Easing functions for smooth animation.
BezierEasing provides Cubic Bezier Curve easing which generalizes easing functions exactly like in CSS Transitions.
A collection of easing functions.
Collection of easing functions in TypeScript
tsParticles easing quad plugin
Simple and fast tweening engine with optimised Robert Penner's equations.
[](https://discord.gg/poimandres)
Create smooth gradients in React Native
Curated collection of data structures for the JavaScript/TypeScript.
PostCSS plugin to create smooth linear-gradients that approximate easing functions.
Utility script that takes an easing function as input and outputs a coordinate set with adjustable precision/resolution.
tsParticles easing sine plugin
tsParticles easing back plugin
tsParticles easing linear plugin
tsParticles easing circ plugin
tsParticles easing expo plugin
tsParticles easing cubic plugin
tsParticles easing quint plugin
Quick and easy spring animations. Works with other animation libraries (animejs, framer motion, motion one, @okikio/animate, etc...) or the Web Animation API (WAAPI).
tsParticles easing quart plugin
Strict borsh compatible de/serializer
A jQuery plugin from GSGD to give advanced easing options
Additional easing equations for the fx module in the svgjs.com library
Easings Js Library
Declartive DSL for handling fixed width records, read and write with ease
Track and debug errors in your Ruby applications with ease using Rollbar. With this gem, you can easily monitor and report on exceptions and other errors in your code, helping you identify and fix issues more quickly. Rollbar's intuitive interface and advanced error tracking features make it the perfect tool for ensuring the stability and reliability of your Ruby applications.
This is a ruby gem that lets you implement categorization systems with ease. Associative memory neural networks make it easy to identify probable patterns between sets of named data points. It can be cumbersome to interface with the neural network directly, however, as a typical implementation has a fixed size and training period, which limits how useful they can be to an integrated system. associative_memory simplifies these kind of machine learning models by offering dynamic input and output sets. This allows your code to concentrate on extrapolating meaningful patterns rather than juggling bitmasks and transposition matrices.
Enigma is a lightweight Ruby gem designed to verify passwords hashed using Firebase's custom scrypt-based algorithm, making it ideal for seamless integrations and migrations involving Firebase authentication systems. It provides a secure, efficient way to compare a user-provided password against a stored hash without exposing sensitive details, ensuring constant-time comparisons to mitigate timing attacks. Key features include: - Full compatibility with Firebase Authentication's password hashing logic, combining scrypt with AES-256-CTR encryption for signing. - Configurable parameters for scrypt (rounds, memory cost), signer keys, and salt separators. - Secure practices using OpenSSL's fixed-length comparisons. - Support for custom logging, with easy integration into Rails or other frameworks. - Minimal dependencies, relying on the 'scrypt' gem alongside Ruby's standard library. A common use case is migrating users from Firebase to systems like Devise in Ruby on Rails. During migration, extract the user's base64-encoded salt and stored hash from Firebase, then use Enigma to verify the input password. If it matches, set the raw password in Devise to generate a new hash, avoiding forced resets and ensuring a smooth transition. Whether for custom auth systems, password audits, or hybrid setups, Enigma simplifies secure verification while prioritizing ease of use.