MSN Virtual Earth (using ATLAS)

-
For those who attended PDC 2005, there was a discussion of ATLAS and how powerful of a tool it is. To further demonstrate its capabilities, Microsoft created virtualearth.msn.com, a very cool map site. To further explain how the site was put together, check out http://weblogs.asp.net/scottgu/archive/2005/09/14/425131.aspx. The ATLAS add-on toolkit will be available for Visual Studio.Net 2005 sometime in 2006.

Comments

Post a Comment

Popular posts from this blog

.NET MAUI with Blazor vs. XAML

-
Image
  What's better for mobile development with .NET Maui, Blazor or XAML? Each has its pros and cons.  Listed below is a brief comparison of the 2 options: Maui with Blazor : Web Tech, Shared Logic Best for: Web developers or teams already using Blazor for web apps who want to reuse components and logic. Pros: - Write UI in Razor syntax (HTML + C#) - Share components across web and mobile - Easier onboarding for web devs - Great for internal tools or hybrid apps Cons: - Slight performance overhead compared to native XAML - Limited access to some native features (though improving) - Smaller ecosystem for mobile-specific Blazor components Maui with XAML : Native Feel, Rich Control Best for: Developers with WPF, UWP, or Xamarin.Forms experience, or those wanting full control over native UI. Pros: - Deep integration with MAUI’s native controls - Rich styling and layout capabilities - More mature tooling and community support for mobile-specific features - Better performance for compl...
Read more

Deployment Types in AI Foundry

-
Deploying a model in Azure AI Foundry can be done in 9 different ways.  Depending on the type of deployment chosen, it may impact one of more factors, such as cost, latency, efficiency for processing large datasets, compliance.  Listed below is a description of each deployment type, along with advantages and disadvantages.  For more details, please visit https://learn.microsoft.com/en-us/azure/ai-foundry/foundry-models/concepts/deployment-types Deployment Type Description Advantage Disadvantage Global Standard Shared global infrastructure for general-purpose model inference. Cost-effective and easy to scale. Performance may vary under high demand. Global Provisioned Dedicated global infrastructure for consistent performance. Reliable throughput and latency. Higher cost due to dedicated resources. Global Batch Asynchronous globa...
Read more

AI Agents vs. Agentic AI

-
AI agents and agentic AI are related but not the same. AI agents are task oriented systems built around LLMs, while agentic AI refers to a broader paradigm where AI systems exhibit autonomy, goal directed behavior, and self improving capabilities.   AI Agents vs. Agentic AI AI Agents: perform tasks but do not necessarily set their own goals. •             Modular systems built around LLMs or LIMs. •             Designed for narrow, task specific automation. •             Operate through tool integration, prompt engineering, and workflow orchestration.   Examples: ·                   A customer service chatbot ·              ...
Read more