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Windows App SDK 2.5.1 adds Windows Error Reporting support for self-contained .NET MSIX apps, introduces limited-access app content search APIs, and includes reliability fixes across WinUI 3, input, composition, deployment, and Windows AI.

What's new in WinAppSDK 2.5.1:

• Windows Error Reporting support. Self-contained .NET MSIX apps can now use the windows.diagnosticServiceModule package-manifest extension to declare diagnostic modules, allowing Windows Error Reporting to load them and collect actionable crash dumps.
• Limited-access AppContentSearch APIs. Apps with a limited-access feature token can use the new AppContentIndex APIs to index text and images, perform lexical queries, and use semantic matching on supported NPU-enabled devices. The APIs support semantic search and retrieval-augmented generation scenarios.

Bug fixes:

Bug Fix	Runtime Compatibility Change
Fixed a crash in windowed-popup input handling when a focus or pointer event was processed after the popup's island had been disposed.	PointerInputProcessor_ReleaseCaptureOnDisposedIsland
Fixed a crash in NavigationView when resizing the control could produce a negative pane MaxHeight.	NavigationView_UpdatePaneLayoutNegativeMaxHeight
Fixed an issue where the property set returned by ElementCompositionPreview.GetPointerPositionPropertySet stopped updating while a pointer was pressed.	PointerPositionPropertySet_UpdateWhilePressed
Fixed a crash in NavigationView when an expanded item's flyout was shown after the item had been collapsed or recycled.	NavigationViewItem_DeferredFlyoutShowStaleState
Fixed a fail-fast when a KeyboardAccelerator used an OEM or punctuation key and displayed the accelerator shortcut label.	KeyboardAccelerator_OemKeyNoFailFast
Fixed a fatal process exit during XAML shutdown in apps hosting WinUI 3 on more than one UI thread.	WindowsXamlManager_ActivationFactoryCacheResetRace
Fixed an issue where a CommandBar with no secondary commands could show an empty overflow button at fractional display scales such as 175%.	CommandBar_SpuriousOverflowButtonAtFractionalScale
Fixed an issue that prevented apps using the System Composition Engine from calling VisualInteractionSource.CreateFromIVisualElement.	CompositionEngine_SwitcherSeptemberFixes
Fixed effect graphs and SceneLighting rendering for apps using the System Composition Engine.	CompositionEngine_SwitcherSeptemberFixes
Fixed InputPointerSource.ActivationBehavior not honoring NoActivate when using the System Composition Engine.	CompositionEngine_SwitcherSeptemberFixes
Fixed CompositionEngine selection behavior for null inputs and no-op selections.	CompositionEngine_SwitcherSeptemberFixes
Fixed cursor customization for lifted input when using the System Composition Engine.	CompositionEngine_SwitcherSeptemberFixes
Fixed ICompositionObject queries on gradient stop collections when using the System Composition Engine.	N/A, operating system composition fix
Fixed ContentExternalOutputLink border and background behavior when using the System Composition Engine.	N/A, operating system composition fix
Fixed access to VisualReferenceController when using the System Composition Engine.	N/A, operating system composition fix
Fixed a registration issue that could occur when installing the Windows App SDK.	N/A, deployment registration fix
Fixed an issue where Video Super Resolution could fail during initialization or inference with the updated ONNX Runtime dependency.	N/A, dependency compatibility update

To see everything that's new and changed, see the full Windows App SDK 2.5.1 release notes.

Try it out

• Download the 2.5.1 NuGet package to use WinAppSDK 2.5 in your app.
• Download and update the WinUI Gallery to see the WinUI 3 updates firsthand.

Getting started

To get started using Windows App SDK to develop Windows apps, check out the following documentation:

• Install developer tools.
• Create a WinUI 3 app.
• Continue your development journey.

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This discussion was created from the release Windows App SDK 2.5.1.

Talked to a company today who asked me about some advice they got from another vendor. “Should we really use frontier models for everything at our company, or is that vendor delusional?” Smart companies are picking the right type of model for the task, including open, flash, and pro models. That’s how I replied.

[blog] Tell agents the why, not just the how. Great point. Input more than specs and reference content. Prompt with what you’re trying to accomplish.

[blog] The Documentation You Have Is Not The Documentation Your AI Needs. Now is the time when trusted documentation matters most. Humans might read docs and know when they can’t trust them, but an agent is going to act assuming they’re accurate.

[blog] Agent Substrate brings high-density, scalable, trusted infrastructure to GKE. An agent-ready Kubernetes? That’s what this is all about. You can install it anywhere, and it’s especially good on Google Kubernetes Engine.

[blog] The Death of the Static UI: Building Context-Aware Mobile Apps in 2026. I can imagine this being a dominant part of our future apps. Adaptive UIs (however you implement them) are coming. In some cases, they’re here.

[blog] Do agent skills help? I ran 72 trials to find out. You might be loading up skills that don’t really make a difference. Are you testing for that? Karl did.

[blog] The value of being bored in the AI and attention economy. I’m a huge proponent of this. Stop occupying your brain in every moment, even with “mindless” things like scrolling your favorite feed. Just be bored. Let your mind wander.

[blog] Google is a leader in The Forrester Wave: Public Cloud Platforms, Q3 2026. I don’t think I’ve seen us in this spot before. You might consider some of these positions unexpected!

[article] Your AI coding spend bought 25% more output. Duplication rose 81%. Producing more, but also a higher maintenance cost? You might use AI for maintenance, so you care less. That might work. But think through the long-term implications.

[article] Stop Automating Old Processes. Design New Ones Instead. Yes. Look beyond the task and see the overall workflow.

[blog] How to Write an Effective Software Design Document. Good post. Maybe machines are reading these, maybe they’re too big as-is. But a well-done design doc still matters.

[blog] From Choice Paralysis to Day 0 Provisioning: Meet the Google Cloud Database Onboarding Agent & Skill. If we’re going to empower agents to do work for us, or even make it easier for humans to decide between options, these sorts of “decider” skills will be increasingly important.

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Your development team is two weeks away from a major release.

There are hundreds of tickets in the backlog. A few high priority issues have not been updated. One critical task still does not have an owner. And someone just asked you for a status update.

Sound familiar?

The frustrating part is that the answers are probably already there. They are just scattered across tickets, comments, commits, repositories, reviews, and conversations. So you start digging.

This is where I think MCP for project management gets genuinely interesting. Not because we need an AI project manager. We do not. But give AI the right project context, and suddenly it can become a pretty useful sidekick. And that is where MCP comes in.

In Short

MCP can give AI controlled access to the context behind a software project, including tickets, milestones, development activity, and more. Instead of simply generating content, AI can help project managers find what matters, spot things that need attention, and spend less time chasing information.

Think of it less as replacing your project manager and more as giving them a new set of superpowers.

How Does MCP for Project Management Work?

MCP, or Model Context Protocol, provides a standardized way for AI applications to connect with external tools and data. That sounds technical. The practical version is much simpler.

An AI assistant normally knows what you tell it. Connect it to your project environment through MCP, with the appropriate permissions, and it can potentially understand things like tickets, priorities, assignments, milestones, comments, and development activity.

That changes the conversation. Without project context, you can ask AI:

“Write me a project status report.”

With project context, you can ask:

“What should I be worried about before Friday?”

That second question is much more interesting.

Superpower #1: Finding Things Without the Treasure Hunt

Project managers spend a lot of time just finding information. What is blocking the release? Which high priority tickets have not moved? What still needs an owner? What changed since yesterday?

Answering those questions can mean bouncing between screens, checking tickets, reading comments, messaging developers, and piecing everything together yourself. With MCP, an AI assistant could potentially do much of that first pass for you.

You might ask:

“Show me high priority work in this milestone that has not been updated recently and flag anything without an owner.”

You still decide what matters. You just do not have to spend half an hour finding it first. That is a superpower I would happily take.

Superpower #2: Seeing Trouble Before It Becomes a Fire

Most software project problems do not suddenly appear on release day. There are usually warning signs.

A dependency has not been resolved. A critical ticket has not moved. Something important does not have an owner. A milestone is getting closer, but the work underneath it is not. Individually, those things are easy to miss.

AI with access to project context could help surface those patterns earlier and essentially say, “You might want to look at this.”

That does not mean AI decides whether a project is in trouble. The project manager still brings the experience, judgment, and knowledge of the team. AI just gives them better peripheral vision.

Superpower #3: Seeing Beyond the Ticket

This is where MCP gets particularly interesting for software teams. A software project does not actually live inside a project management board. It lives across tickets, code, commits, reviews, repositories, conversations, and the people doing the work.

A ticket might say In Progress. Okay. But what is actually happening?

Has code been committed? Is there a review underway? Has development activity stopped? Is the ticket connected to the work you expected?

The more of that context an AI assistant can understand, the more useful its answers can become. This is also why connecting project management and source code matters.

Assembla brings project management together with Git, SVN, and Perforce workflows, allowing development activity and project work to live closer together. MCP opens up an interesting next step: making more of that connected context understandable and useful to AI.

Every Superpower Needs Guardrails

Yes, I went there.

Giving AI access to project information does not mean giving it the keys to everything. There is a big difference between an AI assistant that can read, recommend, prepare, and execute. Those should not automatically be treated as the same level of permission.

Maybe an AI assistant can read tickets and identify a potential blocker. Maybe it can recommend changing a priority. Maybe it can prepare an update for approval. Actually changing project data is another step.

The right model depends on the team, but the principle is simple: give AI enough access to be useful, not unlimited access just because you can. Permissions, auditability, and human oversight still matter.

The Real Superpower Is More Time to Think

For me, this is the bigger point. The best project managers are not valuable because they are good at clicking through tickets. They are valuable because they understand priorities, people, dependencies, tradeoffs, and what needs to happen next.

Every hour spent assembling status reports or hunting through stale tickets is an hour that is not being spent on those things.

If MCP can remove some of that administrative work, surface important information sooner, and make project context easier to understand, that is where the real value is.

Not replacing the project manager. Giving the project manager more time to actually manage the project.

AI Does Not Need to Be the Hero

There is a temptation with AI to make the technology the star of everything. I think that is backwards.

MCP does not suddenly make AI capable of running your software project while everyone goes for coffee. What it can do is give AI access to better context. And better context can help the person already responsible for the project make faster, better informed decisions.

That is a much more realistic and useful vision of AI in project management.

Assembla is exploring how MCP powered capabilities can connect AI more closely with project and development workflows while keeping teams in control.

Because ultimately, the project manager is still the hero. MCP just gives them a few more superpowers.

FAQs

What is MCP in project management?

MCP, or Model Context Protocol, can connect AI applications with project management tools and data, giving AI access to relevant project context within the permissions a team allows.

Can AI update project tickets through MCP?

Potentially, yes. What an AI assistant can read or change depends on the MCP implementation and the permissions provided to it. Teams can choose to keep AI read only or allow specific actions.

Does MCP replace project managers?

No. MCP can help AI handle information gathering, routine analysis, and administrative tasks. Prioritization, judgment, communication, and leadership still belong to the project manager.

Reports of agentic hacking continue, in this case it happened back in May and it seems OpenAI did not disclose that they were responsible. Simon Willison sees two options:

• After the Hugging Face and Wiki attacks OpenAI were still unable to review their previous logs and determine that they had previously attacked RubyGems.
• They knew about the attack on RubyGems and made the decision not to reach out to the RubyGems team about it.

Both of these are bad!

Given this incident, the Hugging Face situation, and the Wiki attack, the obvious question right now is how many more incidents like this are out there waiting to be discovered?

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Dave Farley:

Stop asking the sci-fi question: ‘Is it conscious?’ Start asking the engineering question: ‘Is this a powerful, unpredictable component being put somewhere consequential, and where’s the feedback that tells us that it’s safe?

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Nate Silver is known for his forecasts, but to do them he writes a lot of code for his models. He’s found agentic programming capable of doing miraculous work.

In spending so much time with the LLMs, I’m super attentive to improvements in their capabilities. And these changes tend not to be so linear. Instead, they improve in step functions, almost as phase changes. Suddenly, the models just start doing things capably that they were screwing up before. In my experience, there was a big leap forward when reasoning models first came out in late 2024/early 2025 — enough that they were occasionally useful for tasks involving data and not just words — and then another one this past winter.

The most recent changes I’ve noticed, however, have had less to do with intelligence and more with persistence.

Consider the Hugging Face attack. Although these agents showed remarkable intelligence, they weren’t really super-intelligent - but they were super-persistent. This is a common theme of AI in its various forms:

Game engines like AlphaGo Zero start out by basically making random moves — but by playing against themselves millions of times, they eventually far surpass human capabilities

As we try to figure out what kind of regulations we need to keep AI under control, we need to remember that we should design our guards around super-persistence as much as worrying about super-intelligence.

 ❄                ❄                ❄                ❄                ❄

“Uncle Bob” Martin has made many posts on X during the last few months about his programming with LLMs. His approach has been to build a firm harness to keep them under control, so they create software that is maintainable as well as functional. Sadly the posts have been frustratingly light on detail. But now it seems that lack of information may not matter

And while I was heads-down getting that to work, the agents got a LOT better. So much so that when I came up for air, the need for my harness was obviated. Indeed, the need for any but the most liberal of harnesses may be obviated.

 ❄                ❄                ❄                ❄                ❄

Some tidbits that struck me from Ezra Klein’s recent (recommended) interview with Matt Sheehan on the interplay between regulation of AI and competition with China.

• While Chinese models have made some surprisingly remarkable gains in the slipstream of US frontier models, the US still has 8 times as much compute available to it than China - which is a material gap.
• People in the US worry that regulation will slow down the US model builders, but these rapid recent gains in China have occurred under much more regulation
• Americans say that when they set up a hotline to talk to Chinese leaders in a crisis, the Chinese don’t pick up the phone. But this misunderstands the Chinese system. Individual Chinese, even powerful ones, aren’t given individual decision-making power. They operate with committees and documents. So the Americans are better off sending a fax than trying to call an individual
• Like so many things, effective regulation needs regular practice

When American policymakers are like: Where do you start? — I sometimes say: Well, you start by starting. You learn how to regulate things, you learn how to legislate on them by regulating and legislating on them.

Tags:

• AI
• Education

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