Post Syndicated from Patrick Kennedy original https://www.servethehome.com/patrick-at-micro-center-columbus-ohio-grand-reopening-with-jeff-geerling/
Patrick and Jeff Geerling are at the Micro Center in Columbus, Ohio for the new store’s grand reopening today
The post Patrick at Micro Center Columbus Ohio Grand Reopening with Jeff Geerling appeared first on ServeTheHome.
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:
AWS Glue 6.0 also includes most significant upgrade in Spark 4.1, the modern runtime engine:
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
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:
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
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:
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
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:
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:
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
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.
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.
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.
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.
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.
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.
The 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.
Step 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.
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.
The 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:
To get started, visit the runtime instance in Amazon Bedrock AgentCore documentation and create your first capacity provider.
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.
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.
Post Syndicated from Ritik Khatwani original https://aws.amazon.com/blogs/devops/analyze-and-remediate-technical-debt-autonomously-with-aws-transform-continuous-modernization/
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.
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.
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:
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.
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.
Figure 2: Set up continuous modernization in the AWS Transform web application
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.
Figure 3: Use the AWS Transform cli to analyze your repositories
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.
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
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
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
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.
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
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.
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.
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.
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:
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:
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!
Post Syndicated from Cliff Robinson original https://www.servethehome.com/geekbench-7-is-out-with-a-major-overhaul/
Geekbench 7 is out with major overhauls to scoring, CPU and GPU benchmarks. We have already been testing with it
The post Geekbench 7 is Out with a Major Overhaul appeared first on ServeTheHome.
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.
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.
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:
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:
count, find, delete, or update. No coding is required, even when running at large scale.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
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.
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.
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.
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.
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.
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.
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.
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.
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.
Post Syndicated from Esra Kayabali original https://aws.amazon.com/blogs/aws/amazon-sqs-turns-20-two-decades-of-reliable-messaging-at-scale/
On July 13, 2006, we launched Amazon Simple Queue Service (Amazon SQS) as one of the first three services available to customers, alongside Amazon EC2 and Amazon S3. We had learned firsthand that distributed systems need a reliable way to pass messages between components without creating tight dependencies. If one service called another directly and that service was slow or unavailable, failures cascaded through the entire system. Message queuing solved this by letting services communicate asynchronously: a producer could drop a message into a queue and move on, while a consumer picked it up when ready. This approach kept individual service failures from affecting the rest of the system.
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. The scale, performance, and operational controls around it look very different now though.
Jeff Barr covered the first 15 years of SQS milestones in his 15th anniversary post, from the original 8 KB message limit in 2006 through FIFO queues, server-side encryption, and Lambda integration. Over the last five years, we have continued to scale SQS, added stronger security defaults, and introduced new capabilities that address increasingly complex workload patterns.
Key milestones between 2021 and 2026
High throughput mode for FIFO queues (2021): In May 2021, we launched general availability of high throughput mode for FIFO queues, supporting up to 3,000 transactions per second (TPS) per API action, a tenfold increase over the previous limit. We continued raising this ceiling over the following two years: to 6,000 TPS in October 2022, to 9,000 TPS in August 2023, and to 18,000 TPS in October 2023, before reaching 70,000 TPS per API action in select Regions by November 2023.
Server-side encryption with SSE-SQS (2021): In November 2021, we introduced server-side encryption with Amazon SQS-managed encryption keys (SSE-SQS), giving customers an encryption option that required no key management. In October 2022, we made SSE-SQS the default for all newly created queues, so customers no longer needed to explicitly enable it.
Dead-letter queue redrive enhancements (2021): We progressively expanded how customers recover unconsumed messages from dead-letter queues. In December 2021, we added DLQ redrive to source queue directly in the SQS console. In June 2023, we extended this capability to the AWS SDK and CLI through new APIs, including StartMessageMoveTask, CancelMessageMoveTask, and ListMessageMoveTasks. In November 2023, we added redrive support for FIFO queues.
Attribute-based access control, ABAC (2022): In November 2022, we introduced ABAC, giving customers the ability to configure access permissions based on queue tags rather than maintaining static policies as resources scaled.
JSON protocol support (2023): In November 2023, we added support for the JSON protocol in the AWS SDK, reducing end-to-end message processing latency by up to 23% for a 5 KB payload and lowering client-side CPU and memory usage.
Amazon EventBridge Pipes console integration (2023): We added the ability to connect a queue directly to EventBridge Pipes from the SQS console, routing messages to a broad range of AWS service targets without writing custom integration code.
Extended Client Library for Python (2024): We brought the Extended Client Library, previously available for Java, to Python developers, allowing messages up to 2 GB to be sent through SQS by storing the payload in Amazon S3 and passing a reference through the queue.
FIFO in-flight message limit increase (2024): We increased the in-flight message limit for FIFO queues from 20,000 to 120,000 messages, so consumers can process significantly more messages concurrently without being constrained by the previous ceiling.
Fair queues for multi-tenant workloads (2025): We introduced fair queues to mitigate the noisy neighbor problem in multi-tenant standard queues. By including a message group ID when sending messages, customers can prevent a single tenant from delaying message delivery for others, without any changes required on the consumer side.
1 MiB maximum message payload size (2025): We increased the maximum message payload from 256 KiB to 1 MiB for both standard and FIFO queues, helping customers send larger messages without offloading data to external storage. AWS Lambda event source mapping for SQS was updated in parallel to support the new payload size.
The constant underneath the change
Despite two decades of feature additions, the fundamental use case for SQS has not shifted. Customers use it to decouple services, buffer bursts of traffic, and build systems that stay resilient when individual components fail. That same pattern now extends to AI workloads. Customers use SQS queues to buffer requests to large language models, manage inference throughput, and coordinate communication between autonomous AI agents operating as independent services. For an example of this architecture in practice, read Creating asynchronous AI agents with Amazon Bedrock.
To learn more about Amazon SQS, visit the Amazon SQS product page, review the developer guide, or explore recent updates on the AWS Blogs.
Post Syndicated from Esra Kayabali original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-aws-builder-center-at-one-year-network-scanning-in-security-hub-loom-for-aws-and-more-july-13-2026/
AWS Builder Center turned one year old last week. Launched on July 9, 2025, the platform has grown from a community hub with Wishlist voting, community profiles, and a toolbox into a full ecosystem with sandbox environments, workshops, Spaces, and a Builders’ Library. To mark the anniversary, Rick Suttles published a full feature timeline covering everything shipped over the past year: AWS Capabilities by Region (1,500+ services across 37 Regions), Spaces for community-created groups, workshops with category and complexity filters, badges and streaks, article series, view counts, saved items, student status, availability notifications, sign-in with GitHub and Amazon, and sandbox environments.
Jeff Barr published a retrospective summarizing Builder Center’s first year. Since launch, 5,548 authors have published 6,448 articles with more than 10.4 million page views combined. Builders have earned 99,226 badges since the badge system launched in March 2026. Community members have submitted 565 wishes, 10 of which have shipped with another 20 on the near-term roadmap.
The top community article Building an AWS Study Buddy with MCP + Strands Agents SDK by Dineshraj Dhanapathy reached 50,000+ views. Chris Miller’s Migrating an EOL Linux Server to AWS in 8 Hours with Kiro followed at 45,000+, and Yash Aggarwal’s AIdeas: NeuroVoice – Multimodal AI for Early Screening of Neurological Diseases article reached 38,000+.
The week’s headline addition is Sandbox Environments by Rick Suttles. Sandboxes give you a free, pre-provisioned AWS account to complete a workshop exercise. Each environment is active for 8 hours, after which the account and all its resources are automatically de-provisioned. You can have one active sandbox at a time and request one per week. No personal AWS account, credit card, or manual cleanup required.
Last week’s launches
Here’s what else happened this week.
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:
create-oauth2-token-with-iam API. New governance controls include OAuth-specific IAM condition keys, token introspection and revocation, dynamic client registration, and CloudTrail audit elements.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.
Wishing everyone a restful and enjoyable summer. Whether you’re building, learning, or recharging, I hope you find time for all three. I’ll be heading to Scandinavia for a few weeks to trade the heat for some cooler weather and longer evenings. Come back next week for more news!
Post Syndicated from Ryan Smith original https://www.servethehome.com/aic-gets-flashy-with-32-ssd-bay-jbof-server-for-key-value-caching/
In preparation for the Rubin Vera era, AIC was showing off its F2032-01-G6, a 2U JBOF storage system that can house up to 32 E3 SSDs. The box is designed to be paired with BlueField-4 DPUs, allowing it to be used as a key value caching appliance
The post AIC Gets Flashy with 32 SSD Bay JBOF Server for Key Value Caching appeared first on ServeTheHome.
Post Syndicated from Ryan Smith original https://www.servethehome.com/ibm-expands-z17-and-linuxone-5-mainframe-lineups-with-single-frame-and-rackmount-servers/
IBM is expanding its mainframe lineups with new single-frame and rackmount z17 and LinuxONE 5 offerings for smaller customers. As well, the company is releasing an 18U LinuxONE box for new customers called the LinuxONE Express
The post IBM Expands z17 and LinuxONE 5 Mainframe Lineups With Single Frame and Rackmount Servers appeared first on ServeTheHome.