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Top Trending React JS Libraries of 2019

You may be a Javascript developer looking to experiment with modern frameworks, or maybe you’re a seasoned back-end or systems programmer, or perhaps you’re new to programming in general and want to learn how to build dynamic web applications. Whether you are a sole developer or a project manager — the foremost thing you want is to keep up with the latest trends around the newest core technologies.

According to a Stack Overflow survey — Javascript is the most preferred programming, markup, and scripting language for developers. Javascript (71.5%) followed by HTML (69.4%) and CSS (66.2%) are the top three most used languages. Javascript tools like Node.js, Angular, and React are the most popular frameworks and libraries for developers to work with. New developers are eager to learn React over any other framework. While Javascript React itself seems pretty straightforward — the tooling and ecosystem, however, can become overwhelming.

Facebook’s extensive and open-source library – React is best for large web apps development. Following is an in-depth evaluation of the trending React JS libraries.

1. Redux

As the documentation states, Redux is a predictable state container for JavaScript apps. Redux is one of the most popular libraries in front-end development these days. However, many people are confused about what it is and what its benefits are. It’s an application data-flow architecture, rather than a traditional library or a framework like Underscore.js and AngularJS.

Redux (React JS Library) architecture

[Read documentation]

2. ANTD

Ant Design is a design language for background applications. It is refined by the Ant UED Team with an aims to create a uniform user interface specs for internal background projects, and lower the unnecessary cost of design and implementation. ANTD also liberates the design and front-end development resources.

Specially created for internal desktop applications, Ant Design is committed to improving the experience of users and product designers. User interface designers and user experience designers are collectively considered as product designers. ANTD has also blurred the boundaries of product managers, interaction designers, visual designers, front-end developers, and development engineers. Taking advantage of unitary specifications, Ant Design makes design and prototype more simple and accessible for all project members, which comprehensively promotes experience and development efficiency of background applications and products.

Also read-

  1. Top Javascript Trends for 2020
  2. React Native Framework: an in-depth study
  3. Tips to build an awesome UI using React

3. Blueprint

Blueprint provides reusable UI components for building various apps. Initially, the toolkit was built for desktop solutions. Later, because of its great capabilities and flexibility, Blueprint was implemented for web and mobile solutions as well. However, the contributors say it may not cater to all mobile apps’ needs.

BluePrint is not just a React JS library. It works well with Angular and Vue. Developers can also use it with other JavaScript and TypeScript languages and CSS markup language.

4. Mozaik

Mozaik is a great library for creating lovely dashboards for web applications. It has customization options for developing responsive layouts and personalized themes. Mozaik allows for grid positioning, optimized backend communication, and also provides an option to use more than one dashboard. Mozaik JS library works really great on all platforms, be it on a big screen or a smartphone.

Moziak - one of the popular React JS Libraries

5. Elemental UI

Elemental UI is a pretty flexible and efficient UI framework for building design-heavy web applications.

This framework is very similar to the Material UI framework but is much more lightweight. It is a flexible and beautiful CSS UI framework for ReactJS. It’s designed to be installed from npm and built into your project with Browserify or Webpack.

6. Gatsby

This is a level up from the traditional React JS libraries as we know them. The Gatsby tool allows developing websites on ReactJS and GraphQL faster than with any other web technology.

Most websites use static generators to take the first step towards a high-quality solution. Gatsby allows extending website functionality, seamless maintenance, and support.

Gatsby websites can easily load data from any resources with special plugins, contributing significantly to performance improvement. Image optimization, lazy-loading, and styles-lining speed up the website automatically, without manual modifications.

One of the most important Gatsby features is that websites don’t require a server to run on. For example, you can host the website on Github or Netlify for free.

Trending React JS Libraries: Conclusion

The libraries discussed above are very efficient in carrying out their specific functions. You can use the libraries for:

  • Designing great user interfaces
  • Creating captivating user experiences
  • Testing JavaScript and React code
  • Generating static websites

Each of these React JS libraries described has taken advantage of the features and components of the React JS framework, allowing for an easier experience during development. You can check out GitHub, for a complete and extensive list of React JS components and Libraries. 

About the author: Abhijeet Gupta is a Tech Lead with Mantra Labs. He has over 8 years of experience in developing web and mobile applications.

Related:

  1. Learn Ionic Framework From Scratch in Less Than 15 Minutes! 
  2. Ionic Platform for Mobile App Development: Features & New Releases
  3. 5 Trending PHP Frameworks in 2020
  4. Top Javascript Frameworks and Trends in 2020
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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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