Upgrading a Kubernetes control plane has long been a one way door. Open source Kubernetes doesn’t support control plane rollback, so once you upgrade, there’s no going back. The community is making real progress here, and KEP-4330 introduces emulated versions to ease rollback. But in practice this constraint has pushed organizations to build elaborate compensating mechanisms like bake periods, stagger groups, automated sign offs, and months long upgrade cycles. With Kubernetes releasing three minor versions per year, teams managing hundreds of clusters, especially in regulated environments, often delay upgrades entirely because they aren’t confident they can recover if something goes wrong. The result is clusters stuck on older versions, missing security patches, and eventually running up against extended support timelines.

Today, we’re announcing Kubernetes version rollbacks for Amazon Elastic Kubernetes Service (Amazon EKS), a new feature that gives cluster administrators a safety net when performing cluster upgrades. With version rollbacks, you can reverse a Kubernetes version upgrade within seven days if you encounter issues after upgrading, returning your cluster to its previous working state.

Where approaches like emulated versions keep a cluster in a transitional holding state, EKS version rollback returns your cluster to a fully validated previous version that ran in production, not an emulation of it. Now, if you upgrade a cluster from, say, Kubernetes 1.34 to 1.35 and discover a compatibility issue, you can roll back to 1.34 within seven days. There’s no need to rebuild your cluster or scramble to troubleshoot under pressure. Think of it as an undo button for Kubernetes version upgrades.

The feature supports rolling back one minor version at a time, matching the same incremental approach EKS uses for upgrades. And to help you roll back safely, EKS automatically evaluates your cluster’s rollback readiness through cluster insights, flagging items like node version compatibility or add-on dependencies before you proceed. If you’ve already assessed the situation and want to move quickly, you can use the --force flag to bypass those checks. The above applies to all EKS clusters, whether you manage your own nodes or let AWS handle them. But for customers who have embraced fully managed infrastructure, rollback goes a step further.

Rollback for EKS Auto Mode

EKS Auto Mode gives you one click deployment of production ready Kubernetes clusters, automating compute, networking, and storage management so you can focus on your applications rather than infrastructure. EKS Auto Mode introduces additional considerations for version rollbacks because both the control plane and managed nodes need to be rolled back together. Since node rollbacks respect your pod disruption budgets, the process can take time depending on your configuration.

To give you control over this process, we’ve introduced a cancel API that lets you stop a node rollback at any point. If you decide the rollback is taking too long or you want to change your approach, you can cancel and adjust your disruption budgets to accelerate things, or choose a different path forward.

By default, EKS never bypasses your disruption budgets during a rollback because we prioritize workload stability. You can always choose to modify or remove disruption budgets yourself to speed up the process if needed.

Let’s try it out

To try version rollbacks, I navigated to the Amazon EKS console and selected one of my clusters that I had recently upgraded.

From the cluster’s configuration page, I can see the option to initiate a version rollback, along with information about my current rollback window.

Before initiating the rollback, I reviewed the rollback insights to check for any potential issues. The insights showed me the status of my nodes and flagged anything I should address before proceeding.

After confirming, the rollback began. My cluster remained functional throughout the process. The control plane rollback took about 20 minutes, similar to a standard upgrade. For my EKS Auto Mode cluster, the nodes rolled back gracefully according to my disruption budget settings.

Once complete, my cluster was back on the previous Kubernetes version, running as expected.

Now available

Kubernetes version rollbacks for Amazon EKS are available today at no additional cost in all commercial AWS Regions where Amazon EKS is available. You pay only for the standard EKS and compute costs you would normally incur. There are no extra charges for using the rollback capability.

Control plane rollbacks are available for all EKS clusters, and node rollbacks are available for clusters running EKS Auto Mode. Version rollbacks support clusters running Kubernetes versions available in EKS standard support and extended support.

To get started, visit the Amazon EKS documentation or try it out directly in the Amazon EKS console.

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Today, we’re announcing AWS CloudFormation Express mode, a new deployment mode that accelerates deployments for developers and AI tools iterating on infrastructure. Express mode accelerates deployments by completing when CloudFormation confirms resource configuration is applied, rather than waiting for extended stabilization checks. This reduces deployment time by up to 4 times for iterative development workflows and production scenarios.

How it works

Every CloudFormation deployment performs stabilization checks after resource configuration is applied. These checks serve an important purpose when you need to confirm resources can serve traffic before shifting load.

However, many workflows do not require full stabilization to proceed. Express mode benefits two primary use cases: iterative development workflows and production scenarios where you are comfortable with eventual stabilization. These use cases include iterating on infrastructure configurations during development, testing individual components of your application, and AI-assisted infrastructure development that benefits from sub-minute feedback loops.

With Express mode, CloudFormation completes deployments when resource configuration is applied, without waiting for stabilization checks. Resources continue becoming operational in the background. CloudFormation automatically retries dependent resources that encounter transient failures during provisioning within the same stack, without requiring any customer intervention. This built-in resilience handles timing issues between resources as they stabilize. Express mode changes when the deployment completes, not how resources are provisioned.

For example, when I create an Amazon Simple Queue Service (SQS) queue with a dead letter queue (DLQ), Standard mode takes 64 seconds, but Express mode completes in up to 10 seconds. In the case of deleting an AWS Lambda function with network interface attachment, Standard mode takes 20–30 minutes, but Express mode completes in up to 10 seconds based on my benchmarking test.

Get started with CloudFormation Express mode

When you create a CloudFormation stack in the AWS Management Console, choose Enable in the Express mode under Stack deployment options.

You can also use AWS Command Line Interface (AWS CLI), AWS SDKs, or IaC tools like AWS Cloud Development Kit (CDK), and AI tools such as Kiro.

Activate Express mode by setting the --deployment-config parameter to EXPRESS when creating, updating, or deleting stacks. No template changes are required. Express mode disables rollback by default for the fastest iteration experience. To re-enable rollback, set disableRollback to false in the deployment-config for production environments, or implement monitoring/cleanup mechanisms for failed deployments.

aws cloudformation create-stack  
   --stack-name my-app  
   --template-body file://template.yaml  
   --deployment-config '{"mode": "EXPRESS", "disableRollback": true}' 

For example, use the Express mode when you build infrastructure incrementally, adding resources one at a time. Ensure your IAM role templates follow the principle of least privilege.

# Iteration 1: Deploy IAM role
aws cloudformation create-stack 
--stack-name my-microservice 
--template-body file://iteration1-iam.yaml 
--deployment-config '{"mode": "EXPRESS"}' 
--capabilities CAPABILITY_IAM
--role-arn arn:aws:iam::123456789012:role/CloudFormationDeployRole

# Iteration 2: Add Lambda function
aws cloudformation update-stack 
--stack-name my-microservice 
--template-body file://iteration2-lambda.yaml 
--deployment-config '{"mode": "EXPRESS"}' 
--capabilities CAPABILITY_IAM
--role-arn arn:aws:iam::123456789012:role/CloudFormationDeployRole

# Iteration 3: Add SQS queue and event source mapping
aws cloudformation update-stack 
--stack-name my-microservice 
--template-body file://iteration3-sqs.yaml 
--deployment-config '{"mode": "EXPRESS"}' 
--capabilities CAPABILITY_IAM
--role-arn arn:aws:iam::123456789012:role/CloudFormationDeployRole

For AWS CDK, activate Express mode with the cdk deploy --express command when you deploy your CDK stack. This command retrieves your generated CloudFormation template and deploys it through the CloudFormation Express mode, which provisions your resources as part of a CloudFormation stack.

Express mode works with all existing CloudFormation templates and supports all CloudFormation features including change sets and nested stacks. When you enable Express mode on a parent stack, all nested stacks also use Express mode. If you need resources to be fully operational before proceeding with traffic or testing, continue using the default deployment behavior, which performs stabilization checks before completing.

Now available

AWS CloudFormation Express mode is available today in all AWS commercial Regions at no additional cost. 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. To learn more, visit the CloudFormation documentation.

Start accelerating your deployments today, and send feedback to AWS re:Post for AWS CloudFormation or through your usual AWS Support contacts.

— Channy

The post Accelerate your infrastructure deployments by up to 4x with AWS CloudFormation Express mode appeared first on The NOKIA Blog.

When you run compute-intensive workloads like real-time analytics, batch processing, video encoding, scientific modeling, or CPU-based machine learning inference, every percentage point of performance matters. You need instances that deliver higher throughput per vCPU, faster memory access, and more network bandwidth, all while keeping your costs in check.

Today I am happy to announce the general availability of Amazon Elastic Compute Cloud (Amazon EC2) C9g and C9gd instances, powered by AWS Graviton5 processors. C9g instances are compute-optimized and deliver up to 25% higher performance per vCPU compared to previous-generation C8g instances. They feature the fastest memory of any processor instance in the cloud, with DDR5 8800MT/s DIMMs, 5x more L3 cache, and up to 3x higher packet-processing performance compared to Graviton4-based instances. The faster memory and larger caches mean your workloads spend less time waiting on data, translating into higher throughput for in-memory analytics, faster agentic loops, and more responsive real-time applications.

C9g instances are ideal for batch jobs, video encoding pipelines, or distributed analytics that can utilize Amazon Elastic Block Store (Amazon EBS) for storage. It is also a natural fit for agentic AI workloads, where concurrent environments and CPU-bound reasoning steps benefit from Graviton5’s higher core count and larger caches. As AI shifts from answering questions to taking actions, running code, and orchestrating multi-step tasks, the demand for CPU compute is growing, and C9g instances are built for this shift.

Some workloads also need fast local storage alongside that compute power. Choose C9gd when your application benefits from high-speed, low-latency local NVMe SSD storage, for example scratch space during HPC simulations, temporary caches for ML inference, or local buffers for ad-serving engines.

Graviton5-based instances with NVMe instance store volumes also support detailed performance statistics, providing high-resolution I/O metrics, including latency histograms broken down by I/O size, up to 1-second granularity and accessible via Amazon CloudWatch or nvme-cli at no additional cost.

C9g and C9gd instances at a glance

C9g and C9gd instances are available in 11 sizes ranging from medium to 48xlarge, plus a bare metal option. They offer up to 15% higher network bandwidth and 20% higher EBS bandwidth on average across sizes compared to the previous generation, with the largest 48xlarge size delivering up to 100 Gbps of network bandwidth and up to 72 Gbps of EBS bandwidth, a 2x increase.

C9gd instances add local NVMe SSD storage with up to 30% higher storage performance compared to previous-generation local storage instances.

Both families are well-suited for high-performance computing (HPC), batch processing, gaming, video encoding, scientific modeling, distributed analytics, CPU-based machine learning inference, and ad serving.

Here are some additional capabilities:

• Instance Bandwidth Configuration (IBC) lets you adjust the allocation of bandwidth between Amazon EBS and Amazon VPC networking by up to 25%, helping you optimize performance for workloads with specific bandwidth requirements such as databases and caching.
• ENA Express support for enhanced networking.
• Up to 128 EBS volumes can be attached to virtual instances.
• Support for Savings Plans, On-Demand, Spot Instances, Dedicated Instances, and Dedicated Hosts.

Nitro Isolation Engine

C9g and C9gd instances are the first compute optimized Amazon EC2 instances to feature the AWS Nitro Isolation Engine, a new capability of the AWS Nitro System. The Nitro Isolation Engine is a purpose-built component of the Nitro Hypervisor, implemented in Rust, that enforces isolation between virtual machines. It mediates all access to VM memory, CPU register state, and I/O devices through a minimal set of APIs.

To learn more about the Nitro Isolation Engine, visit the blog post. For details on the formal verification results, including scope and assumptions, see our technical white paper.

Now available

Amazon EC2 C9g and C9gd instances are now available in US East (Ohio, N. Virginia), US West (Oregon), and Europe (Frankfurt). Additional regions will follow.

You can launch C9g and C9gd instances today using the AWS Management Console, AWS Command Line Interface (AWS CLI), or AWS SDKs. For pricing information, visit the Amazon EC2 Pricing page.

To learn more, visit the Amazon EC2 C9g and C9gd instances page and send feedback to AWS re:Post for EC2 or through your usual AWS Support contacts.

— seb

The post Amazon EC2 C9g and C9gd instances powered by AWS Graviton5 processors are now available appeared first on The NOKIA Blog.

If you manage TLS certificates for your applications, you know the challenge: certificates expire, and when they do, your customers see errors or your service goes down. As certificate validity periods get shorter (the Certification Authority (CA)/Browser Forum mandates reduced maximum validity to 100 days starting March 2027, and to 47 days by 2029), manual renewal processes become untenable. You need automation.

Automatic Certificate Management Environment (ACME) is an open protocol for requesting, renewing, and revoking TLS certificates without human intervention. It’s the same protocol behind Let’s Encrypt, and it’s supported by dozens of clients across every platform.

Today we’re announcing ACME support for public certificates in AWS Certificate Manager (ACM). ACM now provides a fully managed ACME server endpoint that works with any ACMEv2-compatible client, such as Certbot, cert-manager for Kubernetes, acme.sh, or any other client you already use. You can issue public TLS certificates from Amazon Trust Services through the standard ACME protocol.

Before today, if you wanted automated certificate management using the ACME protocol, you relied on external certificate authorities alongside ACM, leading to a fragmented visibility experience. Some certificates lived in ACM, others were managed externally with no central dashboard. PKI administrators had limited ability to control who could request certificates or which domains were allowed.

With ACME support in ACM, you can now set up one or more managed ACME endpoint that allows you to centrally manage and monitor ACME certificate usage across your organization.

As a PKI administrator, you get centralized controls that go beyond basic certificate issuance. You can bind IAM roles to ACME accounts for fine-grained access control over which domains each client can request. You can define domain scopes at the endpoint level to enforce organization-wide policies. And you get centralized monitoring and visibility in the same place: AWS CloudTrail logs every certificate request for auditability, Amazon CloudWatch tracks operational metrics, and ACM sends expiry notifications when certificates are approaching renewal. Using ACM, your PKI team can search all certificates, whether issued through the ACM console, an API call, or ACME.

How it works

To get started, you first set up a dedicated ACME endpoint, configure authorization controls using External Account Binding (EAB), validate which domains the endpoint can issue certificates for, and point your existing ACME clients to the new endpoint.

The domain validation step is important: it separates who can set up certificate issuance from who can request certificates. The PKI administrator validates domains once at the endpoint level, using DNS credentials that stay with the admin. Application owners who need certificates never touch DNS. They register with an EAB credential, and the endpoint enforces which domains and scopes they’re allowed to request. This means you can distribute certificate automation broadly across your organization without distributing DNS keys along with it.

I start this demo from the ACME certificates page in the AWS Certificate Manager console.

I already have a few endpoints and certificates in this account, I walk you through creating a new one from scratch. First, I select Create ACME endpoint.

I give my endpoint a name. The Endpoint type is Public. ACME clients will connect over the public internet. The Certificate type is Public. The certificate will be issued by Amazon Trust Services and trusted by browsers and operating systems by default. For the certificate key type, I keep the default ECDSA P-256. RSA 2048 and ECDSA P-384 are also available if your clients require them.

Scrolling down, I configure the domain. I enter my domain name and select the domain scope. The scope controls exactly what certificate patterns your ACME clients are allowed to request for this domain. If I check only Exact domain, clients can only request certificates for that specific domain name. Adding Subdomains allows certificates for any subdomain (for example, api.example.com or dev.example.com). Adding Wildcards allows wildcard certificates (*.example.com). By leaving a scope unchecked, you prevent any client using this endpoint from requesting that type of certificate, even if their ACME request is otherwise valid. For a production endpoint, you might enable only Exact domain and Subdomains while leaving Wildcards unchecked to enforce a stricter security posture.

I also select my Amazon Route 53 hosted zone from the drop down menu. ACM then automatically creates the DNS CNAME records needed for domain validation, so I don’t have to do it manually. When my domain is hosted outside of Route 53, I manually create the provided CNAME record at my DNS provider instead. This is a meaningful difference from typical ACME setups where each client handles its own domain verification independently.

These centralized controls give PKI administrators a single place to authenticate domains, restrict which certificate types (ECDSA or RSA) clients can request, and further limit wildcard issuance. Having these governance capabilities built in means you don’t need to purchase a separate certificate lifecycle management product or invest in building a custom policy layer yourself, both of which come at significant cost and operational overhead.

I select Create ACME endpoint

After a few seconds, the endpoint is created. The console shows a Setup progress tracker with the next steps. My domain shows a “Validating” status. The validation method is DNS validation, where ACM verifies that you control the domain by checking for a specific CNAME record. Because I selected my Route 53 hosted zone during creation, I select Create records in Route 53 to let ACM handle the DNS validation automatically.

The validation completes in a few seconds and the status changes to Success.

Now I need to create External Account Binding (EAB) credentials. EAB credentials are a key identifier and HMAC key pair that lets your ACME client register an account with the ACME server. Once registered, the client generates its own asymmetric key pair, which is then used to authenticate all subsequent certificate requests. On the endpoint details page, I select the External account binding tab, then select Create EAB. I give the credential a name and optionally set an expiration time, ideally no longer than needed to complete client registration.

After I select Create EAB credential, the console shows the Key ID and HMAC Key. I note these values because I need them to configure my ACME client. The setup progress now shows four green checkmarks.

I’m ready to request a certificate. On the endpoint details page, I expand the CLI reference section. The console provides ready-to-use command examples for both Certbot and acme.sh. I copy the Certbot command and run it inside a container using the certbot/certbot image.

certbot certonly --standalone --non-interactive --agree-tos 
    --email <EMAIL> 
    --server https://acm-acme-enroll.us-east-1.api.aws/<ENDPOINT_ID>/directory 
    --eab-kid <EAB_KID> 
    --eab-hmac-key <EAB_HMAC_KEY> 
    --issuance-timeout <ISSUANCE_TIMEOUT> 
    -d <DOMAIN>

I replace the placeholders with my endpoint URL, EAB credentials, and domain name. The --eab-kid and --eab-hmac-key arguments are how Certbot registers with your ACME endpoint using the External Account Binding credentials I generated earlier. Each ACME client has its own syntax for this step, so check your client’s documentation for the exact flags.

Certbot contacts the ACME endpoint and returns a valid certificate signed by Amazon Trust Services.

I use openssl to view the certificate before installing it.

The certificate is now visible in the ACM console under the ACME certificates tab, alongside any certificates issued through the console or API.

Availability and pricing

ACME support in AWS Certificate Manager is available today in all commercial AWS Regions and will be available in AWS GovCloud (US), the China Regions, and the AWS European Sovereign Cloud partitions at a later date.

Pricing is per domain included in each certificate at the time of issuance, with a different price for fully qualified domain names and wildcards. Volume tiers are calculated based on total domain occurrences across all certificates issued per month in your AWS account. For details, see the ACM pricing page.

To get started, visit the ACM section on the AWS console or read the documentation.

— seb

The post Automate public TLS certificate issuance with ACME support in AWS Certificate Manager appeared first on The NOKIA Blog.

We know that right now, a senior engineer is shipping a checkout flow they “built” in a single afternoon. AI assistant does the heavy lifting, happy path runs clean, and a rotating chevron spins on the order summary. Two weeks later, engineering gets a notice from customer support: a blind customer using a screen reader can’t complete the purchase because the “Pay Now” control is a <div> with a click handler. No role. Not focusable. Not working.

That gap — between code that runs and a product people can actually use — is becoming one of the defining engineering challenges of the AI era. Teams can generate UI faster than ever, but they still have to guarantee that what they ship is usable, secure, and maintainable.

Accessibility sits right in the middle of that problem.

This is not an article about compliance checklists or end-of-project audits. It’s about engineering systems. Specifically, why accessibility should be treated as an operational capability — alongside privacy, security, reliability, and observability — and what that looks like in practice.

The Audit Trap

For years, the default way to “do” accessibility was the one-time, audit-only approach: hire a firm, get a list of 200 findings, fix some of them, file the report. A lot of teams have now moved beyond this model — and the reason is worth looking into.

Audits do matter. For sales, procurement, governance — they’re essential. When a buyer asks for a VPAT or an ACR, you need one. When legal asks if you’re meeting requirements, you need documentation. Audits serve those purposes well.

But audits don’t help you build accessible features during sprint planning. Audits can cost points during a sprint. They don’t catch problems before merge requests. They don’t scale with deployment velocity.
The mistake, essentially, is tackling accessibility as a snapshot when you really need constant monitoring. Six months after the audit, the product has shipped dozens of releases, multiple new features, and a redesigned nav. The report is now fiction. Compliance is not a state you reach — it’s a state you maintain, and complexity fights you the whole way.

The WebAIM Million report, which scans the top one million home pages every year, found that 95.9% of pages had detectable WCAG failures in its 2026 run, with an average of 56.1 errors per page. The number of page elements jumped more than 20% in a single year, likely driven by AI-enabled development and ‘vibe coding’ — and more elements mean more places to break. Accessibility debt behaves exactly like technical debt: every inaccessible component you ship becomes a future remediation project, and the interest compounds.

Any strategy that treats accessibility as a periodic event rather than a continuous property of the system is going to lose.

The AI Problem Nobody Wants To Name

With the scale at which teams now generate UI, the gap doesn’t just persist; it multiplies.

Start with how fast this arrived. In February 2025, Andrej Karpathy coined “vibe coding” — a way of working where you “fully give in to the vibes” and “forget that the code even exists”. You describe intent, the model generates, you accept the diffs without reading them. It was meant for weekend projects. It did not stay there. Y Combinator reported that 25% of its Winter 2025 batch had codebases that were 95% AI-generated.

Models don’t land on non-semantic markup by accident — three forces push them there. Most React code on GitHub uses non-semantic “soup”, so that’s what the models learn. Human reviewers and evaluators judge output visually, so the feedback loop rewards looks, not semantics. And <div onClick> is fewer tokens than <button aria-expanded="true" ...>, so absent a constraint, the model takes the cheap path.

Here’s the thing about AI-generated UI: it’s inaccessible by default. Not occasionally — by default. A developer writing in Frontend Masters tested AI-generated React components across multiple tools and documented the pattern. A typical AI-generated sidebar had ten distinct accessibility failures in twenty-nine lines: no landmark, no heading, no list structure, elements with click handlers instead of buttons, no aria-expanded, no keyboard handling, and unlabeled icons. The accessibility tree — the structure screen readers actually read — came back as flat, unstructured text. “Same pixels” as the author put it. “One is a door. The other is a painting of a door”.

Now connect this to security, because the two failures come from the same root. Veracode’s 2025 GenAI Code Security Report tested large language models across dozens of coding tasks and found that a large fraction of AI-generated code introduced security vulnerabilities — including OWASP Top 10 flaws. Cross-site scripting failures were particularly common, and security performance did not meaningfully improve with newer, larger models. The issue wasn’t model intelligence. It was process: developers generating code without specifying security constraints and accepting output without systematic verification.

The same shortcut that skips the security review skips the accessibility review. At scale, AI won’t close the accessibility gap — it has industrialized the very thing that creates it.

The fix is not to ban AI. Your developers are already using it. The fix is to constrain it and verify it — to treat AI as a very fast teammate who always needs guardrails.

Velocity and Accessibility Are Not Enemies

This is usually where someone says, “Guardrails? Sounds great, but they will slow us down.”

In practice, the opposite tends to be true.

Shift-left is the entire DevOps thesis, and it applies cleanly here. An accessibility issue caught during design review is a comment. The same issue found in production is a remediation project.

Catching an accessibility issue as a component is built takes minutes. Fixing one after the fact — discovering it in an audit, diagnosing the root cause, restructuring the markup, applying the necessary fix, writing tests — can easily take hours. Multiply that across hundreds of findings from a late-stage audit, and you have weeks of unplanned work that earlier automated checks — whether in design reviews, development workflows, or CI — could have prevented.

Teams that integrate accessibility into everyday workflows avoid the expensive surprises: emergency audits, remediation sprints, procurement blockers, and redesigns that quietly break core user journeys. Accessibility doesn’t reduce velocity. Unexpected work reduces velocity. In-flow accessibility is one way of eliminating unexpected work.

What Enterprise-Ready Actually Looks Like

The organizations that scale accessibility successfully do not rely on heroes. They rely on systems.

The highest-leverage place to start is the design system. One accessible component can be reused thousands of times. The GOV.UK Design System is a useful example: components undergo both automated and manual testing using assistive technologies such as JAWS, NVDA, VoiceOver, and TalkBack. The team is explicit about the limits of automation and supplements tooling with user testing involving people with disabilities. They’re equally clear that using the design system doesn’t “magically” make a service accessible; it just gives you a higher starting point.

Accessibility becomes infrastructure. That’s the lesson.

From there, it moves into the engineering workflow:

• Accessibility requirements are included in the Definition of Done.
• Pull request reviews include explicit accessibility checks.
• Interactive controls use semantic elements (<button>, <a>) by default.
• Keyboard navigation and focus management are treated as standard engineering concerns, not optional polish.

Finally, accessibility becomes enforceable through automation:

At that point, accessibility stops depending on memory and starts depending on the process. It becomes part of your platform.

Patterns That Actually Scale

A few implementation patterns consistently show up in teams that do this well.

Constrain AI Before It Generates

Instead of fixing accessibility after generation, bake requirements directly into tooling through Cursor rules, Copilot instructions, or repository-level standards. Tell the model to use semantic HTML. Tell it when to use buttons versus links. Tell it to expose the state and labels correctly. Models follow persistent constraints far more reliably than one-off prompts.

Comboboxes, menus, tabs, modals, and similar controls routinely become accessibility hotspots. Libraries such as Radix UI, React Aria, and Headless UI already solve many of these problems. The scalable approach is not about repeatedly implementing accessibility correctly. It’s inheriting accessible behavior from well-tested primitives.

Capture Accessibility During Design Handoff

Focus order, labels, heading hierarchy, and interaction states should be specified before implementation begins. If accessibility requirements are absent from the design artifact, they are often absent from the final product. A simple memo at design handoff — what is the tab order, what are the labels, what happens on error — removes a huge amount of guesswork later.

None of these patterns is exotic. They’re just DevOps and platform thinking applied to accessibility.

The Broader Business Impact

Engineering leaders rarely prioritize accessibility solely because of regulations. But regulations, procurement requirements, user retention, and product quality all point in the same direction.

Legal pressure continues to increase. Digital accessibility lawsuits in the United States have stayed in the thousands per year, and they are not limited to large enterprises. The European Accessibility Act is now enforceable across the EU, applying to e‑commerce, banking, ticketing, telecoms, and more, regardless of where the company is headquartered. The message is clear: accessibility is no longer a “nice-to-have” in the eyes of regulators.

But compliance is only part of the story. The bigger story is the market you leave on the table. The World Economic Forum (December 2023) estimates that the world’s 1.3 billion people with disabilities, “along with their friends and family, has a spending power of $13 trillion”; disabled consumers alone control roughly $8 trillion in annual disposable income, per the Valuable 500.

In the UK alone, the Click-Away Pound Report 2019 found the “Click-Away Pound has risen to £17.1 billion” — more than 4.9 million users with access needs who abandon inaccessible sites and spend elsewhere, up almost 45% from £11.75 billion in 2016. People don’t file a bug report. They leave and buy from a competitor.

There is also a procurement reality that turns accessibility from a cost into a moat. If you sell B2B or to government, you will increasingly be asked for proof of accessibility — VPATs/ACRs or equivalent documentation. According to Level Access’s Seventh Annual State of Digital Accessibility Report, 75% of organizations now require proof of accessibility at least most of the time when purchasing digital products — essentially unchanged from 74% in the previous report, but with a notable shift towards stricter enforcement, as those that always require it rose from 27% to 31%. A strong ACR accelerates the sales cycle; a weak one, or none at all, creates redlines that stall or kill it. For some buyers, this is a hard requirement before your product can even enter evaluation. A strong accessibility story accelerates the sales cycle. A weak one creates redlines that stall or kill it.

Step back and the deeper pattern is clear: accessibility is a proxy for engineering maturity. A team that ships semantic HTML, manages focus, exposes state correctly, and tests it in CI is a team that has its house in order. The same discipline that produces an accessible component produces a maintainable, testable, less buggy one.

For dev and product leaders, that’s the real business case: accessibility work is platform work. It pays off every time a feature ships faster and more smoothly, with less rework, than it otherwise would have.

Systems, Not Sprints

If you take one thing from this, make it this: accessibility doesn’t come from an audit, a hero, or a heroic remediation sprint before launch. It comes from systems.

An accessible design system so components start right. A Definition of Done so they stay right. Automated testing and CI gates so regressions fail the build. Governance, so someone owns it. Guardrails for AI-assisted development so your fastest tool stops being your biggest liability.

None of those practices is particularly glamorous. That’s exactly why they work. They’re the same kinds of boring, reliable systems you already trust for security, reliability, and performance.

But there’s one thing no tool on that list can do. No linter, no automated scanner run, no dashboard will ever tell you what it’s actually like to use your product as a blind person with a screen reader, or to navigate your checkout with a keyboard because a tremor makes a mouse inoperable.
So build the systems — you need them, and they’re the only way accessibility survives contact with a real release schedule. But test with real users with disabilities regularly. The first time you sit behind someone using JAWS to fight through a form your team thought was “done”, something changes. The tooling tells you whether you passed. A real person tells you whether it actually works.

Accessibility is not a feature. It’s an operational capability. Treat it that way, and you get something dev and product leaders already care about: a faster, safer, more reliable way to ship software.

(il, yk)

The post Why Accessibility Is An Operational Capability, Not A Feature appeared first on The NOKIA Blog.

It has been a busy stretch on the AWS Summit circuit. At the New York City Summit, I delivered a workshop called Building AI architectures with AWS Serverless, and it was a lot of fun watching builders wire up agents and serverless services to solve real problems in a single afternoon. This week I am heading down to the Washington, DC Summit, which always puts a spotlight on innovation in the public sector. If you are going to be there, come say hello.

A question I hear a lot at these events is how teams can put AI to work without waiting on a long engineering backlog, and this week’s biggest launch speaks directly to that, with Amazon Connect Customer introducing a no-code way for business teams to design AI powered customer experiences themselves. Now, let’s get into this week’s AWS news.

Headlines


Amazon Connect Customer launched the Agentic CX designer (NLX) in preview, a no-code canvas for designing and deploying AI powered self service experiences. Business teams can build and launch voice and digital experiences that bring agentic and deterministic AI together in one governed flow, going from design to testing and simulation to production ready experiences in weeks rather than months. The launch also includes Live Sync in preview, a patented technology that drives a customer’s web or mobile experience in real time as they speak or type. A caller can complete a form or pull up the right product page without ever leaving the conversation. To see how this reshapes who designs customer experience, read the blog post on how the business user is the new architect of customer experience.

Last week’s launches


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

• AWS Lambda MicroVMs – A new serverless compute primitive that gives each user or job VM level isolation with near instant launch and resume speeds, plus the ability to suspend and resume execution for up to 8 hours. Built on Firecracker, it is made for running user or AI generated code in multi-tenant applications without managing virtualization infrastructure or trading off isolation, speed, and state.
• Amazon EC2 AMI Watermarks – Lets you embed custom identifiers in your private AMIs that automatically carry forward to every derived AMI across copies, Regions, and account shares. You can combine watermarks with Allowed AMIs and Declarative Policies to restrict launches to approved images, available at no additional cost in all AWS Regions.
• AWS Outposts self-service lifecycle management – Adds self service configuration, quoting, ordering, subscription management, renewal, and decommissioning directly from the console, CLI, and API. A new quoting tool generates real time cost estimates in seconds and surfaces account and regional constraints before you submit an order.
• Amazon MSK AI Agent Skills – Gives AI coding assistants like Kiro, Claude Code, and Cursor expert, up-to-date guidance for operating Amazon MSK, covering troubleshooting, sizing, configuring, monitoring, and migrating external Kafka clusters to MSK Express. Tasks that once required specialized knowledge become a guided experience developers can complete on their own.
• Amazon OpenSearch Service AI-assisted migrations – Migration Assistant now includes an agent guided experience that helps you move self managed Apache Solr, Elasticsearch, or OpenSearch deployments to OpenSearch Serverless or Managed Clusters using tools like Kiro and Claude Code, with new live traffic capture and replay support for Solr.
• Amazon GuardDuty AI-powered investigations (preview) – Automatically analyzes findings and accounts to help you separate true threats from benign activity, examining context and related activity from the last 90 days with knowledge graphs and threat intelligence. Each investigation returns a disposition assessment with confidence scoring, MITRE ATT&CK classification, and actionable recommendations in minutes.

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:

• Open Governance for MySQL – Oracle announced a community governance model for MySQL that gives organizations outside Oracle a defined role in the project, including four non Oracle seats on a new Steering Committee and a public GitHub presence. AWS holds a seat and shares why it supports the move and how it already contributes fixes upstream for everyone running MySQL.
• A new way to keep your AWS Certification current -You can now maintain an eligible AWS Certification for an additional year by completing curated training and hands on labs on AWS Skill Builder instead of retaking a full exam. The option is available today in open beta for several Associate and Professional certifications, with more coming later this year.
• The All Builders Welcome Grant insider’s guide for 2026 applicants – A community guide on AWS Builder Center that walks early career builders through applying for the grant, which covers a full conference pass, airfare, and hotel for AWS re:Invent 2026. Applications are open now and close on July 14.

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

Looking for ways to connect with builders in person? Check out the AWS Summits coming to a city near you, find a local AWS Community Day led by user groups around the world, and explore tutorials, community content, and ways to grow your skills over at the AWS Builder Center.

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

-Micah

The post AWS Weekly Roundup: Agentic CX designer for Amazon Connect Customer, EC2 AMI Watermarks, Open Governance for MySQL, and more (June 29, 2026) appeared first on The NOKIA Blog.

Today, we are announcing AWS Lambda MicroVMs, a new serverless compute primitive within AWS Lambda that lets you run code generated by users or AI in isolated, stateful execution environments. You get virtual machine level isolation, near-instant launch and resume, and direct control over environment lifecycle and state, all without managing infrastructure or building expertise in complex virtualization technologies. Lambda MicroVMs are powered by Firecracker, the same lightweight virtualization technology that has powered over 15 trillions of monthly Lambda function invocations.

Why customers need this

Over the past few years a new class of multi-tenant applications has emerged that all share the need to hand each end user their own dedicated execution environment in which to safely run code that the application developer did not write. AI coding assistants, interactive code environments, data analytics platforms, vulnerability scanners, and game servers that run user-supplied scripts all fit this pattern. Building that capability today means making a difficult choice. Virtual machines deliver strong isolation but take minutes to start. Containers launch in seconds, yet their shared-kernel architecture requires significant custom hardening to safely contain untrusted code. Functions as a service are optimized for event-driven, request-response workloads, but are not designed for long-running interactive sessions that need to retain environment state across user interactions. That leaves developers either accepting tradeoffs between performance and isolation, or investing significant engineering resources to build and operate custom virtualization infrastructure to achieve isolated execution while delivering low-latency experiences to end-users. This presents an effort that demands deep expertise and pulls engineering time away from the product they are actually trying to build.

Lambda MicroVMs is purpose-built for exactly this gap. Each MicroVM gives a single end user or session its own isolated environment that launches rapidly, retains memory and disk state for the length of the session, and pauses to a low idle cost when the user steps away. Because the same Firecracker technology already underpins AWS Lambda Functions, you inherit the operational maturity of a service that has been running this stack at scale.

Let’s try it out

To get started, I navigated to the AWS Lambda console, where Lambda MicroVMs now appears in the left-hand navigation menu. I first need to create a MicroVM Image.

I packaged a Flask web app and its Dockerfile into a zip file, uploaded it to an Amazon Simple Storage Service (Amazon S3) bucket.

My Flask API – app.py

import logging

from flask import Flask, jsonify

app = Flask(__name__)
logging.basicConfig(level=logging.INFO)


@app.route("/")
def hello():
    app.logger.info("Received request to hello world endpoint")
    return jsonify(message="Hello, World!")


if __name__ == "__main__":
    app.run(host="0.0.0.0", port=5000)

My Dockerfile

FROM public.ecr.aws/lambda/microvms:al2023-minimal
RUN dnf install -y python3 python3-pip && dnf clean all

WORKDIR /app

COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY app.py .

EXPOSE 5000

CMD ["gunicorn", "--bind", "0.0.0.0:5000", "app:app"]

I used the following command to create my MicroVM Image.

aws lambda-microvms create-microvm-image 
--code-artifact uri=<path/to/s3/artifact.zip> --name <VM_image_name> 
--base-image-arn arn:aws:lambda:us-east-1:aws:microvm-image:al2023-1 
--build-role-arn <IAM role ARN>

You can also create the MicroVM Image in the AWS Console as in the image above. Once I ran the command, Lambda retrieved the zip, ran the Dockerfile, initialized the application, and took a Firecracker snapshot of the running disk and memory state. Build logs streamed in real time to Amazon CloudWatch under /aws/lambda/microvms/<image-name>, and when the image was ready it appeared in the console with its Amazon Resource Name (ARN) and version number.

aws lambda-microvms run-microvm 
--image-identifier arn:aws:lambda:<region>:<acct>:microvm-image:my-image 
--execution-role-arn arn:aws:iam::<acct>:role/MicroVMExecutionRole 
--idle-policy '{"maxIdleDurationSeconds":900,"suspendedDurationSeconds":300,"autoResumeEnabled":true}'

Launching can also be done via the AWS Console or the CLI. I passed the image ARN and an idle policy configured to auto-suspend after 15 minutes of inactivity and auto-resume on the next incoming request. No networking setup was required. Lambda assigned the MicroVM a unique ID, returned a dedicated endpoint URL, and started a new MicroVM with my Flask app already running, since it was resumed from a snapshot. My Flask app was already running the moment the launch completed. One API call to get a fully initialized, bootstrapped compute environment.

To send traffic, I generated a short-lived auth token with the CLI and attached it to a plain HTTPS request using the X-aws-proxy-auth header. The request landed on my Flask app immediately. I then let the MicroVM sit idle past the suspend threshold, at which point the MicroVM was suspended, with its memory and disk state snapshotted and stored. I then sent another request, and it resumed with the application state fully intact. From the client side, the pause never happened.

How it works

Under the covers, Lambda MicroVMs delivers three capabilities that, until today, no single AWS compute service offered together. The first is virtual machine level isolation, which comes from Firecracker. Each session runs in its own dedicated MicroVM with no shared kernel and no shared resources between users, so untrusted code supplied by one user is contained to their execution environment, without access to other environments or the underlying system. The second is rapid launch and resume. The model is image-then-launch: you create a MicroVM Image by supplying a Dockerfile and code packaged as a zip artifact in Amazon S3, and Lambda runs your Dockerfile, initializes your application, and takes a Firecracker snapshot of the running environment’s memory and disk state. Every subsequent MicroVM launched from that image resumes from the pre-initialized snapshot rather than booting cold, which means launches and idle resumes both achieve near-instant startup latency. Even a multi-gigabyte interactive session comes back online quickly enough to feel responsive to the end user. The third is stateful execution. A running MicroVM retains memory, disk, and running processes across the user’s session. During idle periods, a MicroVM can be suspended – with memory and disk state intact – and resumed when traffic arrives. Installed packages, loaded models, and working filesets are readily available when the user resumes their session. MicroVMs support up to 8 hours of total runtime and can be suspended automatically after a configurable idle window, which makes it straightforward to build products as varied as software vulnerability scans that complete in minutes, data analytics applications that run for hours, and interactive coding sessions with extended idle periods. As Lambda MicroVMs are started from pre-initialized snapshots, applications generating unique content, establishing network connections, or loading ephemeral data during initialization may need to integrate with service-provided hooks for compatibility.

Lambda MicroVMs is a new resource within AWS Lambda, with a distinct API surface. Lambda Functions remain the right choice for event-driven, request-response workloads, and Lambda MicroVMs is purpose-built for multi-tenant applications that need to hand each end user or session their own isolated environment to execute user- or AI-generated code. The two complement each other. An application using Lambda Functions for its event-driven backbone can call into Lambda MicroVMs for the steps that need to run untrusted code in isolation. You bring the application, and the service delivers the execution environment.

Now available

AWS Lambda MicroVMs is available today in the US East (N. Virginia, Ohio), US West (Oregon), Europe (Ireland) and Asia Pacific (Tokyo) Regions, on the ARM64 architecture, with up to 16 vCPUs, 32 GB of memory, and 32 GB of disk per MicroVM. Idle MicroVMs can be suspended explicitly through an API call or automatically through a lifecycle policy, which reduces the running cost while preserving full state for fast resume. Pricing details can be found on the AWS Lambda pricing page.

To get started, visit the AWS Lambda console, or learn more on the Lambda MicroVMs product page. For documentation, see the Lambda MicroVMs Developer Guide.

The post Run isolated sandboxes with full lifecycle control: AWS Lambda introduces MicroVMs appeared first on The NOKIA Blog.

With an emphasis on simplicity, elegance, and functionality, they improve user experience by providing a clean and user-friendly interface. They will also help improve your website’s speed and loading times, improve visual hierarchy, and create a stronger brand identity.

A minimal WordPress theme will help you increase engagement, conversions, and overall satisfaction for your website visitors.

This article has a collection of free WordPress themes that fully embrace minimal design. They are all fully responsive and beautifully designed, making them the perfect platform for your portfolio or blog.

plenty of whitespace so your customers can focus on your products, which is sure to increase your conversions. The theme is also highly customizable.

Free to Download

The Celsius theme supports multiple post formats and large featured images that will draw in your visitors. It loads fast and looks great on any device thanks to its responsive design.

Pure WordPress Theme

Free to Download

The Pure theme is an excellent choice for designers, featuring a minimal design focused on typography and portfolio features. You can use the built-in drag-and-drop builder to customize page layouts and style the theme using the powerful admin panel.

Free to Download

Less Reloaded is a minimalist, responsive WordPress theme. It has full support for Gutenberg and offers only the necessary features. It’s quick to set up and easy to use.

Free to Download

The Lightly theme is an excellent choice for a minimal online magazine or a news site. It was optimized for fast loading and features a widgetized homepage, making it easy to showcase posts and post categories.

Free to Download

The Ocin Lite is another excellent theme for minimal WooCommerce stores. The theme is fully responsive and easy to customize using the built-in Live Customizer.

Free to Download

Koji is perfect for writers and bloggers as it offers ample whitespace around your posts. It’s compatible with Gutenberg and has full accessibility support.

Free to Download

The Nuria theme is clean and perfect for online magazines and news sites. The theme focuses on readability and lets you display multiple featured posts as post lists or carousel sliders.

Free to Download

Casper is another theme that’s quick to set up, as it offers minimal features. The theme has a single-column layout and is fully responsive.

Free to Download

The Rams theme offers a standard two-column layout, a colorful sidebar, and gorgeous typography. The theme is perfect for bloggers and authors, and is fully responsive. It also includes full Jetpack support and support for various post formats.

Free to Download

The Vito theme is sure to grab your visitors’ attention thanks to stunning animations that bring your content to life. The theme is also fully responsive, so it will work great on any device.

Free to Download

The Lingonberry theme uses a timeline-like display for your blog posts. It offers a single-column layout and supports multiple post formats and the Gutenberg editor.

Best WordPress Minimal Themes at a Glance

The Perfect Minimal Theme

Minimal WordPress themes are a great choice when you want to make sure your content stands out. They can be used by bloggers and writers as well as designers and creatives who want their portfolio to be the main focus. Use this roundup to find the perfect minimal theme for your website.

–>

Read more articles by Paul Andrew

The post 20+ Best Free Minimal Themes for WordPress in 2026 appeared first on The NOKIA Blog.

Last week AWS Summit New York City brought together thousands of customers, partners, and builders for a free, one-day event showcasing the latest in cloud and AI innovation. Dr. Swami Sivasubramanian, VP of Agentic AI at AWS unveiled a stack of AI launches in his keynote, all built around one thesis: agents that compound value over time.

• Agents for working – You can launch autonomous agents and access a smarter activity feed with new Amazon Quick features, which now let you create and run multi-step agents directly in the desktop app and consolidates email, Slack, calendar, and tasks into a single prioritized view with personalized rules.
• Agents for securing – You can shift from reactive to proactive security with AWS Continuum, a new AI-native security service that reasons, validates, and acts at machine speed across the full code vulnerability lifecycle. AWS Security Agent (now part of AWS Continuum) adds new features: threat modeling; pull request code scanning with remediation across major Git platforms; and IDE integrations via Kiro power, Claude Code plugin, and MCP.
• Agents for building – You can write, ship, and modernize code in one continuous loop with Kiro, AWS DevOps Agent, and AWS Transform. Kiro introduces a native iOS app; AWS DevOps Agent adds release management capabilities to assess code changes before production; and AWS Transform continuous modernization reduces tech debt autonomously.
• Agents customers create – You can go from agent idea to production in minutes with Amazon Bedrock AgentCore, which now includes a GA harness for infrastructure and orchestration, Web Search, Managed Knowledge Base, policy integrations with Guardrails, and the new AWS Context service for mapping organizational data relationships.

To learn more, visit the Summit recap from our top announcements blog post and Amazon News post.

Last week’s launches

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

• AWS Local Zone in Hanoi, Vietnam  —This new Local Zone is one of the first AWS Local Zones in the Asia Pacific with support for Amazon S3 and Amazon EBS Local Snapshots, enabling customers to meet data residency requirements by storing and backing up data locally. To get started, enable the Hanoi Local Zone (ap-southeast-1-han-1a) from the Regions and Zones tab in the AWS Global View or by using the ModifyAvailabilityZoneGroup API.
• AWS Blocks, an open-source TypeScript framework for application developers (preview) — AWS Blocks runs a fully functional local environment with Postgres, authentication, and real-time messaging, no AWS account required. When you’re ready to deploy, the same application code runs on production AWS services with zero changes, and you can drop into AWS CDK at any point for direct resource configuration.
• Grok 4.3 from xAI in Amazon Bedrock —You can use the Grok 4.3 model on Amazon Bedrock, giving you even more choice as you build generative AI applications across reasoning, agentic, and enterprise workflows. Grok 4.3 runs on a new inference engine in Bedrock designed for price performance, with support for tool calling, structured output, and response streaming.
• Amazon S3 annotations: attach rich, queryable context directly to your objects — Amazon S3 now lets you attach up to 1 GB of rich, mutable, and queryable context directly to your objects using annotations, purpose-built for AI agents and autonomous workflows that need to discover, understand, and act on data at scale without maintaining separate metadata systems.
• Amazon ECS announces faster service auto scaling — Amazon ECS service auto scaling now detects and responds to load changes faster with support for high resolution (20-second) metrics and metric publishing optimizations. In AWS benchmarking tests, time to trigger scale-out improved from 363 seconds to 86 seconds (76% faster), and total time to scale and provision new tasks improved from 386 seconds to 109 seconds (72% faster).
• Amazon EC2 G7 instances accelerated by NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs — AWS is the first major cloud provider to support NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs. G7 instances are accelerated by these GPUs with custom sixth-generation Intel Xeon Scalable processors, delivering up to 4.6x AI inference performance and up to 2.1x graphics performance compared to G6 instances.
• Strands Agents introduces new capabilities — Strands is an open source toolkit for building production agents. You can now use better context management in Harness SDK, a new isolated execution environment with Strands Shell, and chaos testing and red teaming in Strands Evals.
• AWS Management Console Private Access – You can access the AWS Console from VPCs without internet connectivity, allowing enterprises to manage their AWS infrastructure through the console while maintaining strict network security controls in air-gapped environments.
• AWS Marketplace Storefront is now generally available – AWS Partners can create and deploy their own branded catalog of solutions and services on their website or application in hours. Channel Partners and Independent Software Vendors can now simplify how they manage their cloud marketplace business and make it easier for customers to discover and purchase their solutions from AWS Marketplace.
• Palo Alto Networks (PANW) Advanced DNS Security on Amazon Route 53 Resolver DNS Firewall (preview) – You can now enforce DNS threat protections from Palo Alto Networks directly on Route 53 DNS Firewall rules, without deploying separate firewalls or modifying VPC configurations — by subscribing to PANW from the DNS Firewall console through the embedded AWS Marketplace widget.

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

Price reductions 

AWS continues to look for ways to increase performance and lower prices for our customers. I noticed a few such efforts last week, so I’d like to share them:

Learn more about AWS, browse and join upcoming AWS-led in-person and virtual events, startup events, and developer-focused events as well as 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’s all for this week. Check back next Monday for another Weekly Roundup!

— Channy

The post AWS Weekly Roundup: NY Summit recap, Local Zone in Hanoi, Grok 4.3 in Bedrock, price reductions, and more (June 22, 2026) appeared first on The NOKIA Blog.

Today, we’re announcing the general availability of Amazon Elastic Compute Cloud (Amazon EC2) G7 instances, delivering high performance GPU acceleration for AI inference, graphics, and data analytics workloads.

AWS is the first major cloud provider to support NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs. G7 instances are accelerated by these GPUs with custom sixth-generation Intel Xeon Scalable processors, delivering up to 4.6x AI inference performance and up to 2.1x graphics performance compared to G6 instances. G7 instances also deliver faster performance for GPU-accelerated analytics on Amazon EMR on Amazon Elastic Kubernetes Service (Amazon EKS). G7 instances are well suited for a broad range of GPU-enabled workloads including AI inference, graphics rendering, video transcoding and analytics, spatial computing, virtual desktop infrastructure (VDI), and data analytics.

Here are improvements of G7 instances compared to previous generation:

• Faster GPU memory – NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs offer 1.33 times the GPU memory capacity and 2.45 times the GPU memory bandwidth compared to G6 instances. With 32 GB of GPU memory per GPU, 5th Gen Tensor Cores, and 4th Gen RT Cores, G7 instances deliver enhanced AI inference and graphics performance.
• High performance networking and storage – G7 instances come with 700 Gbps of EFA-enabled networking throughput (7x compared to G6) enabling the low-latency, high-bandwidth connectivity that AI inference, graphics-intensive applications, and GPU-accelerated data analytics workloads need to perform at their best. G7 instances support up to 7.6 TB local NVMe SSD storage, enabling you to keep large models and datasets close to compute, reduce data transfer overhead, and improve throughput.
• Advanced video encoding and decoding engines – Ninth-generation NVENC and sixth-generation NVDEC engines support 4:2:2 encoding and decoding for high-resolution video workflows, delivering 1.5x concurrent video streams compared to previous-generation G6 instances.

EC2 G7 instance specifications

G7 instances feature up to 8 NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs with up to 256 GB of total GPU memory (32 GB of memory per GPU) and custom Intel Xeon Scalable processors. They also are available in 7 sizes and support up to 192 vCPUs, up to 700 Gbps of network bandwidth, up to 768 GiB of system memory, and up to 7.6 TB of local NVMe SSD storage.

Here are the specs:

* Coming soon

G7 instances support NVIDIA GPUDirect P2P for multi-GPU sizes, NVIDIA GPUDirect RDMA with EFA, and GPUDirect RDMA with EFA for Amazon FSx for Lustre, enabling low-latency GPU-to-GPU communication for multi-GPU and multi-node workloads.

To get started with G7 instances, you can use the AWS Deep Learning AMIs (DLAMI) or NVIDIA Workstation AMIs with prepackaged GPU drivers for your AI inference and graphics workloads. To use G7 instances with Amazon EKS, build EKS AMIs with NVIDIA driver version R595 with EKS-provided automation. G7 instances support multiple operating systems including Amazon Linux, Ubuntu, RHEL, and Windows Server, with comprehensive NVIDIA driver integration providing compatibility with industry-standard graphics libraries including DirectX, Vulkan, and OpenGL.

Get started today

You can start using Amazon EC2 G7 instances today in two AWS regions: US East (Ohio) and US West (Oregon). To check future Regional expansion plans, look up the instance type in the CloudFormation resources tab on the AWS Capabilities by Region page.

G7 instances are offered through multiple purchasing options, including On-Demand, Savings Plans, and Spot Instances. Dedicated Instances are also supported for the 12xlarge, 24xlarge, and 48xlarge sizes. For detailed pricing, visit the Amazon EC2 Pricing page.

Ready to get started? Launch G7 instances from the Amazon EC2 console. For more details, head over to the Amazon EC2 G7 instances page. We’d love to hear your feedback. Share it on AWS re:Post for EC2 or reach out through your usual AWS Support contacts.

– Daniel Abib

The post Announcing Amazon EC2 G7 instances accelerated by NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs appeared first on The NOKIA Blog.

Amazon Elastic Container Service (Amazon ECS) service auto scaling automatically adjusts task counts to meet workload demand with comprehensive scaling policies, including predictive scaling for recurring traffic patterns, scheduled scaling for planned events, and target tracking to scale dynamically on real-time metrics.

You can choose proactive scaling by using predictive scaling (automatic) and scheduled scaling (customer-defined), or reactive scaling by using target tracking with just a target to scale on. Amazon ECS service auto scaling adjusts the number of tasks in an ECS service based on Amazon CloudWatch metrics, such as average CPU/Memory usage, request count per target, a custom metric such as queue depth, or demand surges by using advanced machine learning (ML) algorithms.

With today’s launch, Amazon ECS service auto scaling now detects and responds to load changes faster with support for high resolution (20-second) metrics and metric publishing optimizations. In AWS benchmarking tests, time to trigger scale-out improved from 363 seconds to 86 seconds (76% faster, 4.2x), and total time to scale and provision new tasks improved from 386 seconds to 109 seconds (72% faster, 3.5x)

This launch delivers three key benefits for your applications:

• Improved performance and reliability: Faster scaling means, your application responds faster to demand surges, reducing latencies or failures for end users during demand surges.
• Right-size without compromise: Depending on the workload, you can reduce baseline task counts because scale-out now happens fast enough to handle traffic spikes without preemptive capacity padding. This directly reduces compute costs while maintaining application performance and availability.
• Simpler scaling configuration: Target tracking with high-resolution metrics delivers the aggressive scaling behavior that previously required custom scaling configurations, such as usage of step-scaling policies. One configuration change replaces custom engineering work.

How it works

To use ECS faster service auto scaling, first enable high-resolution metrics for your ECS service, and then configure a target tracking scaling policy which uses high-resolution metrics. ECS faster service autoscaling works across all compute options on ECS: AWS Fargate, ECS Managed Instances, and Amazon Elastic Compute Cloud (Amazon EC2). You can enable these metrics when you create or update your ECS service in the Amazon ECS console, or using AWS SDKs and tools, and AWS CloudFormation.

When you create a service in the console, add 20-seconds resolution metrics in the Monitoring configuration section. These metrics incur additional CloudWatch costs while the standard resolution (60-seconds) is free.

In the Service auto scaling section, check Use service auto scaling and choose Target Tracking for the scaling policy type to use real-time data to scale the number of tasks that your service runs based on demand.

Then, choose a Scaling policy type for the target tracking. You can select ECSServiceAverageCPUUtilizationHighResolution or ECSServiceAverageMemoryUtilizationHighResolution as new metrics.

That’s it – your ECS service will use high resolution metrics for auto scaling.

To update an existing ECS service to use faster auto scaling, you first need to configure high resolution metrics via Update Service. Once deployment completes, your service will generate high-resolution metrics. You can then go to the Service and auto scaling tab from your service details to update scaling policy to use higher resolution metrics.

That’s all you need. Your ECS service now evaluates scaling decisions at 20-second intervals.

You can also use the AWS Command Line Interface (AWS CLI) to enable new metrics in your ECS service through Application Auto Scaling. To learn more, visit the faster auto scaling documentation.

Now available

Faster service autoscaling with high-resolution metrics for Amazon ECS is available today. The feature itself has no additional cost, but high-resolution CloudWatch metrics introduce a new pricing dimension. For details, see the CloudWatch pricing page.

Give it a try today and send feedback to AWS re:Post for ECS or through your usual AWS Support contacts.

— Channy

The post Amazon ECS introduces new high-resolution metrics for faster service auto scaling appeared first on The NOKIA Blog.

Today at the AWS Summit in New York City, Swami Sivasubramanian, AWS VP of Agentic AI, provided the day’s keynote.

Here’s our roundup of the biggest announcements from the event:

New in agents customers create

We’re introducing new capabilities on Amazon Bedrock AgentCore: connecting AI agents to organizational, web, and paid knowledge, helping teams find and fix what’s going wrong in production, and enforcing controls that scale as agents grow more capable.

Together, these capabilities help you build more capable agents faster, govern those agents with controls that scale, and improve them continuously. To learn more, read our blog post covering all the new features.

• Introducing Amazon Bedrock Managed Knowledge Base for faster, more accurate enterprise AI applications — You can build enterprise RAG pipelines with the managed Knowledge Base on Bedrock. It provides native data connectors, Smart Parsing for automatic multi-format data preparation, and an Agentic Retriever for complex multi-step queries—all integrated with AgentCore Gateway so developers can focus on business outcomes rather than infrastructure management.
• Announcing Web Search on Amazon Bedrock AgentCore: Ground your AI agents in current, accurate web knowledge — You can use a fully managed web search tool that enables agents to ground responses in current, cited web knowledge with zero data egress from customer’ secured AWS environment. You can focus on building agents instead of manually adding web search to agents on Bedrock AgentCore and managing its infrastructure.
• AWS WAF adds AI traffic monetization capability to help content owners charge AI bots for content access — You can use a new Bot Control capability that enables content providers and publishers price, meter, and collect payment from AI bots and agents accessing their content and APIs. AWS WAF now lets you set a price for that access, accept payment through third-party providers, and grant scoped access directly at the edge.
• Amazon Bedrock AgentCore harness in now generally available — You can do building and running production-grade AI agents in minutes—without coding orchestration loops—by defining your agent’s model, tools, skills, and instructions in configuration, with Bedrock AgentCore harness.
• Coming soon: AWS Context — This is a new service that automatically maps the relationships across your existing data into a knowledge graph and provides agentic search so AI agents in the organization can access governed data relationships, business rules, and domain knowledge at runtime.

New in agents for securing

• Introducing AWS Continuum: Security at machine speed — AWS Continuum for code vulnerabilities, available in a gated preview, takes findings from across your environment, prioritizes by business impact, proves which are exploitable, and drives a fix through your own process.
• AWS Security Agent (now part of AWS Continuum) adds threat modeling, Kiro power and Claude Code plugin, and more — You can generate the new threat modeling (preview) to understand the full context of your application and identify threats with recommended mitigations using the STRIDE framework. You can also use pull request code scanning with remediation across major Git platforms, and IDE integrations via Kiro power, Claude Code plugin, and MCP — letting developers run security reviews and fix issues without context switching.

New in agents for building

• Introducing Kiro for iOS — Kiro introduces a native iOS app, available in a gated preview, built for real engineering work that gives developers a new surface to kick off, monitor, steer, and interact with their Kiro sessions directly from their phone. That means you can now start sessions, check back when they’re done, review diffs, and approve changes all while staying connected to your work with no laptop running.
• AWS DevOps Agent adds release management capabilities to assess code changes before production — You can use a new release readiness review of code changes and autonomous release testing. These new features verify every change against the natural language standards you give to the DevOps Agent and run change-specific tests in production-like environments.
• Proactively reduce tech debt autonomously with AWS Transform – continuous modernization — You can use continuous analysis (preview) to automatically scan your code repositories against configurable baselines and generates findings in hours, not weeks. Once you’ve identified and prioritized findings, you can configure autonomous remediations that generate pull requests for affected repositories automatically.

In addition to the keynote announcements, we have other important launches this week:

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