Free Modern URL Shortener.

Kutty is a tailwind plugin for building web applications. It has a set of accessible and reusable components that are commonly used in web applications.

KUbernetes Test TooL (kuttl)

Kubernetes wrapper for sshuttle

🔗✂️ Cross-Browser URL Shortener Extension based on Kutt.it

A collection of explicit runge-kutta integrators, written in C++

Node.js client for Kutt.it url shortener

🔪 .NET Package for kutt.it url shortener

An implementation of the Runge-Kutta-methods for solving systems of ODEs with Python.

API wrapper library and cli for kutt.it

Project indefinitely paused and replaced by https://kutt.it/BinauralInterface For updates, join https://discord.com/invite/RhRMbmQ Tool that automates installing the required files to enable 3D audio in games.

Runge-Kutta-Fehlberg 7(8) numerical method for celestial dynamics on C++

A cutter made for the A4T toolhead

4th-order Runge-Kutta method for solving the first-order ordinary differential equation (MATLAB)

Kutt.it API client & CLI written in Go

Prototype implementations of the orders 2 and 4 of the Runge-Kutta method in C++, CUDA and OpenCL applied to vector fields.

Alfred workflow for Kutt.

मेरो कुट्टो कोदालोहरु, हल्का खिया लागेको छ, होसियार है :bangbang:

Arabic typing practice

command line tool for https://kutt.it service

A Web Application displays the tweets about everyone eating. It's sample of Catalyst, DBIx::Class, and Moose.

The optimization field suffers from the metaphor-based “pseudo-novel” or “fancy” optimizers. Most of these cliché methods mimic animals' searching trends and possess a small contribution to the optimization process itself. Most of these cliché methods suffer from the locally efficient performance, biased verification methods on easy problems, and high similarity between their components' interactions. This study attempts to go beyond the traps of metaphors and introduce a novel metaphor-free population-based optimization based on the mathematical foundations and ideas of the Runge Kutta (RK) method widely well-known in mathematics. The proposed RUNge Kutta optimizer (RUN) was developed to deal with various types of optimization problems in the future. The RUN utilizes the logic of slope variations computed by the RK method as a promising and logical searching mechanism for global optimization. This search mechanism benefits from two active exploration and exploitation phases for exploring the promising regions in the feature space and constructive movement toward the global best solution. Furthermore, an enhanced solution quality (ESQ) mechanism is employed to avoid the local optimal solutions and increase convergence speed. The RUN algorithm's efficiency was evaluated by comparing with other metaheuristic algorithms in 50 mathematical test functions and four real-world engineering problems. The RUN provided very promising and competitive results, showing superior exploration and exploitation tendencies, fast convergence rate, and local optima avoidance. In optimizing the constrained engineering problems, the metaphor-free RUN demonstrated its suitable performance as well. The authors invite the community for extensive evaluations of this deep-rooted optimizer as a promising tool for real-world optimization. The source codes, supplementary materials, and guidance for the developed method will be publicly available at different hubs at http://aliasgharheidari.com/RUN.html.

Basic implementation 4th order Runge Kutta method for ODE's

Runge-Kutta Methods in Julia

Shorten your links in VS Code

kutt library for ruby

A Graphical interface for real-time control and monitoring of KuttyPy (ATMEGA32 Board)

A desktop app for the Free Modern URL Shortener. https://kutt.it

使用龙格库塔法解激光的速率方程

Write a MATLAB code to solve the initial value problem y'=e^(t-y) where 0<=t <=1 . with initial condition y(0)=1 Runge-Kutta ORDER FOUR METHOD In each method, N=5,10,20,40,80,160,320,640,1280,2560. Program must be able to display the error (EN) at the final step t=1 for each N and calculate the ratio of errors The exact solution for this problem is y(t)=ln(e^t + e - 1)