fakedata-quickcheck VS falso

Compare fakedata-quickcheck vs falso and see what are their differences.

falso

All the Fake Data for All Your Real Needs πŸ™‚ (by ngneat)
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fakedata-quickcheck falso
- 13
0 3,117
- 0.6%
0.0 7.1
almost 3 years ago 3 months ago
Haskell TypeScript
MIT License MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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fakedata-quickcheck

Posts with mentions or reviews of fakedata-quickcheck. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning fakedata-quickcheck yet.
Tracking mentions began in Dec 2020.

falso

Posts with mentions or reviews of falso. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-08-09.

What are some alternatives?

When comparing fakedata-quickcheck and falso you can also consider the following projects:

fakedata - Haskell Library for producing quality fake data

faker - Generate massive amounts of fake data in the browser and node.js

faker - A library for generating fake data such as names, addresses, and phone numbers. [Moved to: https://github.com/faker-ruby/faker]

chance.js - Chance - Random generator helper for JavaScript

faker - Faker is a Python package that generates fake data for you.

typeorm-seeding - 🌱 A delightful way to seed test data into your database.

Bogus - :card_index: A simple fake data generator for C#, F#, and VB.NET. Based on and ported from the famed faker.js.

community-faker - Generate massive amounts of fake contextual data

mongosh-snippets - An experimental plugin feature for mongosh

mongosh-docker - Unofficial dockerized MongoDB Shell

copycat - Generate deterministic fake values: The same input will always generate the same fake-output.

COB-FAKER - COBOL port (partial) of the Python Faker project - used to generate or anonymize test data requiring names, addresses, etc.