Recently, the FreeBSD Foundation has been making
progress on improving the operating system’s support for modern
laptop hardware. The foundation is now looking to expand testing to
encompass a wider range of hardware; it has announced
a laptop integration testing project to allow the community to easily
test FreeBSD’s compatibility with laptops and submit the results.
With limited access to testing systems, there’s only so much we can
do! We hope to work together with volunteers from the community who
want FreeBSD to work well on their laptops.
While we expect device hardware and software enumeration to be a
fully automated process, we feel that manually-submitted comments
about personal experience with FreeBSD are equally valuable. We plan
to highlight this commentary on our “matrix of compatibility” webpage
for each tested laptop.
We are striving to make it as easy as possible to submit your
results. You won’t have to worry about environment setup, submission
formatting, or any repo-specific details!
Mike Masnick points out that the recent New Mexico court ruling against Meta has some bad implications for end-to-end encryption, and security in general:
If the “design choices create liability” framework seems worrying in the abstract, the New Mexico case provides a concrete example of where it leads in practice.
One of the key pieces of evidence the New Mexico attorney general used against Meta was the company’s 2023 decision to add end-to-end encryption to Facebook Messenger. The argument went like this: predators used Messenger to groom minors and exchange child sexual abuse material. By encrypting those messages, Meta made it harder for law enforcement to access evidence of those crimes. Therefore, the encryption was a design choice that enabled harm.
The state is now seeking court-mandated changes including “protecting minors from encrypted communications that shield bad actors.”
Yes, the end result of the New Mexico ruling might be that Meta is ordered to make everyone’s communications less secure. That should be terrifying to everyone. Even those cheering on the verdict.
End-to-end encryption protects billions of people from surveillance, data breaches, authoritarian governments, stalkers, and domestic abusers. It’s one of the most important privacy and security tools ordinary people have. Every major security expert and civil liberties organization in the world has argued for stronger encryption, not weaker.
But under the “design liability” theory, implementing encryption becomes evidence of negligence, because a small number of bad actors also use encrypted communications. The logic applies to literally every communication tool ever invented. Predators also use the postal service, telephones, and in-person conversation. The encryption itself harms no one. Like infinite scroll and autoplay, it is inert without the choices of bad actors - choices made by people, not by the platform’s design.
The incentive this creates goes far beyond encryption, and it’s bad. If any product improvement that protects the majority of users can be held against you because a tiny fraction of bad actors exploit it, companies will simply stop making those improvements. Why add encryption if it becomes Exhibit A in a future lawsuit? Why implement any privacy-protective feature if a plaintiff’s lawyer will characterize it as “shielding bad actors”?
And it gets worse. Some of the most damaging evidence in both trials came from internal company documents where employees raised concerns about safety risks and discussed tradeoffs. These were played up in the media (and the courtroom) as “smoking guns.” But that means no company is going to allow anyone to raise concerns ever again. That’s very, very bad.
In a sane legal environment, you want companies to have these internal debates. You want engineers and safety teams to flag potential risks, wrestle with difficult tradeoffs, and document their reasoning. But when those good-faith deliberations become plaintiff’s exhibits presented to a jury as proof that “they knew and did it anyway,” the rational corporate response is to stop putting anything in writing. Stop doing risk assessments. Stop asking hard questions internally.
The lesson every general counsel in Silicon Valley is learning right now: ignorance is safer than inquiry. That makes everyone less safe, not more.
The essay has a lot more: about Section 230, about competition in this space, about the myopic nature of the ruling. Go read it.
Traditional business intelligence (BI) integration with enterprise data warehouses has been the established pattern for years. With generative AI, you can now modernize BI workloads with capabilities like interactive chat agents, automated business processes, and using natural language to generate dashboards.
In this post, we provide implementation guidance for building integrated analytics solutions that combine the generative BI features of Amazon Quick with Amazon Redshift and Amazon Athena SQL analytics capabilities. Use this post as a reference for proof-of-concept implementations, production deployment planning, or as a learning resource for understanding Quick integration patterns with Amazon Redshift and Athena.
Common use cases
You can use this integrated approach across several scenarios. The following are some of the most common use cases.
Traditional BI reporting benefits from bundled data warehouse and BI tool pricing, making generative BI the primary use case with significant cost advantages.
Insurance: Automates Solvency II and IFRS 17 regulatory reporting, replacing manual spreadsheet consolidation.
Banking: Accelerates FDIC call report generation and capital adequacy dashboards, cutting month-end close from days to hours.
Interactive dashboards with contextual chat agents give BI teams conversational interfaces alongside their visual metrics.
Gaming: Live ops teams query player retention and monetization KPIs in plain English—no SQL needed.
Financial Services: Trading analysts chat with real-time P&L dashboards to surface anomalies and drill into positions on demand.
Domain-specific analytics workspaces democratize enterprise data exploration through Quick Spaces and natural language queries.
Insurance: Actuarial and underwriting teams query claims and risk data without waiting on data engineering.
Banking: Risk and compliance teams explore credit, market, and operational data through a single natural language interface.
Workflow automation removes repetitive tasks and accelerates self-service analytics.
Financial Services: Automated AR reconciliation flows replace manual ledger matching, shrinking close cycle effort significantly.
Gaming: Telemetry ingestion pipelines trigger reporting refreshes automatically, freeing data engineers from routine work.
Let us examine an end-to-end solution combining these technologies.
Solution flow
AWS offers two native SQL analytics engines for building analytics workloads. Amazon Redshift provides a fully managed data warehouse with columnar storage and massively parallel processing. Amazon Athena delivers serverless interactive query capabilities directly against data in Amazon S3.
You can use either Amazon Redshift or Amazon Athena as a SQL engine while implementing the steps in this post. The following are the steps involved in building an end-to-end solution.
Figure1: Solution steps to integrate SQL Analytics engines with Amazon Quick
Set up your SQL analytics engines: Amazon Redshift or Amazon Athena.
Load data and create business views designed for analytics workloads.
Configure integration between SQL analytics engines and Amazon Quick.
Create data sources in Amazon Quick.
Create datasets and dashboards for visual analytics.
Use Topics and Spaces to provide natural language interfaces to your data.
Deploy chat agents to deliver conversational AI experiences for business users.
Implement business flows to automate repetitive workflows and processes.
Let’s start by walking through steps 1–4 for Amazon Redshift. We then describe the same four steps for Amazon Athena before explaining the Amazon Quick steps 5–8.
Configure and create datasets in Amazon Redshift
Amazon Redshift offers two deployment options to meet your data warehousing needs. Provisioned clusters provide traditional deployment where you manage compute resources by selecting node types and cluster size. Serverless automatically scales compute capacity based on workload demands with pay-per-use pricing. Both options are supported by Amazon Quick. For this walkthrough, we use Redshift Serverless.
Set up SQL analytics engine
To create a Redshift Serverless namespace and workgroup:
On the left navigation pane, select Redshift Serverless.
Follow the steps described in the Creating a workgroup with a namespace documentation page to create a workgroup and a namespace. Note the username and password provided. You will use these details for configuring connections in Amazon Redshift and Quick.
You should see the status as Available for both the workgroup and namespace in the Serverless dashboard.
Figure 2: Amazon Redshift Serverless Workgroup and NamespacesThe deployment will be completed in approximately 3–5 minutes.
Load data and create business views
Now you can load data using the industry-standard TPC-H benchmark dataset, which provides realistic customer, order, and product data for analytics workloads.To load data into Amazon Redshift:
Run the following COPY commands to load data from the public S3 bucket: s3://redshift-downloads/TPC-H/.
Ensure that the IAM role attached to the namespace is set as the default IAM role. If you didn’t set up the default IAM role at the time of namespace creation, you can refer to the Creating an IAM role as default for Amazon Redshift documentation page to set it now.
copy customer from 's3://redshift-downloads/TPC-H/2.18/100GB/customer/' iam_role default delimiter '|' region 'us-east-1';
copy orders from 's3://redshift-downloads/TPC-H/2.18/100GB/orders/' iam_role default delimiter '|' region 'us-east-1';
copy lineitem from 's3://redshift-downloads/TPC-H/2.18/100GB/lineitem/' iam_role default delimiter '|' region 'us-east-1';
Run the following query to validate load status. The status column should show as completed. You can also review the information in other columns to see details about the loads such as record counts, duration, and data source.
select * from SYS_LOAD_HISTORY Where table_name in ('customer','orders','lineitem');
Figure 3: Output of SYS_LOAD_HISTORY showing successful completion of COPY Jobs
Create a materialized view to improve query performance:
Run the following SQL to create a materialized view that pre-compute results set for customer revenues and order volumes by market segment.
CREATE MATERIALIZED VIEW mv_customer_revenue AS
SELECT
c.c_custkey,
c.c_name,
c.c_mktsegment,
SUM(l.l_extendedprice * (1 - l.l_discount)) as total_revenue,
COUNT(DISTINCT o.o_orderkey) as order_count
FROM customer c
JOIN orders o ON c.c_custkey = o.o_custkey
JOIN lineitem l ON o.o_orderkey = l.l_orderkey
GROUP BY c.c_custkey, c.c_name, c.c_mktsegment;
Run the following SQL to review the data in the materialized view.
select * from mv_customer_revenue limit 10;
Configure integration with Amazon Quick
Amazon Quick auto discovers the Amazon Redshift provisioned clusters that are associated with your AWS account. These resources must be in the same AWS Region as your Amazon Quick account. For Amazon Redshift clusters in other accounts or Amazon Redshift Serverless, we recommend that you add a VPC connection following the steps in Enabling access to an Amazon Redshift cluster in a VPC documentation. Usually, these steps are performed by your organization’s cloud security administration team.
For serverless, you will apply the same steps in the workgroup instead of the cluster. You can find the VPC and Security Group settings in the Data Access tab of a workgroup.
Figure 4: Amazon Redshift Serverless workgroup VPC and Security groups
To create a dataset connecting to Amazon Redshift, complete the following steps.
In the Quick left navigation pane, go to Datasets.
Choose the Data sources tab and select Create data source.
Select Amazon Redshift and enter the following:
Data Source Name: Provide customer-rev-datasource as data source name.
Connection type: Select the VPC connection created in the previous step.
Database server: Enter the Amazon Redshift workgroup endpoint (for example, quick-demo-wg.123456789.us-west-2.redshift-serverless.amazonaws.com).
Port: 5439 (default).
Database: dev.
Username/Password: Amazon Redshift credentials with access to the database.
Choose Validateconnection. The validation should be successful.
Figure 5: Amazon Redshift data source configuration
Choose Create Data Source to create a data source.
Now let’s explore how to perform all these four steps to configure Athena in Amazon Quick.
Configure and create datasets in Amazon Athena
Amazon Athena provides immediate query capabilities against petabytes of data with automatic scaling to handle concurrent users. Let’s go through the steps to configure connections between Amazon Quick and Amazon Athena.
For Query result configuration, select Athena managed.
Choose Create workgroup.
Your workgroup is ready immediately for querying data.
Load data and create business views
For Athena, you create tables using the TPC-H benchmark dataset that AWS provides in a public S3 bucket. This approach gives you 1.5 million customer records already optimized in Parquet format without requiring data loading.
To create tables and views in Athena:
Open the Athena Query Editor from the console.
Create a database for your analytics (create S3 bucket if it exists already):
CREATE DATABASE IF NOT EXISTS athena_demo_db
COMMENT 'Analytics database for customer insights'
LOCATION 's3://my-analytics-data-lake-[account-id]/';
Create an external table pointing to the TPC-H public dataset:
CREATE EXTERNAL TABLE IF NOT EXISTS athena_demo_db.customer_csv (
C_CUSTKEY INT,
C_NAME STRING,
C_ADDRESS STRING,
C_NATIONKEY INT,
C_PHONE STRING,
C_ACCTBAL DOUBLE,
C_MKTSEGMENT STRING,
C_COMMENT STRING
)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY '|'
STORED AS TEXTFILE
LOCATION 's3://redshift-downloads/TPC-H/2.18/100GB/customer/'
Create a business-friendly view for analytics:
Run the following SQL to create a view that aggregates customer account balances grouped by market segments.
CREATE VIEW athena_demo_db.customer_deep_analysis AS
SELECT
c_custkey AS customer_id,
c_name AS customer_name,
c_mktsegment AS market_segment,
c_nationkey,
ROUND(c_acctbal, 2) AS account_balance,
CASE
WHEN c_acctbal < 0 THEN 'At-Risk'
WHEN c_acctbal < 2500 THEN 'Low'
WHEN c_acctbal < 5000 THEN 'Mid'
WHEN c_acctbal < 8000 THEN 'High'
ELSE 'Premium'
END
AS balance_tier,
ROUND(AVG(c_acctbal) OVER (PARTITION BY c_mktsegment), 2) AS segment_avg,
ROUND(c_acctbal - AVG(c_acctbal) OVER (PARTITION BY c_mktsegment), 2) AS vs_segment_avg,
ROUND((c_acctbal - AVG(c_acctbal) OVER (PARTITION BY c_mktsegment))
/ NULLIF(STDDEV(c_acctbal) OVER (PARTITION BY c_mktsegment), 0), 2) AS segment_z_score,
RANK() OVER (PARTITION BY c_mktsegment ORDER BY c_acctbal DESC) AS rank_in_segment,
NTILE(5) OVER (ORDER BY c_acctbal DESC) AS global_quintile
FROM athena_demo_db.customer_csv
ORDER BY c_acctbal DESC;
Verify your view from Athena with:
SELECT * FROM athena_demo_db.customer_deep_analysis limit 5;
In the top-right corner, choose your profile icon, then select Manage account.
Under Permissions, choose AWS resources. Figure 7: AWS resource permissions
Enable Athena Access
Under Quick access to AWS services, choose Manage.
Locate Amazon Athena in the list of AWS services.
If Athena is already selected but access issues persist, clear the checkbox and re-select it to re-enable Athena.
Under Amazon S3, select S3 buckets.
Check the boxes next to each S3 bucket that Amazon Quick needs to access—including buckets used for Athena query results and any Redshift COPY source buckets.
Enable Write permission for Athena Workgroup to allow Amazon Quick to write Athena query results to S3 and choose Finish.
Choose Save to update the configuration.
The final step is to grant your Amazon Quick author permissions to query your database, Athena tables, and views. Configuration depends on whether AWS Lake Formation is enabled.
If AWS Lake Formation is not enabled
Permissions are managed at the Quick service role level through standard IAM-based S3 access control. Ensure that the Quick service role (for example, aws-quick-service-role-v0) has the appropriate IAM permissions for the relevant S3 buckets and Athena resources. No additional Lake Formation configuration is required.
If AWS Lake Formation is enabled
Lake Formation acts as the central authorization layer, overriding standard IAM-based S3 permissions. Grant permissions directly to the Amazon Quick author or IAM role.
Choose Permissions, then Data permissions, then Grant.
Select the IAM user or role.
Choose the required databases, tables, and columns.
Grant SELECT at minimum; add DESCRIBE for dataset creation.
Repeat for each user or role that requires access.
Create data source
Follow these steps to create an Athena data source on Amazon Quick.
In the Amazon Quick console, navigate to Datasets and choose Data sources tab.
Choose Create data source, then select the Amazon Athena card.
Enter a Data source name (you can give any name of your choice), select your Athena workgroup (like quick-demo), and choose Validate connection.
Figure 8: Athena data source creation
Choose Create data source.
Your Athena data source is now available for building datasets, dashboards, and Topics.
Use Amazon Quick generative AI features
The next steps, from 5–8, demonstrate Amazon Quick generative AI capabilities using Amazon Redshift as a data source. While we use Amazon Redshift in this example, you can substitute with Amazon Athena based on your specific requirements.
Create dashboards
Let’s start by creating datasets from the Amazon Redshift data source.
In the left navigation pane, choose Datasets.
On the Datasets page, choose Create Dataset.
For the data source, select Amazon Redshift data source customer-rev-datasource.
From the menu, choose mv_customer_revenue.
Figure 9: Select table to visualize
You can choose one of the following query modes. For this post, select Directly query your data option and choose Visualize.
Import to SPICE for quicker analytics – Quick loads a snapshot into its in-memory engine for faster dashboard performance.
Directly queryyour data– Quick runs queries on demand against your query engine.
Select Build icon to open a chat window. Enter “Show me orders by market segments” as the prompt. Note that you need Author Pro access to use this feature.
Figure 10: Build visualization using generative BI feature
You can change the visual type to a pie chart and add it to the analysis.
Choose Publish dashboard. Your dashboard is now available for viewing and sharing.
Create topics and spaces
To fully maximize enterprise data with AI, we must provide the right structure and context. That’s where Topics and Spaces come in. Topics act as natural language interfaces to your structured datasets, automatically analyzing your data, mapping fields, and adding synonyms. Business users can ask “What are total revenues by market segment?” and receive instant, visualized answers without writing a single line of SQL. Spaces bring together all of your related assets into a single collaborative workspace that democratizes data access, reduces context-switching, accelerates team onboarding, so everyone is working from the same trusted, AI-ready data sources.
To create a Quick topic
From the Amazon Quick homepage, choose Topics, then choose Create topic.
Enter a name for your topic. For this post, use Customer Revenue Analytics.
Enter a description. For example:
The Customer Revenue Analytics topic is designed for business users (including analysts, sales operations teams, finance, and market segment owners who need to explore customer and revenue data without SQL expertise. It serves as a natural language interface over the mv_customer_revenue Amazon Redshift dataset, allowing users to ask plain-English questions like “What are total revenues by market segment?” and receive instant, visualized answers. By automatically mapping business language to the underlying schema, it democratizes access to revenue insights across the organization.
Under Dataset, select mv_customer_revenue.
Choose Create. The topic can take 15–30 minutes to enable depending on the data. During this time, Amazon Quick automatically analyzes your data, selects relevant fields, and adds synonyms.
After the topic is enabled, take a few minutes to review and enrich it. The following are some example enrichments.
Add column descriptions to clarify field meaning for business users.
Define preferred aggregations (for example, sum compared to average for revenue fields).
Confirm which fields are Dimensions and which are Measures.
(Optional) To further refine how your topic interprets and responds to queries, add multiple datasets (for example, a customer CSV combined with a database view), custom instructions, filters, and calculated fields.
After your topic is created, its columns are available to add to a Space or to an Agent by selecting it as a data source.
Figure 11: Create a Quick Topic
Create a Space for your team
Spaces bring together dashboards, topics, datasets, documents, and other resources into organized, collaborative workspaces. By centralizing related assets in a single workspace, Spaces reduce context-switching, accelerate onboarding, so everyone is working from the same trusted data sources.
What to include in your Quick Space
Dashboard – Add the dashboard Market Segment Dashboard published from your mv_customer_revenue analysis. This gives team members instant access to visualizations such as revenue by market segment, top customers by order volume, and revenue distribution.
Topic – Connect the Customer Revenue Analytics (built on the mv_customer_revenue materialized view) to enable natural language queries directly against your Amazon Redshift data.
Optionally, you can upload supporting context to ground your team’s analysis:
Data dictionary or field definitions for mv_customer_revenue
Business rules for revenue calculation (for example, how discounts are applied in the TPC-H model)
This implementation guide, so new team members can onboard quickly
To create the Quick Space
From the left navigation menu, choose Spaces, then choose Create space.
Enter a name, for example, Customer Revenue & Segmentation.
Enter a description. For example:
Centralized workspace for customer revenue analysis powered by Amazon Redshift includes interactive dashboards, natural language query access to customer and segment data, and supports documentation for the TPC-H revenue model.
Add knowledge by connecting the Market Segment Dashboard and topic Customer Revenue Analytics.
You can invite team members, such as finance, sales operations, and segment owners, and set appropriate permissions.
Your Space is now ready for collaborative data exploration.
Figure 12: Create a Quick Space
Build chat agents
A custom chat agent delivers conversational AI experiences that understand business context and provide intelligent, grounded responses to user queries. These agents go beyond question-and-answer interactions. They synthesize knowledge from your dashboards, topics, datasets, and documents to explain trends, surface anomalies, guide users through complex analytics workflows, and recommend next steps.
Rather than requiring users to navigate multiple tools or write SQL queries, agents serve as a single conversational interface to your entire analytics environment. Agents can also connect to Actions, pre-built integrations with enterprise tools such as Slack, Microsoft Teams, Outlook, and SharePoint, enabling them to answer questions and trigger real-world workflows, send notifications, create tasks, and interact with external systems directly from the conversation. Custom agents can be tailored to specific business domains, teams, or use cases so that responses align with organizational terminology, data definitions, and business processes. After created, agents can be shared across teams, enabling consistent, actionable, AI-powered data access at scale. For teams working with the mv_customer_revenue dataset, we recommend creating a dedicated Customer Revenue Analysis Agent. This is a purpose-built conversational assistant grounded in your Amazon Redshift data, dashboards, and the Customer Revenue & Segmentation Space.
Create a Quick chat agent
There are two ways that you can use Amazon Quick to create a Quick agent. You can use the navigation menu or directly from Space. The following steps walk you through creating one from the navigation menu.
To create a Quick chat agent
From the left navigation menu, choose Agents, then choose Create agent.
Enter a name for your agent, for example, Customer Revenue Analyst.
Enter a description. For example:
An AI assistant for analyzing customer revenue, market segment performance, and order trends using our Amazon Redshift or data warehouse.
Under Knowledge Sources, add the Customer Revenue & Segmentation Space as a data source. This gives your agent access to the dashboards, topics, and reference documents you’ve already built.
(Optional) Define custom persona instructions to align the agent’s responses with your business context. For example, specifying preferred terminology, response style, or the types of questions it should prioritize.
Choose Launch chat agent.
Start having a conversation with your data. You are welcome to ask any questions. The following are some examples.
Which market segment generated most revenue?
Show me order trends
Figure 13: Create a Quick Chat agent
To share your Quick chat agent
After your agent is published, choose Share and invite team members or share it across your organization. Custom agents can be tailored to specific business contexts so that different teams can get AI assistance that speaks their language, without needing to configure anything themselves.
Create Quick Flows
Quick Flows automate repetitive tasks and orchestrate multi-step workflows across your entire analytics environment. This removes manual effort, reducing human error, and ensuring consistent execution of critical business processes. Flows can be triggered on a schedule or launched on demand, giving you flexible control over when and how automation runs.
You can build flows that span the full analytics lifecycle: monitoring data quality and flagging anomalies, generating and distributing scheduled reports to stakeholders, and triggering downstream actions in integrated systems such as Slack, Outlook. Amazon Quick gives you three ways to create a flow, so whether you prefer a no-code conversation or a visual step-by-step builder, there’s an option that fits how you work.
To create a flow from chat
While conversing with My Assistant or a custom agent, describe the workflow that you want to automate in plain English.
Amazon Quick generates the flow and offers to create it directly from your conversation — no configuration screens required.
To create a flow from a natural language description
From the left navigation menu, choose Flows, then choose Create flow.
Enter a plain-English description of your workflow. For example:
” Query revenue data by market segments. Filter by order count and all dates. Search web for comparable relevant market trends. Generate formatted summary reports providing market summary and look ahead per segment. ”
Amazon Quick automatically generates the complete workflow with all the necessary steps.
Optionally, you can add additional steps.
Choose Run Mode to test the Flow.
After your flow is created, share it with team members or publish it to your organization’s flow library, so everyone benefits from the same automation without having to rebuild it independently.
Figure 14: Create a Quick Flow to generate summaries and publish dashboards
For more complex flow, review weekly customer revenue summary flow as an example.
Queries the mv_customer_revenue materialized view in Amazon Redshift for the latest weekly revenue figures by market segment.
Compares results against the prior week to calculate segment-level variance.
Generates a formatted summary report and publishes it to the Customer Revenue & Segmentation Space.
Sends a notification through email or Slack to finance, sales operations, and segment owners with a direct link to the updated dashboard.
Flags any segment where revenue has declined more than a defined threshold, routing an alert to the appropriate owner for follow-up.
This flow transforms what might otherwise be a manual, multi-step reporting process into a fully automated pipeline, so stakeholders receive consistent, timely revenue insights without analyst intervention and saving analysts an estimated 3–5 hours per week. For detailed guidance on creating and managing flows, see Using Amazon Quick Flows. Also review Create workflows for routine tasks demo.
Cleanup
Consider deleting the following resources created while following this post to avoid incurring costs. We encourage you to use the trials at no cost as much as possible to familiarize yourself with the features described.
Delete the Amazon Quick account used while following this post. If you used an existing account, delete the data sets, dashboards, topics, spaces, agents and flows created.
Conclusion
This integrated approach to business intelligence combines the power of AWS SQL analytics engines with Amazon Quick generative AI capabilities to deliver comprehensive analytics solutions. By following these implementation steps, you establish a foundation for traditional BI reporting, interactive dashboards, natural language data exploration, and intelligent workflow automation. The architecture scales from proof-of-concept implementations to production deployments, transforming how organizations access and act on data insights. For more information about Amazon Quick features and capabilities, see the Amazon Quick documentation. To learn more about Amazon Redshift, visit the Amazon Redshift product page. For Amazon Athena details, see the Amazon Athena product page.
Last week, I visited AWS Hong Kong User Group with my team. Hong Kong has a small but strong community, and their energy and passion are high. They recently started a new AI user group, and we hope more people will join. I was able to strengthen my bond with the community through great food and conversation.
This week, I”ll first take a closer look at some of the key launches.
AWS DevOps Agent helps you run cloud operations—investigating incidents, reducing time to resolution, and preventing issues before they happen. Customers like United Airlines, Western Governors University, and T-Mobile are already using DevOps Agent to accelerate incident response and simplify operations at scale. At WGU, resolution time dropped from hours to minutes, and in preview customers report up to 75% lower MTTR and 3 to 5 times faster resolution. Learn more in Sébastien’s preview blog post and GA announcement.
AWS Security Agent brings continuous, context-aware penetration testing into the development lifecycle. This agent operates like a human penetration tester. Customers including LG CNS, HENNGE, and Wayspring are seeing strong results. At LG CNS, teams estimate over 50% faster testing and ~30% lower costs, along with significantly fewer false positives. Learn more in Esra’s preview blog post and GA announcement.
Both are designed to work across AWS cloud, multicloud, and on-prem environments. You can have an always-available teammate that can handle the heavy lifting, so you can focus on what matters most.
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 March 31, 2026.
Availability change guide for services in maintenance
We understand that changes in availability can impact your operations. For specific guidance, consult the relevant service documentation or contact AWS Support.
Last week’s launches Here are last week’s launches that caught my attention:
The Trusted
Platform Module (TPM) is a widely misunderstood piece of hardware (or
firmware) that lives in most x86-based computers. At SCALE 23x in Pasadena, California,
James Bottomley gave a presentation on the TPM and the work that he and
others have done to enable the Linux kernel to work with it. In
particular, he described the problems with interposer attacks, which target
the communication between the TPM and the kernel, and what has
been added to the kernel to thwart them.
Greg Kroah-Hartman has released the 6.6.133 stable kernel. This reverts
a backporting mistake that removed file descriptor checks which
led to kernel panics if the fgetxattr, flistxattr, fremovexattr, or fsetxattr functions were called
from user space with a file descriptor that did not reference an open
file.
Google says that it will fully transition to post-quantum cryptography by 2029. I think this is a good move, not because I think we will have a useful quantum computer anywhere near that year, but because crypto-agility is always a good thing.
The Arctic Freezer 4U-SP5 is something really interesting. As the name suggests, it is a 4U cooler that is designed to cool AMD SP5 platform processors. For those who are buying AMD EPYC 9004/9005 series processors like Genoa, Bergamo, and Turin. Perhaps the best part is that these sell for around $55, which is less […]
LWN recently reported on the Trivy
compromise that led, in turn, to the compromise of the LiteLLM system; that
article made the point that the extent of the problem was likely rather
larger than was known. The Next Web now reports
that the Trivy attack was used to compromise a wide range of European
Commission systems.
The European Union’s computer emergency response team said on
Thursday that a supply chain attack on an open-source security
scanner gave hackers the keys to the European Commission’s cloud
infrastructure, resulting in the theft and public leak of
approximately 92 gigabytes of compressed data including the
personal information and email contents of staff across dozens of
EU institutions.
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At the last re:Invent, we introduced the 
