Dapr, the Distributed Application Runtime, a graduated OSS project within the CNCF with over 700k weekly NuGet downloads, has released a new version. What it means for you:

• Faster workflows, jobs, and reminders: in nightly performance tests against 1.18.0, workflow throughput is up to 40% higher, actor reminder registration p99 latency is 71% lower, and jobs scheduling p99 latency is 33% lower
• Leaner workflow workers: the .NET SDK now get only the new events of each turn, not the full history
• Easier workflow operations: list, inspect, and rerun workflows from the .NET SDK
• Workflow governance: operate on workflows owned by other apps, with WorkflowAccessPolicy deciding which operations each caller may perform, and requires rules that allow an operation only when the caller's history contains the events you list
• More for AI agents: new alpha APIs to store files, search documents, and query vectors, plus a Dapr MCP server
• A simpler control plane: run actor placement from the Scheduler and drop one service

Read the announcement: https://blog.dapr.io/posts/2026/10/22/dapr-v1.19-is-now-available/

I mean, a single individual doesn’t have an infinite supply of tokens, but with the help of the community, I believe it’s possible; I’ve heard of porting projects being completed in record time.

I’ve been using AI extensively for UI development for some time now.

My recent projects have included:

• React SPA + MUI (with MCP)
• Avalonia Windows applications

Based on what I’ve experienced while developing these applications, I’m starting to feel that AI may be able to handle almost every aspect of UI development.

On our side, we mainly use Kiota to expose the server API specifications and contracts.

Even with relatively simple prompts, AI can inspect those API definitions and understand the available endpoints, request and response models, and related structures. From there, it can build a usable UI from scratch.

We also use AGENTS.md and PLAN.md to define architectural guidelines and conventions for areas such as the service layer and ViewModel layer, and have AI work within those rules.

So far, it has been able to handle the vast majority of the work.

We also use AI for server-side development, but I see a significant difference there.

Backend frameworks have much larger specifications, architectural constraints, and more complex business requirements. Because of this, we still rely heavily on human-written and human-reviewed reference implementations, and then have AI generate code based on those established patterns.

Even with this approach, I still see problems occasionally—for example, AI generating queries with O(N)-style access patterns or introducing other performance issues.

UI development, however, feels fundamentally different.

The improvement in productivity has been remarkable.

What surprised me even more was seeing AI directly launch a native application, interact with it, test it, identify problems, modify the code, and test it again.

Watching that entire process was both shocking and, frankly, almost surreal.

It made me wonder:

Will junior frontend developers eventually become unnecessary?

I’m starting to worry that, in the long run, only developers who can design and maintain the core UI architecture, frameworks, components, and development standards will remain essential.

The implementation of ordinary business screens may increasingly become something AI can handle almost entirely on its own.

What do you think?

I’m the developer of TensorSharp, an open-source .NET 10 inference engine for running local GGUF models. It can be embedded in .NET applications or run through a CLI and an OpenAI/Ollama-compatible server.

The project combines managed C# with native accelerator backends. The benchmarks below use native CUDA/GGML execution, including TensorSharp-specific kernels. The performance improvements come from changes to scheduling, data movement, and model execution.

I recently worked on DeepSeek V4.1 Flash 1017 on five NVIDIA A40s, and thought the engineering details might be interesting to people building performance-sensitive .NET applications.

Making the GPUs work concurrently

One of the biggest problems was in the native tensor-parallel execution path.

The model’s routed experts were distributed across GPUs, but execution repeatedly passed through a synchronous, host-driven path. Each layer involved multiple host round trips and synchronization points.

The revised implementation:

• Schedules work concurrently across GPU ranks.
• Uses CUDA events to order dependent operations.
• Overlaps the shared expert’s computation with the routed experts.
• Removes host waits from inside the layer’s execution path.

Cross-GPU transfers still use pinned host-memory staging because peer access was not working correctly on this machine.

In the implementation comparison, this change took tensor-parallel decode from 15.2 to 36.3 tokens/sec after a 2,048-token prompt with full prefill enabled.

For me, this was a useful reminder that distributing work across devices is only the beginning; the synchronization and data-transfer design determine how much concurrency you actually get.

Making native failures recoverable

The reliability work was just as interesting.

Previously, an exception on a native worker could terminate the server. A failure on the coordinating rank could leave the other workers waiting indefinitely at a barrier.

The fixes coordinate error reporting, ensure every rank reaches the required synchronization points, and drain work already issued by a failed layer. Failure-injection tests now check both the failure path and recovery on the next forward pass.

When native workers run inside a .NET application, that lifecycle becomes part of the application’s reliability story.

A small benchmark snapshot

The comparison used the same Unsloth UD-IQ2_XXS GGUF files on the same five-A40 machine, with the Engram lookup table in host memory.

TensorSharp used --tp 5. The llama.cpp baseline was Unsloth’s dsv41-all fork at be836572, using layer splitting; its row/tensor split modes did not successfully run this model in the tested build.

These are real-text server results for approximately 1,800-token prompts, generating 256 tokens with greedy decoding and thinking disabled. Each configuration had a warm-up and three measured requests, with unique prefixes to prevent prefix-cache reuse.

Prompt processing is also called prefill. The first three rows report medians; the DSpark row reports means, with the drafter enabled in both engines. TensorSharp’s generation rows use its default YOCO configuration.

The speed/quality trade-off

YOCO bounded replay reduces prompt-processing work by running selected later layers over only the final 140 positions of eligible text-prefill calls. It is approximate and can be disabled with:

TS_DSV41_YOCO_KEEP=0

The 3.23× prefill result above has this approximation disabled.

On the report’s WikiText-2 evaluation, perplexity—lower is better—was:

• llama.cpp: 2.4697
• TensorSharp, YOCO disabled: 2.4811
• TensorSharp, YOCO enabled: 2.4978

Enabling YOCO increased perplexity by approximately 0.67% relative to TensorSharp’s full-prefill result. With YOCO enabled, perplexity was approximately 1.14% above llama.cpp’s.

That is a measured trade-off on a limited text evaluation, rather than a general percentage change in model capability. Disabling YOCO also does not make the two engines numerically identical.

Scope of the results

These measurements cover one model quantization and one hardware configuration. The llama.cpp server results came from an earlier session on the same machine and showed some unexplained prefill variability.

At measurement time, the TensorSharp build included uncommitted changes, and the raw validation records were also uncommitted. The report records the build details and remaining limitations; the current repository should not be assumed to reproduce every number directly.

The five-GPU setup is specific to this benchmark. TensorSharp also supports smaller models and managed CPU, native CPU, CUDA, Metal, and Vulkan execution paths, with support varying by model.

Links:

• TensorSharp source and getting started
• Full benchmark report, quality measurements, and limitations
• Build instructions and architecture documentation

If you’re integrating local models into .NET applications, do you prefer an in-process engine or a separate inference server? I’d be interested in what drives that choice in practice, especially around deployment, memory ownership, and recovering from native failures.

I am asking here, because I am interested in opinions of .NET developers specifically, because that is where my passion is. Mods: If you think this question doesn't qualify for this sub, please delete this post.

Now that building new features (or even new software products as a whole) has become cheaper then ever, do you think the YAGNI (you ain't gonna need it) principle is still relevant and exercised in practice?

I have a feeling that developers and companies these days think a lot less about whether something is actually needed and if it provides a real benefit, before building things.
The rise of endless AI slop shows that a lot of people don't think twice before building something.
Just prompt it and Claude goes brrrrrr, no matter if there is an actual audience for it.
But these are probably mostly hobbyists and not professional developers, are they?

So what do you and your team do?

Do you still reflect on if something is actually needed or do you just implement each and every feature request and idea?

And how does that affect the maintainability of your code base?
After all, more features means more to maintain and test and more potential for things to break.

In that regard: How do you keep yourself and your teammates from gold plating and over-engineering and premature optimization?
I know a lot of developers tend to be perfectionists (I have been guilty of that myself in the past) and AI makes it easier than ever to fall into that trap.

I built Magpie over the last year or so to solve a lot of my frustrations with the *arr suite of apps.

• Smarter acquisition, better lifecycle management (no more stuck downloads/manual intervention)
• Real user accounts and RBAC
• One app that can manage just about any content type via extensions
• Multinode cluster support
• Many others that I can't think of at the moment

Most functionality is plugin-based, including content types, so it can handle movies, TV, music, books, and whatever else anyone wants to build.

I’ve been running it as my full *arr replacement for months and shaking out bugs along the way, and I think it’s in a really good state.

Plugins also cover media server, download clients, and indexer integrations, as well as metadata discovery. They can be searched and installed directly from the UI. I have several plugins built already and will be adding lots more.

It also supports several deployment models and database backends.

I’d love for people to check it out and let me know what they think.

GitHub

PS, to share a few cool implementation tidbits (to me anyway):

• Plugins are DI-native, so you can override built-in services, add API endpoints, and even package frontend extensions as web components from your .NET code.
• There is a custom MLFQ-style (multilevel feedback queue) job scheduler so long-running background jobs don’t hog resources (although you can change how it works with plugins, like most things).
• It has a custom query language for library queries and external metadata providers.

Hi all,

Happy weekend. With .NET 9 right around the corner and Native AOT maturing, I wanted to share a real-world project I have been developing: **Corvus v1.6**.

It is an open-source server manager and Docker dashboard tailored for homelabs and cheap VPS instances where keeping memory consumption minimal is critical.

### What is under the hood:

* **.NET 9 Native AOT:** Ahead-Of-Time compiled single binary without the JIT runtime.

* **Zero-reflection data layer:** Embedded SQLite (WAL mode) combined with `Dapper.AOT` so that the binary trims cleanly without reflection warnings. (PostgreSQL is also an optional backend).

* **Memory footprint:** Stays under 30 MB of RAM inside the Docker container at steady state.

* **Real-time pipeline:** Uses Server-Sent Events (SSE) and minimal APIs to feed container stats and health signals to a lightweight React SPA.

* **Terminal & Agent:** Built-in web terminal to shell into containers and a lightweight multi-node edge agent.

Building fully AOT-compliant services in ASP.NET Core came with a few hurdles (especially around dynamic serialization and Docker socket stream handling), but the memory and startup time benefits are definitely worth it.

If you want to play with the UI without running anything locally:

- Live Demo: https://demo.usecorvus.me

- Source Code: https://github.com/brhnshn/Corvus

I'd be glad to discuss the Native AOT trimming process, Dapper.AOT quirks, or any architecture questions you might have!

I've been building line-of-business applications in .NET for 20 years, and most projects end up with the same stack: MediatR, AutoMapper or Mapperly, FluentValidation, MassTransit, Hangfire, Serilog, and plenty of glue code to connect them.

They're good libraries. But each solves its own problem, and keeping everything consistent across application layers is still work developers have to write, review, and maintain. Agents make boilerplate cheap to produce, but not free to review, keep consistent, or maintain.

So I started building Pragmatic Design.

One source generator to rule them all.

The idea is to have application modules share the same declarations instead of treating every layer as a separate concern.

You declare entities and operations using attributes. At build time, a unified source generator produces the surrounding C# code: endpoints, permissions, persistence, validation, mapping, OpenAPI and much more.

And that's just the beginning. The ecosystem also includes specifications, messaging and events, background jobs, caching, logging, documents, imaging, storage, and more. The goal is to have these modules work together through shared declarations rather than stitching everything together by hand.

A few design choices:

• Compile-time consistency: contradictory declarations produce build errors.
• Inspectable generated code: ordinary C# you can open in your IDE, debug, and step through.
• AOT-friendly generation: generated wiring avoids reflection. Full AOT support is coming soon, with a native Pragmatic.Sql module addressing the remaining database-side limitations.
• Flexible deployment: the same modules can run in one application or as separate services.

It's currently in alpha, targeting .NET 10 (11 coming soon). APIs are still evolving, and business logic that can't be expressed declaratively remains handwritten.

Repository and sample applications:
https://github.com/pragmatic-design/Pragmatic.Design

I'm looking for developers willing to challenge the approach.

• Does sharing declarations across application layers solve a real problem, or introduce too much coupling?
• What concerns would you have about relying on generated code for this much infrastructure?
• What would stop you from using it in a production project?

Honest criticism is welcome. I'd rather discover the design's weaknesses now than after people depend on it.

Hey everyone,

I'm curious whether other .NET/C# developers have had a similar experience.

I've been a .NET/C# developer for about 15 years, mostly building cross-platform mobile apps - first with Xamarin and now with .NET MAUI. I've noticed that for developers whose experience is mainly in desktop, web or backend development, transitioning into mobile development can be challenging.

If you’ve made that transition, what did you find most challenging? Were there things you had to learn that weren't obvious from your existing .NET experience?

I'm asking because I'm considering creating an online course for .NET/C# developers making that transition. I'm trying to understand whether there's a real need for it. What would you expect a course like that to cover? Or did existing docs, tutorials, or other resources already give you what you needed?

Genuinely interested in your answers!

Cheers,
Andi

For those new to my posts, Blazor Ramp is an accessibility-first, open-source Blazor component library. This post is about the release v1.0.0 of the GoTo component.

So what is GoTo?

The inspiration comes from those of us who started coding in the 90s with VB: you add GoTo markers to a page, and from anywhere you can jump to them. In an interactive Blazor web app, the marker you jump to becomes the active element, so you can carry on tabbing from that point.

Who is it for?

Those familiar with my posts will know I sometimes feel that sighted keyboard-only users, those who don’t use a screen reader, get the sharp end of the stick. When people build accessible sites, a lot of the effort goes into screen reader users, and this group gets forgotten.

A sighted keyboard-only user doesn’t have screen reader software to bring up lists of headings, landmarks or interactive elements, so they generally navigate with the Tab key and use the arrow and page keys to scroll. When a page has a lot of interactive content, getting to the part you want can take tens, or even hundreds, of Tab presses. For some people that’s a minor irritation, and for others it’s a real barrier.

What about skip links?

You’ve probably all used skip links, which let any user, keyboard-only or screen reader, bypass repeated blocks of content. But how many bi-directional ones have you seen? I’ve never seen one. They all go in one direction, so unless you add a second link going the other way, the user has to make a forward-only round trip to get back to the start.

Say you’re diligent and add a return link for every skip link. If you have five destinations and want users to get from any one to any other, that’s 20 links. With ten it’s 90, and it’s getting silly. The maths is n × (n − 1).

Where GoTo comes in

GoTo isn’t a link. It’s a marker that can receive focus. A GoTo Viewer lists the markers, you select one, you land on it, and you can tab on from there. You can bring up the viewer at any time with a configurable shortcut. When it opens, it lists only the markers that aren’t inside hidden containers, so the list changes depending on where you are and what’s visible.

You may ask: why not just list headings and landmarks, like screen readers do (I may do in the future)? A screen reader user will often use headings and landmarks as a place to start reading from. A sighted keyboard-only user’s need is a little different. They can see the headings, and they can scroll and read from anywhere. The problem is reaching interactive, focusable content when there’s a lot of other focusable content in the way.

To be clear, GoTo isn’t a replacement for skip links. It’s a simple way of placing hidden markers on a page so that keyboard-only users can move through the content in whichever direction they choose.

Have a look

I’ve added markers to my documentation site, and over the next few days I’ll put more of them in the places I think are most useful. If you’re curious, take a look at this page, one of the examples for my Data Table, which shows templated columns and row actions: Data Table Templating

For a keyboard-only user on that page, getting to the border controls after the table means tabbing through the filter, across each sortable column header, down through every row action button, and then across the pager to reach the other side.

With GoTo and its viewer, using the default configuration, a user with one hand can press Alt+Z (Option+Z on a Mac) to bring up the viewer. They can then use Tab, or the arrow, Home and End keys, to move through the list of buttons. As each button is focused, its marker scrolls into view. Pressing Enter or Space activates the button, closes the viewer and puts you on the now-visible label, from where you can tab on to the next item.

I started this project at the end of last year with the Live Region service and Announcement History viewer aimed at screen reader users so at long last I've managed to scratch an itch that has been bugging me for a while now and do something for keyboard-only non-screen reader users - hope you or your users find it useful.

GitHub repo: https://github.com/BlazorRamp/Components

Doc site: https://docs.blazorramp.uk

Test site: https://blazorramp.uk

Regards

Paul

Hi, I have just released version 2.0.0 of ResXLocalization, my localization library for Avalonia and WPF (MIT-licensed).

If you want to localize an Avalonia or WPF app using ResX files, this library might be a good fit.
It's one of the most feature complete localization libraries for Avalonia.

GitHub repo: https://github.com/rent-a-developer/ResXLocalization

Quick start

• Create a .resx file:

​

<root>
  <data name="SaveFiles" xml:space="preserve">
      <value>Save Files</value>
  </data>
  <data name="FilesSaved" xml:space="preserve">
      <value>{0} files saved in {1}</value>
  </data>
</root>

• Install the NuGet package ResXLocalization.Avalonia or ResXLocalization.WPF.
• Let it generate the typed resource keys (source generator)
• Localize in XAML:

​

<Button Content="{l:Localize {x:Static res:StringsKeys.SaveFiles}}" />
...
<TextBlock>
  <TextBlock.Text>
    <l:Localize Key="{x:Static res:StringsKeys.FilesSaved}">
      <l:Argument Value="{Binding Count}" />
      <l:Argument Value="{Binding Location}" />
    </l:Localize>
  </TextBlock.Text>
</TextBlock>

• And in code:

​

this.SaveFilesButton.Content = Localizer.Current.Get(StringsKeys.SaveFiles);
this.SavedFilesTextBlock.Text = Localizer.Current.Get(
    StringsKeys.SaveFiles,
    MessageArguments.FromValues(this.model.Count, this.model.Location)
);

• Live switch the language/culture:

​

Localizer.Current.CurrentCulture = new CultureInfo("de-DE");

Main Features

• Live, no-reload language switching
• Type-safe, compile-checked resource keys
• Full native AOT & trim support (Avalonia only, WPF doesn't support native AOT)
• Arguments for localized texts (improved in version 2.0.0)
• ICU MessageFormat support (opt-in, new in version 2.0.0)
• Built in Enum localization
• Good support for multi-resx file scenarios (scoped and search-all lookups)
• MVVM-friendly and Leak-safe
• Good lookup-failure diagnostics
• Small footprint. ResXLocalization.Avalonia: 79.28 KB , ResXLocalization.Core: 84.17 KB, ResXLocalization.MessageFormat (opt-in): 406,18 KB
• Zero dependencies (except for ResXLocalization.MessageFormat, which uses MessageFormat)
• .NET 8 and later, Avalonia 12 and later, WPF

Typed keys mean you can reference resources like objects and get compile time checking.
This avoids having typos in resource names going unnoticed.

When you change Localizer.Current.CurrentCulture it updates all localization bindings in place. If you assign a string directly in code as above, you need to call Get again after changing the culture.

ICU MessageFormat support

The biggest new feature of 2.0.0 is the optional ICU MessageFormat support (thank you livonium for the feature request that sparked the work on 2.0.0!).

For something like “1 file saved” / “5 files saved”, you can use a plural rule in your resource text:

<root>
    <data name="FilesSaved" xml:space="preserve">
        <value>{Count, plural, one {# file saved in {Location}} other {# files saved in {Location}}}</value>
    </data>
</root>

You need to install the NuGet package ResXLocalization.MessageFormat and call Localizer.Current.UseMessageFormat() at startup for this to work.

Then just bind the arguments like this in XAML:

<TextBlock>
  <TextBlock.Text>
    <l:Localize Key="{x:Static res:StringsKeys.FilesSaved}">
      <l:Argument Name="Count" Value="{Binding Count}" />
      <l:Argument Name="Location" Value="{Binding Location}" />
    </l:Localize>
  </TextBlock.Text>
</TextBlock>

The text block is automatically updated when either the count or the language changes.

Or in code like this:

this.SavedFilesTextBlock.Text = Localizer.Current.Get(
    StringsKeys.FilesSaved,
    MessageArguments.FromTuples(
        ("Count", this.model.Count),
        ("Location", this.model.Location)
    )
);
​
// or:
this.SavedFilesTextBlock.Text = Localizer.Current.Get(
    StringsKeys.FilesSaved,
    MessageArguments.FromObject(this.model)
);

Each translation can use its language/culture plural rules.
The ICU message format support is opt-in. If you don't use it, the default formatter (based on string.Format) is used.

Other changes in version 2.0.0

• Typed keys now finally work without a .Designer.cs file.
• Formatting arguments are now placed via <Argument> children of the Localize/LocalizeEnum markup extension, which replaces the old Arg0…Arg8 attached properties, so arguments now also work in styles and templates.
• Resource keys are bindable, so the view model can dynamically decide which resource to use.
• You can change the preview language (for VS XAML Designer/Rider XAML previewer) with DesignTime.Culture. However, ResXLocalization does not work correctly in Rider's WPF previewer due to a number of bugs (there are quite a few) in it. JetBrains has really made quite a few unfortunate design decisions for their previewer. I tried to work around them as far as possible, but it's nowhere near perfect.

If you are upgrading from 1.x, please read the migration guide, because there are quite a few breaking changes to make it all work.

The repository has runnable sample apps (both for Avalonia and WPF) to help you (or an AI gent) get familiar with the usage:
https://github.com/rent-a-developer/ResXLocalization

AI attribution:
I used AI, mainly Claude, for brainstorming, planning, experiments, and parts of the implementation. It helped me save a great deal of time finding good solutions for a couple of quirks of Avalonia and (especially) WPF and the source generator’s MSBuild integration. I reviewed the generated code and I use ResXLocalization in my own projects, so I would not settle for some half-baked vibe coded garbage. Of course, if you don't like projects where AI is involved, then just ignore this project and that's fine too.

I hope this helps someone localizing their app(s)!

If you find this library useful I would appreciate a star on GitHub.
Of course feature request and (reasonable) criticism is welcome too.

It's free and open-source (MIT licensed), with no licensing fees.

MediCore supports:

CQRS (Commands & Queries) , Request/Response handling , Pipeline interceptors , Validation and transaction handling , Notifications , Automatic handler registration

I built this project to simplify CQRS implementation and learn more about the mediator pattern.

I'm still improving it and would love to hear your feedback or suggestions from .NET developers.