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5 Reasons Why Flutter Framework is Better than React Native

4 minutes read

Flutter has become one of the most hyped cross native frameworks since its stable release. Nowadays, most of the companies are enthusiastic about flutter. It is mainly because one can develop applications for Android, iOS, Windows, Mac, Linux, and web from a single codebase. Despite fast development and flexible UI, many developers still want to hold on to the React Native. Let’s discuss why Flutter Framework is the best followed by a comparison between Flutter and React Native from a developer’s perspective.

What is Flutter?

Flutter, a UI software development kit by Google is known for building impressive, natively compiled apps for web, mobile, and desktop using a single codebase.

Flutter was originally an open-source project for mobile application development. Later it was extended to support platforms like web, Windows, Google Fuchsia and Linux. You might be already aware of Google’s new operating system called Fuchsia. Here, Flutter is the primary source for developing its applications. Recently, Flutter has become more competitive with React Native (Facebook) and Xamarin (Microsoft).

Useful resources:

  1. Mobile support for Flutter
  2. Web support for Flutter
  3. Desktop support for Flutter 

Why Flutter Framework?

1. Fast Development

Flutter is faster than many other application development frameworks. With its “hot reload” feature, you can experiment, build UIs, add/remove features, test and fix bugs faster. Thus reducing the overall app development time.

2. Expressive and Flexible UI

You can really build beautiful apps in Flutter. Also, the end-user experience is similar to native apps. Flutter has a layered architecture that lets you control every pixel on the screen. Thus, customization is very simple in Flutter. With its powerful composting capabilities, you can overlay and animate graphics, text, video, and other controls without any limitations.

You’ll also find a set of widgets that deliver pixel-perfect experiences on Android and iOS. It enables the ultimate realization of Material Design. Just in case you don’t know, Material.io is Google’s initiative to build beautiful, usable products with Material Components for digital experiences.

Useful resources: Material.io

3. Native Performance

Flutter’s widgets incorporate all critical platform differences such as scrolling, navigation, icons and fonts. This provides a native performance experience on both iOS and Android.

4. Dart Language

Dart programming language is developed by Google and is meant for mobile, desktop, backend and web applications. It is a client-optimized language for fast performing apps on multiple platforms.
Dart is AOT (Ahead Of Time) compiled to fast, predictable, native code, allowing writing almost all of Flutter code in Dart. This makes Flutter extremely fast and customizable. Virtually, everything (including all the widgets) can be customized.

5. Important Flutter Tools

Flutter framework supports many different tools including Android Studio and Visual Studio Code. It also provides support for building apps from the command line. Dart DevTools, which is a new debugging tool, is more flexible and allows runtime inspection. You can also view logs, debug apps and inspect widgets for Flutter App Development.

  1. Widget inspector helps to visualize and explore the tree hierarchy. Flutter uses this for UI rendering.
  2. Timeline view helps you to monitor your application at a frame-by-frame level. You can also identify rendering and computational work in timeline view.
  3. Source-level Debugger: It lets you step through code, set breakpoints and investigate the call stack.
  4. Logging View displays events from the Dart runtime, application frameworks and app-level logging events.

Flutter vs React Native

FlutterReact Native
Initial Release20172015
Created ByGoogleFacebook
Open Sourceyesyes
Programming LanguageDartJavaScript
Popularity68,000 Stars on Github (June 2019)78,400 stars on Github (June 2019)
IDEHigher compatibility with IntelliJ idea, Visual code studio & Android studioA wide range of IDE’s and tools support React Native
Documentationclean and easy to followUnclear
ArchitectureBLoCFlux and Redux
Stateful Hot ReloadingAvailableAvailable
Adaptive ComponentsComponents are not adaptive. Need to be configured manually (proprietary widgets)Some are adaptive automatically
(native components)
App PerformanceHigher at 60fps animation standardLower as it uses JavaScript bridge for initiating interaction
Native AppearanceBetter as it has access to the device’s core functionalitiesLess due to its dependency on third-party
3D SupportNoYes
Top apps built using Flutter/ReactXianyu app by Alibaba, Hamilton app for Hamilton Musical, Google Ads app
More
Instagram, Facebook, Facebook Ads, Skype, Tesla

Conclusion

React Native is an older framework is quite popular with its stability and developing time. However, React Native and Flutter framework have their own pros and cons. But, both are actively good looking at the features they’re providing.

React Native sounds like a tooling and dependency nightmare, while Flutter sounds like pleasure but still suffering growing pains. From my personal experience, many developers have expressed extreme frustration with React Native at times. Also, Developers have acknowledged that it’s a pleasure to develop apps in Flutter. 

From my own experience, I’m strongly leaning towards Flutter.


About the Author: Raviteja Aketi is a Senior Software Engineer at Mantra Labs. He has extensive experience with B2B projects. Raviteja loves exploring new technologies, watching movies, and spending time with family and friends.

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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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