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5 Reasons Why Xamarin 3 Is Better Cross-Platform Mobile Development Framework

Xamarin has released Xamarin 3 cross-platform, the newest version of its cross-platform mobile development framework.

Five core reasons to use Xamarin 3 for cross-platform development of mobile applications: xamarin-01

  1. Xamarin Designer for iOS:
    The Xamarin Designer for iOS is a powerful visual designer for iOS, allowing you to quickly lay out sophisticated UIs, intuitively add event handlers, take advantage of auto-layout, and see live previews of custom controls.
    No more gray boxes—you’ll see exactly what your app will look like, right on the design surface. Integrated into both Xamarin Studio and Visual Studio, we think we’ve created the world’s best UI designer for iOS.
  1. Meet Xamarin.Forms:
    Xamarin.Forms is a new library that enables you to build native UIs for iOS, Android and Windows Phone from a single, shared C# codebase.
    It provides more than 40 cross-platform controls and layouts which are mapped to native controls at runtime, which means that your user interfaces are fully native. Delivered as a portable class library, Xamarin.Forms makes it easy to mix and match your shared UI code with the platform-specific user interface APIs Xamarin has always given you.
  1. Major IDE enhancements
  • Massive visual updateXamarin Studio now includes a new welcome screen, hundreds of new icons, improved support for Retina displays, and some nice touches throughout the IDE.
  • Streamlined Visual Studio support – We’ve enhanced and combined our iOS and Android extensions into a single Visual Studio extension, streamlining installation and updates for all users, and improving the build and debugging experience.
  • NuGet – Xamarin 3 includes full support for using NuGet packages in your mobile apps – in Visual Studio or Xamarin Studio – enabling you to take advantage of the many NuGet packages which are are now shipping with Xamarin compatibility.
  • .NET BCL Documentation – Full documentation for the .NET Base Class Libraries (BCL) is now integrated into Xamarin Studio courtesy of our friends at Microsoft.
  • F# Support – Xamarin Studio now ships with built-in support for building iOS and Android apps using the increasingly-popular F# functional programming language.
  1. Improved code sharing:
    Xamarin 3 introduces two great new code sharing techniques for cross-platform apps:
  • Shared Projects
    Shared Projects provide a simple, clean approach to code sharing for cross-platform application developers. Xamarin developers can now use Shared Projects to share code across iOS, Android, and Windows in either Xamarin Studio or Visual Studio.
  • Portable Class Libraries
    Portable Class Libraries are libraries that are consumable across a diverse range of .NET platforms. Xamarin 3 can both produce and consume PCLs from both Xamarin Studio and Visual Studio. Xamarin-Studio-Nuget-1024x700
  1. API integration:
    Xamarin binds the same APIs and UI controls that are used to build iOS, Android and Mac apps in their respective platform specific languages. For Windows development, Xamarin with Microsoft Visual Studio offers Windows Phone and Windows 8 applications. Code can be shared between iOS, Android and Windows using Portable Class Libraries (PCL) and appropriate application architecture.

Quick facts about Xamarin:

  1. Mobile development platforms that span iOS, Android, and Windows without compromising the quality and performance as a rule expected from native apps development.
  2. Xamarin is one of the most cost- and time-efficient tools used for building apps for different operating systems.
  3. Instead of designing an app for each system separately, app developers can share about 75 % of developed code across all major mobile platforms which decreases cost and time-to-market.
  4. Xamarin delivers high performance and excellent UX based on native API.
  5. Ensure seamless integration Xamarin provides quality assurance and functionality testing on a wide range of devices.

Xamarin is gaining more attention everyday and with good reason. In a world where a variety of mobile platforms coexist, we need a toolset that allows us to support multiple platforms with minimal duplication of work. This is what we get with Xamarin.

In case, you any queries on Xamarin 3, feel free to approach us on hello@mantralabsglobal.com, our developers are here to clear confusions and it might be a good choice based on your business and technical needs.

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Machines That Make Up Facts? Stopping AI Hallucinations with Reliable Systems

There was a time when people truly believed that humans only used 10% of their brains, so much so that it fueled Hollywood Movies and self-help personas promising untapped genius. The truth? Neuroscientists have long debunked this myth, proving that nearly all parts of our brain are active, even when we’re at rest. Now, imagine AI doing the same, providing information that is untrue, except unlike us, it doesn’t have a moment of self-doubt. That’s the bizarre and sometimes dangerous world of AI hallucinations.

AI hallucinations aren’t just funny errors; they’re a real and growing issue in AI-generated misinformation. So why do they happen, and how do we build reliable AI systems that don’t confidently mislead us? Let’s dive in.

Why Do AI Hallucinations Happen?

AI hallucinations happen when models generate errors due to incomplete, biased, or conflicting data. Other reasons include:

  • Human oversight: AI mirrors human biases and errors in training data, leading to AI’s false information
  • Lack of reasoning: Unlike humans, AI doesn’t “think” critically—it generates predictions based on patterns.

But beyond these, what if AI is too creative for its own good?

‘Creativity Gone Rogue’: When AI’s Imagination Runs Wild

AI doesn’t dream, but sometimes it gets ‘too creative’—spinning plausible-sounding stories that are basically AI-generated fake data with zero factual basis. Take the case of Meta’s Galactica, an AI model designed to generate scientific papers. It confidently fabricated entire studies with fake references, leading Meta to shut it down in three days.

This raises the question: Should AI be designed to be ‘less creative’ when AI trustworthiness matters?

The Overconfidence Problem

Ever heard the phrase, “Be confident, but not overconfident”? AI definitely hasn’t.

AI hallucinations happen because AI lacks self-doubt. When it doesn’t know something, it doesn’t hesitate—it just generates the most statistically probable answer. In one bizarre case, ChatGPT falsely accused a law professor of sexual harassment and even cited fake legal documents as proof.

Take the now-infamous case of Google’s Bard, which confidently claimed that the James Webb Space Telescope took the first-ever image of an exoplanet, a factually incorrect statement that went viral before Google had to step in and correct it.

There are more such multiple instances where AI hallucinations have led to Human hallucinations. Here are a few instances we faced.

When we tried the prompt of “Padmavaat according to the description of Malik Muhammad Jayasi-the writer ”

When we tried the prompt of “monkey to man evolution”

Now, if this is making you question your AI’s ability to get things right, then you should probably start looking have a checklist to check if your AI is reliable.

Before diving into solutions. Question your AI. If it can do these, maybe these will solve a bit of issues:

  • Can AI recognize its own mistakes?
  • What would “self-awareness” look like in AI without consciousness?
  • Are there techniques to make AI second-guess itself?
  • Can AI “consult an expert” before answering?

That might be just a checklist, but here are the strategies that make AI more reliable:

Strategies for Building Reliable AI

1. Neurosymbolic AI

It is a hybrid approach combining symbolic reasoning (logical rules) with deep learning to improve factual accuracy. IBM is pioneering this approach to build trustworthy AI systems that reason more like humans. For example, RAAPID’s solutions utilize this approach to transform clinical data into compliant, profitable risk adjustment, improving contextual understanding and reducing misdiagnoses.

2. Human-in-the-Loop Verification

Instead of random checks, AI can be trained to request human validation in critical areas. Companies like OpenAI and Google DeepMind are implementing real-time feedback loops where AI flags uncertain responses for review. A notable AI hallucination prevention use case is in medical AI, where human radiologists verify AI-detected anomalies in scans, improving diagnostic accuracy.

3. Truth Scoring Mechanism

IBM’s FactSheets AI assigns credibility scores to AI-generated content, ensuring more fact-based responses. This approach is already being used in financial risk assessment models, where AI outputs are ranked by reliability before human analysts review them.

4. AI ‘Memory’ for Context Awareness

Retrieval-Augmented Generation (RAG) allows AI to access verified sources before responding. This method is already being used by platforms like Bing AI, which cites sources instead of generating standalone answers. In legal tech, RAG-based models ensure AI-generated contracts reference actual legal precedents, reducing AI accuracy problems.

5. Red Teaming & Adversarial Testing

Companies like OpenAI and Google regularly use “red teaming”—pitting AI against expert testers who try to break its logic and expose weaknesses. This helps fine-tune AI models before public release. A practical AI reliability example is cybersecurity AI, where red teams simulate hacking attempts to uncover vulnerabilities before systems go live 

The Future: AI That Knows When to Say, “I Don’t Know”

One of the most important steps toward reliable AI is training models to recognize uncertainty. Instead of making up answers, AI should be able to respond with “I’m unsure” or direct users to validated sources. Google DeepMind’s Socratic AI model is experimenting with ways to embed self-doubt into AI.

Conclusion:

AI hallucinations aren’t just quirky mistakes—they’re a major roadblock in creating trustworthy AI systems. By blending techniques like neurosymbolic AI, human-in-the-loop verification, and retrieval-augmented generation, we can push AI toward greater accuracy and reliability.

But here’s the big question: Should AI always strive to be 100% factual, or does some level of ‘creative hallucination’ have its place? After all, some of the best innovations come from thinking outside the box—even if that box is built from AI-generated data and machine learning algorithms.

At Mantra Labs, we specialize in data-driven AI solutions designed to minimize hallucinations and maximize trust. Whether you’re developing AI-powered products or enhancing decision-making with machine learning, our expertise ensures your models provide accurate information, making life easier for humans

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