Microsoft recently began rolling out its Low Latency Profile CPU Boost technology under the generic label of general performance improvements in the Windows 11 KB5089573 optional update for Windows 11. It works entirely in the background to dynamically spike your processor frequency when you interact with the Start menu, Windows Search, and Action Center.

However, there is no user-facing toggle in the Settings application (which I don’t think it should have in the first place), and there is no notification informing you that the system is active.

Also, if you are running a relatively powerful desktop or a modern premium laptop, you likely will not notice a jaw-dropping change in core Windows shell experiences because your hardware was already fast to begin with.

After enabling Low Latency Profile:

So, how would you know if your PC has Low Latency Profile enabled? Here is a simple way to verify if the hidden CP Boost feature is successfully running on your Windows 11 computer.

Check CPU usage before installing the May 2026 Optional Update

If you haven’t installed the May Windows 11 Optional Update already, you can have a more “scientific” testing.

Open the Task Manager, go to the Performance section, and check the CPU usage while opening the Start menu, Search, and Action Center. Since Microsoft also mentioned “app launches” in the KB5089573 changelog, try opening a few inbox apps as well, like Outlook and Edge.

“[General Performance] This update accelerates app launch and core shell experiences such as Start menu, Search, and Action Center.”

But when I force-enabled Low Latency Profile on my PC, I noticed that the Task Manager CPU usage may not show a CPU spike, as its polling rate is too slow to catch brief microsecond frequency spikes.

So, I recommend that you install the highly reliable HWiNFO software to check CPU usage. Go to the official HWiNFO website and download the free, lightweight hardware analysis utility.

Once downloaded, launch HWiNFO. You can either choose Full mode or Summar-only and click Start.

Normally, you wouldn’t see any CPU spike while opening the Start menu or system flyouts. You may see a brief CPU spike while launching some apps, but Low Latency Profile is mainly for core Windows experiences as of now. This serves as our baseline for comparison.

We know that many of you skip the optional update, but if you’re interested in checking out Low Latency Profile, and you haven’t downloaded the update yet, here’s your cue:

Install the May 2026 Optional Update and check CPU usage

Open Settings > Windows Update > Advanced options > Optional updates. In the Optional updates available area, you’ll find the KB5089573 update.

If you decide to skip this preview patch, you’ll get Low Latency Profile in about two weeks when Microsoft bundles it broadly into the mandatory June 2026 Patch Tuesday security release.

Check for Controlled Feature Rollout restrictions

Once your PC reboots with the latest update, open HWiNFO and keep the monitoring tool running to one side of your screen while you open the Start menu, trigger the Windows Search bar, and pull out the Action Center flyout panel.

Here, I have installed the May 2026 optional update, but the core Windows shell experience doesn’t trigger peak CPU usage. I noticed a spike while opening Microsoft Store, but I suspect that it’s due to the app loading its interface. Also, Store launching wasn’t a quick experience.

Essentially, I’m among the vast majority of users who have installed the update, but Low Latency Profile is disabled due to Microsoft’s policy of gradual release to ensure stability (also called CFR).

If you’re among the lucky few who see CPU spikes and, most importantly, a faster or smoother Start menu and other flyouts, then congratulations, you have got Low Latency Profile.

If you don’t notice a CPU spike or faster core experiences, you can easily bypass Microsoft’s deployment queue. As we detailed in our guide on how to turn on Low Latency Profile in Windows 11, you can open an administrative Command Prompt, navigate to your local ViveTool folder, and execute the command vivetool /enable /id:58989092 to force enable Low Latency Profile.

How to test if Low Latency Profile CPU boost is active on your PC

After forcing the feature on via ViveTool and restarting your machine, you can finally verify if the configuration was successful. Open HWiNFO upon landing back on your desktop. You must wait a few minutes for your background startup applications to settle down, as they cause their own CPU spikes.

Once the CPU usage numbers drop back down to a normal, idle state, begin triggering the core Windows 11 shell experiences. Open the Start menu, click into the Windows Search box, and click your taskbar clock to open the Action Center.

If the Low Latency Profile is successfully running, you will observe two simultaneous confirmation signals.

1. First, and most importantly, you will feel a distinct smoothness across the interface, with the shell menus popping up instantly without the usual rendering lag.
2. Second, your HWiNFO monitoring panel will log an immediate, sharp frequency jump across your active CPU cores every single time you open those specific menus.

Start menu after enabling Low Latency Profile:

Windows Search opens faster after enabling Low Latency Profile:

Action Center is buttery smooth after enabling Low Latency Profile:

As I said before, if your PC is already fast enough, you’ll only notice smoother loading of these core experiences, and hence the relevance of CPU monitoring.

Low Latency Profile behaves differently depending on your system specifications

Earlier, when I tested Windows 11’s hidden Low Latency Profile in a low-powered VM running Insider builds, it yielded immediate, dramatic results.

The hardware response was obvious because the Task Manager would show a massive, sudden CPU spike every single time the system layer was engaged. CPU Boost worked while opening apps, too. Edge, Microsoft Store, and Outlook saw higher CPU utilization in the Task Manager when opened, and lasted for 1 to 2 seconds.

However, when I forced the configuration on my primary daily-driver PC, instead of raw speed, what I noticed was smoothness. I was almost used to the micro-stuttering of the Start menu. So, seeing it glide up smoothly caught me off guard.

More powerful machines may see an even more fluid Start menu and system flyouts, as they were already quick enough. Either way, I like that my PC feels more premium now.

CPU boost is a necessary step forward while Microsoft cleans up its code

There is still an immense amount of unnecessary social media backlash surrounding Low Latency Profile CPU scheduling technique. Many users still continue to dismiss the tool as an unoptimized shortcut.

However, as we previously reported, engineering leads like Scott Hanselman have correctly pointed out that hardware-level CPU boosting is a standard industry practice that maximizes UI responsiveness.

That said, Windows 11 oftentimes feels slower than legacy operating systems like Windows 7 because modern shell elements are bogged down by web-wrapped slop and ad-filled frameworks.

I truly hope that Microsoft continues its commitment to rewriting the Start menu and taskbar using native, lightweight code. Until those foundational software overhauls are complete, the criticisms will continue.

Either way, as of now, Low Latency Profile is a good feature that makes some Windows 11 experiences feel as snappy and responsive as they should be.

The post What actually happens to your CPU when Windows 11’s Low Latency Profile is working appeared first on Windows Latest

An anonymous reader quotes a report from Ars Technica: Now sites have a new way to spy on their visitors: measuring subtle interactions with their solid-state drives. The technique, named FROST (fingerprinting remotely using OPFS-based SSD timing), allows sites to monitor other sites a visitor is viewing and what apps are open on their devices. The technique, laid out in a research paper (PDF), exploits a side channel, a form of leak resulting from physical manifestations such as electromagnetic emanations, data caches, or the time required to complete a task. By measuring the manifestations, attackers can decrypt encrypted traffic and infer other confidential data. The attack that FROST uses is known as a contention side channel, which measures the interaction of various processes all using (or competing for) a given resource. By measuring the timing of certain I/O (input-output) operations of the SSD a visitor is using, the researchers were able to determine the websites open in other tabs -- even on other browsers -- and the apps that were open on the visitor's device. FROST requires no interaction from the visitor other than opening the site hosting the attack. [...] Unlike previous contention side-channel attacks on SSDs, FROST runs exclusively in the browser. It uses JavaScript that interacts with the OPFS (origin private file system), an allocated storage space that's reserved for a specific site to run code needed to complete a given task. Websites can create one with no interaction required by the visitor. While each file system is sandboxed, meaning it's isolated from other websites and from the device system itself, the JavaScript can measure the I/O interactions. Then, by running those interactions through a pretrained convolutional neural network -- a system that uses deep learning to analyze text, audio, and images -- the attacker can deduce various apps and websites open on the device. "The attacker continuously measures SSD contention by performing random reads from a large OPFS file," the researchers explained. "SSD contention caused by user activity causes measurable latency differences for these read operations. By training a convolutional neural network (CNN) on these traces, the attacker can fingerprint user activity on the host system by classifying new traces using the trained model."

Read more of this story at Slashdot.

So far this year, authorities have seized or demanded ballots from elections in four states. Experts fear the trend could throw the midterms into chaos unless courts draw a line.

In my recent experience as a Senior Consultant at Microsoft, I’ve been actively involved in designing and delivering AI-driven solutions, with a strong focus on building intelligent agents using modern frameworks.

Along the way, I've built agents using both Microsoft Foundry Agents SDK (hereafter "Agents SDK") and Microsoft Agent Framework (MAF)

Both approaches are powerful and capable. However, once you move beyond simple proofs of concept, the developer experience and architectural patterns start to differ significantly.

This article provides a practical comparison based on real implementation experience and aims to help developers choose the right approach.

Approach 1: Agents SDK

Agents SDK provides a straightforward way to create agents with integrated tools and models.

Example: Creating an Agent

from azure.ai.projects import AIProjectClient

from azure.ai.agents.models import AzureAISearchTool, AzureAISearchQueryType

from azure.identity import DefaultAzureCredential

 

client = AIProjectClient(credential=DefaultAzureCredential(), endpoint=os.getenv("AZURE_AI_PROJECT_ENDPOINT"))

 

# Configure tools

ai_search = AzureAISearchTool(

    index_connection_id=conn_id,

    index_name="my-index",

    query_type=AzureAISearchQueryType.SEMANTIC,

)

 

# Create agent (persisted in Foundry portal)

agent = client.agents.create_agent(

    model=os.getenv("AZURE_AI_AGENT_DEPLOYMENT_NAME"),

    name="MyAgent",

    instructions="You are a helpful assistant.",

    tool_resources=ai_search.resources,

    tools=ai_search.definitions,

)

 

# Run conversation

thread = client.agents.threads.create()

client.agents.messages.create(thread_id=thread.id, role="user", content="Hello")

run = client.agents.runs.create(thread_id=thread.id, agent_id=agent.id)

What this approach provides

• Native integration with Azure AI services (OpenAI, AI Search, MCP)
• Managed execution environment
• Simple and quick agent setup

Conceptually, this approach can be summarized as:

Model + Tools + Execution

Strengths

• ✅ Rapid development and onboarding
• ✅ Strong integration within the Azure ecosystem
• ✅ Well-suited for single-agent or tool-driven use cases
• ✅ Minimal infrastructure overhead

Challenges observed in practice

As the complexity of scenarios increases, certain limitations become more visible:

• Multi-agent workflows require custom orchestration logic
• Agent handoffs must be implemented manually
• Context sharing across agents requires additional design effort

While this approach offers flexibility, it shifts orchestration complexity to the developer.

Approach 2: Microsoft Agent Framework (MAF)

Microsoft Agent Framework introduces a higher-level abstraction, focused on agent orchestration and system design.

Creating an Agent

from agent_framework import Agent, WorkflowBuilder, Message

from agent_framework.foundry import FoundryChatClient

from azure.identity import DefaultAzureCredential

 

client = FoundryChatClient(

    project_endpoint=os.getenv("FOUNDRY_PROJECT_ENDPOINT"),

    model=os.getenv("FOUNDRY_MODEL_DEPLOYMENT_NAME"),

    credential=DefaultAzureCredential(),

)

 

# Create agents (in-process only, not persisted in portal)

researcher = Agent(client, name="ResearcherAgent", instructions="Research topics thoroughly.")

writer = Agent(client, name="WriterAgent", instructions="Write concise summaries.")

 

# Build and run multi-agent workflow

workflow = WorkflowBuilder(start_executor=researcher).add_edge(researcher, writer).build()

 

async for event in workflow.run(Message("user", "Summarize migration best practices"), stream=True):

    print(event.content)

What this approach provides

• Built-in orchestration capabilities
• Native support for multi-agent workflows
• Structured agent lifecycle management
• Context and memory handling

Conceptually, this can be viewed as:

Agents + Orchestration + System Design

Observations from implementation

When implementing similar use cases using MAF:

• Agent responsibilities became clearly defined
• Routing and delegation patterns were significantly simplified
• Overall system architecture became easier to maintain and scale

This approach encourages thinking in terms of agent ecosystems rather than isolated agents.

Architecture Comparison

Agents SDK

 

 

 

 

 

 

 

 

Microsoft Agent Framework (MAF)

 

Choosing the Right Approach

Use Agents SDK when:

• You need rapid development for a single-agent use case
• The workflow is relatively straightforward
• You prefer flexibility and lower-level control

Use Microsoft Agent Framework when:

• You are designing multi-agent systems
• Your solution requires routing, delegation, or handoffs
• Long-term scalability and maintainability are essential

Pros and Cons Summary

Agents SDK

Pros

• Easy to get started
• Strong Azure integration
• Flexible design

Cons

• Manual orchestration required
• Limited native multi-agent support
• Complexity increases as scenarios grow

Microsoft Agent Framework (MAF)

Pros

• Built-in orchestration
• Native multi-agent support
• Scalable and structured architecture

Cons

• Learning curve for new developers
• More opinionated framework design
• Reduced low-level control compared to SDK-based approach

References and Repositories

🔗 Microsoft Agent Framework (MAF)

• Microsoft Agent Framework – GitHub Repository
• Microsoft Agent Framework Samples – Tutorials & Examples
• Workflow Samples (Multi-agent patterns)
• FoundryChatClient sample (Python)
• Agent Framework demos - GitHub Source

📘 Documentation

• Microsoft Agent Framework Overview (Microsoft Learn)
• Agent Framework + Microsoft Foundry provider docs

🔗 Azure AI Projects / Agents SDK

• Azure AI Projects SDK – Python (GitHub Source)
• Azure AI Projects Agents (.NET SDK repo)

📘 Documentation

• Azure AI Projects SDK (Python) – Microsoft Learn
• Azure AI Agents SDK – Microsoft Learn

Conclusion

Azure AI Projects and Microsoft Agent Framework both play important roles in the modern agent development landscape.

• Agents SDK enables quick and flexible agent development
• Microsoft Agent Framework enables structured, scalable agent systems

In practice, the choice depends on whether you are building a single agent feature or a multi-agent system.

Final Thought

Agents SDK helps you get started quickly.
Microsoft Agent Framework helps you scale with confidence

In a follow-up blog, I’ll dive into how the M365 Agents SDK compares with Microsoft Agent Framework, especially in the context of enterprise productivity and Copilot experiences.

Copilot agents have been talking the talk—summarizing information, drafting content, and answering questions. But soon they’ll be walking the walk—executing workflows across systems in policy-controlled Cloud PCs. 

With Windows 365 for Agents (now in public preview), you can run AI agents in a secure environment and use natural language to direct them to work across software and complete tasks, such as processing invoices or updating CRM data.

What’s changing?

It may sound like a small shift, but Windows 365 for Agents introduces a fundamentally different runtime model.

For the first time, you’ll be able to automate workflows that live outside APIs across real applications—including legacy and UI-based systems—without giving up enterprise security or control.

Much of today’s work still lives in browsers, desktop apps, and legacy systems—environments that assume intentional, human behavior. But agents behave differently:

• Humans operate intermittently and with judgment
• Agents can operate continuously and at scale

Agents depend on IT-defined boundaries, such as identity, policy, access, and monitoring, to keep execution aligned with intended workflows.

Without boundaries, agents can:

• Access unintended systems
• Act beyond their intended scope
• Amplify small mistakes across workflows

Agents need a dedicated execution space designed for autonomous activity but governed by humans by default.

Windows 365 for Agents introduces the right execution environment

Windows 365 for Agents provides a dedicated Cloud PC environment that lets you define and control agents in various ways:

• Independently and continuously, or on demand
• Under your existing identity, policy, and management controls, such as Microsoft Entra ID and Intune
• As repeatable, multi-step workflows across real applications, including legacy and UI-based systems, within the boundaries you set

Running agents in this controlled environment helps isolate risk and enforce security boundaries so act autonomously while remaining fully governed by your policies and without negatively impacting production systems.

Get started with Windows 365 for Agents

Interested in how this works and what Windows 365 for Agents unlocks for your environment? Read full blog, Windows 365 for Agents: run AI agents on secure cloud PCs, to learn more.

 

A brand new issue of Raspberry Pi Magazine has arrived. Our favourite article within this month’s pages is a little left-field – we fell for all the Pi-related Easter eggs hidden in this build.

We are big fans of the Unix philosophy here at Raspberry Pi Towers: devices should do one thing and one thing well. It’s great that you can have a calculator app on your phone or a laptop, but a dedicated physical calculator somehow imbues the numbers with more gravitas. That’s certainly the case for this 3D printed calculator built by computer scientist Chris Haynes. The Flapulator, as he calls it, is a calculator sporting a 3D printed mechanical flap display, powered by a Raspberry Pi Pico, and featuring the same Gateron KS-33 low-profile mechanical switches as seen on Raspberry Pi 500+.

To be frank, that’s enough for us: any device built in the modern age that uses an obsolete display technology gets our vote, but there’s more to this than meets the eye… or pi. As well a dedicated pi button, all the trigonometric functions only work in radians, and all of the angles used when designing the shell, lid, and body are either some form of pi or a derivative of pi.

Chris told us: “… I have found myself using the Flapulator more often than I expected. For quick and simple calculations, it does add some extra enjoyment that you just can’t get with a digital equivalent. I think we can all appreciate the joy of using a highly tactile device, and the Flapulator does a great job of stimulating all of the senses related to that. There is the satisfying feel of clicking the keyboard, the auditory pleasure in hearing the digits turn, and the visual enjoyment of watching the display update.”

Issue 166 of Raspberry Pi Official Magazine is out now!

If you liked this article, there are many more like it in the latest issue of Raspberry Pi Official Magazine. You can purchase a copy from the Raspberry Pi Store in Cambridge. It’s also available from our online store, which ships around the world. And you can get a digital version via our app on Android or iOS.

You can also subscribe to the print version of our magazine. Not only do we deliver worldwide, but those who sign up for a six- or twelve-month print subscription will receive a FREE Raspberry Pi Pico 2 W!

The post Raspberry Pi Official Magazine presents: Flapulator, the 3D printed calculator appeared first on Raspberry Pi.