Open Source Linux Education Software - Page 12
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SIS SED PLUS
School information system.
The School Information System (SIS) mobile app is used by the personnel of the School Education Department (SED) for real time data collection from the schools; for data monitoring by the AEOs; and for eTransfer by the teachers. -
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90DaysOfDevOps
The journey towards a better foundational knowledge of DevOps
This repository is used to document my journey on getting a better foundational knowledge of DevOps. I will be starting this journey on the 1st January 2022 but the idea is that we take 90 days which just so happens to be January 1st to March 31st. The reason for documenting these days is so that others can take something from it and also hopefully enhance the resources. The goal is to take 90 days, 1 hour a day, to tackle over 13 areas of DevOps to foundational knowledge. This will not cover all things DevOps but it will cover the areas that I feel will benefit my learning and understanding overall. What is and why do we use DevOps. Learning a Programming Language. Knowing Linux Basics. Understand Networking. Stick to one Cloud Provider. Use Git Effectively. Automate Configuration Management. Learn Infrastructure as Code. And much more! -
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AI Agents for Beginners
12 Lessons to Get Started Building AI Agents
AI Agents for Beginners is a comprehensive educational repository created to introduce developers to the concepts and practical implementation of AI agents. It provides step-by-step lessons that guide users through building autonomous systems capable of reasoning, planning, and interacting with tools. The curriculum combines theory with hands-on exercises, allowing learners to experiment with real agent architectures and workflows. It covers key topics such as prompt design, tool integration, memory management, and multi-agent coordination. The repository is structured to be accessible to beginners while still providing depth for more advanced learners. It also includes practical examples that demonstrate how agents can be applied to real-world problems. Overall, it serves as a foundational resource for understanding and building agent-based AI systems. -
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AI Researcher
An autonomous AI researcher
AI Researcher is an experimental open-source project that demonstrates how multiple AI agents can collaborate to conduct complex research tasks from start to finish with minimal human intervention. It orchestrates agents that can generate research questions, perform literature reviews, execute experiments, analyze results, and synthesize findings into structured outputs like reports or code. Each agent operates with clear roles — such as researcher, analyst, and summarizer — and they communicate through a task-management interface that ensures progress tracking and iterative refinement. The system emphasizes modularity, so teams can swap in new reasoning modules, data retrieval strategies, or domain knowledge bases depending on the research topic. Through self-supervised feedback loops, agents adjust their strategies based on prior outcomes, improving both the quality and relevance of results over time. -
Stop Storing Third-Party Tokens in Your DatabaseRolling your own OAuth token storage can be a security liability. Token Vault securely stores access and refresh tokens from federated providers and handles exchange and renewal automatically. Connected accounts, refresh exchange, and privileged worker flows included.
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Advanced Java
Comprehensive guide to advanced Java topics and backend tech
advanced-java is a curated knowledge base for Java developers seeking to deepen their understanding of advanced topics, including JVM internals, multithreading, networking, databases, system design, and common backend technologies. Written in Chinese, it serves as both a study guide and reference for preparing for technical interviews or mastering large-scale system development with Java. -
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Ansible Automation Platform Workshops
Training course for Ansible automation platform
The Red Hat Ansible Automation Workshops project is intended for effectively demonstrating Ansible's capabilities through instructor-led workshops or self-paced exercises. These interactive learning scenarios provide you with a pre-configured Ansible Automation Platform environment to experiment, learn, and see how the platform can help you solve real-world problems. The environment runs entirely in your browser, enabling you to learn more about our technology at your pace and time. The demos are intended for effectively demonstrating Ansible capabilities with prescriptive guides on the Ansible Automation Workshop infrastructure. Check out the optional website which is rendered automatically from markdown files using Github Pages. -
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Ansible-lint
Best practices checker for Ansible
Ansible Lint is a command-line tool for linting playbooks, roles and collections aimed towards any Ansible users. Its main goal is to promote proven practices, patterns and behaviors while avoiding common pitfalls that can easily lead to bugs or make code harder to maintain. Ansible lint is also supposed to help users upgrade their code to work with newer versions of Ansible. Due to this reason we recommend using it with the newest version of Ansible, even if the version used in production may be older. As any other linter, it is opinionated. Still, its rules are the result of community contributions and they can always be disabled based individually or by category by each user. ansible-lint checks playbooks for practices and behavior that could potentially be improved. As a community-backed project ansible-lint supports only the last two major versions of Ansible. -
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BPF Performance Tools
Official repository for the BPF Performance Tools book
BPF Performance Tools Book is the companion repository for Brendan Gregg’s book on Linux performance analysis using eBPF and BCC tracing technologies. The project contains scripts, examples, and reference material that demonstrate how to inspect kernel behavior, application performance, CPU usage, networking activity, file systems, and system bottlenecks in real time. It serves as both an educational resource and a practical toolkit for Linux engineers, SREs, and performance analysts working with modern observability workflows. The repository showcases how eBPF enables safe, low-overhead tracing directly inside the Linux kernel without requiring intrusive instrumentation. It includes tools for profiling, latency analysis, syscall tracing, and troubleshooting distributed systems or production environments. Overall, the project is regarded as a foundational resource for understanding advanced Linux performance engineering and observability techniques. -
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Beginner JavaScript
Slam Dunk JavaScript
Beginner JavaScript is the companion repository to a hands-on course that teaches modern JavaScript from the ground up using practical exercises and real code. It focuses on fundamentals—variables, types, functions, arrays, objects, DOM manipulation, and events—while introducing ES6+ features in approachable steps. The materials encourage learning by doing, with interactive challenges, starter files, and solutions you can compare against your own attempts. Clear explanations and incremental projects help learners build confidence before tackling more complex topics. Because everything runs in the browser with minimal tooling, setup is simple and newcomers can focus on the language rather than the build system. The repo doubles as a reference you can revisit later when you need to refresh syntax or patterns. -
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Binarytree
Python library for studying Binary Trees
Binarytree is Python library that lets you generate, visualize, inspect and manipulate binary trees. Skip the tedious work of setting up test data, and dive straight into practicing algorithms. Heaps and BSTs (binary search trees) are also supported. Binarytree supports another representation which is more compact but without the indexing properties. Traverse trees using different algorithms. -
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Catalyst
Accelerated deep learning R&D
Catalyst is a PyTorch framework for accelerated Deep Learning research and development. It allows you to write compact but full-featured Deep Learning pipelines with just a few lines of code. With Catalyst you get a full set of features including a training loop with metrics, model checkpointing and more, all without the boilerplate. Catalyst is focused on reproducibility, rapid experimentation, and codebase reuse so you can break the cycle of writing another regular train loop and make something totally new. Catalyst is compatible with Python 3.6+. PyTorch 1.1+, and has been tested on Ubuntu 16.04/18.04/20.04, macOS 10.15, Windows 10 and Windows Subsystem for Linux. It's part of the PyTorch Ecosystem, as well as the Catalyst Ecosystem which includes Alchemy (experiments logging & visualization) and Reaction (convenient deep learning models serving). -
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Category Theory for Programmers
'Category Theory for Programmers' unofficial PDF and LaTeX source
This is an unofficial PDF version of "Category Theory for Programmers" by Bartosz Milewski, converted from his blogpost series (with permission). Category Theory for Programmers was published 12 August, 2019. Based off release tag v1.3.0. See errata-1.3.0 for changes and fixes since print. Scala Edition is now available in paperback. Publish. Based off release tag v1.3.0. See errata-scala for changes and fixes since print. The best way to build the book is using the Nix package manager. After installing Nix, type nix-shell in the root directory of the project. This will download all the needed dependencies and tools to build the book (TeXLive, required fonts and packages, Pygments theme for syntax highligting, etc.). -
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Clojure Koans
A set of exercises for learning Clojure
Clojure Koans is a collection of exercises aimed at teaching the Clojure functional language by filling in tests to make them pass. Each “koan” gradually introduces key Lisp and Clojure concepts, from basic data structures to macros. It runs via leiningen or CLI tools, ideal for TDD learning and hands-on practice. -
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Countries
Laravel countries and currencies
This package has all sorts of information about countries. Amongst many other information, you'll be able to plot country maps. This package is not tied to Laravel and doesn't require it to be installed (we have a bridge for this purpose), but it has Laravel Collections in its core, all methods in Collections are available, this way you can do things like filter, map, reduce, search, sort, reject, and a lot more. It, actually, uses Coollection, which is Laravel Collections with a fluent syntax, allowing us to have access to array keys (and values) as object properties. -
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Dasar Pemrograman Golang
Source Code E-book Dasar Pemrograman Golang
Goal, or Go is a programming language that was born in 2009. The goal has many advantages, as evidenced by a large number of large companies using this language in the development of their products, up to the level of production of course. This ebook is one of the many references that can be used as learning material for Go programming. The topics provided vary greatly from basic ( from 0), to adventurous chapters. Basic Go Programming topics discussed are very basic, and suitable for people who have never or have not used the Go language. The discussion starts from installation, execution, hello word, followed by topics such as the discussion of several keywords Go, pointers, struct, interface, reflect, goroutine, channel, date time, and others. We will focus on learning basic science needed for developing web applications using Go, including such as: routing, multiplexers, middleware, cookies, and others. -
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Deep Learning 500 Questions
500 Questions on Deep Learning using a question-and-answer format
DeepLearning-500-questions is a comprehensive handbook that compiles 500 important questions on deep learning, curated to serve as a valuable reference for AI engineer interviews and self-study. Edited by Tan Jiyong with contributions from Guo Zizhao, Li Jian, and Dian Songyi, the book systematically covers both theoretical foundations and practical applications of deep learning. The first sections focus on essential mathematics, machine learning basics, and deep learning foundations, establishing the groundwork for more advanced topics. Later chapters explore classic neural network structures such as CNNs, RNNs, and GANs, as well as key applications in computer vision like object detection and image segmentation. The resource also delves into optimization methods, including transfer learning, network architecture design, hyperparameter tuning, model compression, and acceleration techniques. -
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DeepSqueak
DeepSqueak Using Machine Vision to Accelerate Bioacoustics Research
Using Machine Vision to Accelerate Bioacoustics Research. -
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DevOps Exercises
Linux, Jenkins, AWS, SRE, Prometheus, Docker, Python, Ansible, Git
DevOps Exercises is a massive, community-maintained collection of questions, tasks, and mini-challenges that cover the breadth of modern DevOps and platform engineering. It spans Linux, networking, Docker, Kubernetes, CI/CD, monitoring, cloud providers, security, and even soft skills and troubleshooting. The idea is to give candidates and teams a realistic practice ground for interviews, certifications, and day-to-day operational work. Because it’s structured as Q&A and exercises, you can go through it progressively or dip into specific domains where you need strengthening. The repository gets frequent contributions, keeping it aligned with current tooling and practices. It is widely used by people preparing for DevOps roles because it mirrors the style and depth of questions companies actually ask. -
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DevOps for Beginners
Learn Docker, Kubernetes, Terraform, Ansible, Jenkins and Azure Devops
DevOps Master Class is a comprehensive educational repository that teaches the fundamentals of DevOps practices and tools through hands-on examples. It covers key technologies such as Docker, Kubernetes, Terraform, Ansible, Jenkins, and cloud platforms like AWS and Azure. The course is structured to guide beginners from zero experience to building and deploying real-world applications. It emphasizes practical learning through exercises involving containerization, orchestration, and infrastructure as code. The repository also introduces continuous integration and delivery concepts with real implementations. It is designed to provide a complete foundation for modern DevOps workflows. Overall, it serves as a practical roadmap for developers transitioning into DevOps roles. -
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Diciotech
Diciotech is a collaborative online tech dictionary
Diciotech is a collaborative online tech dictionary, built with HTML, SASS and JavaScript, and open source. Our mission is to help people of all levels understand terms and concepts related to technology in a clear and simple way. -
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Dopamine
Framework for prototyping of reinforcement learning algorithms
Dopamine is a research framework for fast prototyping of reinforcement learning algorithms. It aims to fill the need for a small, easily grokked codebase in which users can freely experiment with wild ideas (speculative research). This first version focuses on supporting the state-of-the-art, single-GPU Rainbow agent (Hessel et al., 2018) applied to Atari 2600 game-playing (Bellemare et al., 2013). Specifically, our Rainbow agent implements the three components identified as most important by Hessel et al., n-step Bellman updates, prioritized experience replay, and distributional reinforcement learning. For completeness, we also provide an implementation of DQN (Mnih et al., 2015). For additional details, please see our documentation. We provide a set of Colaboratory notebooks which demonstrate how to use Dopamine. We provide a website which displays the learning curves for all the provided agents, on all the games. -
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Elixir Koans
Elixir learning exercises
Elixir Koans is an interactive learning project designed to teach the fundamentals of the Elixir programming language through a series of self-guided coding exercises. Inspired by the style of koans used in other programming communities, it provides incomplete code snippets and failing tests that learners must solve to progress. Each exercise builds on the previous one, gradually introducing core concepts such as pattern matching, recursion, processes, and concurrency in Elixir. By debugging and filling in missing pieces, users gain practical, hands-on experience while reinforcing theoretical knowledge. The project is ideal for both complete beginners and developers transitioning from other languages who want to learn Elixir in a structured, exploratory way. Its design emphasizes discovery, experimentation, and reflection, making it a powerful tool for deepening understanding of Elixir’s unique paradigms. -
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Fanyi
A 🇨🇳 and 🇺🇸 translate tool in your command line
Fanyi is a tool for translating words between the Chinese and English languages, right in your command line. It’s a good supportive tool for learning and reading the Chinese language from English, or the other way around. All translation data is fetched from iciba.com and fanyi.youdao.com, and with each translation comprehensive and related samples are given for better understanding and proper usage. There are translations for words as well as sentences, and in Mac/Linux bash, words can even be pronounced by the ‘say’ command. -
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Graphics Programming Black Book
Markdown source for Michael Abrash's Graphics Programming Black Book
This is the source for an ebook version of Michael Abrash's Black Book of Graphics Programming (Special Edition), originally published in 1997 and released online for free in 2001. Reproduced with blessing of Michael Abrash, converted and maintained by James Gregory. The version which Michael and Dr. Dobbs released in 2001 was a collection of PDF files. That version is still available. However, the structure (multiple files) and the format (PDF) result in a poor user experience on an ebook reader or other mobile device. This version has been thoroughly cleaned of artifacts and condensed into something which can easily be converted into an ebook-friendly format. You can read this version online at GitHub, or download any of the EPUB or Mobi releases. You can clone the repository and generate your own version with pandoc if necessary. -
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LLM Course
Course to get into Large Language Models (LLMs)
LLM Course is a hands-on, notebook-driven path for learning how large language models work in practice, from data curation to training, fine-tuning, evaluating, and deploying. It emphasizes reproducible experiments: each step is demonstrated with runnable code, clear dependencies, and references to commonly used open-source models and libraries. Learners get exposure to multiple adaptation strategies—LoRA/QLoRA, instruction fine-tuning, and alignment techniques—so they can choose approaches that fit their hardware and budgets. The materials also cover inference optimization and quantization to make serving LLMs feasible on commodity GPUs or even CPUs, which is crucial for side projects and startups. Evaluation is treated as a first-class topic, with examples of automatic and human-in-the-loop methods to catch regressions and verify quality beyond simple loss values. By the end, students have a mental model and a practical toolkit for iterating on datasets, training configs, etc.
