GNU LibreJS for Firefox stops non-free non-trivial JavaScript LinkedIn’s Feathr joins the LF AI & Data Foundation

A Firefox and Firefox based browser extension called GNU

LibreJS automatically blocks nonfree, non-trivial JavaScript. GNU LibreJS operates like NoScript on first appearance. One of the primary differences is that NoScript blocks most JavaScript by default, while GNU LibreJS makes a distinction between non-free non-trivial JavaScript and free or trivial JavaScript.

The essay The JavaScript Trap by Richard Stallman served as the basis for the creation of GNU LibreJS. Stallman contends that non-free software, primarily written in JavaScript but also in other languages, is run by browsers. Many of these apps are proprietary or not open, and some of them are harmful or problematic. Stallman claims that a JavaScript program used by Google Docs has a size of half a megabyte. It is compressed, which makes it challenging to understand and analyse it.

Stallman advises against running JavaScript that is regarded as being complicated or expensive. Scripts loaded from external pages, those that modify the DOM, and calls to eval are all examples of JavaScript code that fits the description. The GNU website publishes the complete list. When GNU LibreJS is installed in Firefox and other compatible browsers, it makes these distinctions for the user. It enables JavaScript that it deems trivial and prevents all JavaScript that it deems non-trivial.

The extension adds a toolbar icon that indicates how many blocked JavaScript references are present on the page. In addition to controls to change the status of the entire website or specific scripts or pieces of code, a click displays accepted and blocked JavaScript. An entire website, as well as specific scripts or code snippets, can be whitelisted or blacklisted.

LinkedIn, a business networking site owned by Microsoft Corp., has announced that its Feathr feature store has joined the AI & Data Foundation of the Linux Foundation. Feathr was created by LinkedIn and launched in 2017 to simplify, accelerate, and scale up feature serving in machine learning, particularly for real-time AI applications. According to a blog post by LinkedIn engineers Hangfei Lin and Jinghui Mo, the company’s AI teams use Feathr to store, transform, serve, and distribute features with high throughput and low latency.

Feathr acts as an abstraction layer between the raw data and machine learning models, making it easier to standardise and make machine learning workflows and apps for feature definition, transformation, serving, storage, and access. When Feathr connects to numerous databases, developers can concentrate more on feature engineering while letting Feathr handle data serialisation types. Additionally, it offers options for managing credentials and optimising performance.

Feathr enables authors of machine learning algorithms to create features only once and use them across a variety of contexts, including model training and model serving. Additionally, they have the ability to connect to multiple offline data sources, like data lakes and data warehouses, and convert the data contained within into machine learning characteristics.

Since its debut, Feathr has grown to power numerous AI applications at LinkedIn, where it is employed to manage a large number of features.