Search Results for "ai python ide"
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Showing 49 open source projects for "ai python ide"
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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... -
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codex-orange-book
A Full-Link Guide to Using Codex from Installation to Real-World Cases
...It is written as a full learning resource rather than a conventional software package. The guide covers installation, configuration, core concepts, standard workflows, practical examples, and extension paths. It explains Codex App, Codex CLI, Codex IDE Extension, Codex Web, cloud workflows, Skills, MCP, Git, GitHub, automation, and memory-related usage. It is aimed at developers, independent builders, AI tool users, and technical teams that want a structured way to adopt Codex. Overall, it functions as a practical handbook for moving from basic Codex setup to real project execution and review-ready deliverables. -
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Maths, CS & AI Compendium
Become a cracked AI/ML Research Engineer
...It also addresses operating systems, algorithms, production software, GPU programming, AI inference, and ML systems design. Readers need only elementary mathematics and basic Python knowledge to begin. A bundled MCP server lets compatible AI assistants use the locally cloned compendium as a knowledge base. The repository is useful for self-study, interview preparation, and deeper AI research engineering training. -
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The Art of Debugging Open Book
The Art of Debugging Open Book
...The repository combines methodology chapters with practical copy-and-paste recipes and tool recommendations. Dedicated material covers Unix debugging utilities, compiled programs, Python applications, and PyTorch workloads. Topics include tools such as strace, GDB, ldd, nm, profiling utilities, memory investigation, and performance analysis. The content is organized as a practical reference rather than a conventional programming library. PDF and EPUB editions are available, and a companion skill file adapts the debugging methodology for AI coding agents. -
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AutoResearchClaw
Autonomous research from idea to paper. Chat an Idea. Get a Paper 🦞
AutoResearchClaw is an open-source framework designed to automatically generate full academic research papers from a single idea or topic. 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... -
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Local Deep Research
95% on SimpleQA (e.g. Qwen3.6-27B on a 3090)
Local Deep Research is an open-source AI-powered research assistant designed to perform deep, iterative investigations by combining large language models with multi-source search capabilities. It runs locally, giving users full control over their data, privacy, and infrastructure while supporting both local and cloud-based LLMs. The system breaks down complex queries into smaller steps, performs parallel searches across web and academic sources, and generates structured, citation-backed... -
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Book5_Essentials-Probability-Statistics
The book 5 of statistics in simplicity
...The material connects probability theory directly to real analytical workflows, helping learners understand how statistics supports predictive modeling. Like the other books in the series, it blends mathematical explanation with Python-based experimentation. Overall, the project provides a practical statistical foundation for students advancing into AI and data science. -
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US Job Market Visualizer
A research tool for visually exploring Bureau of Labor Statistics
US Job Market Visualizer is a research tool for interactively exploring Occupational Outlook Handbook data from the U.S. Bureau of Labor Statistics. It organizes information for 342 occupations into a treemap where area represents employment and color can represent different metrics. Users can compare projected growth, median pay, education requirements, and estimated digital AI exposure. The repository includes tools for scraping BLS pages, converting them to structured data, and building... -
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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... -
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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Book3_Elements-of-Mathematics
From Addition, Subtraction, Multiplication, and Division to ML
Book3_Elements-of-Mathematics is an open learning resource in the Visualize-ML collection that introduces core mathematical foundations required for modern data science and AI. The repository presents topics such as algebra, calculus fundamentals, and mathematical reasoning using a highly visual and beginner-friendly approach. Its goal is to reduce the intimidation barrier often associated with formal mathematics by combining diagrams, structured explanations, and applied examples. The... -
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Open Science
Open Science is an open-source, local-first, model-agnostic research
Open Science is an open-source, local-first AI research workbench built for scientific discovery on macOS, Windows, and Linux. Researchers can describe a task in plain language and let an agent inspect files, execute Python or R, search the web, and call scientific data connectors. The system is model-agnostic, allowing users to connect supported providers, compatible gateways, or subscription-based backends. -
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TextSight AI Detector API Client
AI detector and AI humanizer API client for Python and JavaScript
Free, open-source Python and JavaScript client for the TextSight AI detector and AI humanizer API. Detect if text was written by ChatGPT, Claude or Gemini with a sentence-by-sentence AI score, and rewrite AI-sounding text so it reads naturally, in 3 lines of code. A developer-friendly alternative to the GPTZero, Originality.ai and ZeroGPT APIs. Zero dependencies, TypeScript types, automatic retries and a command line tool. -
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QuizSolver
AI-powered quiz solver for Windows. Free to use, easy to set up.
QuizSolver is a free Windows app that uses AI vision to automatically read and answer quiz questions on your screen. It takes a screenshot, detects the answer buttons, sends the question to an AI model, and clicks the correct answer in seconds. Built-in support for Quizalize and Quipper. A Custom mode is available for other quiz sites, though results may vary. HOW TO SET UP: 1. Download and unzip QuizSolver — no installation needed 2. Get a free API key from Groq at... -
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The Ultimate Python Course
Source code and all the details for the Ultimate Python Course
...Each chapter is accompanied by source code and problem sets for hands-on practice. Projects include small games as well as larger applications such as a virtual assistant and an AI auto-reply bot. Additional learning materials include a Python handbook, handwritten notes, and supporting reference resources. -
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The Grand Complete Data Science Guide
Data Science Guide With Videos And Materials
The Grand Complete Data Science Materials is a repository curated by a data-science educator that aggregates a wide range of learning resources — from basic programming and math foundation to advanced topics in machine learning, deep learning, natural language processing, computer vision, and deployment practices — into a structured, centralized collection aimed at learners seeking a comprehensive path to data science mastery. The repository bundles tutorials, lecture notes, project outlines, course materials, and references across topics like Python, statistics, ML algorithms, deep learning, NLP, data preprocessing, model evaluation, and real-world problem solving. Its broad scope makes it particularly suitable for beginners or self-taught programmers who want an end-to-end learning track — from fundamentals all the way to building and deploying ML or AI systems. -
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Programming Without Coding Technology
Create software using a general-purpose visual programming system
...Using PWCT we developed a textual programming language Compiler and Virtual Machine without writing a single line of code where the programming process done using the PWCT visual components. This language called Supernova and it's free-open source. Many database, Multi-Media, Network, AI, Simulation & Math applications are developed using PWCT You can see/edit the generated source code. PWCT support Harbour, Supernova, C, Python, & C#.NET and you can extend PWCT to support code generation in any text based programming language. PWCT comes with many samples, tutorials and movies. -
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KherveBook
Computational notebook mixing Python, Markdown, LaTeX, JS and Sheet
KherveBook is a Jupyter-inspired computational notebook built as a native PyQt5 desktop application. A single document mixes five kinds of cell: runnable Python code, formatted Markdown, typeset LaTeX, JavaScript/HTML, and live spreadsheets. Python runs in an in-process kernel with NumPy, SciPy, pandas and Matplotlib preloaded. A side AI chat that knows the API of KherveBook that can write your Python program for you. Spreadsheet cells evaluate A1-style formulas in Python; LaTeX cells compile full documents with tectonic; JavaScript/HTML cells render in an embedded web view (D3, Plotly, canvas); and images or PDF pages import as cells. ... -
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ktrain
ktrain is a Python library that makes deep learning AI more accessible
ktrain is a Python library that makes deep learning and AI more accessible and easier to apply. ktrain is a lightweight wrapper for the deep learning library TensorFlow Keras (and other libraries) to help build, train, and deploy neural networks and other machine learning models. 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. ... -
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Lingueez - Vocabulary Builder
Collect vocabulary from anywhere, organize, and master it.
Lingueez helps you build vocabulary across languages and actually remember it. Add a word the moment you meet it — from any app, with a global shortcut — and Lingueez looks up its definition and translation for you. It then turns your personal word list into spaced-repetition flashcards that resurface each word right before you'd forget it, so your study time goes where it's needed most. And when recognizing a word is no longer enough, the quiz asks you to pick the right answer — or... -
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Guia do Desenvolvedor Back-end
Everything you need to become a back-end developer
Guia do Desenvolvedor Back-end is a back-end developer guide designed to help learners understand the technologies and concepts behind server-side development. It presents a roadmap and curated resources for both new developers and professionals who want to expand their stack. The guide covers Linux, Git, GitHub, HTTP, APIs, programming languages, databases, cloud platforms, Docker, architecture patterns, and related technical areas. It also includes resources for data science, machine... -
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The Art of Programming
A collection of practical tips can be found at the bottom of this page
The Art of Programming (Second Edition) is a curated collection of programming problems and solutions originally derived from the Microsoft 100 Interview Questions blog series, later refined into a long-running tutorial and ultimately a published book. Created by July, the series began in 2010 and has since evolved into an in-depth exploration of algorithmic thinking, data structures, and coding interview preparation. The repository brings together 42 classic programming problems from the... -
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data-science-on-gcp
Source code accompanying book: Data Science on the GCP
The data-science-on-gcp repository is a comprehensive collection of code examples and end-to-end workflows that accompany the book Data Science on the Google Cloud Platform, designed to teach developers how to build scalable data science and machine learning systems using Google Cloud services. It provides structured, chapter-aligned implementations that guide users through the full lifecycle of a data science project, including data ingestion, storage, processing, analysis, model training,... -
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DeepMind Educational Resources
DeepMind's repo of educational notebooks for learning AI and research
Educational is an open collection of interactive tutorials created by Google DeepMind to make the fundamentals of machine learning and artificial intelligence accessible to learners of all backgrounds. The repository provides hands-on, beginner-friendly resources that introduce essential AI concepts through Google Colab notebooks, combining intuitive explanations with executable code. The tutorials cover a broad range of topics—from foundational Python programming and data handling to supervised, unsupervised, and reinforcement learning, as well as graph neural networks and scientific reasoning. Specialized notebooks also explore creative AI applications, language modeling, generative models, and protein folding. ... -
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Hello AI World
Guide to deploying deep-learning inference networks
Hello AI World is a great way to start using Jetson and experiencing the power of AI. In just a couple of hours, you can have a set of deep learning inference demos up and running for realtime image classification and object detection on your Jetson Developer Kit with JetPack SDK and NVIDIA TensorRT. The tutorial focuses on networks related to computer vision, and includes the use of live cameras. -
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Brain Tokyo Workshop
Experiments and code from Google Brain’s Tokyo research workshop
The Brain Tokyo Workshop repository hosts a collection of research materials and experimental code developed by the Google Brain team based in Tokyo. It showcases a variety of cutting-edge projects in artificial intelligence, particularly in the areas of neuroevolution, reinforcement learning, and model interpretability. Each project explores innovative approaches to learning, prediction, and creativity in neural networks, often through unconventional or biologically inspired methods. The...
