 Mac instances (2020): The first Mac instances (mac1) were built on Apple Mac mini with Intel Core i7 (Coffee Lake) on the AWS Nitro System. Mac M1 (mac2) instances launched in July 2022 as the first Arm-based macOS instances on EC2. M2 Pro Mac instances followed in 2023, M4 and M4 Pro Mac instances in 2025, M3 Ultra Mac instances and M4 Max Mac instances in 2026, giving Apple developers a complete range of cloud-based build and test environments for macOS, iOS, iPadOS, tvOS, watchOS, and visionOS apps.
  • AWS Trainium for full-stack AI workloads at scale (2021): In November 2021, we previewed Trn1 instances with AWS Trainium accelerators optimized for high-performance deep learning training. In December 2024, Trn2 instances powered by AWS Trainium2 launched, with Trn2 UltraServers linking 64 Trainium2 accelerators via NeuronLink for training trillion-parameter foundation models. At AWS re:Invent 2025, Trn3 UltraServers powered by AWS Trainium3 deliver the best token economics for next-generation agentic, reasoning, and video generation applications. A single Trn3 UltraServer interconnects up to 144 Trainium3 chips to train and serve the largest frontier models. Now, AWS Trainium3 delivers the leading price-performance for high-performance AI training and inference at scale.
  • EC2 Capacity Blocks for ML (2023): This new EC2 usage model further democratizes ML democratizes ML by making it easy to access GPU instances to train and deploy ML and generative AI models. You reserve the GPU capacity you need (initially P5 instances) for a future date and only for the duration you require. In November 2024, EC2 Capacity Blocks for ML added supported for provisioning in a matter of minutes and extending up to six months. Now, EC2 Capacity Blocks for ML supports P6-B300, P6-B200, P5e, P5en, P4d, P4de, Trn1, Trn2, and Trn3 instances in addition to P5.
  • AWS Graviton5 (2025): Building on eight years of Graviton innovation since 2018, we previewed Graviton5 chips in AWS re:Invent 2025 and launched M9g and M9gd instances powered by Graviton5 and built on the sixth-generation AWS Nitro System. C9g and C9gd followed in June 2026. Now, Graviton5 features 192 cores, a 5x larger cache, and up to 33% lower inter-core latency, making it well suited for the growing demands of agentic AI workloads such as real-time reasoning, code generation, and multi-step task orchestration that require continuous, high-throughput CPU compute at scale.
  • AWS Nitro Isolation Engine (2026): Customers wanted to see, not just hear from us, proof of workload isolation in the Nitro Hypervisor. The Nitro Isolation Engine is a purpose-built component inside the Nitro Hypervisor, harnessing formal verification to provide mathematical assurance that customer workloads are isolated from each other and AWS operators, pioneering a new standard for mathematically proven cloud security. This feature is also based on the sixth-generation AWS Nitro System which has continued to evolve since our introduction in 2017.

The foundation underneath it all
Despite two decades of innovation, the fundamental value proposition of Amazon EC2 has not changed. Customers use it to get secure, resizable compute capacity in minutes, pay only for what they consume, and scale on demand without long-term commitments. That same flexibility now extends to AI workloads at a scale no one anticipated in 2006.

EC2 remains the foundational compute layer of AWS. Amazon ECS, Amazon EKS, AWS Lambda, AWS Fargate, AWS Batch, Amazon EMR, Amazon SageMaker AI, and Amazon Bedrock ultimately run on EC2 capacity. Every architectural pattern customers have built over the past twenty years, from simple web servers to trillion-parameter foundation model training clusters, starts with a decision to launch an instance.

We made strong foundational decisions in 2006, and we left room for the service to grow. Twenty years later, that strategy of creating services that are minimal-yet-useful, launching quickly, and iterating rapidly in response to your feedback continues to guide how we build. The next twenty years of cloud computing will demand capabilities we have not yet imagined. Amazon EC2 will continue to be the foundation where your workloads run.

To learn more about Amazon EC2, visit the Amazon EC2 product page or check out what’s new with EC2.

— Channy

Arm’s AGI Data Center CPU at Hot Chips 2026

Post Syndicated from Ryan Smith original https://www.servethehome.com/arms-agi-data-center-cpu-at-hot-chips-2026/

At Hot Chips 2026, Arm is showcasing their AGI data center CPU, the company’s first complete commercial CPU design. Based around Arm’s Neoverse V3 CPU cores, AGI is designed to power the next generation of agentic AI servers

The post Arm’s AGI Data Center CPU at Hot Chips 2026 appeared first on ServeTheHome.

Intel Core Series 3 (Wildcat Lake) CPU at Hot Chips 2026

Post Syndicated from Ryan Smith original https://www.servethehome.com/intel-core-series-3-wildcat-lake-cpu-at-hot-chips-2026/

At Hot Chips 2026, Intel went deeper into its newest budget SoC design, Wildcat Lake, which is being used in consumer and edge devices

The post Intel Core Series 3 (Wildcat Lake) CPU at Hot Chips 2026 appeared first on ServeTheHome.

AWS Weekly Roundup: Student Rewards on AWS Builder Center, Local Zone in Las Vegas, and more (August 24, 2026)

Post Syndicated from Esra Kayabali original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-student-rewards-on-aws-builder-center-local-zone-in-las-vegas-and-more-august-24-2026/

During my time at AWS, I have always looked for opportunities to work with students. I have delivered over 50 talks at universities across the region, and watching the potential in the room is always a strong motivator. It reminds me of why I do this work, and that the students I meet today may well become our customers and collaborators tomorrow. That is why I am happy to open this week with Student Rewards on AWS Builder Center.

Rick Suttles published Introducing Student Rewards on AWS Builder Center, a new benefit for verified higher education students. When you verify your enrollment through SheerID and complete your Builder Center profile, you unlock 12 months of premium AWS Skill Builder access (900+ courses, hands-on labs, certification exam prep, and game-based learning). From there, you earn badges through actions on Builder Center: publishing articles, commenting, and maintaining engagement. At 7 badges, you unlock $10 in AWS Credits. At 14 badges, another $20 in credits. At 21 badges, you earn an AWS Foundational Certification exam voucher ($100 value).

This represents a commitment of over $500 million in resources during this back-to-school season, providing students with the training, tools, and certification needed to start building their careers in cloud and AI. Student Rewards is available to students 18 years or older and enrolled at accredited higher education institutions worldwide, subject to verification and applicable terms.

Verify your student status and start learning, earning badges, and unlocking rewards!

Last week’s launches
Here’s what else happened this week.

  • A new AWS Local Zone in Las Vegas, Nevada – This new Local Zone supports Amazon EC2 C7i, M7i, R7i, and C8gn instances, Amazon EBS, Amazon ECS, Amazon EKS, Application Load Balancer, and AWS Direct Connect. AWS Local Zones are now available in more than 30 metropolitan areas worldwide. In addition, AWS added a fourth Availability Zone to the Europe (London) Region, delivering next-generation AI and ML capacity with Trn3 and P6 accelerated instances alongside general-purpose compute.
  • Amazon EC2 Auto Scaling now supports batch instance termination – You can now pass up to 100 instance IDs to the TerminateInstanceInAutoScalingGroup API to terminate them as a batch, reducing the number of API calls needed to scale down your Auto Scaling groups. Batch termination is designed for workloads that need to rapidly scale down, such as AI/ML training jobs, container orchestrators, or event-driven architectures that spin up large fleets temporarily.
  • AWS CloudShell now includes a built-in visual file editor – CloudShell now includes a visual file editor that you can launch directly from your shell session using a single edit command. The editor supports syntax highlighting, find-and-replace, multi-line selection, copy-paste, and undo-redo in a single browser session. Whether you are updating a deployment script, modifying an agent steering file, editing a CloudFormation template, or fixing a Lambda function, the editor provides a seamless edit-and-run experience without leaving CloudShell.
  • Amazon Bedrock now supports SpaceXAI Grok 4.6 with cross-Region inference – Grok 4.6, a frontier model built for coding, agentic tasks, and knowledge work, is now available on Amazon Bedrock. The model runs on the bedrock-runtime endpoint with support for the Responses, Chat Completions, and Converse APIs, and works with existing account-level controls including model invocation logging, Amazon CloudWatch metrics, and cost itemization in AWS Cost Explorer.
  • Amazon Bedrock expands API support and introduces cross-Region inference for OpenAI models – Amazon Bedrock now supports OpenAI GPT-5.6 models (Sol, Terra, and Luna) with the Responses, Converse, and Chat Completions APIs, and adds cross-Region inference. Geo cross-Region inference routes requests within a predefined geography (including new US Geo support with this launch), while Global cross-Region inference serves requests from any commercial AWS Region at a lower per-token cost.
  • AgentCore payments is now generally available in Amazon Bedrock AgentCore – At general availability, AgentCore payments includes Quick Create for Coinbase credential provisioning directly within the AgentCore console, a curated Coinbase Bazar MCP server of pay-per-use x402 endpoints via AgentCore gateway, support for the Machine Payment Protocol (MPP), and the “upto” scheme in the x402 protocol for pay-per-inference and dynamic pricing use cases. To learn more, visit the AI Blog post.
  • AWS Glue 6.0 delivers 30% price reduction and Iceberg v3 support – AWS Glue 6.0 is built on a fully modernized runtime, Apache Spark 4.1, Python 3.13, and Scala 2.13, delivering 30% lower pricing than previous AWS Glue versions. With Iceberg v3, Glue 6.0 adds the VARIANT data type with automatic shredding for faster reads on semi-structured data, deletion vectors for high-performance row-level updates, geometry and geography data types for spatial processing, and flexible schema evolution.

For a full list of AWS announcements, be sure to keep an eye on the What’s New with AWS page.

Other AWS news
Here are some additional posts you may find useful:

  • Updates to your AWS Sign-In experience – AWS is gradually introducing updates to the sign-in and sign-up experience. The redesigned sign-in page introduces a unified email entry point for root users and customers using the new email-based sign-in method, while IAM users continue signing in with their account ID, username, and password. The page also includes sign-in options for customers whose AWS account was created using a supported identity provider (Google, GitHub, Apple, or Amazon.com). A redesigned session selection page simplifies viewing and managing multiple active account and role sessions. If your organization relies on browser automation or scripted workflows that interact with the sign-in page, review the post to understand how these changes might affect your configuration.
  • In the works: AWS Builder Lofts in Berlin, Hyderabad, and São Paulo – My colleague Channy announced plans to open new Builder Lofts in three cities. Since the first Builder Loft opened in San Francisco in July 2025, it has welcomed more than 22,500 developers through its doors. Each new location will be a permanent community space offering free workshops, networking events, pitch nights, content creation spaces, and co-working areas. Berlin will focus on digital sovereignty and security-readiness, Hyderabad on AI and cloud-native architecture, and São Paulo on supporting Latin America’s developer ecosystem.
  • AWS and Amazon WorkSpaces recognized as a Leader in the 2026 Gartner Magic Quadrant for Desktop as a Service – AWS has been named a Leader in the 2026 Gartner Magic Quadrant for Desktop as a Service (DaaS) for the third consecutive year, evaluated on Completeness of Vision and Ability to Execute. Gartner noted strengths in operations, geographic strategy, and overall viability. This is also the first year the evaluation includes Amazon WorkSpaces for AI agents, a capability that runs AI agents within the same desktop environment, security perimeter, and audit trail as human users.

For a full list of AWS blog posts, be sure to keep an eye on the AWS Blogs page.

Upcoming AWS events
Check your calendar and sign up for upcoming AWS events:

Visit the AWS Builder Center to meet other builders, contribute solutions, and find resources that help you keep building.

Summer is slowly coming to an end, and I am already planning a few days off in the coming months to keep me motivated through the rainy autumn ahead. I hope you are doing the same. Come back next week for more!

— Esra

AWS Glue 6.0 now available with 30% lower price and full Apache Iceberg v3 support

Post Syndicated from Channy Yun (윤석찬) original https://aws.amazon.com/blogs/aws/aws-glue-6-0-now-available-with-30-lower-price-and-full-apache-iceberg-v3-support/

Today, we are announcing the general availability of AWS Glue 6.0, delivering 30% lower pricing than previous AWS Glue versions and introducing full support for Apache Iceberg v3 features. AWS Glue 6.0 is built on a fully modernized runtime, Apache Spark 4.1, Python 3.12, and Scala 2.13, delivering faster performance.

With this release, AWS Glue provides the most complete Iceberg v3 implementation on any fully serverless managed Spark service, along with new capabilities that simplify ETL authoring, improve PySpark performance, and enable real-time streaming with single-digit millisecond latency.

What is new in AWS Glue 6.0
AWS Glue 6.0 delivers the complete Apache Iceberg v3 specification, built on Iceberg 1.11.0. The headline feature is the VARIANT data type with shredding support, which achieves faster query read performance compared to traditional string data type columns for semi-structured data.

With VARIANT shredding, you can store and query JSON, logs, and event data without flattening schemas, eliminating duplicate data copies, custom parsing code, and pipeline breakage when schemas change. This capability transforms how teams handle semi-structured data at scale.

Additional Iceberg v3 capabilities include:

  • Geometry and Geography data types: Enable native spatial processing for GIS analytics, location intelligence, and geospatial data pipelines directly on managed Spark.
  • Nanosecond-precision timestamps: Support IoT sensor data, scientific computing, and high-frequency financial workloads that require precision beyond standard milliseconds.
  • Unknown type handling: Process data with unexpected or evolving schemas without pipeline failures, providing resilience against upstream schema changes.

AWS Glue 6.0 also includes most significant upgrade in Spark 4.1, the modern runtime engine:

  • Spark declarative pipelines: Spark Declarative Pipelines introduces a simplified approach to ETL authoring. Data engineers declare transformations, specifying what data should look like, while the engine automatically determines execution order and optimization. This reduces the complexity of pipeline development and eliminates manual orchestration overhead.
  • Arrow-native Python UDFs and UDTFs: AWS Glue 6.0 introduces Arrow-native execution for Python User-Defined Functions (UDFs) and User-Defined Table Functions (UDTFs). This eliminates serialization overhead between Python and the JVM, improving PySpark performance for complex transformations.
  • Real-time streaming mode: For stateless streaming use cases, AWS Glue 6.0 introduces a real-time streaming mode that achieves single-digit millisecond latency. Built on Spark 4.1’s Real-Time Mode with Glue-optimized execution, this capability supports real-time event processing, low-latency data transformation pipelines, and time-sensitive data routing.

Getting started with AWS Glue 6.0
No API changes are required to use AWS Glue 6.0. You can select the new version using the existing --glue-version parameter in the create-job or update-job APIs through AWS Command Line Interface (AWS CLI), AWS SDK, AWS Glue Studio, Amazon SageMaker Unified Studio, and your preferred IDE.

To get started with AWS Glue 6.0 jobs in the AWS Glue Studio console, open the AWS Glue job and on the Job Details tab, choose the version Glue 6.0 – Supports Spark 4.1, Scala 2, Python 3. You can create new AWS Glue jobs on AWS Glue 6.0 to get the benefit from the improvements, or migrate your existing AWS Glue jobs.

To start using AWS Glue 6.0 on an AWS Glue Studio notebook or an interactive session through a Jupyter notebook, set 6.0 in the %glue_version magic. You can also upgrade existing jobs to Glue 6.0 using the Spark upgrade agent on AWS Glue Studio or use the auto-upgrade feature in their existing Glue jobs to automatically upgrade them to Glue 6.0.

To learn more, visit the AWS Glue 6.0 version detail and Migrating AWS Glue for Spark jobs to AWS Glue version 6.0 in the AWS documentation.

Now available
AWS Glue 6.0 is generally available today in all AWS Regions where AWS Glue operates. For Regional availability and a future roadmap, visit the AWS Capabilities by Region. If you want to call APIs, search documentation, find regional availability, and check troubleshooting about this new feature, try using the AWS MCP Server and plugins with your preferred AI tool.

You pay an hourly rate, billed by the second, for crawlers (discovering data) and extract, transform, and load (ETL) jobs (processing and loading data). For the AWS Glue Data Catalog, you pay a simplified monthly fee for storing and accessing the metadata. The first million objects stored are free, and the first million accesses are free. To learn more, visit AWS Glue Pricing page.

Give it a try in the AWS Glue Studio console, and send feedback to AWS re:Post for AWS Glue or through your usual AWS support contacts.

— Channy

In the works: AWS Builder Lofts in Berlin, Hyderabad, and São Paulo

Post Syndicated from Channy Yun (윤석찬) original https://aws.amazon.com/blogs/aws/in-the-works-aws-builder-lofts-in-berlin-hyderabad-and-sao-paulo/

In the early days of cloud computing, AWS supported intensive learning for builders in a physical space called the AWS Pop-up Lofts in cities worldwide. These spaces were accessible to startup entrepreneurs, developers, and others interested in learning more about AWS for events, meetings, and co-working. With the recent emergence of generative AI, AWS Gen AI Lofts provided pop-up style collaborative spaces across the world and immersive experiences for startups and developers.

We realized the need of permanent community spaces give students and developers a place to learn, connect, and contribute through hands-on experiences, community-led sharing, and technical collaboration. Since opening in San Francisco in July 2025, the first AWS Builder Loft has welcomed more than 22,500 developers through its doors, hosting hackathons, workshops, demo nights, and community-led events that bring the local tech community together under one roof.

Today, we are announcing plans to open new Builder Lofts in Berlin, Hyderabad, and São Paulo. Each location will be a permanent community space to offer free workshops, networking events, pitch nights, content creation spaces, collaboration/co-working areas, and event hosting for developers, students, or tech professionals who want to walk through the doors.

You’ll still be able to meet AWS experts there, but beyond that we further want to establish a home for local tech communities from AWS User Groups and AWS Student Builder Groups, to independent developer groups you’re already part of. As a tech community leader, you are welcome to request booking of our space to host your meetup at no cost.

Why three cities
The expansion reflects fast-growing developer cities which are important talent and innovation hubs for each region:

  • Berlin: After launching AWS European Sovereign Cloud, we’re deepening our commitment to Europe’s developer community. Berlin’s Builder Loft will host educational sessions on digital sovereignty, hackathons, security-readiness workshops, and community meetups that connect Germany’s growing startup ecosystem with the broader European and global tech landscape.
  • Hyderabad: The Hyderabad Builder Loft will give Indian developers a dedicated space to upskill on AI, explore cloud-native architecture, and connect with peers building the next generation of applications.
  • São Paulo: Brazil’s cloud market is growing at 30% annually, and São Paulo sits at the center of Latin America’s tech boom. The Builder Loft will serve as a hub for the region’s developers, offering free programming in partnership with local communities, universities, and startup networks.

A typical week at the Builder Loft
The Builder Loft in San Francisco hosts four to eight community events weekly from technical deep dives on generative AI to startup pitch nights, from coding workshops for students to networking sessions that bring together developers from across the region.

The spaces are designed to be flexible. A training room fills with over 50 students on a Tuesday morning. By evening, it transforms into a demo stage where a startup showcases its latest prototype to a room of potential collaborators. On weekends, community groups host their own meetups.

What makes the model work is that it’s driven by the community itself. Local developers, meetup organizers, and tech leaders shape the programming. AWS provides the space, the infrastructure, and the support, but the energy comes from the builders who participate. Find upcoming events or request to host your own event at the Builder Loft San Francisco.

Stay tuned
We’ll announce Builder Loft openings in three cities in future blog posts, so stay tuned for updates! To learn more about Builder Lofts for details and to follow along for updates, read Rick’s blog post and visit the AWS Builder Loft page.

— Channy

AWS Weekly Roundup: EC2 application status checks, IAM role manager, OpenAI Daybreak on Bedrock, and more (August 17, 2026)

Post Syndicated from Channy Yun (윤석찬) original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-ec2-application-status-checks-iam-role-manager-openai-daybreak-on-bedrock-and-more-august-17-2026/

Last week, the OpenSearch and Valkey teams visited Seoul to meet open source developers and contributors in the Open Source Summit Korea 2026 and MCP DevSummit Seoul 2026. At the four-day event, community leaders and users of open source projects and emerging agent AI gathered to share knowledge, collaborate on solutions, and push the projects forward.

Leaders of the Korean OpenSearch communities volunteered to participate in the booth, and also had time to network and interact in the user group meetup.

OpenSearch is an open source, enterprise-grade search and observability suite that brings order to unstructured data at scale. On June 9, 2026, OpenSearch 3.7 introduced new tools designed to query, alert, and track SLOs across logs, traces, and metrics through a single interface and retrieve vectors up to 5.5x faster for improved search performance. Since July 30, 2026, you can run OpenSearch version 3.7 on Amazon OpenSearch Service for improvements in vector search performance, search relevance, and Query Insights.

Valkey is an open source high-performance key/value datastore that supports a variety of workloads such as caching, message queues, and it can act as a primary database. On May 19, 2026, Valkey 9.1 introduced a redesigned I/O threading model that improves throughput by up to 17% and reduces memory usage for strings under 128 bytes by up to 20%. Since June 23, 2026, you can run Valkey 9.1 in Amazon ElastiCache for node-based clusters, delivering higher throughput, improved memory efficiency, and stronger access control for multi-tenant workloads.

You can meet our open source teams at upcoming OpenSearch and Valkey events.

Last week’s launches
Here are some launches that got my attention:

For a full list of AWS announcements, be sure to keep an eye on the What’s New with AWS page.

Other AWS news
Here are some additional projects and news items you may find interesting:

  • The deprecation of email validation in AWS Certificate Manager: ACM will discontinue support for email-validated public certificates by September 30, 2027. If you use email validation for your ACM public certificates, you need to migrate to DNS validation before that date. For Amazon CloudFront distributions, HTTP validation is also available.
  • The next-generation AWS VPN Client with CLI support and admin controls: You can use a new AWS VPN Client built on OpenVPN3. With the new client, you get full backward compatibility with existing AWS Client VPN endpoints while delivering the automation capabilities and security posture that enterprise networking teams have been asking for.
  • Oracle Exadata on Exascale for Oracle AI Database@AWS: ExaDB-XS brings Exadata-class performance and availability through a consumption-based model. With ExaDB-XS, you can scale compute and storage independently in small increments and pay only for what you consume.

For a full list of AWS blog posts, be sure to keep an eye on the AWS Blogs page.

Learn more about AWS, browse and join upcoming AWS-led in-person and virtual events, startup events, and developer-focused events including AWS Summits and AWS Community Days. Join the AWS Builder Center to connect with builders, share solutions, and access content that supports your development.

That is all for this week. Check back next Monday for another Weekly Roundup!

— Channy

AWS Weekly Roundup: AWS Heroes Summit, Web Search on Amazon Bedrock, Dogwood, Kiro Crew, and more (August 10, 2026)

Post Syndicated from Channy Yun (윤석찬) original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-aws-heroes-summit-web-search-on-amazon-bedrock-dogwood-kiro-crew-and-more-august-10-2026/

Last week, we brought together AWS Heroes from around the world to connect, collaborate, and celebrate the builders who go above and beyond for the AWS community.

The AWS Heroes Summit, an invite-only annual gathering, brings global experts specializing in fields like AI, serverless, and containers together for direct collaboration, technical deep-dives, and feedback sessions with internal AWS product and service teams.

Day 1 started with an inspiring fireside chat from AWS CEO Matt Garman. From an insightful AMA with James Hamilton on Day 2 to breakout sessions from various product teams that sparked new ideas, our AWS Heroes excelled at sharing knowledge, lifting each other up, and turning conversations into collaborations. To learn more, read the attendee feedback on LinkedIn.

Last week’s launches
Here are some launches that got my attention:

  • Web Search on Amazon Bedrock: Amazon Bedrock now enables OpenAI models (GPT-5.4, GPT-5.5, and GPT-5.6 Sol/Terra/Luna) to browse and retrieve information from the internet, allowing AI applications to access up-to-date information beyond their training data. This capability opens new possibilities for building AI agents and applications that can answer questions using real-time web content while maintaining data residency within your secured AWS environment with zero data egress. To get started, visit the AI blog post and the Amazon Bedrock User Guide.
  • Runtime Instances on Amazon Bedrock AgentCore: You can now deploy and run AI agents on dedicated runtime instances through Amazon Bedrock AgentCore, providing more control over agent execution environments with predictable performance and cost. To get started, visit Sébastien’s blog post and AgentCore documentation.
  • Vector search for Amazon DynamoDB: You can store and query vector embeddings alongside your existing data in DynamoDB without managing a separate vector database. DynamoDB already supports storing memory for AI agents, and with vector search you can now add semantic retrieval over that memory for agentic grounding, with predictable performance. To learn more, visit Esra’s blog post and Amazon DynamoDB Developer Guide.
  • AWS Transform continuous modernization now generally available: This capability helps engineering teams analyze and remediate technical debt across source code repositories at scale. You can modernize mainframe and legacy workloads with an ongoing, automated approach rather than a one-time migration event. To learn more, visit Micah’s preview blog post. You can also try the AWS Transform Kiro Power and agent plugins.
  • Up to 3,000 Mbps for AWS Lambda function bandwidth: AWS Lambda functions now support increased network bandwidth, enabling data-intensive workloads and faster communication between Lambda functions and other AWS services. This feature enables functions outside a VPC that are configured with 2 GB of memory or more to access network bandwidth that scales proportionally, from 625 Mbps at 2 GB up to 3,000 Mbps at 10 GB.

For a full list of AWS announcements, be sure to keep an eye on the What’s New with AWS page.

Other AWS news
Here are some additional projects and news items that you may find interesting:

  • Introducing Dogwood: Runtime Verification for AI Agents: AWS open-sourced Dogwood, a purpose-built governance language for AI agents to support Cedar policies and add temporal conditions. Powering Dogwood, Amazon Bedrock AgentCore introduced temporal policies whose decisions depend on the history of an agent’s actions within a session, not on the current request alone.
  • AWS supports Agent Plugins: An Open Standard for Portable Agent Extensions: AWS announced support for Agent Plugins, an open source, vendor-neutral specification that gives AI agent extensions a common packaging format so you can package an extension once and ship it to any client, including Kiro, VS Code, Cursor, or any tool that implements the spec.
  • Introducing Kiro Crew: Kiro Crew is a persistent, self-evolving workspace that keeps work moving, online or off, enabling collaborative multi-agent development workflows within the Kiro IDE. It’s built for engineering work that goes beyond a single chat session, and spans repos, tools, and days. You can run several efforts in parallel or hand work to subagents that report back, so nothing waits in line.

For a full list of AWS blog posts, be sure to keep an eye on the AWS Blogs page.

Learn more about AWS, browse and join upcoming AWS-led in-person and virtual events, startup events, and developer-focused events including AWS Summits and AWS Community Days. Join the AWS Builder Center to connect with builders, share solutions, and access content that supports your development.

That is all for this week. Check back next Monday for another Weekly Roundup!

— Channy

Runtime instances: persistent compute for production AI agents on Amazon Bedrock AgentCore

Post Syndicated from Sébastien Stormacq original https://aws.amazon.com/blogs/aws/runtime-instances-persistent-compute-for-production-ai-agents-on-amazon-bedrock-agentcore/

When you move AI agents from prototype to production, the infrastructure challenges multiply. Your agents need to persist state across multi-step workflows that run for hours or days. They need to coordinate with other agents, share context, and sometimes access GPUs for specialized tasks. Amazon Bedrock AgentCore runtime microVMs provide a fully managed environment for invocations that can run for up to 8 hours and support stateful workflows through managed session storage. Some workloads also benefit from dedicated, larger-capacity environments — for example, when agents need to run continuously for multiple days, access GPUs or the underlying OS, or run multiple collaborating agents on the same host.

Today, I’m happy to announce runtime instances, a new complementary compute option in Amazon Bedrock AgentCore Runtime that gives your agents persistent, managed infrastructure purpose-built for complex agent workloads.

What you get
Runtime instances provides AWS-managed EC2 infrastructure where you deploy multiple agents in a single runtime, each with their own dependencies and artifact types. Your agents can collaborate on the same host within shared sessions that persist for up to 14 days. The service supports GPU acceleration for compute-intensive tasks, session stop/restart to save costs during idle periods, and containerized deployments for teams that want to ship independently. For knowledge that needs to survive beyond a session, runtime instances pairs naturally with Amazon Elastic Block Store (Amazon EBS) and AgentCore Memory, which gives your agents long-term recall across sessions and environments.

Before today, if you wanted to keep your agents running for days or they needed GPU access, or multi-agent coordination, you had to build and manage that infrastructure yourself. You provisioned EC2 instances, configured networking, set up session management, handled scaling, and stitched together monitoring. Runtime instances handles all of that for you while integrating with the same AgentCore APIs, identity controls, and observability you already use with AgentCore Runtime microVMs.

A few things that should make agent developers smile: your agents can call each other as tools within a shared session, iterating autonomously until the job is done. You bring any framework (CrewAI, LangGraph, LlamaIndex, Strands) and any model. Packaging is minimal, a @app.entrypoint decorator and a zip file or container image. And if your workflow spans days, hibernate Monday night and resume Wednesday morning with everything intact.

Runtime microVMs and runtime instances are complementary compute options that you can use independently or together through the same AgentCore runtime APIs. A lightweight orchestrator agent on runtime microVM can coordinate and dispatch work to specialized worker agents running on instances. The orchestrator handles API calls, task routing, and result aggregation using runtime microVM’s fast scaling, while workers on Instances perform compute-intensive tasks like code compilation, security scanning, or GUI automation that require persistent state and direct OS access.

Let me show you how it works
I built two agents for this demo: a code writer agent that generates Python code from natural language descriptions, and a code reviewer agent that analyzes the generated code for bugs, security issues, and style improvements. Both agents share the same file system, so the reviewer can read whatever the writer produces without any data transfer or API calls between them.

Here is the code writer (simplified, no error handling):

writer = Agent(
    model="us.anthropic.claude-sonnet-4-5-20250929-v1:0",
    system_prompt=(
        "You are a senior Python engineer. "
        "Given a task, return ONLY a single Python code block — no prose."
    ),
)

@app.entrypoint
def handler(event, context):
    task = event.get("task") or event.get("prompt")
    session_id = getattr(context, "session_id", None) or event.get("session_id")
    session_dir = SHARED_DIR / session_id
    session_dir.mkdir(parents=True, exist_ok=True)

    code = str(writer(task))
    (session_dir / "code.py").write_text(code)

    return {"agent": "writer", "wrote": str(session_dir / "code.py"), "code": code}

Here is the code reviewer agent (simplified, no error handling):

reviewer = Agent(
    model="us.anthropic.claude-sonnet-4-5-20250929-v1:0",
    system_prompt=(
        "You are a strict Python code reviewer. "
        "Given code, return 3 bullet points: bugs, style, suggestions."
    ),
)

@app.entrypoint
def handler(event, context):
    session_id = getattr(context, "session_id", None) or event.get("session_id")
    code_path = SHARED_DIR / session_id / "code.py"
    code = code_path.read_text()
    review = str(reviewer(f"Review this code:\n\n{code}"))

    return {"agent": "reviewer", "read": str(code_path), "review": review}

Each agent is a Python application using Strands Agents with an @app.entrypoint decorator and a model of its choice. I package each one as a zip file. For this demo, I use the AWS Management Console. You can also use the AgentCore CLI, the AWS Command Line Interface (AWS CLI) or infrastructure as code.

Step 1: Create a capacity provider.

A capacity provider defines the EC2 infrastructure your agents run on. In the AgentCore console, I select Runtime in the left navigation, then select the Capacity providers tab and Create capacity provider.

ACI Create Capcity Provider 1

I give it a Name, select Linux (64-bit ARM) as the Operating system, and choose c7g.2xlarge as the Allowed instance types. This gives me 8 vCPUs and 16 GiB of memory, enough for both agents to run comfortably side by side.

Further down, I configure the VPC, subnets, and security groups for network access. Under Storage configuration, I keep the default gp3 volume. Under Service access, I select Create a new service role and let the console create the infrastructure role that manages EC2 instances on my behalf.

I select Create capacity provider and wait a few seconds. The status moves to Active.

ACI Create Capacity Provider 2

ACI Create Capacity Provider 3

Note the capacity provider configuration summary: operating system, instance type, subnets, security group, instance profile, and infrastructure role. Once created, only the description can be edited, so verify your settings before you proceed.

ACI Create Capcity Provider 2

Step 2: Create a runtime and deploy the first agent.

Back on the Runtime page, I select Create runtime. I give it a Name, select Instances as the Compute type, and choose the Capacity provider I created in the previous step.

ACI Create Runtime 1

Under Agent source, I select S3 Source, then Upload to S3. I choose my agent zip file (ACIDemoWriter.zip), set the Language runtime to Python 3.13, and specify agent.py as the Agent entry point. This is the file that contains my @app.entrypoint decorated function. Under Permissions, I select Create default role to let the console provision the IAM role my agent needs.

ACI Create Runtime 2

I select Create runtime and wait for the status to become Ready.

I repeat the same process for my code reviewer agent. I create a second runtime, select the same capacity provider, upload my reviewer agent zip file, and wait for it to become Ready. Both agents now share the same underlying EC2 infrastructure.

AgentCore Runtime Instances - Agent ReadyThe console shows me a View invocation code section with ready-to-use Python, TypeScript, and JavaScript snippets to invoke my agent programmatically. But for this demo, I use the built-in test feature. I select Test on the writer agent’s page.

AgentCore Runtime Instances - Show invocation codeStep 3: Invoke agents and observe collaboration.

The Runtime playground opens. At the top, I see three fields: Runtime agent, Endpoint, and Session ID. The console generates a session ID automatically. I take note of it because I will reuse it with the reviewer agent.

In the Input field, I type a JSON payload asking the writer agent to generate code:

{"prompt": "write a fibonacci suite"}

I select Run. After a few seconds, the Output panel shows the agent’s response. The writer agent generated a Python module with two implementations of a Fibonacci sequence (a list-based function and a generator) and wrote it to /tmp/agentcore-session/ca5ec24d-07f5-4eeb-add1-5ba416bf9eb2/code.py. Notice the session ID in the file path. That directory is the shared file system for this session.

AgentCore Runtime Instances - Invoke code writer agent

Step 4: Invoke the reviewer agent in the same session.

Now I switch the Runtime agent dropdown to ACIDemoReviewer. The important part: I paste the same session ID (ca5ec24d-07f5-4eeb-add1-5ba416bf9eb2) in the Session ID field. This is what connects the two agents.

I type a simple prompt:

{"prompt": "review the code"}

I select Run. The reviewer agent reads the file the writer produced from the shared session directory and returns a detailed code review. It finds no critical bugs but suggests adding type hints, input validation, and simplifying the edge case handling.

AgentCore Runtime Instances - Invoke code reviewer agentThe two agents never exchanged messages or called each other’s APIs. They collaborated through the shared file system that runtime instances provide within a session. You can extend this pattern to any number of agents: a test agent that runs the code, a documentation agent that generates README files, a security agent that scans for vulnerabilities, all sharing the same working directory.

Key details
Here are a few things to know as you get started:

  • Supported OS: Linux (ARM64 and x86_64) at launch.
  • Session persistence: Sessions persist for up to 14 days.
  • Runtimes: Python 3.11-14 with native code support. Container images also supported.
  • GPU: Support for GPU-accelerated instance types.
  • Integration: Uses the same AgentCore APIs, identity, observability, and policy controls as AgentCore Runtime.
  • Pricing: Standard EC2 pricing plus a management fee for AgentCore orchestration.
  • Regions: US East (Ohio, N. Virginia), US West (Oregon), Asia Pacific (Mumbai, Singapore, Sydney, Tokyo), and Europe (Frankfurt, Ireland)

To get started, visit the runtime instance in Amazon Bedrock AgentCore documentation and create your first capacity provider.

— seb

Amazon DynamoDB now supports real-time vector search at any scale

Post Syndicated from Esra Kayabali original https://aws.amazon.com/blogs/aws/amazon-dynamodb-now-supports-real-time-vector-search-at-any-scale/

Today, we’re announcing the general availability of vector search in Amazon DynamoDB. You can now store vector embeddings alongside your operational data in DynamoDB and run similarity searches directly against that data, without replicating it to a separate vector store.

DynamoDB supports native vector search with single-digit millisecond latency at 99%+ recall, and is designed for any scale, even trillions of vectors. There are no servers to provision, patch, or manage, and no software to install, maintain, or operate. The service has no versions, no maintenance windows, and zero downtime maintenance.

Vector indexes have no storage limits and scale horizontally as your data grows. You can now build applications that require semantic retrieval on agentic memory, retrieval augmented generation, recommendation engines, personalized experiences, anomaly detection, and more using DynamoDB and its native vector search.

If your application already uses DynamoDB, adding vector search previously required copying data into a dedicated vector database while maintaining a synchronization pipeline between the two services. This added operational overhead, data movement costs, licensing costs, and the challenge of maintaining predictable low latency at scale. With vector search built into DynamoDB, your vectors and operational data share the same serverless infrastructure and the same pay-per-request pricing model.

Vector search in DynamoDB introduces a new index type that you create on an attribute storing vector embeddings. You generate embeddings using a model of your choice, such as Amazon Bedrock Titan Text Embeddings, Cohere Embed, or OpenAI text embedding models, and store them as a list of floats in your table using a standard PutItem call. You then create a vector index on that attribute and specify the number of dimensions, the distance function, and any non-vector attributes you want to use as filters to narrow search results at query time. The SearchVectors API accepts a query vector, the number of results to return (up to 100), and optional filter conditions. It returns results ranked by similarity.

Use vector search in DynamoDB when your operational data already lives in DynamoDB and you want to add similarity search without provisioning a separate database or managing a synchronization pipeline. DynamoDB is fully serverless, so vector search scales automatically with no infrastructure to manage. It supports up to 4096 dimensions, Euclidean, Cosine, and Dot product distance functions, and inline filtering.

Getting started with vector search in DynamoDB
This walkthrough shows how to add vector search to an existing DynamoDB table using the DynamoDB console. The scenario contains an online sporting goods store with a product catalog table. Each item has standard operational attributes such as productId, category, description, marketplace, name, and price. The goal is to add semantic search so shoppers can find products using natural language queries rather than exact keyword matches.

1. Prepare DynamoDB table
To enable semantic search, I first generate vector embeddings for the product descriptions already in my table. Embeddings are numerical representations of text generated by a machine learning model that capture the meaning of the content. Two items with similar descriptions will have embeddings that are close to each other in vector space, which is what makes similarity search possible.

I can generate embeddings using Amazon Bedrock Titan Text Embeddings or another embedding model, then add them to my table using the AWS Management Console, AWS Command Line Interface (AWS CLI), AWS SDKs, AWS CloudFormation, or other infrastructure-as-code (IaC) tools.

For an existing table like ProductCatalog, I add the embeddings to each item as a new attribute named descriptionEmbedding using an UpdateItem call. DynamoDB stores vector embeddings using its existing List data type. Each element in the list is a Number that represents a single float value of the embedding vector. This means I do not need a new data type or schema change to start storing vectors alongside my existing operational attributes.

2. Create vector index
In the DynamoDB console, open the ProductCatalog table and choose the Indexes tab. I choose Create vector index. On the Create vector index page, I fill in the index details as follows. I enter ProductDescriptionIndex as the Index name and descriptionEmbedding as the Vector attribute.

I enter the number of Dimensions that matches my embedding model’s output and select Cosine as the Distance function. Cosine measures the angle between vectors rather than their magnitude, which makes it effective for comparing semantic similarity of text embeddings. Vector search in DynamoDB also supports Euclidean and Dot product distance functions.

  • Euclidean: Use when the magnitude of the vectors is meaningful, such as clustering items by a numeric value like purchase count.
  • Dot product: Use when both direction and magnitude matter, such as in recommendation systems that weight interest alignment and frequency together. As a general rule, match the distance function to the one used to train your embedding model for the best accuracy.

I enter marketplace as the Partition key. The vector index partition key controls how DynamoDB distributes vectors across partitions, allowing the index to scale out while maintaining predictable latencies. Each search is scoped to a single partition key value, so a product catalog serving multiple marketplaces can search within one marketplace’s inventory without scanning the entire index. The partition key is optional, but recommended for large datasets with high query throughput.

I expand Inline filter attributes and add category as a filter attribute. This helps me narrow search results to a specific product category at query time. Filter conditions support exact-match values only; range conditions such as BETWEEN or BEGINS_WITH are not supported. I leave Attribute projections set to All so that all table attributes are returned with my search results. Choose Create vector index and wait for the index status to change to Active.

3. Run vector search
I generate a query vector from a natural language search term such as “lightweight running shoes for summer” using the same embedding model I used for the product descriptions. In the DynamoDB console, I choose Explore items in the left navigation pane and select the ProductCatalog table.

Choose Search to switch to vector search mode. I select ProductDescriptionIndex from the Select a vector index dropdown, paste the query vector into the Search vector field, and set Number of results (Top K) to 5. I enter US as the Partition key value to scope the search to the US marketplace. I expand Inline filter attributes and set category equal to footwear to narrow the search to footwear products only. Now, choose Run.

DynamoDB returns the five most semantically similar products in the footwear category, ranked by similarity score, alongside the standard operational attributes such as name and price in the same response. The similarity score’s meaning depends on the distance function selected for the index. For Cosine and Euclidean distance functions, lower similarity score values indicate higher similarity, with a score of 0 indicating identical vectors. For the dot product distance function, higher similarity score values indicate higher similarity.

To interact with vector search programmatically, including calling APIs and searching documentation, try the AWS MCP Server and plugins with your preferred AI coding tool. To learn more, visit the Amazon DynamoDB Developer Guide.

Get started today
Vector search in Amazon DynamoDB is generally available in all commercial AWS Regions, including the AWS GovCloud (US) Regions. For Regional availability and a future roadmap, visit the AWS Capabilities by Region. For pricing details, visit the Amazon DynamoDB pricing page.

Start exploring vector search in DynamoDB today and send feedback to AWS re:Post for Amazon DynamoDB or through your usual AWS Support contacts.

— Esra

Analyze and remediate technical debt autonomously with AWS Transform – continuous modernization

Post Syndicated from Ritik Khatwani original https://aws.amazon.com/blogs/devops/analyze-and-remediate-technical-debt-autonomously-with-aws-transform-continuous-modernization/

Introduction

In a recent post, my colleague Micah Walter introduced AWS Transform – continuous modernization in public preview. Today, this capability is generally available in regions supported for AWS Transform .

Development velocity continues to increase. But velocity without maintenance accumulates technical debt at speed. The faster software scales, the faster technical debt compounds. At the same time, more sophisticated exploits and attack vectors are emerging – and the risk is increasing (Figure 1). For organizations, this makes staying on top of tech debt and maintaining a strong security posture across an increasing sphere of responsibility not only important, but business-critical.

Two pie-circle diagrams connected by an arrow labeled "AGENTIC AI." In the left circle, most of the area is labeled "BUY, SAAS, COTS" while a small blue wedge labeled "BUILD, MAINTAIN" faces three arrows labeled "VULNERABILITIES." In the right circle, the blue "BUILD, MAINTAIN" section has grown to roughly half the circle and faces many more vulnerability arrows, illustrating how agentic AI expands the amount of code organizations build and maintain — and the corresponding vulnerability surface.

Figure 1: Changing landscape of software maintenance

To contend with compounding technical debt, engineering organizations have typically stitched together point tools, spent app-by-app cycles wasting engineering capacity, and relied on self-reports for status that lags reality and hides regressions.

This is the problem continuous modernization capability was built to solve: shift code transformation from a periodic project into an automated, always-on practice. Rather than scheduling modernization sprints or relying on manual audits, your repositories are analyzed on demand or on a recurring schedule, with findings prioritized by severity and impact, and validated pull requests are generated autonomously to resolve them.

In this post, I’ll recap the preview launch and then I’ll walk you through additional capabilities we’ve added since. I’ll also show how you can use it today to get started.

What continuous modernization provides

AWS Transform – continuous modernization connects to your source control systems (GitHub, GitLab, Bitbucket, or local repositories), scans repositories, and generates prioritized findings. At your direction, it autonomously creates pull requests with validated code changes.

The capability supports several analysis types:

  • Rapid tech debt analysis: fast metadata-only scans of package manifests (pom.xml, package.json, requirements.txt) to identify stale versions and outdated dependencies
  • Comprehensive tech debt analysis: deep code-level analysis examining source code for debt patterns, code quality issues, architecture concerns, and improvement opportunities
  • Security analysis: Common vulnerability detection within the source code and dependencies via AWS Security Agent (now part of AWS Continuum)
  • Agentic readiness: assesses your code base readiness for agent integration
  • Modernization readiness: evaluates candidates for containerization, serverless migration, and platform upgrades
  • Custom analysis: run your own transformation definition as an analysis, including organization-specific policies your platform team already enforces

You can run these analyses on demand or schedule them on a recurring cadence. The system accumulates findings over time, giving you trend data and portfolio-wide visibility that periodic manual audits can’t match.

Initiate and schedule recurring analysis from the AWS Transform web app

We’re excited to add the ability to connect your source code management (SCM) provider and initiate an analysis directly from the AWS Transform web application so you can get value from real insights faster than before (Figure 2). You can also schedule recurring analysis, review findings, and create remediations from within the web app. To learn more about the web app and how to set it up, see AWS Transform web application.

Screen recording of the AWS Transform Continuous Modernization console (N. Virginia region). A "Connect new source" dialog is filled in with GitHub as the provider, then repositories refresh and "agentcore-samples" is selected from the awslabs-agentcoresamples source. A new analysis is configured with type "Tech debt (comprehensive)" and named "agentcore samples tech debt comprehensive." In the Schedule step, "Recurring" is chosen with a weekly cadence on Mondays, a start date of 2026/07/28, and a start time, ending with the "Run now and schedule" button.

Figure 2: Set up continuous modernization in the AWS Transform web application

Interact via your IDE or terminal with the new CLI and developer tools

The new version of the AWS Transform CLI and it’s atx ct(v3.8.0) introduce additional capabilities that simplify how you setup and work with continuous modernization. The new atx ct remote sub-commands allow you to provision infrastructure and run scheduled analyses and remediations with Amazon Elastic Compute Cloud (Amazon EC2) and AWS Batch (Figure 3). To learn more about the CLI commands, refer to working with continuous modernization. The updated AWS Transform Kiro power and plugin make it even easier to configure your source repositories and run analyses directly from your IDE or terminal. To learn more, see developer tools.

You can also leverage labels with your repositories to group them and organize operations in batches. In the example below, I create a subset of repositories from my GitHub organization that I want to run agentic readiness analysis on and trigger a one-time analysis.

Screen recording of a macOS zsh terminal running AWS Transform CLI commands. First, atx ct repository update tags two repositories (ritikk::ecsdemo-nodejs and ritikk::ecsdemo-frontend) with the label "ecs-demos"; the output confirms "Bulk label update complete: Targeted 2, Updated 2." Then atx ct remote analysis --types "agentic-readiness" --mode "batch" --labels "ecs-demos" --sources "ritikk" --stack-name "AtxInfrastructureStack" kicks off a batch analysis. Pre-flight checks pass, a Lambda function is invoked, an S3 manifest is written, and two jobs are submitted to Batch with a batch ID and a poll command shown to check status.

Figure 3: Use the AWS Transform cli to analyze your repositories

Real-world results

Across industries, partners and enterprises are already seeing the impact of continuous modernization. The results speak to a consistent theme: what used to take months of manual effort now happens in minutes, at a scale that was previously impractical with manual review.

From weeks of manual assessment to insights in days

Quantiphi ran continuous modernization across a large portfolio and compressed a multi-week assessment into a matter of days:

“At Quantiphi, we’re helping enterprises accelerate application modernization through AI-powered engineering. Using AWS Transform continuous modernization, we analyzed more than 500 repositories and uncovered over 3,000 technical debt findings in less than a week, a time frame that traditionally required nearly three weeks of manual assessment. By automating technical debt discovery and providing actionable remediation insights, we reduced assessment effort by more than 60%, accelerated modernization planning, and enabled our customers to focus engineering investments on innovation instead of analysis. We believe AWS Transform continuous modernization is a foundational capability for delivering continuous, AI-driven enterprise modernization at scale.”

Sanchit Jain, Migration and Modernization Practice Leader, Quantiphi AWS Practice

Shifting from reactive maintenance to proactive modernization

For Hexaware, the value is in continuously surfacing what needs attention across large portfolios, so teams can get ahead of debt rather than react to it:

“At Hexaware, we’re constantly looking for ways to help clients modernize faster while controlling cost and complexity. AWS Transform continuous modernization provides a scalable approach to identifying technical debt, modernization opportunities, and AI-readiness gaps across large application portfolios. By automating analysis and continuously surfacing remediation recommendations, organizations can shift from reactive maintenance to proactive modernization. This capability has the potential to significantly accelerate transformation roadmaps and help enterprises build more resilient, future-ready applications, very quickly.”

Inderjeet Gurtatta, Vice President, Hexaware Technologies

An 80 percent reduction in assessment time

Tech Mahindra measured the impact directly, cutting assessment time from 40 hours to 8 across 25 repositories:

“Achieving an 80 percent reduction in assessment time, from 40 hours down to 8, across 25 enterprise repositories is just the beginning of what we see as a transformative shift in how organizations approach continuous modernization. AWS Transform’s scanning and analysis engine is solid, and the structured output allows our teams to validate findings quickly and build prioritized remediation plans with confidence. As the platform matures with features like scan resume capabilities and broader platform support, we expect to embed AWS Transform continuous modernization into our standard delivery methodology while maintaining the depth and accuracy our enterprise clients demand.”

Sanjeev Agarwal, Global Head of AWS Business, Tech Mahindra

Uncovering risks that traditional scanners miss

Cybage found that the continuous modernization capability surfaced hidden security risks that conventional scans overlooked, then fed those findings into their own delivery framework:

“AWS Transform continuous modernization completes codebase analysis in under an hour, work which normally takes weeks, while uncovering risks that traditional scanners miss like disabled security warnings, vulnerable code copied into applications, and security controls intentionally switched off. Cybage’s CLEAR Framework builds on these findings by converting them into a prioritized, customer-specific modernization plan with confidence scoring, technical-debt measurement, and integration with tools like GitHub, Jira, and SonarQube. Together, AWS Transform discovers hidden risks at speed, and CLEAR determines what matters most and how teams move from analysis to execution.”

Mohammad Mahdee-uz Zaman, Vice President, AWS Strategic Alliances, Cybage Software Inc.

Straight from discovery to a concrete migration plan

3Pillar moved directly from discovery to an actionable migration plan:

“For most IT leaders, application modernization is the bane of their existence. We put AWS Transform continuous modernization to the test across more than 25 repos, and came away very impressed. Analysis that would have taken our engineers an estimated 3-4 weeks of manual code reviews surfaced in an hour, uncovering over 190 tech debt findings, including outdated dependencies, dead code, and migration risks. The service’s ability to build migration rules purpose-built for each repo let us move straight from discovery to a concrete migration plan, without weeks of manual mapping. Based on our testing, we estimate AWS Transform continuous modernization can cut 40-50% off the overall modernization lifecycle. This speed means tech leaders can embark on modernization initiatives with confidence that it won’t drag on for many years.”

Pankaj Chawla, CTO, 3Pillar

Conclusion

AWS Transform – continuous modernization helps you go from one-off projects and campaigns to a fully operational tech debt management program: connecting sources, running analyses, triaging findings, launching remediation campaigns, and scheduling recurring scans. The web app dashboard and reports provide prioritization signals directly from your code. When a repository diverges from your baseline, the next analysis can surface the change and help teams understand its severity and breadth. This reduces reliance on manual status collection and periodic code-health audits.

To get started, you can access the capability through the AWS Transform Kiro power, the AWS Transform web application, or directly via the atx ct CLI. To learn more, visit the AWS Transform documentation.

Ritik Khatwani

Ritik Khatwani

Ritik is a Sr Worldwide Specialist Solutions Architect at AWS based in New York City. He has deep expertise in software engineering and currently works with customers to modernize their development workflows using generative AI.

AMD’s Physical AI Plans Come Into Focus as Company Launches Ryzen Embedded AI X100

Post Syndicated from Ryan Smith original https://www.servethehome.com/amds-physical-ai-plans-come-into-focus-as-company-launches-ryzen-embedded-ai-x100/

At Advancing AI 2026, AMD laid out their plans for a comprehensive product stack for physical AI hardware. From SoCs to modules to dev kits, AMD is eyeing physical AI as their next big growth opportunity

The post AMD’s Physical AI Plans Come Into Focus as Company Launches Ryzen Embedded AI X100 appeared first on ServeTheHome.

AWS Weekly Roundup: Local Zone in Athens, Claude Opus 5 on AWS, Lambda durable execution for .NET, and more (July 27, 2026)

Post Syndicated from Daniel Abib original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-july-27-2026/

Last week I had the privilege of spending three days in São Paulo with technical builders from across Latin America, brought together for a regional tech event full of deep-dive sessions, hands-on workshops, and conversations with customers and partners. What struck me most wasn’t any single session, it was the energy of a technical community that so rarely gets to be in the same room. People traded architecture ideas over coffee, sketched out solutions on whiteboards, and left with a longer list of things to try than they arrived with. It’s a good reminder that, for all the tooling we build, the community around it is what makes the technology stick.

That community spirit connects nicely to the week’s biggest infrastructure news, which is all about bringing AWS closer to where builders actually are.

Now, let’s get into this week’s AWS news…

Headlines
AWS Local Zone in Athens, Greece: AWS has opened a new Local Zone in Athens, Greece, the second Local Zone in EMEA with support for Amazon S3 and Amazon EBS Local Snapshots, so you can store and process data within Greece to help meet local data residency requirements. The Athens Local Zone supports Amazon EC2 (C7i, M7i, and R7i instances), Amazon S3 with the One Zone-Infrequent Access storage class, Amazon EBS, and Amazon ECS.

Athens, Greece skyline

AWS Local Zones place AWS infrastructure much closer to large population and industry hubs, enabling applications that require single-digit millisecond latency, such as real-time gaming, media production, and financial services, to run where end users actually are. For builders in Greece, you can now run latency-sensitive workloads locally while connecting seamlessly to the nearest AWS Region for services that don’t require low latency, giving you the flexibility to architect hybrid, latency-optimized applications without managing your own data center infrastructure. To learn more, visit AWS Global Infrastructure and Sustainability Blog post.

Last week’s launches
Here are some launches and updates from this past week that caught my attention:

  • Claude Opus 5 on AWS: You can use Anthropic’s Claude Opus 5, the most advanced Opus model yet, matching Claude Fable 5’s top-tier intelligence in many domains at Opus-tier pricing. Amazon Bedrock offers Claude Opus 5 with zero data retention (ZDR) enabled by default, giving you Opus’ top-tier intelligence while meeting your data governance requirements unlike Claude Fable 5. You have two ways to access Claude Opus 5: Amazon Bedrock and Claude Platform on AWS. To learn more, visit the deep dive blog post.
  • AWS Lambda durable execution SDK for .NET is now generally available: You can now build resilient, long-running workflows in C# using Lambda durable functions, without implementing custom progress tracking or integrating an external orchestration service. The SDK is a natural fit for multi-step applications like payment processing pipelines, AI agent orchestration, and human-in-the-loop approvals, it checkpoints progress automatically and can pause execution for up to a year. If you’re a .NET developer building serverless workflows, this removes a lot of the plumbing you used to write by hand.
  • Amazon Bedrock AgentCore now delivers unified observability with traces and logs in a single log group: Amazon Bedrock AgentCore now delivers agent traces and prompts to the same Amazon CloudWatch log group as your agent’s logs. Previously, telemetry was split across destinations, trace spans went to a shared log group while prompts, inputs, and outputs went to a separate one, so debugging a single agent invocation meant searching in multiple places. You can now debug an invocation in one place, and apply fine-grained access control and customer-managed key (CMK) encryption at the individual agent level.
  • Amazon Connect delivers more natural agentic voice experiences: Amazon Connect now supports more natural, human-sounding agentic voice experiences across 50+ languages, including Portuguese, Spanish, French, Italian, Japanese, Korean, and Thai, with over 100 new voice options and conversational improvements that make AI interactions sound more fluid. Connect’s agentic self-service lets AI agents understand, reason, and take action across voice and digital channels, adapting to a customer’s tone and sentiment. You can now build contact center experiences that feel natural to callers in far more of the languages your customers actually speak.
  • Amazon SageMaker Unified Studio now supports Amazon OpenSearch: You can now query and analyze your search and log analytics data from Amazon OpenSearch directly alongside other data assets in Amazon SageMaker Unified Studio. With this connection, you can combine operational search data in OpenSearch with data from sources like Amazon Redshift, Amazon S3, and relational databases, all within a single, governed environment. It’s especially useful when you need to correlate analytical and operational workloads, such as joining application logs with transactional data to uncover insights.
  • Amazon CloudWatch announces coding agent insights: Amazon CloudWatch now gives engineering leaders visibility into how AI coding tools are driving value across their organization. Coding agent insights integrates with the Claude apps gateway for AWS to collect telemetry from Claude Code without additional instrumentation, and also supports agents like Codex and GitHub Copilot. As teams scale AI coding adoption, you can now measure the return on that investment with metrics built on OpenTelemetry, no custom instrumentation required.

For a full list of AWS announcements, be sure to keep an eye on the What’s New with AWS page.

Other AWS news
Here are some additional posts and resources that you might find interesting:

Upcoming AWS events
Check your calendar and sign up for upcoming AWS events:

  • AWS Summits: AWS Summits are free events that bring the cloud and AI community together to connect, learn, and explore the latest technologies. Browse the full calendar to find a Summit near you in the second half of 2026.
  • AWS Community Days: Community-led conferences where content is planned, sourced, and delivered by community leaders. If you’re in Latin America, don’t miss AWS Community Day Belo Horizonte on August 22, registration is open at awscommunityday.com.br.

Join the AWS Builder Center to connect with builders, share solutions, and access content that supports your development. Browse here for upcoming AWS-led in-person and virtual events and developer-focused events.

That’s all for this week. Check back next Monday for another Weekly Roundup!

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS!

AMD Advancing AI 2026 Keynote Live Coverage

Post Syndicated from Ryan Smith original https://www.servethehome.com/amd-advancing-ai-2026-keynote-live-coverage/

It’s time for AMD’s 2026 Advancing AI conference. Join ServeTheHome for our live blog coverage of the AAI keynote, where AMD will announce their latest and greatest hardware and software technologies

The post AMD Advancing AI 2026 Keynote Live Coverage appeared first on ServeTheHome.

Intel To Add Support for Gen 2 MRDIMMs and Faster DDR5 RDIMMs to Xeon 6 Platform

Post Syndicated from Ryan Smith original https://www.servethehome.com/intel-to-add-support-for-gen-2-mrdimms-and-faster-ddr5-rdimms-to-xeon-6-platform/

Intel this morning is announcing that parts of the Xeon 6 family are going to be getting mid-generation memory upgrades with support for DDR5-8000 RDIMMs as well as Gen 2 MRDIMMs

The post Intel To Add Support for Gen 2 MRDIMMs and Faster DDR5 RDIMMs to Xeon 6 Platform appeared first on ServeTheHome.

AWS Weekly Roundup: One-click Lambda setup prompt, OpenAI GPT-5.6 models on Bedrock, and more (July 20, 2026)

Post Syndicated from Channy Yun (윤석찬) original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-one-click-lambda-setup-prompt-openai-gpt-5-6-models-on-bedrock-and-more-july-20-2026/

Last week, my team visited Seoul to meet AWS Korea User Group (AWSKRUG) leaders. AWSKRUG is the largest cloud developer community in Korea, with 20 meetup groups organized by topic and area that collectively host over 100 events each year, primarily in Seoul.

My team regularly visits countries across the Asia-Pacific region, listens to feedback from user group leaders, and works to support their communities. At this meeting, leaders honestly shared what they did well in the first half of the year, what needs improvement, and what they asked of AWS Developer Experience team. We also enjoyed a pleasant conversation during our Chimaek time together.

Now, let’s take a closer look at key launches of last week.

A one-click Lambda setup prompt for coding agents caught my eye most last week. This prompt configures your agent with AWS Serverless skills and the Serverless Model Context Protocol (MCP) server, embedding serverless best practices from the start. This prompt references the Lambda agent setup guide, which includes installation commands for Claude Code, Kiro, Cursor, GitHub Copilot, Codex, Devin Desktop, and OpenCode.

To get started, choose the Copy agent prompt button on the Lambda console screen or copy fetch https://docs.aws.amazon.com/lambda/latest/dg/samples/aws-lambda-agent-setup.md directly, and paste this URL in your preferred AI agent.

You can also use Agent Toolkit for AWS to give your coding agent current AWS knowledge and safe resource access. Use fetch https://raw.githubusercontent.com/aws/agent-toolkit-for-aws/refs/heads/main/setup-instructions/setup.md for installing AWS MCP Server.

Last week’s launches
Here are last week’s launches that caught my attention:

  • OpenAI GPT-5.6 Sol, Terra, and Luna on Amazon Bedrock: You can use the smartest family of models from OpenAI yet on Bedrock’s next-generation inference engine built for high performance, security, and reliability. The three models span capability tiers from flagship reasoning (Sol) to balanced performance (Terra) to fast, cost-efficient inference (Luna), all accessible through the Responses API on Amazon Bedrock.
  • Same-day transitions to Amazon S3 Standard-IA and S3 One Zone-IA: You can now transition objects to S3 Standard-Infrequent Access (S3 Standard-IA) and S3 One Zone-Infrequent Access (S3 One Zone-IA) as soon as the day they are created, without the previous 30-day minimum retention period in S3 Standard. These storage classes offer up to 40% lower storage costs than S3 Standard while still providing millisecond access when needed, making them ideal for backups, log analytics, and compliance workloads where data becomes cold within hours or days.
  • Self-managed code storage on AWS Lambda: With self-managed Amazon S3 buckets for code storage, you can reference source code directly from your own S3 buckets without Lambda creating intermediate copies. This eliminates code storage limits and reduces function activation time after function creates and updates by removing the copy step.
  • Importing users with password hashes on Amazon Cognito: You can now import users with password hashes in CSV user imports. Previously, imported users had to reset their passwords on first sign-in. Now, you can include password hashes in the CSV import, enabling users to sign in immediately with their existing credentials. When creating a CSV import, you specify the password hashing algorithm used by your source system.

For a full list of AWS announcements, be sure to keep an eye on the What’s New with AWS page.

Additional updates
Here are some additional news items that you might find interesting:

  • Amazon SQS turns 20: Two decades of reliable messaging at scale: When Amazon SQS launched publicly in July 2006, it made this pattern available to every AWS customer. Twenty years later, that core function, decoupling producers from consumers, remains the reason customers use SQS. Let’s look back important milestones after Jeff’s 15th anniversary post.
  • Open Protocols with the Strands Agents SDK: Learn how open AI protocols such as MCP, A2A, UTCP, AG-UI, and x402 work together using Strands Agents SDK for building AI agents as an example implementation, though the patterns apply to any agent framework.
  • Open source Bulk Executor for Amazon DynamoDB: Performing bulk operations against all items in a DynamoDB table has historically required custom coding. The Bulk Executor for DynamoDB simplifies bulk tasks like these. You can use this feature to invoke commands like count, find, delete, or update. No coding is required, even when running at large scale.
  • Transform AWS Support Case Workflows with Kiro CLI: Explore how Kiro CLI’s MCP integration accelerates support case workflows by combining investigation, documentation lookup, and case creation into a single conversational interface across three real-world scenarios: AWS Glue job failures, AWS Lambda cold start investigation, and AWS WAF false positive analysis.

For a full list of AWS blog posts, be sure to keep an eye on the AWS Blogs page.

Learn more about AWS, browse and join upcoming AWS-led in-person and virtual events, startup events, and developer-focused events including AWS Summits. Join the AWS Builder Center to connect with builders, share solutions, and access content that supports your development.

Finally, some customers experienced an issue with Cost Explorer displaying inaccurate estimated billing data in last weekend. They may have received erroneous budget and cost anomaly detection alerts, and observed inflated estimated cost and usage data. The issue has been resolved, and all AWS services are operating normally. We apologize for the concern this incident caused our customers and are conducting a thorough retrospective to prevent events like this from reoccurring, as well as improving our response when billing incidents occur. For more information, visit the AWS Health Dashboard.

That’s all for this week. Check back next Monday for another Weekly Roundup!

— Channy

NVIDIA Announces Expanded Jetson Thor Lineup with Mid-Range T3000 and T2000 Modules

Post Syndicated from Ryan Smith original https://www.servethehome.com/nvidia-announces-expanded-jetson-thor-lineup-with-mid-range-t3000-and-t2000-modules/

NVIDIA has announced the addition of two new mid-range boards to their Jetson Thor lineup, the T3000 and T2000, which will arrive in Q1’27. The new boards aim to be a cheaper offering for customers feeling pressured by the high cost of memory

The post NVIDIA Announces Expanded Jetson Thor Lineup with Mid-Range T3000 and T2000 Modules appeared first on ServeTheHome.

Security Hub adds AI workload protection and multicloud support for Microsoft Azure

Post Syndicated from Michael Fuller original https://aws.amazon.com/blogs/security/security-hub-adds-ai-workload-protection-and-multicloud-support-for-microsoft-azure/

Security Hub is our foundation for full-stack enterprise security across clouds. It centralizes your security operations and turns raw signals into prioritized insights, so your team spends its time managing real risk instead of stitching tools together. Today that foundation grows in two directions our customers asked for most. We are adding purpose-built protection for AI workloads, and security monitoring for Microsoft Azure. Both are steps toward a bigger idea, that your best security tools should get smarter by working together.

These expansions came directly from customers, and they reflect where security is heading, not where it has been. The old promise of security tooling was a place to collect everything in one view. Collecting findings was never the hard part. The hard part is understanding them, connecting them, and acting before an attacker does, and doing it at the speed attacks now move. The programs that win from here will be the ones that see across their whole estate and respond fast, not the ones with the most dashboards. That is what we are building toward, and these launches are steps on that path.

Multicloud security management for Microsoft Azure

Customers across industries have made Security Hub a core part of how they run security on AWS. Most of them have run in more than one cloud for years, and they have been clear with us that they want Security Hub to also cover the rest of their estate. Today we do that for Microsoft Azure, with more clouds following quickly.

Security Hub now discovers Azure Virtual Machines, container images, Function Apps, and identities, then evaluates them for misconfigurations, internet exposure, and software vulnerabilities, with posture checks against the CIS Microsoft Azure Foundations Benchmark™. Azure findings are prioritized next to your AWS findings using the same finding format, automation, and response workflows, so your team works from one understanding of risk across your entire estate. Azure resources are priced at the same rates as equivalent AWS resources with no additional fees, and there’s an independent 30-day free trial. To learn more, see the What’s New post.

This is not actually our first move beyond AWS. Earlier this year we introduced Security Hub Extended, bringing best-in-class partner solutions across nine security categories into the same experience you already use. Those partner solutions protect endpoints, identities, email, browsers, and data wherever they run, across any cloud, on-premises, and everywhere your enterprise operates. Extended was already our first multicloud and multi-workload step. Today we broaden what our own native capabilities cover, and the two lines of work now advance together.

Protecting AI workloads

Every customer I talk to is building with AI. Generative AI on Amazon Bedrock, model training on SageMaker, agents orchestrating workflows through AgentCore. These workloads are reaching production faster than most security programs can keep up, and teams often don’t yet have the tools to monitor model invocations, track agent behavior, or even know what AI assets exist across the organization. One security leader told me his team only caught a compromised service account, one that had been invoking a foundation model thousands of times, because finance questioned the bill. They found a security incident through an accounting review. The visibility gap is real, and it is already expensive.

This summer we start closing it with three launches. Two are GuardDuty capabilities for threat detection and investigation, and a third is a new Security Hub AI inventory.

GuardDuty AI Protection (generally available)

Amazon GuardDuty AI Protection delivers threat detection purpose-built for Bedrock and SageMaker. It detects anomalous model invocations, cost harvesting attacks where adversaries abuse stolen credentials to run inference at your expense, and prompt injection attempts through integration with Bedrock Guardrails.

Cost harvesting is accelerating. When credentials are compromised, attackers increasingly use them to invoke foundation models. Inference is expensive, demand is high, and stolen access converts straight to value without deploying any infrastructure. GuardDuty analyzes CloudTrail data events, learns what normal invocation looks like at scale, and flags the deviations that signal compromise or abuse. This is detection that only works at AWS scale, because you have to see the signal across millions of workloads to know what normal is. GuardDuty AI Protection is now available to all GuardDuty customers with a 30-day free trial.

GuardDuty AI-powered investigations (preview)

AI-powered investigations take on the manual investigation work that drives alert fatigue and slows response. The capability automatically analyzes GuardDuty findings and the accounts around them to separate true threats from benign activity.

It examines finding context, related activity from the last 90 days, affected resources, and threat indicators, using knowledge graphs and threat intelligence to complete in minutes what used to take hours. Each investigation returns a disposition assessment with confidence scoring, MITRE ATT&CK® classification, supporting evidence, and clear recommendations to suppress, contain, or remediate. Your team focuses on genuine threats, whether across a single account or an entire AWS Organization, and mean time to resolution drops. GuardDuty AI-powered investigations is available in preview in 10 AWS Regions.

Security Hub AI inventory (generally available)

You can’t secure what you don’t know exists. Security Hub now provides an AI inventory, a continuously updated, organization-wide view of your AI assets and their security posture. As teams deploy models, agents, and pipelines, security often can’t see what’s running, and without connecting those assets to active threats and misconfigurations, it’s difficult to know what to secure first.

Security Hub AI inventory discovers and catalogs AI workloads across your AWS environment two ways. For managed services, it inventories AWS Config resources across Bedrock, SageMaker, and AgentCore. For self-hosted and external workloads, it finds models running on EC2, ECS, and EKS through runtime analysis, and identifies the external model endpoints your workloads make calls to. It maps each asset to the infrastructure beneath it, including compute, networking, IAM roles, and data stores, and correlates it with security signals such as GuardDuty findings. So when GuardDuty AI Protection flags an anomalous invocation, AI inventory immediately shows you which infrastructure is involved, what’s connected to it, and where it belongs in your priority order.

AI assets multiply fast. A developer spins up a Bedrock agent for a proof of concept. A data science team stands up a SageMaker endpoint for internal testing. Another team wires in an external model API through a Lambda function. Multiply that across hundreds or thousands of accounts and you can quickly lose track. AI inventory gives you that view across every account in your organization, available in your Security Hub Essentials plan at no additional cost.

A different approach to full-stack security

These launches share something worth pausing on. You didn’t procure AI protection as a separate product, and you won’t stand up separate operations for Azure. You add them to the Security Hub you already run, and they show up in your prioritized view of risk. That same idea is what Security Hub Extended extends to the rest of the security estate.

Security Hub Extended now has 21 curated partners across nine categories: 7AI, Britive, CrowdStrike, Idira (CyberArk), Cyera, Island, LayerX, Native Security, Noma, Okta, Oligo, Opti, Proofpoint, SailPoint, SentinelOne, Splunk, Sublime, Upwind, Varonis, Zenity, and Zscaler. These are best-in-class solutions across endpoint, identity, email, network, data, browser, cloud, AI, and security operations. None of them are here by default. Each one earned its place by committing to a shared view of where enterprise security is going, and by investing alongside us to build it. Curation is the point. A recommendation only means something if it can be turned down.

The commercial benefits of Extended are real today. Pay-as-you-go pricing, a single AWS bill, EDP eligibility, and no long-term commitments. But the work we’re most excited about goes further, and it’s not about procurement at all. Findings from every participating solution are emitted in the Open Cybersecurity Schema Framework (OCSF) and aggregated in Security Hub, and we’re building toward a single correlation across all of them, so a signal from an endpoint solution, an identity solution, and a cloud solution combine into one exposure and one attack path instead of three disconnected alerts. We’re working to reduce the deployment and onboarding effort between subscribing and seeing value. And we’re building the exchange that lets partner findings enrich each other, so the best-in-class tools you already trust become more than the sum of their parts. That is the differentiated future we’re investing in, and we’re building it in the open, guided by what customers ask for next. To learn more about Extended, see the What’s New post.

Accelerating forward

Step back and the shape of it is clear. Security Hub reaches across cloud providers, starting with Azure and expanding from there. It reaches across workload types with purpose-built AI protection and inventory. And it reaches across security categories through Extended and its curated partners. What began as a way to bring order to AWS security findings has become how more enterprises run full-stack security.

Detection and visibility are the foundation. What we build on top of them is a security experience that connects signals across every source you trust and helps you respond faster. It’s still Day 1, and Security Hub will keep extending as your environment, and the threats you face, continue to change.

If you have feedback about this post, submit comments in the Comments section below.

Michael Fuller

Michael has been with AWS for 16 years and led product for AWS Security Services for 11 years. Michael has 29 years in the industry and held several roles in product management, business development, and software development for IBM, Cisco, and Amazon. Michael has a Bachelor’s of Science in Computer Engineering from the University of Arizona and an MBA from the University of Washington.