Top Javascript Frameworks and Trends in 2020

Published on Dec 31, 2019

Updated on Jan 3, 2020

With a fast-growing MEAN stack for web and mobile applications, Javascript is becoming the first choice for developers. It is the favorite technology for most professional developers (71.5% in 2018 and 69.7% in 2019), according to the Stack Overflow survey report. Let’s now look at the emerging trends and Javascript applications that are shaping for 2020.

Javascript Trends 2020 Overview

All modern browsers support Javascript and that’s why over 10400 companies in the world use Javascript in their stacks, according to stackshare.io. Here’s an overview of rising Javascript trends for 2020.

Booming TypeScript: It is a superset of Javascript and existing JS programs function smoothly on TypeScript.
In 2018, Vue.JS surpassed React, but since 2019, React is getting back its momentum.
Framework independent web components are growing rapidly.
Developers are considering modular and reusable components.
Angular 7 is on the rise with new interesting features.
More focus on solution-driven APIs through GraphQL.

TypeScript is designed by Microsoft to expand Javascript capabilities. Developers love TypeScript because of fewer bugs, easy to read codes, and self-documentation feature. Visit Github for TypeScript resources.

Top Javascript Frameworks in 2020

Although we see growing traction for framework-agnostic components, the demand for simple and scalable frameworks is still intact. Let’s look at the top 3 frontend and backend JS Frameworks.

The Top 3 Javascript Frameworks for Frontend Development

#1 Angular 7

AngularJS is a Javascript-based, open-source frontend framework developed by Google. Since its release in 2010, there have been several releases to make AngularJS more user-friendly and effective for web development. Following are some of the exceptional features that make Angular 7 even more desirable.

Styling with CSS, SCSS, LESS using ng new / ng add commands.
Virtual scrolling is now possible with ScrollingModule.
Sorting and moving elements between the list has become simpler with the new DragDropModule function.
Using TypeScript, Angular 7 has updated and enhanced dependencies.

#2 React

Facebook introduced React in 2013 and it has been trending for the last four years. Although last year’s release did not include any major release (just minor bug fixes in React DOM and Is), it still boasts of its biggest knowledgebase, support, and community. The following are the reasons for the popularity of React among developers –

Native UI: HTML 5 UI comes with several compromises in terms of intuitive user experience. React brings forth a new and highly functional approach to constructing user interfaces. In other words, the application UI is simply the function of the current application state.
Component-based approach: It allows building encapsulated components that can be assembled to make complex UIs.

Also read – What is React Native Framework: An In-depth Study

#3 Ionic

Ionic is a framework to build and deploy cross-platform (android, native iOS, web) apps. Developers prefer this framework especially because of its intuitive UI components. Apart from simple CLI, and impressive icons, the best features that Ionic delivers are –

Deeplinking – it allows loading a specific view on the app from a web link.
AoT Compiling – to load apps at a lightning-fast speed.
Live Reload – to compile and redeploy apps at every stage of development.

The Top 3 Javascript Frameworks for Backend Development

#1 Express

Express is one of the four components of the rapidly growing MEAN stack (MongoDB, Express, AngularJS, and Node.js). Developers prefer this framework for building APIs and web applications because of its robustness and minimalism. There have been 7 releases to upgrade the performance of the Express framework and no doubt, it retains the position of trending javascript frameworks even for 2019-20.

Express is one of the oldest active Javascript framework since 2010 and is termed as a standard server framework for Node.js.

#2 Next.js

It is a small framework particularly for developing applications in React. Next.js allows developing complex applications with minimal coding. This year, React and Next.js are going hand-in-hand. The growing popularity of React is driving developers’ inclination towards using Next.js. Its key features are –

Allows building universal JS applications that are compatible with any server or device.
Irrespective of the data source, server rendering is easier in Next.js.
It has in-built styling but also works with every CSS-in-JS solution.

#3 Meteor

It is an open-source isomorphic (applications that run on both client and server) javascript framework. Meteor has grown exponentially since its release in 2012. Its key features are-

Compact code: It has an integrated Javascript stack that allows writing compact codes. For instance, a 1000 lines of code can be written in just 10 lines in Meteor.
Hot push: It allows new feature releases without app store approval or users having to download the native app again.
Technology integration: It allows integrating popular frameworks and tools. Thus, utilizing the best features from other tech stacks/frameworks.

Special mention: GraphQL

GraphQL is a query language for APIs and provides a runtime environment for fulfilling queries. It is developed by Facebook and is a star performer in the Github community. Also, many developers believe — GraphQL can replace REST APIs very soon.

GraphQL addresses the following data issues-

It gets all the necessary data to the client via a single request to the server.
The client can communicate in a universal query language. Thus, the server need not specify the structure and composition of the returned data.
It follows a declarative approach of describing the data requirements for the interface. Here, the focus is more on “what” rather than “how” making the information retrieval process simpler.

What about Node.js?

Node.js is the most popular Javascript-based runtime environment for MEAN stack. One should not confuse it with frameworks. It allows developers to use Javascript on both front and backend. Its resourceful APIs make backend development easier. It is a great choice for developing real-time and collaborative applications to date.

Probably, you’ll be interested to know what has changed in Javascript technology over the past few years. Take a look at the past trends-

Bonus – Top trending React Libraries of 2019!

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

Top Javascript Frameworks and Trends in 2020

Javascript Trends 2020 Overview

Top Javascript Frameworks in 2020

The Top 3 Javascript Frameworks for Frontend Development