Scribbler articles on JavaScript Libraries

Stable Diffusion, a text-to-image AI model, generates high-quality images by iteratively transforming noise into visuals, with WebGPU enabling in-browser GPU acceleration for efficient and private local processing.

TensorFlow.js provides a powerful way to build and deploy machine learning models directly in the browser.

With WebLLM, you can bring AI to your web applications without needing a backend, making it a powerful tool for building private, responsive, and portable AI-powered solutions.

JavaScript offers a rich ecosystem of math libraries for different purposes - from basic arithmetic and matrix manipulation to high-precision arithmetic and symbolic computation.

A list of libraries in JavaScript for performing statistical analysis in the browser or in the backend.

This article explores the diverse applications of JavaScript across various domains, including scientific computation, simulations, machine learning, fluid dynamics, equation solving, engineering design, data visualization, statistical analysis, bioinformatics, financial modeling, and robotics.

Fabric.js provides a rich set of features for advanced image manipulation in the browser. Whether you need to crop, resize, rotate, or apply filters, Fabric.js makes it easy to perform these operations with a simple API.

Scribbler is a powerful tool for unit testing JavaScript code, APIs, and libraries. Its interactive environment, combined with the ability to dynamically load testing frameworks and libraries, makes it an ideal choice for developers.

Leaflet.js provides a powerful and flexible framework for creating interactive maps on the web. With its simple API and extensive documentation, developers can easily integrate maps into their web applications and provide users with engaging and informative experiences.

TensorFlow.js empowers web developers to harness the full potential of machine learning directly within the browser environment. With its support for both model training and inference, developers can create powerful AI-driven applications without the need for extensive backend infrastructure.

For developers looking to leverage the full potential of parallel computing in JavaScript, mastering the use of Web Workers is essential. This article discussed leveraging Web Workkers for parallel programming. It also gives examples of using Paralle.js library.

In this article, we delve into the world of JavaScript utility libraries by comparing two popular choices- Underscore.js and Lodash. Through code examples and practical insights, this article aims to equip JavaScript developers with the knowledge to make informed decisions when choosing between Underscore.js and Lodash

Lodash is a popular JavaScript utility library that provides a collection of functions to help developers write concise and efficient code. It offers a wide range of utility functions, including ones for data manipulation, functional programming, and simplifying common tasks.

Open-source image processing libraries are invaluable assets in modern web development. Whether you are building a simple image editor, an advanced computer vision application, or a responsive e-commerce platform, these libraries provide the tools needed to deliver powerful functionality and seamless user experiences.

Numeric.js is a JavaScript library that provides a range of numerical computing and linear algebra/matrix computing functionalities.

Underscore JS provides several utilites and functions for quick development in JavaScript.

JavaScript has several frameworks and libraries for AR with different functionalities and features.

Brain.js is a popular open-source JavaScript library for building neural networks useful for solving a wide range of problems.

Tensorflow.js is a powerful tool for doing maching learning in JavaScript.

Several text classification usecases can be easily implemented in JavaScript using a plathora of libraries.

Python has been the main language for machine learning but JavaScript is good alternative especially for decentralized machine learning.