Search Results for "code-server"
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Showing 110 open source projects for "code-server"
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Claude How-To
A visual, example-driven guide to Claude Code
...Its main value is helping developers move from casual Claude Code usage to more organized, automated, and agent-driven software development. -
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Python Tutorial
Xiaobai Python Tutorial
...The course is based on Python 3.10+ and marks newer language features from Python 3.11, 3.12, and 3.13 where relevant. It offers both a document-style reading site and an interactive learning site where users can write code in the browser. The interactive version includes exercises, automatic judging, progress tracking, and unlockable lesson flow. The content covers basic syntax, code style, data types, variables, lists, tuples, dictionaries, sets, loops, functions, iterators, generators, and object-oriented programming. -
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Hello Python
Comprehensive tutorial repository aimed at teaching the Python program
Hello-Python is a comprehensive tutorial repository aimed at teaching the Python programming language from scratch for beginners. It includes over 100 classes and about 44 hours of video instruction, combined with code samples, projects, and a chat community for support. The material covers the fundamentals—variables, data types, loops, functions—as well as intermediate topics like date handling, list comprehensions, file IO, regular expressions, modules, and packages. The course is designed to be accessible: no prior programming experience required, and the resources are freely available. ... -
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Open Source Vizier
Python-based research interface for blackbox
...Define a problem statement and study configuration. Setup a local server, setup a client to connect to the server, perform a typical tuning loop, and use other client APIs. -
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AutoResearchClaw
Autonomous research from idea to paper. Chat an Idea. Get a Paper 🦞
...Built in Python, it orchestrates a multi-stage research pipeline that gathers literature, formulates hypotheses, runs experiments, analyzes results, and writes the final paper. The system retrieves real academic references from sources such as arXiv and Semantic Scholar to ensure credible citations. It can automatically generate code for experiments, run them in a sandbox environment, and analyze the results with statistical methods. The platform also uses multi-agent debate and automated peer review processes to refine research findings and improve paper quality. By combining literature discovery, experimentation, and writing automation, AutoResearchClaw aims to turn research ideas into conference-ready papers with minimal human intervention. -
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ML for Beginners
12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
...It emphasizes ethical considerations and model evaluation—accuracy is not the only metric—so students learn to validate and communicate results responsibly. By the end, participants can build end-to-end ML experiments, interpret outputs, and iterate with confidence rather than just copying code. -
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Book1_Python-For-Beginners
The Iris Book: Addition, Subtraction, Multiplication, and Division
...The repository emphasizes clarity and gradual skill building, starting from fundamental syntax and moving toward practical programming patterns. It integrates visual aids and annotated code examples to help learners understand not just how Python works but why certain patterns are used. The material is structured to support self-paced learning, making it suitable for students, career switchers, and hobbyists. Because the book is part of a larger data science pathway, it also prepares readers for later work in visualization and machine learning. ... -
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Google Open Source Project Style Guide
Chinese version of Google open source project style guide
Each larger open source project has its own style guide, a series of conventions on how to write code for the project (sometimes more arbitrary). When all the code maintains a consistent style, it is more important when understanding large code bases. easy. The meaning of "style" covers a wide range, from "variables use camelCase" to "never use global variables" to "never use exceptions". The English version of the project maintains the programming style guidelines used in Google. ... -
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PocketFlow Tutorial Codebase Knowledge
Codebase to Tutorial
PocketFlow Tutorial Codebase Knowledge is a project that demonstrates how to build an AI agent capable of analyzing arbitrary codebases and generating beginner-friendly tutorials that explain how they work, turning complex source code into clear educational content. The repository builds on a lightweight 100-line LLM framework and uses natural language models to inspect repository structures, identify core abstractions, map dependencies, and articulate the reasoning behind code design and interactions. By crawling code files, extracting higher-level patterns, and using large language models to narrate explanations, the system aims to help developers — especially those new to a codebase — understand unfamiliar projects without manual deep reading. ... -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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nanoGPT
The simplest, fastest repository for training/finetuning models
NanoGPT is a minimalistic yet powerful reimplementation of GPT-style transformers created by Andrej Karpathy for educational and research use. It distills the GPT architecture into a few hundred lines of Python code, making it far easier to understand than large, production-scale implementations. The repo is organized with a training pipeline (dataset preprocessing, model definition, optimizer, training loop) and inference script so you can train a small GPT on text datasets like Shakespeare or custom corpora. It emphasizes readability and clarity: the training loop is cleanly written, and the code avoids heavy abstractions, letting students follow the architecture step by step. ... -
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ThinkStats2
Text and supporting code for Think Stats, 2nd Edition
ThinkStats2 is the code and text companion for the second edition of Think Stats, an introduction to statistics and data science for Python programmers. It teaches probability and statistical reasoning through short programs, experiments, and analysis of real datasets. The material emphasizes exploratory methods that help readers ask and answer practical questions with data. -
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The Data Engineering Handbook
Links to everything you'd ever want to learn about data engineering
The Data Engineering Handbook is a comprehensive, community-curated repository that aggregates essential learning resources for anyone interested in becoming a professional data engineer. Rather than being a code project itself, it’s a learning handbook that links to books, articles, tutorials, community groups, boot camps, and real-world project examples that collectively form a roadmap to mastering data engineering skills. It includes beginner and intermediate boot camps, interview guides, data cleaning and transformation resources, and curated lists of newsletters and industry communities, making it useful both for self-study and technical interview preparation. ... -
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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. ... -
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Ajenti 2
Ajenti Core and stock plugins
Ajenti is a Linux & BSD modular server admin panel. Ajenti 2 provides a new interface and a better architecture, developed with Python3 and AngularJS. Ajenti 2 can be easily installed with pip and the provided script. Picks up your current configuration and works on your existing system as-is, without any preparation. Does not overwrite your config files, options and comments. -
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PythonPark
Python open source project "The Road to Self-Study Programming"
PythonPark is a large, curated “learning playground” for Python — essentially a comprehensive self-study meta-repository aimed at helping learners progress in Python programming, data science, machine learning, web scraping, and software engineering practices. It aggregates tutorials, learning guides, project examples, and resources across topics: from Python basics and data structures to machine learning, web scraping, and even interview preparation and “programmer life” guidance. Because... -
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Web Dev for Beginners
About 24 Lessons, 12 Weeks, Get Started as a Web Developer
...The course also offers global accessibility with translations in more than 40 languages and built-in support for running in GitHub Codespaces or locally in Visual Studio Code. This makes it a practical and engaging way for beginners to gain a solid foundation in web development. -
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LangChain Open Deep Research
Fully open source deep research agent
Open Deep Research is a configurable, fully open-source agent for producing detailed research reports from complex questions. It separates work across models used for summarization, active research, information compression, and final report generation. Users can select from multiple language model providers as long as the chosen models support tool calling and structured outputs. Search can be powered by several APIs, native provider search, or external tools connected through MCP. The agent... -
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AI Researcher
An autonomous AI researcher
...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. ... -
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Scientific Visualization
An open access book on scientific visualization using python
The Scientific Visualization book is a freely available open-access textbook that introduces how to produce effective scientific visualizations using Python, focusing especially on leveraging the popular plotting library Matplotlib (and related tools). It goes beyond simple plotting tutorials and emphasizes design principles: how to choose colors, layout subplots, annotate graphs, and present data in a way that is both accurate and visually compelling. As such, it serves as a guide for... -
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Recommenders
Best practices on recommendation systems
...Independent or incubating algorithms and utilities are candidates for the contrib folder. This will house contributions which may not easily fit into the core repository or need time to refactor or mature the code and add necessary tests. -
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CodeCampus
CodeCampus is a learning-first Linux OS for CSE and IT students
CodeCampus OS is a custom Linux distribution based on Ubuntu 24, designed for beginner developers, computer science students, and self-learners who want to start coding without friction. Unlike traditional setups that require hours of installation and configuration, CodeCampus provides a fully pre-configured, learning-first environment right out of the box. From the moment you boot, everything is ready—so you can focus on learning, experimenting, and building instead of... -
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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... -
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ktrain
ktrain is a Python library that makes deep learning AI more accessible
...Inspired by ML framework extensions like fastai and ludwig, ktrain is designed to make deep learning and AI more accessible and easier to apply for both newcomers and experienced practitioners. With only a few lines of code, ktrain allows you to easily and quickly. ktrain purposely pins to a lower version of transformers to include support for older versions of TensorFlow. If you need a newer version of transformers, it is usually safe for you to upgrade transformers, as long as you do it after installing ktrain. As of v0.30.x, TensorFlow installation is optional and only required if training neural networks. -
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Simulation of Urban MObility
SUMO is a microscopic, multi-modal traffic simulation.
SUMO is an open source, highly portable, microscopic and continuous traffic simulation package designed to handle large networks. It allows for intermodal simulation including pedestrians and comes with a large set of tools for scenario creation. The code and the issue tracker can be found at https://github.com/eclipse-sumo/sumo/ The documentation can be found at https://sumo.dlr.de/docs/ -
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CodeJo
Web-based introductory coding environment
CodeJo is a web app that allows users to write Python code to control a web canvas element (a pixel grid) or another graphical display element. At this time there are four display canvases available: grid (web canvas) fabric (a library that also uses a web canvas) web (also web canvas) and doc (html). New modules can be added by creating a translation layer.
