At the United Nations General Assembly in New York City, Google.org is committing $4 million to Digital Promise to help equip teachers and faculty with free, practical A…

Googlebook pre-orders begin September 21, and they'll be on shelves October 4.

With tools like a multimodal cursor, smart dictation, and more, Googlebook anticipates your needs to simplify your day.

Google and partners are finally announcing the first laptops under the new Googlebook banner. There are five models from Acer, Asus, Dell, HP, and Lenovo to kick things off. They're available for preorder starting today and arrive in stores on October 4th.

Starting prices range from $899 to $1,299, putting the Googlebooks in the same territory as the MacBook Air and a plethora of Windows laptops. Each Googlebook also comes with 12 months of Google AI Pro with 5TB of cloud storage, along with three-month trials of YouTube Premium, Adobe Photoshop, and CapCut. Google's also promising up to 10 years of updates.

Here's a look at the hardwar …

Read the full story at The Verge.

Chester Ismay, a data science educator and AI consultant, created schedule viewers to keep up with the sports he follows, including the WNBA, NFL, NBA, and Premier League. But he still had to decide which games deserved his attention each week.

In this episode of Zero to Agent in 30 Minutes, Chester built a sports concierge agent to surface the games he should watch. It reads his preferences and current schedules, then sends a weekly summary to his phone. He took the audience through the setup, which combines a prompt file, limited tool permissions, a schedule, and notifications.

How to create your own sports concierge

1. Define your preferences. Chester started with a structured preferences file that identifies the teams he follows and adds context about why he follows them. He built a simple web interface for editing those preferences rather than working directly with the underlying JSON.
2. Give the agent access to current schedule data. His existing sports viewers pull schedule information from sources such as ESPN and store it in repositories on GitHub. A read-schedules tool, run with Node, pulls the latest schedule files and combines them with Chester’s preferences, giving the agent the information it needs without requiring it to search for each game on its own.
3. Write the agent’s policy. Chester spent most of the walkthrough on CONCIERGE.md, the file that defines the agent’s job. The policy sets the goal, identifies the data sources, and defines the rules for deciding which games to recommend. It also specifies how to handle finished tournaments and duplicate matchups, along with the expected output and delivery format. Chester noted that when the output misses the mark, he goes back to the policy and adds more detail to the instructions or adjusts them to better match with the goals of the project.
4. Run the agent with limited permissions. Chester used Claude Code to read the files, apply the policy, and generate the weekly recommendations. He configured permissions so the agent worked only with the files and tools required for the task.
5. Schedule delivery and check the results. Chester used launchd on his Mac to run the concierge every Wednesday and ntfy to send the result to his phone. He checks the recommendations against the underlying schedules and uses tests and multiple data sources to catch errors. Time zone handling required another adjustment. Games could fall on the wrong day when the system defaulted to UTC, so Chester added explicit time zone instructions.

Coming up next

Next week, AI engineer Sajal Sharma gives an agent its own computer in the cloud using services such as E2B and Scrapybara. He’ll demonstrate how sandboxing lets an agent install packages, run code, drive a browser, and control a remote desktop.

Follow along with Zero to Agent in 30 Minutes on Radar, or watch the latest episode on YouTube, Spotify, Apple, or wherever you get your podcasts. If you’re an O’Reilly member, you can watch live. Save your seat.

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Today we published our latest Global AI Diffusion Report. Global AI diffusion continued to climb in the second quarter of 2026. In June, AI usage was 18.8% of the working-age population worldwide, an increase of about 1% from the first quarter of this year. Growth was broad-based: almost every economy saw increased usage.

Notably, the UAE (73.3%) and Singapore (64.3%) continue to top the leaderboard. South Korea again saw the largest absolute change, with an increase of 3.5 percentage points. Saudi Arabia climbed five places from 30th to 25th, the largest rise in the rankings. Japan recorded the largest relative change, growing by 2.2 percentage points, or roughly 10% relative to its Q1 usage of 22.5%. Worldwide, the quarter brought a continued widening of the AI gap between the Global North and South, with usage now at 28.8% in the North and 16.2% in the South.

To track all these trends, we continue to measure AI diffusion as the share of people worldwide ages of 15 and 64 who have used a generative AI product during the reported period. This measure is derived from aggregated and anonymized Microsoft telemetry data and is adjusted to reflect differences in operating system and device market share, internet penetration, and population. Additional details on the methodology are available in our AI Diffusion technical paper.

No single metric is perfect, and this one is no exception. Through the Microsoft AI Economy Institute, we continue to refine how we measure AI diffusion globally, including how adoption varies across countries in ways that best advance priorities such as scientific discovery and productivity gains. For this report, we rely on the strongest cross-country measure available today. Over the past year, there has been a significant rise in new AI tools and models. For our next report, we will expand our measure to include usage of these new tools and expect this update to increase AI user share across almost all economies, with larger projected increases in China.

In addition to our comparison of AI diffusion between the Global North and the Global South, we also find important differences in how AI is being used. Using consumer Microsoft Copilot usage insights, we examined these differences. The most notable difference is a higher share of education and learning use in the Global South.

This report also includes a substantive discussion of open-weight AI models. Open-weight AI model use is increasingly visible through application programming interface (API) usage of AI, where open models have captured a growing share of token volume. This shift could especially benefit the Global South by lowering access costs and enabling adaptation to local languages and needs, although barriers such as infrastructure, connectivity, and skills remain.

Download the latest Global AI Diffusion report and explore the data here.

The post The continued state of global AI diffusion in 2026 appeared first on Microsoft On the Issues.