Sr. Content Developer at Microsoft, working remotely in PA, TechBash conference organizer, former Microsoft MVP, Husband, Dad and Geek.
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Nvidia is reportedly preparing to fire Jensen Huang

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Nvidia's board is preparing to dismiss CEO Jensen Huang following a report from the New York Times about a second alleged extramarital affair, just days after he admitted to a separate affair with a woman alleged to be a Chinese spy, The Wall Street Journal reports. The second alleged affair was with an executive who reports to him, Nvidia's vice president of health care, Kimberly Powell, according to the Times.

The Nvidia board will meet as soon as today to decide on Huang's future at the company, according to the Journal. Nvidia did not immediately respond to a request for comment from The Verge.

Huang and Powell, who are both marri …

Read the full story at The Verge.

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Storm Clouds Ahead For Apple?, Debating Amazon’s Bee Frontier, AI as UI

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M.G. Siegler is the author of Spyglass. Siegler joins Big Technology Podcast in London to discuss whether Apple can keep its distance from the AI spending race. Tune in to hear how the data center boom is driving up the cost of memory and squeezing Apple’s margins even as its rivals foot the bill for new infrastructure. We also cover the foldable iPhone Duo and what happens to apps when AI can build an interface on the fly. Hit play for a show and tell episode, including a debate over whether you’d want Amazon’s Bee Frontier wristband listening to your conversations and telling you what to do next.

Learn more about your ad choices. Visit megaphone.fm/adchoices





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Your Path to Production for Agents

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From: Microsoft Developer
Duration: 1:02:29
Views: 607

Building an AI agent that impresses as a demo has never been easier. Building one that is secure, governable, cost-effective, and capable of delivering consistent business value in production is a very different challenge. In this first episode in our Path to Production for Agents master class series, Adelina Balasa, AI Global Strategy Lead at Microsoft, shares a practical framework developed from working with hundreds of customers and partners, helping organizations navigate the journey from AI experimentation to enterprise-scale deployment. Rather than focusing solely on models, the series explores the architectural, operational, security, and organizational foundations required to successfully move agents from concept to production and beyond.

The Path to Production for Agents series is built around six essential pillars that help organizations overcome the most common barriers to production success: AI Center of Excellence, AI Governance, AI Landing Zones, AI Security & Trust, Agent Ops, and Solution Optimization. Together, these pillars provide a roadmap for establishing the right people, processes, platforms, and practices to deploy and scale AI responsibly and efficiently. Each episode dives deeper into one of these pillars, offering practical guidance and actionable steps that teams can apply immediately.

What You'll Learn in this Episode
- Why so many AI projects succeed as demos but struggle to make the leap into production.
- The common challenges organizations encounter when moving agents from experimentation to deployment and scale.
- Why production readiness is typically an architectural and organizational challenge, not simply a model selection problem.
- An introduction to the six pillars of the Adelina Balasa Path to Production framework and how they work together to accelerate AI transformation.
- How an AI Center of Excellence helps align people, processes, and best practices for successful AI adoption.
- The role of AI Governance, AI Landing Zones, and AI Security & Trust in creating a secure and scalable foundation for AI.
- How Agent Ops and Solution Optimization help improve deployment speed, operational reliability, performance, and cost efficiency.
- How to use the Path to Production framework as a practical roadmap for taking AI agents from ideation to real-world business impact.

Connect
- Adelina Balasa https://www.linkedin.com/in/adelina-balasa-b2006a67/

Related episodes
- Watch the entire Path to Production series https://aka.ms/PathToProductionForAgents

Chapters
00:00 Introduction
00:56 Demo to Production
02:50 AI Journey
03:11 Production Gap
03:37 Framework Overview
04:29 AI CoE
13:39 Governance
23:16 AI Landing Zones
32:00 AU Security and Trust
38:39 AgentOps
45:42 Solution Optimization
53:33 Pulling it all together
54:37 Agent Architectures
1:00:00 Remember these three things

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What's worth building when AI can build anything?

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From: GitHub
Duration: 29:55
Views: 1,528

AI can build almost anything you ask it to, which makes a harder question unavoidable: what's actually worth building? In this GitHub Universe 2026 conversation, Sue Black of Durham University and Kent C. Dodds of kody.codes make the case for building software deliberately rather than reflexively. This session explores what responsible software looks like and takes a deeper dive into the daily reality of shipping, choosing developer tools, and maintaining what is already built. You'll leave with a sharper set of questions to bring to your own work, along with a clearer sense of when development speed is worth it and when it isn't.

Speakers:

Kent C. Dodds, Product Engineer, kody.codes
Sue Black, Professor of Computer Science and Technology Evangelist, Durham University

Watch more videos from GitHub Universe 2026: https://www.youtube.com/watch?v=AArI948SDz8&list=PLENrlACZ7mnU

— CHAPTERS —

00:00 Welcome to GitHub Universe 2026
00:22 What's worth building with AI?
01:06 Sue Black's journey in Technology
03:15 Kent C. Dodds' AI optimism & kodi
04:45 Understanding the ripple effect
07:44 Building with restraint in AI
10:30 Educating the next generation of developers
14:41 Ensuring software is fit for purpose
20:19 AI and diversity in tech
23:14 Invisible software & individual impact
28:25 The million-person impact challenge
29:08 Join the conversation & feedback

#GitHubUniverse #SoftwareEngineering #OpenSource

Stay up-to-date on all things GitHub by connecting with us:

YouTube: https://gh.io/subgithub
Blog: https://github.blog
X: https://twitter.com/github
LinkedIn: https://linkedin.com/company/github
Insider newsletter: https://resources.github.com/newsletter/
Instagram: https://www.instagram.com/github
TikTok: https://www.tiktok.com/@github

About GitHub
It’s where over 180 million developers create, share, and ship the best code possible. It’s a place for anyone, from anywhere, to build anything—it’s where the world builds software. https://github.com

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Test your applications against AWS locally with AWS Emulator

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Today we’re announcing AWS Emulator, a local emulator for Amazon Web Services (AWS) that you run on your machine. The AWS Command Line Interface (AWS CLI), the AWS SDKs, and your coding agents speak to it over a local HTTPS endpoint using the same AWS APIs they would use in the cloud, from your laptop. AWS Emulator has no additional service fee. You pay only for AWS resources when you deploy on AWS. Its source is on GitHub. You install it from an RPM or Debian package on Linux, and on macOS and Windows you run it in Docker.

How it works
You install the emulator, start it, and point your tools at the local endpoint. When AWS Emulator starts, it generates its own credentials, a config file, and a certificate, and writes them to ~/.aws/emulator/9297/. You set environment variables so the AWS SDK talks to your laptop instead of AWS, and your application code stays unchanged.

From there, your existing tests, CLI scripts, and coding agents keep working the way they already do. A unit test that creates an Amazon DynamoDB table, writes an item, and reads it back is the same test, and it runs on your machine without a deployment. A coding agent can create resources, check the result, and iterate again without waiting on a deployment in a Region.

The emulator is implemented in Rust so that it stays light on CPU and memory on a laptop. It supports the APIs of AWS services that have an API model in the AWS API models repository on GitHub, and because it’s built from those models, the same ones the AWS SDKs use, your requests and the errors you get back have the same shape as on AWS.

If you are a developer, especially one working with coding agents such as Kiro or Claude Code, you can use that loop to test AWS interactions of real complexity locally.

Let’s try it out
To get started, I installed the package on Amazon Linux 2023. The package adds the aws-emulator command to /usr/bin/, so it’s on the path.

PKG=https://downloads.aws.com/aws-emulator/packages/aws-emulator/LTS/latest
sudo dnf install -y $PKG/aws-emulator-x86_64.rpm

aws-emulator -v

On macOS and Windows, you build a Docker image from the short Dockerfile in the documentation and run the emulator in a container. We don’t publish a ready-made image.

I started the emulator with aws-emulator. It serves HTTPS on 127.0.0.1:9297, so only my own computer can reach it, and it writes its log as JSON lines that show the address, the config file, and the certificate file.

aws-emulator

In another terminal, I set the environment variables that point the AWS CLI at the emulator, and unset any variables that could supply real AWS credentials.

export AWS_ENDPOINT_URL=https://127.0.0.1:9297
export AWS_SHARED_CREDENTIALS_FILE=~/.aws/emulator/9297/emulator_credentials
export AWS_CONFIG_FILE=~/.aws/emulator/9297/config
export AWS_EC2_METADATA_DISABLED=true
export AWS_DISABLE_HOST_PREFIX_INJECTION=true
export AWS_REGION=us-east-1
export AWS_DEFAULT_REGION=us-east-1
unset AWS_ACCESS_KEY_ID AWS_SECRET_ACCESS_KEY AWS_SESSION_TOKEN AWS_PROFILE

The emulator generates new keys each time it starts. Its access key IDs start with EMULATOR, so you can’t mistake them for real AWS keys, and it rejects requests signed with any other keys. To confirm that my shell was talking to the emulator, I checked the health endpoint and the caller identity. The emulator uses a single account, 000000000000.

curl --cacert ~/.aws/emulator/9297/ca.pem https://127.0.0.1:9297/__admin/health

aws sts get-caller-identity

Then I created a bucket, copied in a small object, and listed it.

$ aws s3 mb s3://local-getting-started
make_bucket: local-getting-started

$ echo "hello from the emulator" > hello.txt

$ aws s3 cp hello.txt s3://local-getting-started/
upload: ./hello.txt to s3://local-getting-started/hello.txt

$ aws s3 ls s3://local-getting-started/

With the AWS CLI pointed at my laptop, I had the same commands I already use and a local endpoint that answered them.

Next I wanted to see it the way I actually work now, so with the emulator still running I opened Kiro and asked it to create an Amazon DynamoDB table, write an item, and read it back, all against AWS Emulator rather than my AWS account.

kiro> Using the local AWS Emulator (the environment variables
      are already set in this shell), create a DynamoDB table
      "kiro-demo" with a string key "id", put an item
      {id: "item-1", msg: "hello from kiro"}, then read it
      back with get-item.

$ aws dynamodb create-table --table-name kiro-demo \
    --attribute-definitions AttributeName=id,AttributeType=S \
    --key-schema AttributeName=id,KeyType=HASH \
    --billing-mode PAY_PER_REQUEST

$ aws dynamodb put-item --table-name kiro-demo \
    --item '{"id": {"S": "item-1"}, "msg": {"S": "hello from kiro"}}'

$ aws dynamodb get-item --table-name kiro-demo --key '{"id": {"S": "item-1"}}'

To use the same setup with an existing test suite, I would start the emulator, set the same environment variables in the script that runs the tests, and run them as usual.

Using the emulator in process
Running the emulator as a local process gives you an HTTPS endpoint that works with the AWS CLI and the AWS SDKs through environment variables. Some SDKs need an extra setting to trust the emulator certificate, for example a trust store for the AWS SDK for Java. For tests, there is a second option. AWS Emulator ships an embedded mode that you instantiate directly inside your test process and wire into the SDK through its HTTP client, rather than talking to a separate server over the network. Each embedded instance keeps its own state, so tests do not share buckets, tables, or queues, and you can run many of them in parallel. Embedded mode is available for the AWS SDK for Rust. You add the aws-emulator-embedded crate from GitHub to the [dev-dependencies] section of your Cargo.toml file. The following example creates an Amazon DynamoDB table, writes an item, and reads it back, entirely in process.

use aws_emulator_embedded::{AwsEmulatorEmbedded, EmbeddedOptions};
use aws_sdk_dynamodb::types::{
    AttributeDefinition, AttributeValue, BillingMode, KeySchemaElement, KeyType,
    ScalarAttributeType,
};

#[tokio::test]
async fn put_then_get_returns_the_item() {
    let emulator = AwsEmulatorEmbedded::new(EmbeddedOptions::default());
    let config = aws_config::defaults(aws_config::BehaviorVersion::latest())
        .http_client(emulator.http_client())
        .region(aws_config::Region::new("us-east-1"))
        .credentials_provider(emulator.aws_credentials_provider())
        .load()
        .await;
    let ddb = aws_sdk_dynamodb::Client::new(&config);

    ddb.create_table()
        .table_name("emulator-demo-table")
        .attribute_definitions(
            AttributeDefinition::builder()
                .attribute_name("id").attribute_type(ScalarAttributeType::S)
                .build().unwrap())
        .key_schema(
            KeySchemaElement::builder()
                .attribute_name("id").key_type(KeyType::Hash)
                .build().unwrap())
        .billing_mode(BillingMode::PayPerRequest)
        .send().await.unwrap();

    ddb.put_item()
        .table_name("emulator-demo-table")
        .item("id", AttributeValue::S("item-1".into()))
        .item("msg", AttributeValue::S("hi".into()))
        .send().await.unwrap();

    let got = ddb.get_item()
        .table_name("emulator-demo-table")
        .key("id", AttributeValue::S("item-1".into()))
        .send().await.unwrap();
    assert_eq!(got.item().unwrap().get("msg").unwrap().as_s().unwrap(), "hi");
}

When it runs as a local process, the emulator requires every request to be signed with Signature Version 4 (SigV4), the same way AWS does, and it exposes a /__admin/health endpoint on the same port so a script or a container can wait until it is ready before running tests. By default it also sends anonymous usage and error data, and you can turn it off.

How we measure fidelity
An emulator is only useful if a test that passes against it would also pass against AWS, so we measure that directly with fidelity tests. A fidelity test is a short program that uses an AWS SDK to run a workflow, such as storing an object and then reading and deleting it, and asserts how AWS behaves. We verify each fidelity test against real AWS. A fidelity test passes only if the emulator matches AWS on the response to every call, including the status code, the body, and the exact error code and message, and on the resources and data that the workflow creates.

With each release, we publish how many fidelity tests pass for each service, as k of n. For example, 40 of 50 means that 40 of the service’s 50 fidelity tests pass. The set of fidelity tests is public in the aws-emulator-evaluation-suite repository on GitHub, including the ones that don’t pass yet, so you can see what we verified and run the fidelity tests yourself. Read a score as a snapshot of a product that’s improving. When we add fidelity tests, a score can go down even while the emulator gets better.

If the emulator doesn’t implement an operation yet, it returns HTTP 501 (Not Implemented), so your tests fail right away. If the emulator acts differently from AWS, you can tell us by opening an issue in the AWS Emulator repository on GitHub.

How the emulator is built
AWS Emulator is built and maintained by agents from the AWS API models using Amazon Bedrock AgentCore Runtime Instances, AWS CodePipeline, and AWS CodeCommit, so it stays current with AWS as those models change.

One group of agents looks for gaps, both by comparing emulator responses against real AWS and by statically comparing the emulator’s code against the published Smithy models, and files an issue for each gap it finds. Another group picks up an issue, writes tests, implements the behavior, and reviews the change for correctness, performance, and security. When the change is ready, it is pushed to a source repository, and that push triggers an AWS CodePipeline run that runs the fidelity tests before the change is published to the public GitHub repository.

How we keep it secure
Because the code is written by agents, an independent security agent reviews changes adversarially, both while the fix is being made and again in the release pipeline as a final check before anything ships.

Security of your own use remains a shared responsibility. We build the emulator to run safely on your machine: it listens only on your own computer by default and rejects requests that aren’t signed with its own keys. You decide where it runs, what data you put in it, and how you test before production. Don’t use the emulator to test whether your application is secure. It doesn’t evaluate AWS Identity and Access Management (IAM) policies and resource-based policies the way AWS does, so a request that AWS denies might succeed in the emulator. Keep real credentials out of the environment of your test jobs, and test your security settings and your whole application on AWS before you go to production.

Now available
AWS Emulator is generally available today. It has no additional service fee. You pay only for AWS resources when you deploy on AWS. You can install it from RPM or Debian packages on Linux, run it in Docker on macOS and Windows, or build from source, and it works with the AWS SDKs and the AWS CLI once you set environment variables. At launch, it has some limits. It doesn’t evaluate IAM policies the way AWS does, and each emulator acts as a single account, with no cross-account or cross-Region behavior. How closely it matches AWS varies by service and feature. To see how well the emulator covers the services that you use, check the fidelity scores in the AWS Emulator documentation.

If you want to check regional availability, visit the AWS Capabilities by Region page in AWS Builder Center. If you want to call APIs, search documentation, find regional availability, and check troubleshooting about this new feature, try using the AWS MCP Server and plugins with your preferred AI tool.

To get started, see the AWS Emulator documentation.

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Uno Platform Studio 3.3: Your AI Workbench, Now on Desktop and Mobile

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&&

Generating code is getting easier and faster, but developers tell us that the real challenge is staying in control of what AI agents change, seeing the results immediately, and validating that everything works as intended.

And while AI tools continue to evolve, one thing hasn’t changed: enterprise .NET developers do their real work on desktop, alongside their IDE, terminal, source control, and the agents they already trust. They want new tools that fit into those tried and tested workflows.

Uno Platform Studio 3.3 is built for that. In addition to the browser, Studio now runs on your desktop, right alongside your IDE and AI agents. A new mobile companion app lets you experience your application on a real device, and the new Theme Editor makes it possible to reshape your application’s entire look in a few clicks.

The major improvements in Uno Platform Studio 3.3:

Uno Platform Studio Desktop

Develop from Windows, macOS, or Linux. Check and fix your dev setup from the start, and build alongside your agent from a solid foundation.

Uno Platform Studio Mobile

Test your app on a real device, with over-the-air updates.

Theme Editor

Restyle your whole application’s colors, shapes, spacing, and type, live, on the running application.

Uno Platform Studio 3.3 wordmark with the 3.3 version badge and four brand-colored diamonds, over a dark field of glowing particle waves.

Together, these additions bring us closer to our vision for Uno Platform Studio: a workbench for agentic application development where developers and AI agents create, collaborate, validate, and ship .NET applications together.

That’s three of the many updates in this release. Let’s unpack it.

Uno Platform Studio Desktop

Build where the real work happens

Uno Platform Studio now installs as a desktop application on Windows, macOS, and Linux. It runs on the same machine as your IDE, your terminal, your source control, and the agents you already use, so it fits into the way you work, acting as a workbench for you and your agents to ship and validate applications together.

Uno Platform Studio Desktop builds.
PlatformRuns on
macOSApple silicon and Intel
Windowsx64 and Arm64
Linuxx64

Your tools, your agent, side by side

Uno Platform Studio Desktop has its own window and its own place in the Dock or taskbar. Cmd+Tab or Alt+Tab takes you between Uno Platform Studio, your IDE, and your agent like any other application, with no hunting through browser tabs.

Uno Platform Studio: the shared workbench for developers and AI agents.

A machine that’s ready to build

Uno Platform Studio Desktop sets up your machine for you, so you can spend your time building instead of chasing dependencies. Every time Studio starts, it runs uno-check and shows anything missing from your setup, from the .NET SDK to workloads and Uno Platform templates. You fix it all from Uno Platform Studio before your first build, without opening a terminal, so you and your agent start every project from a working environment.

The Environment page runs the uno-check checks on start and fixes what is missing, without leaving Uno Platform Studio.

A shout-out to Jonathan Dick, whose .NET MAUI Sherpa inspired us to build a similar experience into Uno Platform Studio.

Checkpoints: undo for your agent

Experiment freely with your agent, knowing you can always get back to a version that worked. Pick any earlier instruction in your conversation and roll your project back to that point, with the code and the conversation restored together. If a direction doesn’t pan out, you return to a known good state in one step instead of undoing changes file by file.

Uno Platform Studio Desktop with the UnoSales app in preview and the Agent panel open. Each instruction carries a timestamp and a restore control; a Checkpoints callout reads: step back to any step, instantly.
Every instruction is a checkpoint. Restoring one brings back the code and the conversation as they were at that moment.

Previews: see every variant at a glance

Uno Platform Studio 3.3 also improves how you preview your application. To make it easier to validate what you or your AI agent have built, the new Preview Variants show your application in both form factors and in light and dark themes, so you can inspect how it looks across layouts and themes at a glance.

This is the change from 3.2 to 3.3:

Studio 3.3 · preview variantsStudio 3.3 with the Previews panel open: the CartPage shown four times, as Phone Light, Desktop Light, Phone Dark, and Desktop Dark, with the Agent panel on the right.
Studio 3.2 · one preview per pageStudio 3.2 Previews panel: a list of pages, each shown once in a single theme.
Drag to compare. In 3.2, one preview per page. In 3.3, each page opens into its variants: phone and desktop, light and dark.

Uno Platform Studio Mobile

Validate on real devices

Simulators and browser previews only get you so far. Touch targets, safe areas, scrolling, the on-screen keyboard, fonts, and performance all behave differently on a real phone.

Uno Platform Studio Mobile puts a real device in your development loop. Sign in with your Uno Platform Studio account, open your project on your phone or tablet, and keep working wherever you prefer: Uno Platform Studio, your IDE, or the CLI. Every change Hot Reloads to the device over the air. No rebuild, no redeploy.

Open the project in Uno Platform Studio Mobile and every change made on the desktop Hot Reloads to the phone over the air.

Your phone becomes part of how you check your work instead of a final step before release. When you or your agent changes something, you see it on real hardware seconds later.

Uno Platform Studio Mobile is available now for testing on iOS and Android.

Try Uno Platform Studio on iOS and Android

  1. Download Uno Platform Studio Mobile for iOS or Android and sign in with your Uno Platform account, or create one to get started.
  2. Explore the Gallery to run sample applications full screen and experience Uno Platform firsthand, no coding required.
  3. Preview your own project by opening an existing project or creating one at platform.uno, then opening it from the mobile app.
  4. See changes live. Keep working in Uno Platform Studio Web or Desktop, and your updates appear over the air on your mobile device, without rebuilding or reinstalling.

Whether you’re exploring samples or building your own app, you can see and test the experience directly on a real device.

Theme Editor

Make your application’s look and feel your own

With the new Theme Editor in Uno Platform Studio, you shape your application’s entire visual identity from one place. Customize colors, typography, shapes, and spacing, and see each change reflected instantly across the running application, without hand-editing XAML or rebuilding.

Theme editing is available in Studio on Pro and Business plans. Learn more about theme editing.

Theme Editor: one change to corner radius, spacing, or color updates every control, in light and dark, on the running application.

Theme Editor works at the level of your whole design system. Instead of maintaining that system by hand across XAML resources, you shape it visually, with immediate feedback on every screen it touches.

It builds on the Themes workspace introduced in Uno Platform Studio 3.2, which lets you visualize and inspect your design system across controls, colors, and typography in light and dark modes. Theme Editor takes you from seeing your design system to actively shaping it. Make a change, check it in both modes and across your controls, and refine it until it’s right.

Whether you’re aligning an application with your brand, exploring a new visual direction, or fine-tuning the details, Theme Editor keeps your look and feel consistent across the entire application. And because every change is saved directly to your application’s XAML as production-ready code you own, what you design is what you ship.

Start building with Uno Platform Studio 3.3

Your IDE. Your AI agents. Your workflow. Now with Uno Platform Studio alongside them. Whether you prefer working on your desktop or starting in the browser, Uno Platform Studio 3.3 brings developers and AI agents together to create, collaborate, validate, and ship cross-platform .NET applications faster, without giving up control of the code.

Get Uno Platform Studio 3.3

Download Uno Platform Studio Desktop for Windows, macOS, or Linux, or launch Uno Platform Studio Web and start building in your browser.

And when you’re ready to take your development workflow further, now is a good time to upgrade: get 25% off Uno Platform Studio Pro or Business while our limited-time offer lasts. Get 25% off Uno Platform Studio.

The post Uno Platform Studio 3.3: Your AI Workbench, Now on Desktop and Mobile appeared first on Uno Platform.

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