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Ionic Platform for Mobile App Development: Features & New Releases

Since its release in 2013, the Ionic platform has gained huge respect in the hybrid and mobile development community.

As of January 2019, Ionic has a git repo of 39.9k stars with an active support community. Its open-source mobile app development framework helps to build top-quality native and progressive applications. 

Before we dig into details, let’s first look at the three main features of the Ionic framework.

  • OSS Framework: Ionic provides open-source, free mobile UI toolkit for building cross-platform apps (iOS, Android, and web) from a single codebase.
  • Mobile DevOps: It addresses the complete lifecycle of mobile DevOps with automated native builds, live-updates, and CI/CD.
  • AngularJS: It uses the AngularJS frontend Javascript framework for building scalable applications.
  • Integrations: Ionic allows integrating community plugins along with premium supported integrations.
  • Compiler: It uses Capacitor, Cordova, and PhoneGap compiler for native mobile applications with CSS, HTML, and Javascript.
  • App Builder: Through Ionic Studio, it provides a visual development environment to build functional apps in minutes.

Packed with lots of useful & out-of-box CSS components, the Ionic platform provides extensive documentation, examples, and starter videos. It helps to simplify the learning curve and get developers up and running quickly.

ionic platform benefits

Advantages of Ionic Platform

Ionic is an open-source SDK for hybrid mobile app development. It wraps the AngularJS framework with HTML5 and CSS and uses Cordova to access native device functions. The biggest advantage of using Ionic is — it allows building hybrid apps with amazing UI, which behave like native apps and rely on a single shared codebase. 

If you’re scared of Javascript, then trust me, with Ionic it’s not as bad as you might think. Look at these advantages of the Ionic platform.

  1. Cross platform: The code you write once runs effectively on web, iOS, and android. 
  2. Open source: It’s free to use with a number of community generated plugins integration.
  3. Development environment: Majority of components are developed in HTML, CSS, and JS, which almost all web developers are familiar with.
  4. Frontend development: AngularJS is embedded in Ionic by default allowing to build scalable applications.
  5. Deeplinking: To load a specific page on the app through a web link.
  6. Live Reload: It helps developers to compile and redeploy apps at every stage of development.

The Ionic platform provides a set of Angular directives (custom HTML elements) for its components. This makes using widgets as easy as writing a line of HTML code. Plus, the developers get access to Angular’s touch recognizers, view animation logic, HTML sanitation, and asynchronous communication.

While the developers can use Ionic straight after cloning or unpacking the library zip, they can also install their Node.js-based CLI through NPM and start quickly with their seed project.

Also read – Trending JavaScript Frameworks in 2020

What’s New in Ionic

Ionic React

On October 14, 2019, Ionic released Ionic React — a native react version of the Ionic framework. Using the standard react-dom library, Ionic has successfully expanded its ecosystem around the web platform.

With developers’ growing intent towards progressive web apps, Ionic’s integration with React provides one of the best PWA solutions to generate highly efficient web components. 

Read the details about the Ionic React announcement here.

Capacitor

Addressing many developers’ concerns about Cordova’s performance, Ionic released Capacitor 1.0 on May 22, 2019. Ionic still supports Cordova. However, new apps will run on this new cross-platform engine — Capacitor, which majorly aims at supporting progressive web apps. 

The capacitor is available as a Javascript API and developers can import it directly to their apps. Also, exposing new native SDKs to Capacitor is extremely easy! Developers will need just a little wrapper code with first-class support for Swift on iOS and Java on Android.

Read more about how the Capacitor is different from Cordova here.

Ionic Studio

Ionic Studio is an IDE (Integrated Development Environment) for an amazing developer experience for building Ionic apps. It offers a robust code editor with TypeScript support, auto compilation, and debugging. 

It brings forth a visual development environment with AWS Amplify for backend integrations. Ionic Studio also eliminates the need for developer docs.

Ionic Framework Use Case

Recently, Mantra Labs has built a mobile application using Ionic for its client — Ezetap. The app generates an invoice for merchants and takes payment against it. It helps the merchants manage their purchase records, accounts, and customer data. The app is currently used by leading businesses like Dominos, Indian Oil, Deccan Herald, Indane, and many more.

Read case study

Ionic Platform Installation Guide

For installation help and smart suggestions from developers, you may refer to the Ionic Documentation.

Please note, the platform is the actual hardware/software on which an application or software is built. Whereas, a framework is a collection of libraries and classes that provide a structure for app development. Ionic is thus, both a platform and a framework for building web and mobile applications.

If you’re planning for mobile app development or have queries related to the Ionic framework, feel free to reach out to us at hello@mantralabsglobal.com. Our developers are here to clear doubts and offer suggestions based on your business and technical needs.

[Related: Learn Ionic Framework From Scratch in Less Than 15 Minutes!]

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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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