Showing 7 open source projects for "work breakdown structure"

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    Gemini 3 and 200+ AI Models on One Platform

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  • 1
    Swift Algorithm Club

    Swift Algorithm Club

    Algorithms and data structures in Swift, with explanations

    Swift Algorithm Club is a project that implements a broad collection of algorithms and data structures in the Swift programming language, with clear commentary and educational intent. Its purpose is not primarily to be a utility library, but rather to teach the how and why behind algorithms—readers can study implementations, complexity, and design choices in a Swift context. The repository includes common classic algorithms (sorting, searching, trees, graphs, dynamic programming, etc.) and...
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  • 2
    Yangshun Lago

    Yangshun Lago

    Data Structures and Algorithms library in TypeScript and JavaScript

    ...Instead of aiming to be a production runtime, it serves as a reference you can step through to understand how arrays, stacks, queues, linked lists, trees, heaps, graphs, and sorting/searching routines actually work. The implementations favor clarity over micro-optimizations, making them approachable for learners who are new to algorithmic thinking or coming from non-CS backgrounds. Because the code is idiomatic JavaScript, it also helps front-end engineers strengthen fundamentals without switching languages. The repository’s structure lets you browse topic by topic and compare trade-offs such as time versus space complexity. ...
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  • 3
    Gym

    Gym

    Toolkit for developing and comparing reinforcement learning algorithms

    ...Gym provides the environment, you provide the algorithm. You can write your agent using your existing numerical computation library, such as TensorFlow or Theano. It makes no assumptions about the structure of your agent, and is compatible with any numerical computation library, such as TensorFlow or Theano. The gym library is a collection of test problems — environments — that you can use to work out your reinforcement learning algorithms. These environments have a shared interface, allowing you to write general algorithms.
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  • 4
    Data Algorithm/leetcode/lintcode

    Data Algorithm/leetcode/lintcode

    Data Structure and Algorithm notes

    This work is some notes of learning and practicing data structures and algorithms. Part I is a brief introduction of basic data structures and algorithms, such as, linked lists, stack, queues, trees, sorting and etc. This book notes about learning data structure and algorithms. It was written in Simplified Chinese but other languages such as English and Traditional Chinese are also working in progress.
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    Streamline Azure Security with Palo Alto Networks VM-Series

    Centrally manage physical and virtualized firewalls with Panorama

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  • 5
    React Fiber Architecture

    React Fiber Architecture

    A description of React's new core algorithm, React Fiber

    The React Fiber Architecture project is a detailed technical document that explains the internal design and behavior of React Fiber, the core algorithm that powers modern React rendering. Rather than being a traditional code library, it serves as an educational deep dive into how React manages updates, scheduling, and reconciliation under the hood. The document explores how Fiber replaces the older stack-based reconciliation algorithm with a more flexible system that breaks rendering work...
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  • 6
    Data Structure & Algorithm Library(DSAL)
    The Data Structure & Algorithm Library (DSAL) is a library of classical data structure and algorithm.
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  • 7
    cosmos

    cosmos

    Algorithms that run our universe | Your personal library of every algo

    Cosmos (by OpenGenus Foundation) is your personal offline collection of every algorithm and data structure one will ever encounter and use in a lifetime. This provides solutions in various languages spanning C, C++, Java, JavaScript, Swift, Python, Go and others. This work is maintained by a community of hundreds of people and is a massive collaborative effort to bring the readily available coding knowledge offline.
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