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Showing 154 open source projects for "guide"
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AI CODE GUIDE
AI Code Guide is a roadmap to start coding with AI
AI CODE GUIDE is an open educational repository that provides a comprehensive roadmap for learning how to use artificial intelligence tools for software development. The project organizes resources related to AI-assisted programming, including tutorials, research papers, developer tools, and practical guidelines. Its goal is to help both experienced programmers and beginners understand how AI models can assist with writing, reviewing, and designing software. -
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Start Machine Learning in 2026
A complete guide to start and improve in machine learning
...The repository emphasizes flexibility by allowing learners to choose their own path through the material depending on their interests, preferred learning style, and level of prior knowledge. Many of the resources referenced are free or widely accessible, making the guide practical for self-learners who want to study independently without formal coursework. -
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Key-book
Proofs, cases, concept supplements, and reference explanations
The book "Introduction to Machine Learning Theory" (hereinafter referred to as "Introduction") written by Zhou Zhihua, Wang Wei, Gao Wei, and other teachers fills the regret of the lack of introductory works on machine learning theory in China. This book attempts to provide an introductory guide for readers interested in learning machine learning theory and researching machine learning theory in an easy-to-understand language. "Guide" mainly covers seven parts, corresponding to seven important concepts or theoretical tools in machine learning theory, namely: learnability, (hypothesis space) complexity, generalization bound, stability, consistency, convergence rate, regret circle. ... -
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Phantasm
Toolkits to create a human-in-the-loop approval layer
Phantasm offers toolkits to create a human-in-the-loop approval layer to monitor and guide AI agents' workflows in real-time, ensuring safety and reliability in AI operations. -
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AgentGuide
AI Agent Development Guide, LangGraph in Action, Advanced RAG
...Instead of presenting scattered resources, the repository organizes them into a systematic learning roadmap that guides learners from foundational concepts to advanced AI agent systems. The guide covers topics such as agent frameworks, retrieval-augmented generation systems, multi-agent collaboration, memory management, and tool usage. It also includes practical projects, interview preparation materials, and curated research papers related to AI agents and LLM engineering. The project is designed not only for learning but also for career preparation, helping developers understand how to build portfolio projects and prepare for AI engineering roles. -
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CAMEL AI
Finding the Scaling Law of Agents. A multi-agent framework
...Our approach involves using inception prompting to guide chat agents toward task completion while maintaining consistency with human intentions. We showcase how role-playing can be used to generate conversational data for studying the behaviors and capabilities of chat agents, providing a valuable resource for investigating conversational language models. -
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Writing AI Conference Papers
Writing AI Conference Papers: A Handbook for Beginners
WritingAIPaper is an open-source guide designed to help beginners understand and navigate the process of writing and publishing academic papers in the field of artificial intelligence. The project provides structured guidance on how to transform research ideas into complete manuscripts, covering topics such as defining the core contribution, organizing the paper structure, and refining technical details. -
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TorchServe
Serve, optimize and scale PyTorch models in production
...REST and gRPC support for batched inference. Export your model for optimized inference. Torchscript out of the box, ORT, IPEX, TensorRT, FasterTransformer. Performance Guide: built-in support to optimize, benchmark and profile PyTorch and TorchServe performance. Expressive handlers: An expressive handler architecture that makes it trivial to support inferencing for your use case with many supported out of the box. Out-of-box support for system-level metrics with Prometheus exports, custom metrics and PyTorch profiler support. -
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Kiro
Kiro is an agentic IDE that works alongside you from prototype
...It introduces the concept of “specs,” which act as executable documentation that defines system behavior, constraints, and acceptance criteria, allowing developers to guide AI agents more precisely. Kiro also incorporates event-driven automation through “hooks,” enabling AI agents to perform tasks such as generating tests, updating documentation, or optimizing code whenever specific development events occur. -
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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12-Factor Agents
What are the principles we can use to build LLM-powered software
12-Factor Agents is a conceptual engineering guide that defines a set of principles for building reliable, scalable, and maintainable LLM-powered applications. Inspired by the original Twelve-Factor App methodology, the project reframes best practices specifically for agentic systems and AI software. It outlines patterns such as treating prompts as first-class assets, owning the context window, and converting natural language into structured tool calls. -
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YData Synthetic
Synthetic data generators for tabular and time-series data
...It consists a set of different GANs architectures developed using Tensorflow 2.0. Several example Jupyter Notebooks and Python scripts are included, to show how to use the different architectures. YData synthetic has now a UI interface to guide you through the steps and inputs to generate structure tabular data. The streamlit app is available form v1.0.0 onwards. -
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Happy Coder
Mobile and Web client for Codex and Claude Code, with realtime voice
...At its core, Happy wraps existing AI coding tools with a unified interface, providing real-time voice interactions, encrypted communication, and seamless device switching between desktop and mobile. You can start a coding session locally through the Happy CLI or connect from a phone or browser, allowing developers to inspect, interact with, and guide the AI as it generates, tests, or explains code. The project includes components like a dedicated backend server for encrypted sync, a rich front-end experience across web and native apps, and support for push notifications when your coding agent encounters permission requests or errors. Happy prioritizes security with end-to-end encryption so your code and interactions remain private and auditable. -
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OpenClaw-RL
Train any agents simply by 'talking'
...The project focuses on enabling agents to improve their behavior through interactive learning rather than relying solely on static prompts or predefined skills. One of its key ideas is allowing users to train an AI agent simply by interacting with it conversationally, using natural language feedback to guide the learning process. The system incorporates reinforcement learning techniques to refine the agent’s policies for tool use, decision making, and task completion over time. It also explores approaches such as online policy distillation and hindsight feedback signals to strengthen training signals from real interactions. The framework operates asynchronously and does not require external API keys, making it easier to experiment with local agent training workflows. -
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Antigravity Kit
AI Agent templates with Skills, Agents, and Workflows
...It comes with an extensive library of predefined agent personas and domain-specific skills that help in performing targeted tasks such as frontend development, backend engineering, quality assurance, and more. With this kit, developers can rapidly initialize a project scaffold and gain access to specialist agents and command workflows that guide the agent through multi-step tasks and slash-command procedures. The repository follows a clear pattern for defining agents and their behaviors, making it easier to extend with new skills or tailor existing ones to your use case. Because it’s framework-agnostic at its core, the Antigravity Kit can integrate with IDEs and other development tooling for smoother AI-augmented coding. -
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Agent Skills
Skills for AI coding agents
...The goal of the project is to make it easy for AI assistants like Claude Code, OpenCode, Cursor, Codex, and others that support this open ecosystem to automatically apply best practices or perform concrete actions when a relevant user intent is detected. For example, some skills guide the agent in applying React and Next.js performance best practices, auditing UI and accessibility standards. -
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ML Retreat
Machine Learning Journal for Intermediate to Advanced Topics
ML Retreat is an open-source learning repository that serves as a structured journal documenting advanced topics in machine learning and artificial intelligence. The project compiles detailed notes, technical explanations, and curated resources that guide readers through complex concepts across modern AI research. Rather than functioning as a traditional tutorial series, the repository is organized as a learning journey that progressively explores increasingly advanced subjects. Topics include large language models, graph neural networks, mechanistic interpretability, transformer architectures, and emerging research areas such as quantum machine learning. ... -
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Zero to Mastery Deep Learning TensorFlow
All course materials for the Zero to Mastery Deep Learning with TF
This project is a comprehensive, code-first deep learning curriculum built around TensorFlow and Keras, designed to guide learners from foundational concepts to practical model deployment through hands-on experimentation. It is structured as a series of progressively complex Jupyter notebooks that emphasize writing and understanding code before diving into theory, reinforcing learning through repetition and application. The material covers core machine learning workflows including regression, classification, computer vision, natural language processing, and time series forecasting, allowing users to build a well-rounded understanding of modern AI tasks. ... -
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autoresearch
AI agents autonomously run and improve ML experiments overnight
...Each experiment runs for a fixed five-minute training window, enabling rapid iteration and consistent comparison across architectural or hyperparameter changes. The system centers on a simple workflow where the agent modifies a single training file while human researchers guide the process through a program.md instruction file. Designed to run on a single GPU, it keeps the research loop minimal and self-contained to make autonomous experimentation practical. Over time, the agent logs experiments, evaluates improvements, and gradually evolves the model through automated trial-and-error. -
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Generative AI for Beginners (Version 3)
21 Lessons, Get Started Building with Generative AI
...It also walks through modern application techniques such as function calling, RAG with vector databases, working with open source models, agents, fine-tuning, and using SLMs. Each lesson includes a short video, a written guide, runnable samples for Azure OpenAI, the GitHub Marketplace Model Catalog, and the OpenAI API, plus a “Keep Learning” section for deeper study. -
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OpenAI Quickstart Node
Node.js example app from the OpenAI API quickstart tutorial
OpenAI Quickstart Node.js is an example application designed to help developers learn how to use the OpenAI API with Node.js. The repository provides structured sample code for a variety of API endpoints, including chat completions, assistants, embeddings, fine-tuning, moderation, batch processing, and image generation. Each folder contains runnable scripts that demonstrate both basic usage and more advanced scenarios. By following the examples, developers can quickly understand how to... -
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SuperPrompt
Experimental prompt framework exploring reasoning structures in AI
SuperPrompt is an experimental open source project focused on designing complex prompts intended to help researchers and developers better understand how AI agents reason and respond. It explores structured prompt engineering techniques that combine symbolic expressions, logical constructs, and conceptual frameworks to guide large language models toward deeper reasoning processes. Its main concept revolves around a highly structured prompt format that includes tagged sections for reasoning, analysis, conceptual expansion, and recursive thinking patterns. These sections act as a kind of meta-instruction system intended to influence how an AI model approaches problem solving and conceptual exploration. ... -
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The Hundred-Page Machine Learning Book
The Python code to reproduce illustrations from Machine Learning Book
The Hundred-Page Machine Learning Book is the official companion repository for The Hundred-Page Machine Learning Book written by machine learning researcher Andriy Burkov. The repository contains Python code used to generate the figures, visualizations, and illustrative examples presented in the book. Its purpose is to help readers better understand the concepts explained in the text by allowing them to run and experiment with the underlying code themselves. The book itself provides a... -
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supervision
We write your reusable computer vision tools
We write your reusable computer vision tools. Whether you need to load your dataset from your hard drive, draw detections on an image or video, or count how many detections are in a zone. You can count on us. -
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Context7 Platform
Up-to-date code documentation for LLMs and AI code editors
...When a user writes a prompt and appends something like “use context7,” the system detects the libraries or frameworks being asked about, fetches the latest docs/snippets from the source repositories, filters and packages relevant context, and injects them into the LLM’s prompt to guide it toward accurate, up-to-date code. The upstream codebase provides an MCP server implementation, enabling clients to easily interface with the Context7 service over standard channels (HTTP, stdio) and treat it as an external “knowledge tool.” -
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Techniques
Techniques for deep learning with satellite & aerial imagery
This repository is a comprehensive, curated collection of deep learning techniques and best practices specifically applied to satellite and aerial imagery. It covers everything from preprocessing and annotation to model architectures and open datasets. The guide includes code snippets, links to research papers, and hands-on tools, making it valuable for researchers, engineers, and enthusiasts working in remote sensing and geospatial AI.
