Faker.js VS casual

Early this month, the malicious attack on free-to-use libraries, namely color.js and faker.js, created a real uproar in the development community. These tools are used in thousands of projects and their downloading rate from npm is estimated in millions per week. To everyone’s surprise, it turned out to be an inside job. Marak Squires, the creator of these libraries, intentionally committed malicious code to his projects and published updated codebases on GitHub and npm. It is said that this sabotage was caused by unsuccessful attempts of Mr. Squires to monetize his projects. Fortunately, malicious packages were quickly removed and the attacker’s account was suspended. The story sparked a new wave of discussion in the development community on possible steps to make the development and maintenance of open-source projects more sustainable.

For anyone else curious about the allusion to Aaron Swartz, it can be found here and reads (as of posting):

Sorry little boy--- I needed to update my LinkedIn profile, hire a professional to write my resume and photograph me, and work on an open-source project no one will use (or worse- work on something everyone uses)"

So people, I've been reading the news regarding some great packages on GitHub, like the Colors and the Faker. I understand that this isn't related entirely with the linux community, but it is something that we should pay attention.

A developer appears to have purposefully corrupted a pair of open-source libraries on GitHub and software registry npm — “faker.js” and “colors.js” — that thousands of users depend on, rendering any project that contains these libraries useless, as reported by Bleeping Computer.

I mean he also maliciously changed all of the links on a faker.js issue to point to conspiracy theories (which I am pretty sure is against Github's TOS): https://github.com/Marak/faker.js/pull/2

About Four (4) Days Ago, the Author of Fakerjs a popular JavaScript library with more than 2 million weekly Download from NPM Deleted the repository and replaced it with one that only has the modified ReadMe "What really happened with Aaron Swartz?" and no content, and pushed an empty package to npm as the latest version (6.6.6).

Faker looks cool! We've been using Casual [0] for ages but Faker looks more featureful and more actively maintained.

0: https://github.com/boo1ean/casual

In order to create the mockserver we need 2 npm dependencies. json-server and casual so we run npm install json-server casual --save-dev in our project.

https://npm.im/casual (57k weekly downloads) https://github.com/boo1ean/casual (2.8k stars)

In the past, I have relied on Google Sheets and a combination of array formulas, vlookups, random value generators to create fake datasets. This approach worked for small, one off datasets. Any time I needed to generate more than 1000 records or needed to update a dataset, this approach proved to be tedious. I also explored Faker and Casual (both of which are fantastic tools) but found that the data they generate was too abstract and that the implementation wasn't ideal for large datasets. I wanted a tool that: