Amazon Prime Day 2026 was exclusively for Prime members and ran June 23–26, 2026 with millions of deals across more than 35 categories.

As part of our annual tradition of sharing how AWS powered Prime Day (2016, 2017, 2019, 2020, 2021, 2022, 2023, 2024, and 2025), I want to share the services and chart-topping metrics from AWS that made your amazing shopping experience possible.

Prime Day 2026 – all the numbers
As always, Prime Day was powered by AWS. Here are some of the most interesting and/or mind-blowing metrics:

Amazon Elastic Compute Cloud (Amazon EC2) and AWS Graviton – During Amazon Prime Day 2026, AWS Graviton powered up to 49% of the Amazon EC2 compute used by Amazon.com.

Amazon Elastic Block Store (Amazon EBS) – During Prime Day 2026, Amazon EBS, our high-performance block storage service, peaked at over 24.8 trillion I/O operations, moving over an exabyte of data daily.

AWS Lambda – AWS Lambda handled over 2.3 trillion invocations per day during Prime Day 2026.

Amazon Elastic Container Service (ECS) and Fargate – During Prime Day 2026, Amazon ECS launched an average of 158.3 million tasks per day on AWS Fargate, representing a 47.7 percent increase from the previous year’s Prime Day average.

Amazon CloudFront – Amazon CloudFront delivered over 2.1 trillion HTTP requests during the global week of Prime Day 2026, a 5 percent increase in total requests compared to Prime Day 2025.

Amazon DynamoDB – Amazon DynamoDB, a serverless, fully managed, distributed NoSQL database, powers multiple high-traffic Amazon properties and systems including Alexa, the Amazon.com sites, and all Amazon fulfillment centers. Over the course of Prime Day 2026, between June 23 and June 26, 2026, DynamoDB processed over 59 trillion requests. DynamoDB maintained high availability while delivering single-digit millisecond responses and peaking at 192 million requests per second.

Amazon Aurora – On Prime Day, Amazon Aurora, a relational database management system (RDBMS) built for high performance and availability at global scale for PostgreSQL, MySQL, and DSQL, processed hundreds of billions of transactions, stored 5,491 terabytes of data, and transferred 1,194 terabytes of data.

Amazon ElastiCache – During Prime Day, Amazon ElastiCache peaked at serving over 2.3 quadrillion daily requests and 2.1 trillion requests in a minute.

Amazon Kinesis Data Streams – Amazon Kinesis Data Streams, a fully managed serverless data streaming service, processed a peak of 988 million records per second during Prime Day 2026.

Amazon Simple Notification Service (SNS) – Amazon SNS, a fully managed pub/sub messaging service for application-to-application and application-to-person communication, delivered 5 trillion messages in a single day during Prime Day 2026.

Amazon Simple Queue Service (SQS) – Amazon SQS, a fully managed message queuing service for microservices, distributed systems, and serverless applications, received a peak of 213 million messages per second during Prime Day 2026

AWS CloudTrail – AWS CloudTrail processes billions of API activity events per day for governance, compliance, and operational auditing. During Prime Day 2026, that volume surged to 3.6 trillion events in just four days, a 44% increase over Prime Day 2025.

AWS CloudWatch – Amazon CloudWatch processed over 2.15 quadrillion metric observations per day during Prime Day 2026.

Amazon GuardDuty – During Prime Day 2026, Amazon GuardDuty monitored an average of 14.08 trillion log events per hour, a 59% increase from last year’s Prime Day.

AWS Fault Injection Service (FIS) – We ran over 44,000 AWS FIS experiments – more than six times what we conducted in 2025 – to help ensure Amazon.com remains highly available on Prime Day.

Prepare to scale
If you’re preparing for similar business-critical events, product launches, and migrations, I recommend that you take advantage of AWS Countdown Premium. From retail peak seasons to major sporting events, elections, and healthcare enrollment periods, we help you deliver flawless experiences when it matters most. Our experts help you manage your infrastructure to handle massive traffic spikes while maintaining security and performance. We work alongside your team to scale infrastructure, optimize costs during demand surges, strengthen security measures, and monitor real-time demands. To learn more, visit AWS Countdown Premium for business critical events.

I look forward to seeing what other records will be broken next year!

— Channy

The post All the numbers: Amazon Prime Day 2026 powered by AWS appeared first on The NOKIA Blog.

Naming is hard. The language we use shapes the way we think about things, but also the conversation we have about it. That’s why we have so much confusion about colors, icons, UI components, features — and that’s why we struggle to name design tokens, HTML classes, and variables.

Sometimes, the names we choose are too generic, so it’s difficult to understand what exactly is meant. And sometimes they are too specific, leaving little room for flexibility and reuse. How do we choose the right names? And which naming conventions work best? Let’s take a closer look.

How To Name Things

If you’re looking for some inspiration for naming HTML classes, CSS properties, or JavaScript functions, Classnames is a wonderful resource jam-packed with ideas that get you thinking outside the box.

The site provides thematically grouped lists of words perfect for naming. You’ll find terms to describe different kinds of behavior, likeness between things, order, grouping, and association, but also themed collections of words that wouldn’t instantly come to one’s mind when it comes to code, among them words from nature, art, theater, music, architecture, fashion, and publishing.

How To Name Colors

How would you choose the right name for a given color? Fortunately, David Aerne maintains a tremendous repository of color names, currently with 30355 unique color names, sourced from various references and 1000s of user contributions.

Of course, the repository comes with a color picker and name search and plenty of other color pickers as well. Tremendous work, which is also a fantastic reference to keep nearby.

How To Name Layers and Groups

What makes a good name? Javier Cuello summarized a set of naming best practices that help you name your layers, groups, and components in a consistent and scalable way.

As Javier points out, a good name has a logical structure, is short, meaningful, known by everyone, and not related to visual properties. He shares do’s and don’ts to illustrate how to achieve that and also takes a closer look at all the fine little details you need to consider when naming sizes, colors, groups, layers, and components.

How To Name Design Tokens

How to build a flexible design token taxonomy that works across different products? That was the challenge that the team at Intuit faced. The parent company of products such as Mailchimp, QuickBooks, TurboTax, and Mint developed a flexible token system that goes beyond the brand theme to serve as the foundational system for a wide array of products.

Nate Baldwin wrote a case study in which he shares valuable insights into the making of Intuit’s design token taxonomy. It dives deeper into the pain points of the old taxonomy system, the criteria they defined for the new system, and how it was created. Lots of takeaways for building your own robust and flexible token taxonomy are guaranteed.

Frequent Names For UI Components

Looking at what other people call similar things is a great way to start when you’re struggling with naming. And what better source could there be than other design systems? Before you end up in the design system rabbit hole, Iain Bean did the research for you and created the Component Gallery.

It’s a collection that includes plenty of examples for more than 50 UI components — from accordion to visually hidden — and also lists other names that the UI components go by. A fantastic resource, and not only with regard to naming.

Bonus: Name That UI, a visual dictionary of common UI components.

How To Name New Features

Features often have low adoption due to poor discoverability. Feature adoption needs time — first the feature must be discovered, then understood, then tried, learned, and eventually built into existing workflow. Erin Gannon has written a helpful practical guide on how to make sure all of it happens.

Good names are driven by user needs and problems. They should signal the value or outcome of a feature (the job-to-be-done), and — most importantly! — tap into the user’s own language. So ask people to explain a feature in their own words and then use the words they use.

How To Name Variables (Taxonomy Map)

A wonderful example of naming guidelines for a complex multi-brand, multi-themed design system comes from the Vodafone UK Design System team. Their Variables Taxonomy Map breaks down the anatomy and categorization of a design token into a well-orchestrated system of collections.

The map illustrates four collections required to support the system and connections between tokens — from brand and primitives to semantics and pages. It builds on top of Nathan Curtis’ work on naming design tokens and enables everyone to gather insight about where a token is used and what it represents, just from its name.

If you want to explore more approaches to naming design tokens, I’ve put together a list of useful Figma kits and resources that are worth checking out.

Design Token Naming Guide + Inventory

Romina Kavcic created a helpful little tool for naming design tokens — from learning how they work and how to name them well to configuring your own naming structure with components, categories, states, and roles.

Bonus: The Design Token Names Inventory spreadsheet, with a simple structure with four levels to give you a bird’s-eye view of all your design tokens. You can easily add rows, themes, and modes without losing track, and filter your tokens.

Naming Products or Services

If you are looking for the right name for your product or service, Onym is a fantastic open-source repository that organizes tools and resources for finding names for brainstorming, vetting, guides, sprints, etymologies — all the way to cautionary tales and agencies that name things, as well as useful books to take a look at.

Useful Bookmarks for Naming Things

A quick-reference list of tools and guides for naming just about… well, anything, in one place:

Wrapping Up

The right name is the one that’s well-understood and used by the team and users. So much time is wasted speaking about the very same thing but in different dialects — the designer’s dialect, the developer’s dialect, the product manager’s dialect, and the user’s dialect. That’s what often causes frustration and confusion.

So if you see low adoption, it might be merely because of the name. And pay attention to potential naming conflicts and put them in a backlog of things to address later. It’s one of those fine little details that can reduce frustration and confusion and help teams work together better.

Meet “Design Patterns For AI Interfaces”

Meet Design Patterns For AI Interfaces, Vitaly’s new video course with practical examples from real-life products — with a live UX training happening soon. Jump to a free preview.

(yk)

The post A Practical Guide To Naming Things appeared first on The NOKIA Blog.

Last week, we announced a public preview of Amazon Bedrock Managed Agents powered by OpenAI, built on a customized version of OpenAI’s Agents API engineered to be AWS-native and integrated with AWS resources. You can now build agents optimized for OpenAI models that run entirely inside AWS with the identities, permissions, and governance controls you already use.

You can choose an execution environment: self-hosted compute to use an existing development machine, container, or compute environment or Amazon Bedrock AgentCore Runtime, for managed runtime sessions and configurable storage in your AWS account. To learn more, visit the Amazon Bedrock documentation.

In addition, we are adding new frontier models on Amazon Bedrock to expand your model choices:

• OpenAI GPT-6.1 Sol: An upgrade to GPT-6 Sol, GPT-6.1 Sol delivers exceptionally strong performance on agentic coding, computer use, and professional work. According to OpenAI, it approaches GPT-6 Astra across demanding evaluations at roughly one-fifth of the cost, giving developers more room to build and run capable agents at scale. To learn more, visit the GPT-6.1 Sol model card.
• OpenAI GPT-6 Astra UltraFast mode: Ultrafast is a premium speed tier for GPT-6 Astra, built for workloads where speed matters most. According to OpenAI, Ultrafast delivers up to 6x faster inference in the API, with up to 300 tokens per second. The Amazon Bedrock inference engine delivers the performance, security, and reliability required for production workloads. To learn more, visit the GPT-6 Astra model card.
• Anthropic Claude Sonnet 5.5: Claude Sonnet 5.5 is a smarter, more efficient Sonnet and a step up from Sonnet 5, making it a natural upgrade for teams already building on Sonnet. It’s stronger for coding, completing well-scoped tasks as part of a larger coding strategy such as building and fixing features with Claude in the same session or verifying output against requirements. To learn more, visit the Claude Sonnet 5.5 model card.
• SpaceXAI Grok 4.7: Grok 4.7 builds on Grok 4.6 with better mixed-document handling, more dependable repo-scale coding with planning and error recovery, and enhanced browser-use agents for form fills and portal navigation. To learn more, visit the Grok 4.7 model card.

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

• AWS Well-Architected Agent (preview): You can use an AI-powered agent service that analyzes your AWS environment to deliver targeted, contextual recommendations for improving your applications’ cost, security, performance, and resilience. The agent analyzes your infrastructure, understands your unique business goals, and delivers contextual recommendations.
• Amazon Aurora PostgreSQL supports direct querying of Apache Iceberg and Parquet data: You can directly query operational data together with data stored in data lakes in Apache Iceberg and Parquet formats using your existing PostgreSQL applications and tools, without extract, transform, and load (ETL) pipelines or data duplication.
• Amazon S3 Tables support all Apache Iceberg V3 data types: Amazon S3 Tables add support for geometry, geography, unknown, and nanosecond timestamp data types, along with column default values, as defined in the Iceberg V3 specification. You can now store geospatial coordinates and nanosecond-precision event times natively instead of encoding them in strings or integers.

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

AWS service availability updates
When the availability of an AWS service or feature changes, we provide customers guidance in AWS Product Lifecycle Changes on available alternatives and support for migration so that disruptions to your operations are minimized. The following lifecycle changes were updated on September 29, 2026.

Services moving to Maintenance (no longer accessible to new customers starting October 29, 2026):

Services entering Sunset:

Services reaching End of Support (as of September 29, 2026):

• Amazon Mechanical Turk

We understand that changes in availability can impact your operations. For specific guidance, consult the relevant service documentation or contact AWS Support.

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

• Introducing Kiro workflows: Kiro workflows enable you to carry out complex tasks from start to finish with multiple agents and less supervision. We’ve been building Kiro itself with workflows, including the new cloud configuration, cloud sessions, and most of the workflow experience.
• Introducing Strands Decider: Strands Decider is one of a new class of decision models or system one models, a type of model that has been gaining significant attention since TypeSafe AI’s launch of Jev earlier this month. Strands Decider 2B is a small, open source, decision model optimized for fast experimentation, local development, and innovation.
• New FDE pathways for AWS Partners: On June 30, AWS announced the Forward Deployed Engineering (FDE) organization, backed by a $1 billion investment, and extended this hands-on delivery approach to AWS Partners through the Partner-Led FDE motion. Now, AWS Partners have a structured way to build and validate that depth with three new Partner FDE pathways and credentials that recognize the applied proficiency required to deliver production agentic AI.

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 upcoming AWS re:Invent 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

The post AWS Weekly Roundup: Amazon Bedrock Managed Agents powered by OpenAI, Q3 service availability updates, Kiro workflows, and more (October 5, 2026) appeared first on The NOKIA Blog.

This brings up an important question for WordPress agencies: How does using AI on client projects affect pricing?

On the surface, it feels like a tricky question. AI is supposed to improve efficiency. A more efficient process should take less time to complete, which means lower costs. But it’s not that simple.

The point of increased efficiency is to make more profit. Faster results and decreased overhead shouldn’t mean that your business takes a financial hit, right? If that’s the case, then agencies have no incentive to use AI for, well, anything.

So, should we reassess the value of our services in the age of AI? And how do we present it to clients? Let’s try to find the answers.

Time-Based Billing Is Outdated

The hourly rate has been a standard for many freelancers and agencies. We charge based on how much time we spend on a given task. This policy worked well enough when everything was done by hand. However, it doesn’t fit with how most of our work is done today.

Let’s take a custom WordPress plugin as an example. In this scenario, our client needs a niche plugin that adds functionality to their website.

This type of project could have taken several hours before AI came into the picture. Much depends on your level of expertise, but it’s safe to say you’d need to conduct research, write the code, then test. There would inevitably be bugs to fix and more tests to run. It’s easy to get stuck on an issue that delays progress.

An hourly rate could make sense here, as you risk getting burned by a project-based fee. What if the project takes longer than anticipated? You might lose money on the deal.

Using AI accelerates the entire process. You should still conduct research, and there will still be bugs to fix. You’ll also want to test for security. But the increased efficiency will likely lead to faster turnaround times.

Here, a project-based fee is a more reasonable solution. You’re charging based on the outcome, rather than time spent. And the result is the most important part of the equation – regardless of how long it took.

Using AI Requires Different Thinking and Communication

Working with AI also means a shift in our approach on both the project and client sides. Agencies and developers must focus on the big picture more than the process of getting it done. Again, the idea is that time isn’t the most relevant factor.

AI-based workflows often turn us into orchestra conductors, rather than old-school code jockeys. We’re using plain language to guide the tool in the right direction. We spend less time on the foundational aspects and more on the structure and feel. It’s a different role that can take time to grasp fully.

This also affects how we communicate with clients. We should emphasize the value in what we build. Clients must understand the benefits of a new feature and how it moves them closer to their goals.

In that regard, it doesn’t matter if building a new plugin took you five days or five hours. The only question worth asking is: Did we provide the best possible solution within their budget?

This isn’t much different than other service-based industries. You probably didn’t care what tools the carpenter used to build your new deck. You were likely more excited about how it looked and functioned.

AI is akin to the carpenter’s power tools. In the right hands, it becomes easier to achieve the desired outcome. You still need a certain level of expertise, however.

AI Should Increase Your Agency’s Revenue

The bottom line is that AI provides a path to do more with less. Perhaps the biggest hurdle for agencies and freelancers is embracing this philosophy.

Remember, there have been many changes in how we build websites. We went from hand-coding HTML to using content management systems (CMS) like WordPress. Tools like WYSIWYG editors and page builder plugins incrementally increased our productivity. Little by little, web professionals adopted these solutions and adapted their businesses to take advantage of them.

AI is more of a revolutionary technology, as it increases our output exponentially. Naturally, a change this big leads to questions about how we work and how much we charge.

You may run into a client who expects lower pricing because of your increased efficiency – don’t fall for it. Your knowledge and experience are still guiding the ship, and they are worth a premium. Once you realize this, you can use AI to encourage growth. These new tools simply offer a faster way to get there.

here. He recently started a writing service for WordPress products: WP Product Writeup. He also has an opinion on just about every subject. You can follow his rants on Bluesky @karks.com.

Read more articles by Eric Karkovack

The post Should Your Agency Charge Less for AI-Assisted Solutions? appeared first on The NOKIA Blog.

Today, we’re announcing the public preview of AWS Well-Architected Agent, an AI-powered service that analyzes your AWS environment to deliver targeted, contextual recommendations for improving your applications’ cost, security, performance, and resilience. The AWS Well-Architected Agent analyzes your infrastructure, understands unique business goals, and delivers contextual recommendations with ready-to-implement fixes. It delivers context-aware optimization without relying on manual audits or generic checklists.

The agent evaluates your environment as an experienced cloud architect would. It automatically correlates utilization metrics, resource configurations, and application topology, and analyzes against Well-Architected best practices across 65+ AWS services. It generates recommendations aligned to your declared business goals, delivers implementation packages with every finding, and surfaces cross-pillar trade-offs making it simpler to remediate the findings.

Here are the three main features of this service:

• Goal-aligned intelligence: AWS Well-Architected Agent replaces flat, undifferentiated findings with context-aware, prioritized recommendations. You declare your business objectives and share your application context. The agent automatically generates and prioritizes recommendations by impact and effort against those goals.
• Three-level recommendations: AWS Well-Architected Agent provides individual resource findings with specific dollar impact (where applicable) and step-by-step remediation, consolidated findings across multiple resources scoped to your application, and broad architectural patterns and designs with Infrastructure as Code (IaC) code changes needed to align with Well-Architected best practices.
• Optionality in remediation: You can choose your path on how you want to remediate with a complete implementation steps tailored to your environment: the console walk-throughs, updated IaC changes for architecture-level recommendations, and AWS Command Line Interface (AWS CLI) commands.

AWS Well-Architected Agent in action
To get started, create an agent profile to define the scope of what Well-Architected Agent can access and provide recommendations on, complete the IAM role setup to access resources, conduct architecture review, and remediate recommendations.

Create an agent profile
In the AWS Well-Architected console, choose Get started with Well-Architected Agent. You can define an agent profile that specifies which AWS accounts and applications to monitor, which optimization pillars to focus on, and the permissions required.

You can choose AWS accounts or AWS Regions to monitor and optimization pillars that matter most to your business. You can also set goals for each pillar: cost optimization, performance, resilience, and security.

To give access to the agent for your AWS environment, provision customer-managed IAM roles the agent uses to read resource configurations, utilization metrics, and application topology. To learn more, visit the IAM prerequisite for AWS Well-Architected Agent.

When you choose Get Started, the agent creates your agent profile. Resource and application recommendations will be generated within 24 hours after profile creation.

You can conduct an architecture review on pre-deployment workloads by uploading an IaC project in Terraform, AWS CloudFormation, or AWS Cloud Development Kit (CDK) to be analyzed. Choose Conduct architecture review in the dashboard, upload a.zip file containing IaC project or repository file, and select which Well-Architected lens to use for reviewing your infrastructure.

You can define your applications to add context which will enhance the relevancy and further contextualize recommendations. Choose Add application context in the dashboard, add your applications with AWS accounts, AWS Regions, AWS services, tags if you want to narrow the scope to specific resources, and the details of applications.

Review prioritized recommendations and start remediating
Now you can see generated prioritized recommendations generated by the agent across your resources and applications, selected pillars, ranked against your declared goals, with automation-ready remediation included.

When you choose the specific recommendation, you can see the details, insights into why the agent are suggesting the recommendation, impacts and trade-off, and recommended fixes across affected AWS resources.

Choose Start remediation to address recommended fixes. You can choose the console, updated IaC template, CLI commands to remediate by the resolution type. It provides detailed step-by-step instructions and you can roll out this instruction and verify the result.

When you choose Using updated IaC template, the agent provides the code changes needed to update your existing IaC templates such as the CDK function shown above which you can copy directly into your codebase.

You can also configure API access to integrate recommendations directly into your existing development and operations workflows. To interact with the agent 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 AWS Well-Architected Agent documentation.

Things to know
Here are some things that you should know about the Well-Architected Agent.

• Automation: You can receive recommendations with the exact IaC code changes needed to remediate, with risks identified by pillar, catching issues before they reach production. Recommendations are delivered through the console and API so you can act without context-switching. Recommendations are also updated periodically, so new recommendations are available for your team to track regularly.
• Evaluation: Generative AI capabilities produce this recommendation, which may contain errors or incomplete information. You are responsible for evaluating the recommendation in your specific context and implementing appropriate oversight and safeguards. Learn more about AWS Responsible AI practices.

You can still use existing AWS Well-Architected Tool to manually evaluate your cloud architecture with user-defined lenses that measure your workload using your own best practices.

Join the preview
Access to the AWS Well-Architected Agent and its recommendations is available in US East (N. Virginia), US East (Ohio), and US West (Oregon). You can onboard workloads from any AWS commercial Region. AWS Well-Architected Agent is delivered by AWS Support and available to AWS customers with an AWS Support plan.

Give it a try today in the AWS Well-Architected console and send feedback through your usual AWS Support contacts.

— Channy

The post Announcing AWS Well-Architected Agent, an AI-powered intelligence to optimize your cloud environment (preview) appeared first on The NOKIA Blog.

Amazon S3 Tables now support all data types in the Apache Iceberg V3 specification. You can create V3 tables or upgrade existing V2 tables to take advantage of V3 features like deletion vectors, row lineage, and new data types such as variant, nanosecond timestamps, unknown, geometry, and geography.

Apache Iceberg has become the open standard for managing large analytics datasets. It lets you manage petabyte-scale tables with features like schema evolution, hidden partitioning, and time travel queries, while keeping your data in open Parquet files in data lakes on object storage like Amazon S3. Amazon S3 Tables offer storage purpose-built to keep Iceberg tables performant and cost-effective as they grow, with fully managed features like automatic compaction, maintenance, replication, and Intelligent-Tiering.

Teams running analytics on Apache Iceberg V2 tables often hit the same limits as their data grows. A compliance request to delete 50,000 user records from a 2-billion-row table leaves behind positional delete files that slow queries until compaction runs. Semi-structured events land as JSON strings that every query has to parse. Geospatial coordinates and nanosecond-precision timestamps get encoded as strings or integers. Each workaround adds storage cost, query latency, and pipeline code. With V3, Iceberg solves these challenges by offering native support for semi-structured and geospatial data, faster row-level operations, and built-in row lineage for data governance.

Starting today, Amazon S3 Tables support all V3 data types, including variant, nanosecond timestamps, geometry, geography, and unknown, along with deletion vectors and row lineage. You can create new V3 tables or upgrade existing V2 tables in place, and S3 Tables continue to run compaction and maintenance for you.

Apache Iceberg V3
V3 is the latest version of the Iceberg specification. Among its many improvements, V3 introduces capabilities that address the most common pain points in V2. This includes:

Deletion vectors replace V2’s positional delete files with a compact binary format. That 50,000-row compliance delete now writes a single deletion vector file instead of thousands of small deletes, significantly reducing compaction time and delete file overhead.

Row lineage adds _row_id and _last_updated_sequence_number to each record automatically. Your downstream pipelines can query these fields to find changed rows without scanning the full table.

New data types let you store semi-structured, geospatial, and nanosecond-precision data natively instead of encoding it as strings or integers:

• Nanosecond timestamp(tz) for nanosecond-precision timestamps
• Geometry and geography for geospatial data
• Unknown for columns with no known type

Variant data type stores semi-structured data in columnar format. During writes, the engine shreds variant data into hidden columns and collects statistics. At query time, those statistics enable file pruning that significantly reduces I/O compared to parsing JSON strings.

The following sections walk through how to use these V3 capabilities in practice, with examples that show how to create tables, work with the new data types, and manage data at scale.

Getting started
A retail analytics team tracks user behavior across web and mobile apps. Each event has a different structure: page views include URLs and duration, purchases include items and amounts, and searches include query terms and result counts. With V3’s variant type, you store all event shapes in one table without predefined schemas:

CREATE TABLE my_catalog.namespace.clickstream (
  event_id bigint,
  event_time timestamp,
  user_id string,
  payload variant
)
USING iceberg
TBLPROPERTIES ('format-version' = '3')

Insert events with different payload shapes without worrying about schema evolution:

INSERT INTO my_catalog.namespace.clickstream VALUES
  (1, current_timestamp(), 'user-42',
   PARSE_JSON('{"action": "purchase", "amount": 99.99, "items": ["laptop_stand"]}')),
  (2, current_timestamp(), 'user-17',
   PARSE_JSON('{"action": "page_view", "url": "/products/webcam", "duration_ms": 4200}'));

Now query the variant column directly, without PARSE_JSON at read time. With Amazon EMR Spark, use variant_get:

SELECT
  event_id,
  user_id,
  variant_get(payload, '$.action', 'string') AS action,
  variant_get(payload, '$.amount', 'double') AS amount
FROM my_catalog.namespace.clickstream
WHERE variant_get(payload, '$.action', 'string') = 'purchase'
  AND variant_get(payload, '$.amount', 'double') > 50.00

To enable deletion vectors for write operations, configure merge-on-read mode:

ALTER TABLE my_catalog.namespace.clickstream
SET TBLPROPERTIES (
  'write.delete.mode' = 'merge-on-read',
  'write.update.mode' = 'merge-on-read',
  'write.merge.mode' = 'merge-on-read'
)

Now when you run a compliance delete, V3 writes a small deletion vector instead of rewriting data files:

DELETE FROM my_catalog.namespace.clickstream
WHERE user_id = 'user-42'

S3 Tables compaction handles these deletion vector files automatically on the next maintenance cycle.

Upgrading from V2
AWS provides backwards compatibility for both versions to minimize disruption during migration to V3. Existing V2 readers continue to work on upgraded tables until you’re ready to fully adopt V3 features. For more details, see the S3 Tables Iceberg V3 documentation.
Upgrade an existing table atomically without rewriting data:

ALTER TABLE my_catalog.namespace.existing_table
SET TBLPROPERTIES ('format-version' = '3')

On the next compaction cycle, S3 Tables remove old V2 delete files. New modifications use deletion vectors automatically. Row lineage fields initialize on the first data modification after the upgrade.

This is a one-way operation. The Apache Iceberg specification does not support downgrading from V3 to V2. Verify that all engines accessing the table support V3 before upgrading.

Using row lineage for incremental pipelines
After your table has V3 data, use row lineage to build efficient incremental pipelines:

SELECT *, _row_id, _last_updated_sequence_number
FROM my_catalog.namespace.clickstream
WHERE _last_updated_sequence_number > 42

This returns only rows modified after sequence number 42. Your downstream jobs can checkpoint this value and process only new changes on each run, instead of scanning the full table.

Compatibility across AWS analytics services
AWS offers the broadest native Apache Iceberg support of any major cloud provider, with Iceberg-compatible services at every layer of the data stack: ingestion, storage, catalog, and analytics. You can store and automatically optimize V3 tables in Amazon S3 Tables, write data with Amazon EMR Spark, integrate and manage data with AWS Glue, and run analytics with Amazon Redshift. To learn more about AWS analytics support for V3, see the Apache Iceberg on AWS prescriptive guidance.

Both S3 Tables and AWS Glue Data Catalog support the Iceberg REST Catalog (IRC) API, enabling interoperability across engines regardless of the catalog endpoint.

Things to know

• S3 Tables compaction fully supports V3 deletion vector files and preserves row lineage metadata.
• The new V3 data types (variant, nanosecond timestamps, geometry, geography, and unknown) require an engine built on Apache Spark 4.0 or later, such as AWS Glue 6.0 or later, or Amazon EMR release 8.1 or later.
• You can create V3 tables from the Amazon S3 console, AWS CLI, or any engine that supports the Iceberg REST Catalog API.
• The new V3 data types are supported only for tables that use the Parquet file format (not ORC or Avro).
• Columns of type variant, geometry, geography, or nanosecond timestamp can’t be included in a table’s sort order for compaction. Tables containing these columns still compact under the sort and Z-order strategies when the sort order uses columns of other types.

Now available
Amazon S3 Tables support for all Apache Iceberg V3 data types is now available in all AWS Regions where S3 Tables are supported. Apache Iceberg V3 support is available at no additional charge; standard S3 Tables pricing applies.

To get started, visit the Amazon S3 Tables documentation or create a table bucket from the Amazon S3 console. If you want to call APIs, search documentation, find regional availability, and check troubleshooting about this feature, try using the AWS MCP Server and plugins with your preferred AI tool. Send feedback to AWS re:Post or through your usual AWS Support contacts.

– Daniel Abib

The post Amazon S3 Tables now support all Apache Iceberg V3 data types appeared first on The NOKIA Blog.

Today, we’re announcing metadata pre-filtering for Amazon S3 Vectors, which delivers higher recall on filtered queries by evaluating your metadata filter before the similarity search. You can filter on attributes such as tenant, category, status, or time, and pre-filtering adds prefix matching with $startsWith for paths, URLs, and hierarchical keys. Each vector carries up to 2 KB of filterable metadata, and a single query supports up to 100 filter constraints. There is no additional cost, no re-ingestion, and no change to your queries.

Most applications never search a whole index. They search the part of it that belongs to a particular user, account, or category, and they express that scope as a metadata filter. Semantic search, retrieval-augmented generation (RAG), and agentic applications all need the same thing from a filtered query: a similarity search that covers the vectors matching the filter, and returns the closest of them. With pre-filtering, a filtered query returns more of the relevant matches your index contains, giving you higher recall on filtered searches.

Common use cases
Pre-filtering applies wherever results have to be both relevant and correctly scoped:

• Legal and professional services: A law firm or e-discovery platform searches documents scoped to a single client, and with $startsWith narrows further by matter number, folder path, or document ID prefix. A single client is a small share of a firm-wide archive, and filters this narrow are where pre-filtering improves recall most.
• Financial services: An investment research platform searches analyst notes, filings, and call transcripts scoped by issuer, document type, and publication date.
• Media and entertainment: A streaming service filters by content rating and regional licensing before the semantic search, finding similar titles restricted to G and PG content licensed in one territory.
• Agentic applications: An agent working within a user’s session filters on fields such as owner, document set, and timestamp so its searches cover the material relevant to the task at hand. Higher recall means more of that material reaches the agent, which improves task reliability

How pre-filtering works
Each vector in an S3 Vectors index can carry application-defined metadata, and a query can filter on those fields.

Every vector index has an index mode. On an index whose index mode is ENHANCED, S3 Vectors resolves your filter first, then searches only the vectors that match. On an index whose index mode is CLASSIC, S3 Vectors performs the vector search and filter evaluation in tandem, validating each candidate vector against your filter as it searches. Existing indexes use CLASSIC until you update them.

Consider a support knowledge base of 8 million tickets, where an agent searches one customer’s history for a recurring error. If that customer accounts for 400 of those tickets, resolving customer_id first means the similarity search runs across all 400 of them, so the agent sees that customer’s prior occurrences. Before the index was updated, the same query drew its candidates from the full 8 million, and the result set contained fewer of that customer’s matching tickets.

On highly selective filters, pre-filtering returns up to 5x more of the matching vectors than the same query returned before on CLASSIC indexes.

Getting started
Before you start, make sure your IAM policy grants permissions for the new actions.

You can get started in three steps. The walkthrough below builds a small product-catalog index and runs a selective filter against it, the same pattern you would use for a multi-tenant RAG store or a document search scoped to one client.

First, create a vector index:

aws s3vectors create-index  
  --index-name product-catalog  
  --vector-bucket-name my-vector-bucket  
  --dimension 1536  
  --distance-metric cosine

The dimension must match the output size of your embedding model, and distance-metric should match how that model was trained (cosine is common for text embeddings). Second, write vectors with the PutVectors API, attaching up to 2 KB of filterable metadata to each vector:

aws s3vectors put-vectors 
  --index-name product-catalog 
  --vector-bucket-name my-vector-bucket 
  --vectors '[{
    "key": "doc-001",
    "data": {"float32": [0.1, 0.2, 0.3, ...]},
    "metadata": {
      "tenant_id": "t-10428",
      "category": "legal",
      "created_date": "2026-03-15",
      "active": true
    }
  }]'

Each vector carries the attributes your application filters on. In this example, tenant_id scopes results to a single customer, category narrows by document type, created_date records when the document was created, and active is a boolean flag. By default every metadata field is filterable, so you can query on any of them without declaring a schema up front.

Third, run a filtered similarity query with the QueryVectors API. The filter uses a compact JSON syntax where a bare key-value pair is an equality match, and operators such as $and, $or, and $gt combine or refine conditions. Pass --return-metadata so the query returns each vector’s metadata:

aws s3vectors query-vectors 
  --index-name product-catalog 
  --vector-bucket-name my-vector-bucket 
  --query-vector '{"float32": [0.1, 0.2, 0.3, ...]}' 
  --top-k 50 
  --return-metadata 
  --filter '{"$and": [
    {"tenant_id": "t-10428"},
    {"category": "legal"},
    {"active": true}
  ]}'

The expected result is a single vector, doc-001, the only one matching all three filter conditions (tenant_id, category, and active):

{
  "vectors": [
    {
      "distance": 0.9717477560043335,
      "key": "doc-001",
      "metadata": {
        "tenant_id": "t-10428",
        "category": "legal",
        "created_date": "2026-03-15",
        "active": true
      }
    }
  ],
  "distanceMetric": "cosine"
}

S3 Vectors first narrows the search space to vectors matching all three filter conditions, then returns the 50 most similar vectors from that subset. Because the filter is applied before the search, those results are drawn from across all the vectors that match it.

Prefix matching with $startsWith
Pre-filtering adds a prefix match operator for filtering on paths, URLs, and hierarchical keys. A document store that encodes case and folder structure into a document ID can scope a search to a subtree in one condition:

--filter '{"$startsWith": {"document_id": "matter-4417/exhibits/"}}'

$startsWith joins the existing operators: equality, numeric range, set membership, existence checks, and boolean logic with $and and $or.

Turning on pre-filtering for existing indexes
Call UpdateIndexMode on an existing index to turn on pre-filtering:

aws s3vectors update-index-mode 
  --vector-bucket-name my-vector-bucket 
  --index-name product-catalog 
  --index-mode ENHANCED

Pre-filtering takes effect in place. Your existing vectors are not re-ingested, your queries do not change, and the new filter operators are available immediately.

Here is the difference on the same index and the same query. Before the update, a query scoped to one tenant returns two of the ten results requested:

aws s3vectors query-vectors 
  --vector-bucket-name my-vector-bucket 
  --index-name product-catalog 
  --query-vector '{"float32": [0.1, 0.2, 0.3, ...]}' 
  --top-k 10 
  --return-metadata 
  --filter '{"tenant_id": "t-10428"}'

{
  "vectors": [
    { "key": "doc-114", "distance": 0.41 },
    { "key": "doc-322", "distance": 0.55 }
  ],
  "distanceMetric": "cosine"
}

After the update, the same query returns a full result set drawn from across that tenant’s documents:

{
  "vectors": [
    { "key": "doc-018", "distance": 0.09 },
    { "key": "doc-207", "distance": 0.13 },
    { "key": "doc-114", "distance": 0.41 },
    ... 7 more
  ],
  "distanceMetric": "cosine"
}

Rolling out across your indexes
Once you have validated pre-filtering on an index, set the default index mode on the vector bucket so that new indexes use ENHANCED without a follow-up call:

aws s3vectors put-vector-bucket-default-index-mode 
  --vector-bucket-name my-vector-bucket 
  --default-index-mode ENHANCED

To bring the rest of your existing indexes across, list them and check the index mode on each one, then call UpdateIndexMode on the ones still using CLASSIC:

aws s3vectors list-indexes 
  --vector-bucket-name my-vector-bucket

aws s3vectors get-index 
  --vector-bucket-name my-vector-bucket 
  --index-name product-catalog

Things to know 

• Indexes created in vector buckets created on or after September 30, 2026 use index mode ENHANCED. Indexes in buckets that existed before that date use CLASSIC until you set the bucket default, including indexes created in those buckets afterward.
• A single query supports up to 100 filter constraints, counted per value the filter evaluates. If a query exceeds that, you can usually consolidate the filter, replacing a 300-value $in over legal cases with a single caseId field, for example, or split it into smaller queries, run them in parallel, and merge the results by distance.

Now available
Metadata pre-filtering is available at no additional cost in all commercial AWS Regions where Amazon S3 Vectors is available, and in the AWS China Regions. You pay standard S3 Vectors pricing for storage, PUT requests, and queries. For full pricing details, visit the Amazon S3 pricing page. For regional availability, visit Amazon S3 Vectors Regions and quotas.

Whether you’re scoping a RAG application to one tenant, scoping an agent’s searches to one user’s documents, or narrowing a catalog search to a licensing window, pre-filtering lets you apply those filters without trading away recall. To learn more and get started, visit the Amazon S3 Vectors documentation. Send feedback to AWS re:Post for S3 or through your usual AWS Support contacts.

— Daniel Abib

The post Amazon S3 Vectors now supports metadata pre-filtering for higher recall on filtered searches appeared first on The NOKIA Blog.

Today, we’re announcing a new capability for Amazon Aurora PostgreSQL that you can use to directly query operational data together with data stored in your data lake in Apache Iceberg and Apache Parquet formats, using your existing PostgreSQL applications and tools. By eliminating the need to extract, transform, and load (ETL) structured data from data lakes into your operational database, you can reduce operational complexity and simplify application development. You can also use Aurora PostgreSQL to query data from data lakes managed in Iceberg REST Catalog (IRC)-compatible catalogs, giving you access to data across a breadth of analytics systems without moving or duplicating it. Whether you’re powering real-time dashboards, enriching transactions with historical context, or building AI agents that reason over both live and archived data, you can now do it all through a single, familiar interface.

Previously, if your application needed to combine recent transactional data in Aurora with historical records stored in Amazon S3, a common approach was to build reverse ETL pipelines that duplicated data, increased infrastructure costs, and required ongoing engineering effort to keep everything synchronized. This challenge only grows as you increasingly embed AI agents into your applications, where it is impractical to predict and pre-replicate every dataset an agent might need.

DuckLabs, the team that maintains the DuckDB project, recently joined Amazon, and this capability is an example of how the efficiency of DuckDB is being integrated into our services. DuckDB is now embedded directly within Aurora PostgreSQL, so you can query live operational data (including uncommitted writes) alongside your data lake in a single query. Query processing stays within Aurora, with no additional network hops and no ETL pipelines that duplicate data. You can query Apache Iceberg tables managed through the AWS Glue Data Catalog, as well as Parquet and Iceberg data stored in Amazon S3 and S3 Tables. You do all of this using familiar PostgreSQL syntax and your existing applications and tools.

We’re excited to bring the speed and simplicity of DuckDB directly into Aurora PostgreSQL, so you and your agents can query and combine operational and Iceberg data using the familiar PostgreSQL applications, tools, and endpoints already in use. By building this capability around DuckDB, future improvements to the open source engine can continue to bring performance and functionality gains to Aurora and other AWS services.

What is new
This capability is supported on two Aurora PostgreSQL major versions: 17 (starting with 17.11) and 18 (starting with 18.6). To use it, you create an Aurora PostgreSQL cluster, attach an IAM role with the AuroraAnalytics feature, and enable the aurora_analytics extension. The IAM role is what gives Aurora access to your data in Amazon S3 and the AWS Glue Data Catalog. You then create foreign tables that point to your Iceberg or Parquet data in the data lake, and query them using familiar PostgreSQL syntax. You can complete this setup through the Amazon RDS console, or with any PostgreSQL client such as psql. The process is well documented in the Aurora PostgreSQL documentation.

You can query data across external IRC-compatible catalogs through AWS Glue Data Catalog federation. You register the external catalog once with Glue, and then create foreign tables for the tables you want to query, the same way you would for any Glue-native table. A single query can then join data stored in Aurora with Iceberg tables registered across multiple catalogs, so applications get a unified view without moving data or replacing your existing catalog investments.

Aurora also applies optimizations such as predicate pushdown and column pruning so that only the relevant data is read. This keeps queries efficient even as the underlying data grows. Frequently accessed data is also cached in your Aurora instance, so subsequent queries against the same data return faster. You can inspect this behavior per query using aurora_analytics_stat_statements(), which reports metrics such as rows scanned, bytes read from Amazon S3, and cache hits.

To see how direct querying works, I connected to my Aurora PostgreSQL database using psql and created the extension:

CREATE EXTENSION aurora_analytics;

For my walkthrough, I set up a simple financial scenario. I have a recent_transactions table in Aurora with the last 7 days of customer transactions, and a Parquet file in Amazon S3 containing 5 years of historical transaction data. To make Aurora aware of the historical data, I created a foreign table pointing at the Parquet file in S3:

CREATE FOREIGN TABLE transaction_history ()
SERVER aurora_analytics_server
OPTIONS (
    location 's3://<my-bucket>/finance/transaction_history.parquet',
    format 'parquet'
);

Notice the empty parentheses in the CREATE FOREIGN TABLE statement. Aurora automatically reads the schema from the Parquet file metadata, so you do not need to define columns manually. For workloads with many tables, you can skip creating them one at a time: a single IMPORT FOREIGN SCHEMA statement bulk-creates foreign tables for every Iceberg or Parquet table in an AWS Glue Data Catalog database, inferring schemas automatically.

With both tables in place, I ran a single query that combines the recent operational data in Aurora with the historical data in S3:

SELECT merchant, category, amount, transaction_date, 'recent' AS source
FROM recent_transactions
WHERE customer_id = 'C-1001'
UNION ALL
SELECT merchant, category, amount, transaction_date, 'historical' AS source
FROM transaction_history
WHERE customer_id = 'C-1001'
  AND transaction_date >= CURRENT_DATE - INTERVAL '5 years'
ORDER BY transaction_date DESC
LIMIT 15;

The result shows both recent and historical transactions in a single result set. The 7 most recent rows come from Aurora, and the rest come directly from the Parquet file in S3. DuckDB handles the analytical scan of the Parquet data under the hood, while Aurora handles the operational data. That single query would have previously required a pipeline to move the historical data into the database first.

If a query pattern needs single-digit-millisecond latency, you can materialize data from the data lake into a native Aurora PostgreSQL table using familiar commands such as CREATE TABLE AS SELECT, INSERT INTO ... SELECT, or MERGE INTO. The materialized table lives in Aurora and is queried like any other PostgreSQL table, giving you a low-latency path for hot data without operating a separate ingestion pipeline. The read queries can run on any Aurora PostgreSQL instance in your cluster, whether the writer or a read replica, so you can offload analytical scans from your operational workload. The materialization commands write data into Aurora, so they run on the writer instance.

Get started today
Direct querying of Apache Iceberg and Parquet data from Amazon Aurora PostgreSQL is available today in all commercial AWS Regions and AWS GovCloud (US) Regions, at no additional charge. You pay only for the incremental Aurora compute the queries consume and Amazon S3 request costs for reading data lake files.

To learn more, visit the Amazon Aurora features page, read the Aurora PostgreSQL documentation, or try it in the Amazon RDS console. We welcome your feedback through AWS re:Post or through your usual AWS Support contacts.

— Esra

The post Amazon Aurora PostgreSQL now supports direct querying of Apache Iceberg and Parquet data in your data lake appeared first on The NOKIA Blog.

Today, we’re excited to introduce the newest members of the AWS Heroes program. AWS Heroes are a vibrant, worldwide group of AWS experts who go above and beyond to share knowledge, mentor others, and build thriving communities. These individuals make a real difference in helping developers and organizations succeed with AWS.

This month, we welcome three exceptional community leaders from across the globe, each bringing unique expertise and a deep commitment to empowering builders everywhere.

Avinash Shashikant Dalvi – Bengaluru, India

Serverless Hero Avinash Shashikant Dalvi is a tech architect and co-organizer of AWS User Group Bengaluru who is focused on serverless, containers, and production-ready applications on AWS. He has delivered over 40 community talks, publishes the AWS for Product Builders newsletter, and creates technical content covering Amazon ECS, AWS Fargate, AWS Lambda, and AWS Amplify.

Joanne Skiles – Orlando, USA

Serverless Hero Joanne Skiles is an engineering leader and educator with over 16 years of experience building full-stack systems, including serverless architecture and AI systems on AWS. She organizes the Orlando AWS User Group and teaches cloud and AI concepts through her YouTube channel, conference talks, and her podcasts Chaotic Commits and Her Career Unplugged. Joanne is also a professor in the Computer Science department at Rollins College, where she runs the Transparent Systems lab.

Xiaofei Li – Shanghai, China

Community Hero Xiaofei Li is an AWS Golden Jacket holder and is an active community leader in the Greater China Region, leading the Kiro, Amazon Quick, and Tokyo Chinese AWS communities. He founded the Kiro Chinese User Community (5,000+ members) and initiated the Chinese localization of AWS Builder Cards across 15 cities and 3,000+ participants. Xiaofei also mentors underserved students and supports Women in Tech initiatives.

Learn More

Visit the AWS Heroes webpage if you’d like to learn more about the AWS Heroes program, or to connect with a Hero near you. To learn more about how to get involved with the AWS community, visit our AWS Builder Center.

— Taylor

The post Celebrating Our Newest AWS Heroes – September 2026 appeared first on The NOKIA Blog.

Misty mornings, leaves shining in bright red, yellow, and orange hues, and spooky creatures making an appearance — October has a very special charm. So, how about some new desktop wallpapers to celebrate October’s beauty, and the upcoming Halloween season, of course? Well, we’ve got you covered.

Just like it has been a monthly tradition here at Smashing Magazine for more than 15 years already, the wallpapers in this post were created by artists and designers from across the globe. All of them come in a variety of screen resolutions and can be downloaded for free. A huge thank-you to everyone who shared their designs with us — you are truly smashing!

Now, before you pick your favorite for the month, did you know that you can also get featured in one of our upcoming wallpapers posts? We are always looking for creative talent, so please don’t hesitate to join in. We’d love to see what you come up with!

• You can click on every image to see a larger preview.
• We respect and carefully consider the ideas and motivation behind each and every artist’s work. This is why we give all artists the full freedom to explore their creativity and express emotions and experience through their works. This is also why the themes of the wallpapers weren’t anyhow influenced by us but rather designed from scratch by the artists themselves.

A Night Of Universal Magic And Joy

“From candlelit streets in Europe to vibrant festivals across the Americas, Halloween has become a global celebration that unites people of all ages. It is a magical night where children step into the shoes of their favorite heroes for candy-filled adventures, while adults rediscover the pure, playful excitement of childhood. Across cultures and continents, glowing pumpkins and festive costumes light up the autumn dark, bringing neighbors together, sparking laughter, and proving that joy knows no age limit.” — Designed by PopArt Studio from Novi Sad, Serbia.

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• with calendar: 320×480, 640×480, 800×480, 800×600, 1024×768, 1024×1024, 1152×864, 1280×720, 1280×800, 1280×960, 1280×1024, 1400×1050, 1440×900, 1600×1200, 1680×1050, 1680×1200, 1920×1080, 1920×1200, 1920×1440, 2560×1440
• without calendar: 320×480, 640×480, 800×480, 800×600, 1024×768, 1024×1024, 1152×864, 1280×720, 1280×800, 1280×960, 1280×1024, 1400×1050, 1440×900, 1600×1200, 1680×1050, 1680×1200, 1920×1080, 1920×1200, 1920×1440, 2560×1440

Voodoo You

Designed by Ricardo Gimenes from Spain.

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• without calendar: 640×480, 800×480, 800×600, 1024×768, 1024×1024, 1152×864, 1280×720, 1280×960, 1280×1024, 1366×768, 1400×1050, 1440×900, 1600×1200, 1680×1050, 1680×1200, 1920×1080, 1920×1200, 1920×1440, 2560×1440, 3840×2160

Hanging

Designed by Ricardo Gimenes from Spain.

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• without calendar: 640×480, 800×480, 800×600, 1024×768, 1024×1024, 1152×864, 1280×720, 1280×800, 1280×960, 1280×1024, 1366×768, 1400×1050, 1440×900, 1600×1200, 1680×1050, 1680×1200, 1920×1080, 1920×1200, 1920×1440, 2560×1440, 3840×2160

Under The Floodlights

“October football begins under the lights. This geometric illustration captures the quiet moment before kickoff: an empty pitch, warm floodlights, and a dark autumn sky. A few fallen leaves hint at the season, while the open composition leaves room for the everyday things on your desktop.” — Designed by Football Charts from Serbia.

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• with calendar: 320×480, 640×480, 800×480, 800×600, 1024×768, 1024×1024, 1152×864, 1280×720, 1280×800, 1280×960, 1280×1024, 1366×768, 1400×1050, 1440×900, 1600×1200, 1680×1050, 1680×1200, 1920×1080, 1920×1200, 1920×1440, 2560×1440, 3840×2160
• without calendar: 320×480, 640×480, 800×480, 800×600, 1024×768, 1024×1024, 1152×864, 1280×720, 1280×800, 1280×960, 1280×1024, 1366×768, 1400×1050, 1440×900, 1600×1200, 1680×1050, 1680×1200, 1920×1080, 1920×1200, 1920×1440, 2560×1440, 3840×2160

Coffee Weather

“October is here, which means colder mornings, warmer clothes, and coffee that somehow tastes better outside. A small autumn scene for one of the coziest months of the year.” — Designed by Ginger IT Solutions from Serbia.

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• with calendar: 320×480, 640×480, 800×480, 800×600, 1024×768, 1024×1024, 1152×864, 1280×720, 1280×800, 1280×960, 1280×1024, 1400×1050, 1440×900, 1600×1200, 1680×1050, 1680×1200, 1920×1080, 1920×1200, 1920×1440, 2560×1440
• without calendar: 320×480, 640×480, 800×480, 800×600, 1024×768, 1024×1024, 1152×864, 1280×720, 1280×800, 1280×960, 1280×1024, 1400×1050, 1440×900, 1600×1200, 1680×1050, 1680×1200, 1920×1080, 1920×1200, 1920×1440, 2560×1440

Midnight Mischief

Designed by Libra Fire from Serbia.

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Magical October

“‘I’m so glad I live in a world where there are Octobers.’ (L.M. Montgomery, Anne of Green Gables)” — Designed by Lívi Lénárt from Hungary.

The Night Drive

Designed by Vlad Gerasimov from Georgia.

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Bird Migration Portal

“When I was young, I had a bird’s nest not so far from my room window. I watched the birds almost every day, because those swallows always left their nests in October. As a child, I dreamt that they all flew together to a nicer place, where they were not so cold.” — Designed by Eline Claeys from Belgium.

The Crow And The Ghosts

“If my heart were a season, it would be autumn.” — Designed by Lívia Lénárt from Hungary.

Spooky Town

Designed by Xenia Latii from Germany.

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Hanlu

“The term ‘Hanlu’ literally translates as ‘Cold Dew.’ The cold dew brings brisk mornings and evenings. Eventually the briskness will turn cold, as winter is coming soon. And chrysanthemum is the iconic flower of Cold Dew.” — Designed by Hong, ZI-Qing from Taiwan.

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Ghostbusters

Designed by Ricardo Gimenes from Spain.

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Hello Autumn

“Did you know that squirrels don’t just eat nuts? They really like to eat fruit, too. Since apples are the seasonal fruit of October, I decided to combine both things into a beautiful image.” — Designed by Erin Troch from Belgium.

Transitions

“To me, October is a transitional month. We gradually slide from summer to autumn. That’s why I chose to use a lot of gradients. I also wanted to work with simple shapes, because I think of October as the ‘back to nature/back to basics month’.” — Designed by Jelle Denturck from Belgium.

AI

Designed by Ricardo Gimenes from Spain.

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First Scarf And The Beach

“When I was little, my parents always took me and my sister for a walk at the beach in Nieuwpoort. We didn’t really do those beach walks in the summer but always when the sky started to turn gray and the days became colder. My sister and I always took out our warmest scarfs and played in the sand while my parents walked behind us. I really loved those Saturday or Sunday mornings where we were all together. I think October (when it’s not raining) is the perfect month to go to the beach for ‘uitwaaien’ (to blow out), to walk in the wind and take a break and clear your head, relieve the stress or forget one’s problems.” — Designed by Gwen Bogaert from Belgium.

Shades Of Gold

“We are about to experience the magical imagery of nature, with all the yellows, ochers, oranges, and reds coming our way this fall. With all the subtle sunrises and the burning sunsets before us, we feel so joyful that we are going to shout it out to the world from the top of the mountains.” — Designed by PopArt Studio from Serbia.

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Tea And Cookies

“As it gets colder outside, all I want to do is stay inside with a big pot of tea, eat cookies, and read or watch a movie, wrapped in a blanket. Is it just me?” — Designed by Miruna Sfia from Romania.

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Autumn Deer

Designed by Amy Hamilton from Canada.

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Discovering The Universe

“Autumn is the best moment for discovering the universe. I am looking for a new galaxy or maybe… a UFO!” — Designed by Verónica Valenzuela from Spain.

King Of The Pirates

Designed by Ricardo Gimenes from Spain.

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Autumn In The Forest

“Autumn is a wonderful time to go for walks in the forest!” — Designed by Hilda Rytteke from Sweden.

Autumn Gate

“The days are colder, but the colors are warmer, and with every step we go further, new earthly architecture reveals itself, making the best of winters’ dawn.” — Designed by Ana Masnikosa from Belgrade, Serbia.

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Game Night And Hot Chocolate

“To me, October is all about cozy evenings with hot chocolate, freshly baked cookies, and a game night with friends or family.” — Designed by Lieselot Geirnaert from Belgium.

A Very Pug-o-ween

“The best part of October is undoubtedly Halloween. And the best part of Halloween is dog owners who never pass up an o-paw-tunity to dress up their pups as something a-dog-able. Why design pugs specifically in costumes? Because no matter how you look at it, pugs are cute in whatever costume you put them in for trick or treating. There’s something about their wrinkly snorting snoots that makes us giggle, and we hope our backgrounds make you smile all month.” — Designed by Trillion from Summit, NJ.

Roger That Rogue Rover

“The story is a mash-up of retro science fiction and zombie infection. What would happen if a Mars rover came into contact with an unknown Martian material and got infected with a virus? What if it reversed its intended purpose of research and exploration? Instead choosing a life of chaos and evil. What if they all ran rogue on Mars? Would humans ever dare to voyage to the red planet?” Designed by Frank Candamil from the United States.

Halloween 2040

Designed by Ricardo Gimenes from Spain.

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Dope Code

“October is the month when the weather in Poland starts to get colder, and it gets very rainy, too. You can’t always spend your free time outside, so it’s the perfect opportunity to get some hot coffee and work on your next cool web project!” — Designed by Robert Brodziak from Poland.

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Happy Fall!

“Fall is my favorite season!” — Designed by Thuy Truong from the United States.

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Get Featured Next Month

Feeling inspired? We’ll publish the November wallpapers on October 31, so if you’d like to be part of the collection, please don’t hesitate to submit your design. We are already looking forward to it!

The post Under Autumn’s Spell (October 2026 Wallpapers Edition) appeared first on The NOKIA Blog.

If there’s one theme that defined last week, it’s choice. The frontier models keep arriving, and the interesting question is no longer just “how smart is it?” but “which model fits this step, at this cost, at this latency?” That’s exactly what landed on Amazon Bedrock over the past few days: GPT-6 Sol and GPT-6 Luna from OpenAI, giving you two new points on the intelligence-versus-efficiency curve, and Claude Opus 5.5 from Anthropic, the first of the Claude 5.5 family.

GPT-6 Sol is built for the demanding, recurring work of development and operations, while GPT-6 Luna makes focused, repeatable tasks practical at high volume, and both ship at significantly lower pricing than their GPT-5.6 predecessors. Claude Opus 5.5, meanwhile, does more with fewer tokens than Opus 5 and is tuned for agentic coding and long-running tasks. What I like about all three is that they push toward the same idea: match the model to the job instead of reaching for the biggest one every time. The other thread was observability catching up to this agentic world, including a launch I had the pleasure of writing about myself.

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

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

• Introducing Amazon CloudWatch Omni – You can now observe your applications and AI agents together in a single, collaborative experience. Amazon CloudWatch Omni is built on OpenTelemetry, so your existing telemetry shows up with nothing to reconfigure, and your whole team reaches it through one URL with enterprise SSO — no console access required. It auto-discovers your services, maps dependencies, and brings AWS DevOps Agent into investigation sessions to correlate signals and trace root causes. There’s a companion post on the agent-observability side, a deeper dive on the AWS Cloud Operations blog on what observability for the AI era looks like, and the announcement on What’s New with the specifics. If you want the bigger picture, Matt Wood’s Wrong, not broken is a great read on why correctness now has to be measured at the level of the run.

• Enhanced custom event buses in Amazon EventBridge – Amazon EventBridge now offers an enhanced custom event bus purpose-built for organizations scaling event-driven applications across teams and accounts. You can now deploy a single centralized bus shared across every account in your organization through AWS RAM, with optional event ordering, a simplified Subscriber resource that bundles filtering, targets, and retries, content-based deduplication, and synchronous invocation for targets like AWS Lambda. A new ingress/egress pricing model replaces the compounding cross-account routing charges of multi-bus setups, and your existing buses keep working unchanged as “classic.”

• Amazon SageMaker HyperPod Inference Gateway – You can now front your LLM inference on Amazon SageMaker HyperPod with a Kubernetes-native, GPU-aware routing layer that deploys as a single Amazon EKS managed add-on with zero application changes. Instead of round-robin load balancing, it routes on real-time inference signals — KV cache utilization, queue depth, prefix cache hits, predicted latency, and more — cutting first-token latency by up to 82% in mixed-hardware and bursty scenarios. It works with any OpenAI-compatible model server, including vLLM and SGLang.

• AI agent skills for AWS End User Messaging and Amazon SES – You can now build and send messages by asking your AI coding agent in plain language. Amazon SES and AWS End User Messaging publish AI agent skills for the AWS MCP Server, giving your agent step-by-step, validated guidance for tasks like verifying a sending identity, sending a production email, or building a branded RCS agent with cards and buttons. The skills work with Claude Code, Codex, Cursor, and Kiro, so you can complete messaging workflows without hopping between docs and console screens.

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:

• Introducing Strands harness – The Strands Agents team released Strands harness, a fully assembled, general-purpose agent harness you can run locally or deploy anywhere, under Apache 2.0. It takes one line of Python or TypeScript to wire up your model of choice across Amazon Bedrock, Anthropic, OpenAI, Google, or a local Ollama model, and it ships with sensible defaults for prompt caching and context management (truncating bulky tool results, compacting when the context window fills up, and keeping memory across runs). The team reports it costs about 28% less than comparable harnesses on the same models while holding accuracy steady.

• Announcing the new AWS Reimagine report on AI – The AWS Executive in Residence team spent nine months interviewing 154 leaders across 27 countries about what separates organizations that turn AI into value from those that don’t. The report is candid (including where AI hasn’t worked at Amazon), and the recurring insight is that once building gets fast, the bottleneck moves to deciding, funding, and governing the work. Well worth a read if you’re thinking about how your teams adopt AI in practice.

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:

• AWS re:Invent – AWS re:Invent returns to Las Vegas from November 30 to December 4, and session times, locations, and speakers are live. Reserved seating opens October 6, so register now and be ready to claim your spot in chalk talks, workshops, and builders’ sessions.

• AWS Summits – With re:Invent on the horizon, the Summits are wrapping up for the year. The last stop is Dubai (September 30) at the Dubai World Trade Center, with 60+ sessions, an AWS Village, and hands-on workshops.

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!

— Daniel Abib

The post AWS Weekly Roundup: GPT-6 Sol and Luna, Claude Opus 5.5 on Amazon Bedrock, Strands harness, and more (September 28, 2026) appeared first on The NOKIA Blog.

The problem is that we can’t always remember what we did or why we did it. This is why documentation is so important. It’s a resource we can reference when jumping back in to add or fix something. Not to mention that it’s handy for clients and colleagues as well.

Documenting your projects has traditionally meant taking good notes. We add comments to our code, update readme files, and log what’s new in myriad other forms. Consistency is key, but difficult to achieve. After all, everyone gets busy and forgets to write something down.

Perhaps AI is just the tool we need to document projects without missing a beat. These models, along with the apps they connect with, can take notes for you. They’ll keep track of changes, explain what a feature does, and why it was built a specific way.

With that in mind, here are a few ideas for using AI to create and maintain your project documentation.

Use AI To Analyze and Remember Your Work

The popular AI models like ChatGPT, Claude, and Gemini all have “memory” features. Set your preferred tool to remember what you’ve done, and the model will record it for future reference. This is a goldmine for documenting your WordPress projects.

Before you start, ensure that you understand how your chosen model remembers things. Some have a setting for this purpose. However, you might also directly instruct an AI agent within your chat session. There are different types of memory, so you’ll want to check to see what is (and isn’t) included.

It’s a must-have feature when vibe-coding plugins. Use AI to keep track of your project from the start. That way, you’ll prevent any knowledge from being lost along the way.

But what about existing projects? You’re covered – to a point. AI can analyze your files and note specific functions and features. It’s also a wonderful tool for troubleshooting an issue or inspecting code written by someone else.

As always, double-check the tool’s interpretation of your work for accuracy. You can even use multiple models to inspect the same thing. It’s better to find a mistake early than a year down the road when you really need to know something.

Decide What Project Information You’ll Need

AI can document what’s been done. However, you’ll still need to figure out what information you’ll need to keep.

Much depends on the type of project you have and with whom it needs to be shared. But it’s safe to say that a changelog makes sense for every use case. You’ll want to know what changed, when it was changed, and potentially why.

Custom plugins or theme features would also benefit from usage instructions. It’s easy to forget how something works. As such, a quick step-by-step guide is valuable. Bonus points if you can add screenshots or videos to the mix. It’s perfect for onboarding new team members.

You might even document client requests. This enables you to stay on top of busy collaborative projects. Use it to assess time and energy spent (and how it impacts your billing). This info could also help you build a project roadmap for future changes.

The idea is to keep track of what matters to you and anyone else who touches your project. AI makes it easy to generate.

Create a System for Documenting Your Projects

Next, you’ll need a place to access your documentation and a plan for maintaining it. This is a very personal decision that depends on your preferences. For example, you could generate a custom text file in each project’s folder and be done with it. But there are more in-depth possibilities.

We’re working with WordPress, so using it as a knowledge base is a natural fit. You could build a centralized resource categorized by client or project. There are even support plugins available to help you get started, and the WordPress Connectors API will connect the site to your chosen AI model.

Solo freelancers could get by with a local WordPress installation, while agencies might want to use an internal or external server. A project’s documentation may also live on your client’s site. It’s a convenient feature and a potential selling point.

A Git repository would be great for those who want to version control their documentation. From there, you can feed updated information to virtually anywhere you want. Using AI here also streamlines the workflow.

Then, as things change, AI can automatically write to the destination. Or, you can manually review it and publish when ready. This ensures that you’ll always have the latest data.

The Pain-Free Way To Document Your Projects

Writing documentation can be a tedious process. You complete a task and then have to remember to record it. The chore is even harder when you’re juggling multiple clients and websites. Even the best of us will forget something.

AI’s automation capabilities make it seamless. Tell your AI tool what to document, and it remembers to keep track so you don’t have to. Some models may let you create blanket rules that cover all of your current and future projects.

You also get to choose where to store this valuable knowledge. It’s something you can share with others or keep to yourself. Regardless, you’ll have access whenever you need it. That will serve you well when it’s time to dive into a theme you haven’t touched in years.

If you’ve avoided project documentation, there’s no better time than the present to get started. There’s also an opportunity to upgrade what you already have. So, pick your favorite AI tool and let it do the work!

here. He recently started a writing service for WordPress products: WP Product Writeup. He also has an opinion on just about every subject. You can follow his rants on Bluesky @karks.com.

Read more articles by Eric Karkovack

The post How AI Can Help You Document Your WordPress Projects appeared first on The NOKIA Blog.