Introduction

We're seeking contributors to join us in the exciting overhaul of our random data generator library, previously built on top of faker-js. In this rewrite, we're striving to enhance performance, flexibility, and maintainability by reconstructing all data generators from scratch. Here's why you should get involved:

Benefits of the Rewrite

1. Independence from External Libraries: By removing the dependency on faker-js, we achieve greater control over the functionality and performance of our random data generation. This independence allows us to tailor the library precisely to our needs and streamline its operation.

2. Customization at Its Core: With the new implementation, we're introducing an array of customization options right at the core of the generator functions. Users will have more granular control over the generated data, enabling them to fine-tune parameters to suit their specific use cases.

3. Enhanced Documentation and Support: Alongside the code rewrite, we're committed to improving documentation and providing comprehensive support resources. Clearer explanations, detailed examples, and an active community will ensure a smooth transition for both existing and new users of the library.

How You Can Contribute:

We're looking for passionate developers who share our vision for a robust, versatile, and user-friendly random data generation tool. Whether you're keen on diving into the codebase, contributing to documentation, or providing feedback and testing, there's a place for you here.

Together, let's build a library that empowers developers worldwide to create realistic, customizable datasets with ease.

Ready to contribute? Head over to our GitHub repository and get involved today!

Sample Usage (Updated):

import { Blueprint as Bp } from '@cicerotcv/blueprint'

// Describe schemas without faker-js dependency
const dateSchema = Bp.date
  .between("2000-01-01", "2022-12-31")
  .transform((date) => date.toISOString());

const idSchema = Bp.datatype.uuid();

const emailSchema = Bp.internet.email()

// Combine schemas into an ObjectSchema
const userSchema = Bp.object({
  id: idSchema,
  createdAt: dateSchema,
  email: emailSchema,
});

// Generate an array of items of the same kind
const userCollection = Bp.array({
  minLength: 1,
  maxLength: 4,
  schema: userSchema,
});

// Compile and generate the items
console.log(userCollection.compile());

Conclusion:

Our rewrite of the random data generator library represents a significant step forward in terms of flexibility, performance, and user empowerment. Join us on this journey to create a tool that meets the diverse needs of developers across various domains.