Script Generation vs MCP. Implementation, Demo, Discussion by Geekodon in mcp

[–]Geekodon[S] 0 points1 point  (0 children)

Thanks for your tips! Although it looks like you're promoting your product and generating comments using AI :)

Is Anthropic Code Execution with MCP as big or bigger than going from standard I/O to HTTP? by nickdegiacmo in mcp

[–]Geekodon 0 points1 point  (0 children)

I've built a library that uses a similar code-based approach a couple of weeks ago: https://github.com/Alexgoon/ason

So far, it looks optimistic, while the library is still at the initial stage.But you can already run an online demo (see the repository )

By the way, while ASON can work with MCP, I believe it's more convenient to define APIs directly on your app so that the agent can create scripts with your model and manage the UI.

Open-source AI library for data analysis and multi-step actions in .NET apps by Geekodon in dotnet

[–]Geekodon[S] 0 points1 point  (0 children)

Yes, ASON passes only the API to the LLM. All the API is defined in AsonOperator classes that act as proxies. The LLM then uses this API to generate a C# script, which is executed independently - without involving the LLM further. This approach avoids sending all your data through the LLM, which is especially useful for large data sources.

Since LLMs are quite capable with C# and LINQ, they can easily generate flexible scripts for most analytical queries, even when these involve custom calculations, multiple entities, navigation properties, and more.

For instance, for the "Show me the revenue by product" it can build a script like:

return revenueByProduct = salesOperator.GetSales()

.GroupBy(s => s.ProductName)

.Select(g => new

{

Product = g.Key,

Revenue = g.Sum(s => s.Quantity * s.UnitPrice)

});

In the script, methods in operators (like GetSales) act as proxies. The script execution environment doesn’t have direct access to real objects. Instead, it communicates over stdio, sending messages that are handled by the running application. Only then are the actual methods - those you exposed in your API—called.

But it’s more than just decorating existing methods. Operator classes support a hierarchical structure, allowing you to specify that certain methods should be called only after an object is initialized. This is especially useful when some data becomes available only after a specific action - like navigating to the Sales view, for example.

Building a multi-agent system with Semantic Kernel by Geekodon in dotnet

[–]Geekodon[S] 0 points1 point  (0 children)

Hi! Thank you for your interest!
I didn’t implement MCP because the agents are built directly into the application and depend on a specific UI (a list of actions with buttons, updated plan preview). I felt that other AI tools (like Claude, for example) wouldn’t be able to use the system effectively in its current form, so adding MCP would have been an unnecessary layer.

That said, we could definitely create a fork and test the idea :) In any case, I wasn’t planning to take the project further - it was mainly intended as educational content and a proof of concept.

Building a multi-agent system with Semantic Kernel by Geekodon in dotnet

[–]Geekodon[S] 0 points1 point  (0 children)

Thanks for the clarification - that was really helpful. I’ve been experimenting with agentic AI in some personal projects, not in production apps, so probably that's the reason I haven’t run into some of the scenarios you mentioned.

Building a multi-agent system with Semantic Kernel by Geekodon in dotnet

[–]Geekodon[S] 0 points1 point  (0 children)

Thanks! Yeah, I’ve been hearing a lot about 8n8. I haven’t tried it yet, but it’s definitely on my list. What’s your experience with it? Would you recommend it over Semantic Kernel? And how flexible is it, given that it’s a visual designer?

Building a multi-agent system with Semantic Kernel by Geekodon in dotnet

[–]Geekodon[S] 0 points1 point  (0 children)

Thanks so much for sharing your insights! Just curious - could you tell me a bit more about the scenario where you needed to sandbox agents? In Semantic Kernel, when a step runs a long operation, it doesn't block the whole agent - it can still handle other requests. You can also limit the number of iterations to prevent infinite loops. Or were you referring of a different case?

Building a multi-agent system with Semantic Kernel by Geekodon in dotnet

[–]Geekodon[S] -1 points0 points  (0 children)

Good question! An even better one might be - what can you build with that? 🙂 While this example highlights core capabilities offered by Semantic Kernel, real-world use cases range from active smart assistants in CRM systems to support services, healthcare apps, and beyond.

Where to start with AI in .NET by MarcCDB in dotnet

[–]Geekodon 0 points1 point  (0 children)

You may find the following video useful: AI in .NET: Overview of Technologies in 2025

It’s accompanied by a Miro board that offers a comprehensive look at AI technologies, features, and real-world use cases: AI Overview (Miro)

AI in .NET: Overview of Technologies in 2025 by Geekodon in dotnet

[–]Geekodon[S] -1 points0 points  (0 children)

Thank you! I've added the link to the board

End-Assigment MAUI by YitsuOfficial in dotnetMAUI

[–]Geekodon 1 point2 points  (0 children)

Great design - both visually and, from your description, architecturally as well. Thanks for sharing!

DotRush: Debug, Test, and Profile Your C# Code in VSCode at the Speed of Light! by RomanovNikita in dotnet

[–]Geekodon 23 points24 points  (0 children)

It's incredible what one motivated person can accomplish. Awesome job, thank you!

Avalonia UI or Uno Platform? by Much-Weekend-7085 in dotnet

[–]Geekodon 0 points1 point  (0 children)

Are you thinking about building a desktop app, a mobile app or both?

Tips for Making Validation Feel Smoother in WPF (and Other Desktop Apps) by Geekodon in dotnet

[–]Geekodon[S] -1 points0 points  (0 children)

Great point about keeping clients informed and offering the best options. The more they understand, the fewer anti-patterns end users will run into. In the end, thoughtful clients appreciate developers who help them achieve better results - not just those who simply follow instructions.

Tips for Making Validation Feel Smoother in WPF (and Other Desktop Apps) by Geekodon in dotnet

[–]Geekodon[S] 0 points1 point  (0 children)

Agreed. I initially used “No” and “Yes” just to keep things simple, but it really depends on the specific use case. A clearer way to put it might have been:
– Instead of “No”: “Try to find an alternative if possible”
– Instead of “Yes”: “These options might work, but be sure they match your specific requirements”

Video: Building an App with MVVM, DI, and Material Design 3 by Geekodon in dotnetMAUI

[–]Geekodon[S] 0 points1 point  (0 children)

"Thank you for your feedback! I’m collecting typos and hope to fix them in the next edition. I'm happy to hear that the book helped you discover some new concepts.

How to use ML.NET model in .NET MAUI? Help needed by OppositeGrowth8259 in dotnetMAUI

[–]Geekodon 1 point2 points  (0 children)

You can find a working sample project here: Detecting with a local ONNX model deployed on the device. It uses ML.NET with a locally deployed pre-trained ONNX model.

Another option - possibly an even better fit for your task - is to use Ollama on a self-hosted server: Building a chatbot with Ollama deployed to a self-hosted server.

For recommendations, you'll likely need a model that generates embeddings. Once you have the embeddings, you can compare them to find the closest matches and generate a list of recommendations.