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Showing 15 open source projects for "structured basic"
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Basic Memory
Persistent AI memory using local Markdown knowledge graphs
Basic Memory is an open source knowledge system that turns AI conversations into persistent, structured knowledge you control. Instead of losing context after each chat, it stores information as simple Markdown files on your device, allowing both you and AI to read and write to the same knowledge base. It uses the Model Context Protocol (MCP) so compatible AI tools can access, update, and build on your notes across sessions. -
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Template MCP Server
Template to quickly set up your own MCP server
The template-mcp-server is a TypeScript-based template designed to expedite the setup of Model Context Protocol (MCP) servers. It provides a basic server configuration with options for both standard input/output and HTTP transport using FastMCP. The template includes a structured framework for defining MCP tools, resources, and prompts, along with TypeScript configuration and development scripts. -
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Swift Concurrency Agent Skill
Add expert Swift Concurrency guidance to your AI coding tool
...Rather than teaching basic Swift, it targets the nuanced behaviors of concurrency primitives, actor isolation, and safety annotations like @MainActor and Sendable. It also clarifies how to reason about structured tasks, cancellation, and performance trade-offs. -
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Hindsight
Hindsight: Agent Memory That Learns
...It addresses one of the core limitations of modern AI agents, which is their inability to retain and meaningfully use past experiences over time, by introducing a structured, biomimetic memory architecture inspired by how human memory works. Instead of relying solely on vector similarity or basic retrieval techniques, Hindsight organizes information into distinct categories such as facts, experiences, beliefs, and observations, allowing agents to differentiate between raw data and inferred knowledge. ... -
Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
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llm_interview_note
Mainly record the knowledge and interview questions
...By organizing topics in a hierarchical documentation format, it enables readers to progress from basic NLP concepts to advanced topics like mixture-of-experts architectures and large-scale training frameworks. -
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Super comprehensive deep learning notes
Super Comprehensive Deep Learning Notes
Super comprehensive deep learning notes is a massive and well-structured collection of deep learning notebooks that serve as a comprehensive study resource for anyone wanting to learn or reinforce concepts in computer vision, natural language processing, deep learning architectures, and even large-model agents. The repository contains hundreds of Jupyter notebooks that are richly annotated and organized by topic, progressing from basic Python and PyTorch fundamentals to advanced neural network designs like ResNet, transformers, and object detection algorithms. ... -
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All Agentic Architectures
Implementation of 17+ agentic architectures
All Agentic Architectures is an open educational repository that provides hands-on implementations of modern AI agent architectures. The project acts as a practical learning resource that bridges the gap between theoretical research on autonomous agents and real software implementations. It contains more than a dozen agent architectures implemented using frameworks such as LangChain and LangGraph. Each architecture is explained through runnable notebooks that illustrate how the agent works... -
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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 authenticate with an API key, send requests, and handle responses within a Node.js environment. ... -
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GenAI Agents
Implementations for various Generative AI Agent techniques
GenAI Agents is a large, tutorial-driven repository that teaches you how to design, build, and experiment with generative AI agents. It spans a spectrum from simple conversational bots and basic question-answering agents to complex multi-agent systems that coordinate on research, education, business workflows, and creative tasks. The implementations leverage modern frameworks such as LangChain, LangGraph, AutoGen, PydanticAI, CrewAI, and more, showing how each can be wired into realistic agent workflows. The repo is structured by categories like beginner agents, framework tutorials, educational agents, business agents, creative agents, analysis agents, news bots, shopping assistants, task management agents, QA bots, and advanced systems such as controllable RAG agents. ... -
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AI Agents Masterclass
Follow along with my AI Agents Masterclass videos
...Students of the masterclass can follow written modules or Jupyter notebooks that illustrate concepts step by step and progressively build more capable agents. The content is suitable for both beginners and intermediate developers because it starts with basic principles and escalates to advanced architectures like multi-agent coordination. -
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Build Your Own OpenClaw
A step-by-step guide to build your own AI agent
Build Your Own OpenClaw is a step-by-step educational framework that teaches developers how to construct a fully functional AI agent system from scratch, gradually evolving from a simple chat loop into a multi-agent, production-ready architecture. The project is structured into 18 progressive stages, each introducing a new concept such as tool usage, memory persistence, event-driven design, and multi-agent coordination, with each step including both explanatory documentation and runnable... -
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Prompt Engineering Techniques
Collection of tutorials for Prompt Engineering techniques
Prompt Engineering Techniques is a focused companion repository that teaches prompt engineering systematically, from fundamentals to advanced strategies. It contains around twenty-plus hands-on Jupyter notebooks, each dedicated to a specific technique such as basic prompt structures, prompt templates and variables, zero-shot prompting, few-shot prompting, chain-of-thought, self-consistency, constrained generation, role prompting, task decomposition, and more. The tutorials are designed to be... -
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From Zero to Research Scientist guide
Detailed and tailored guide for undergraduate students
From-0-to-Research-Scientist-resources-guide is an open-source educational roadmap that helps learners progress from basic programming knowledge to becoming a research scientist in artificial intelligence. The repository focuses primarily on deep learning and natural language processing, providing structured guidance for individuals who want to pursue research careers in these fields. It compiles recommended courses, textbooks, tutorials, and academic resources needed to build expertise in machine learning research. ... -
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Machine Learning Glossary
Machine learning glossary
Machine Learning Glossary is an open educational project that provides clear explanations of machine learning terminology and concepts through visual diagrams and concise definitions. The goal of the repository is to make machine learning topics easier to understand by presenting definitions alongside examples, visual illustrations, and references for further learning. It covers a wide range of topics including neural networks, regression models, optimization techniques, loss functions, and... -
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Sklearn TensorFlow
Sklearn and TensorFlow: A Practical Guide to Machine Learning
...It aims to make practical machine learning education more accessible to Chinese-speaking learners by translating the technical explanations, examples, and exercises from the original English material. The repository organizes the content as structured documentation that can be compiled into multiple formats such as HTML, PDF, EPUB, and MOBI, allowing users to read the material both online and offline. It focuses on teaching core machine learning concepts using Python while demonstrating practical workflows with popular libraries like Scikit-Learn and TensorFlow. The material covers topics ranging from basic machine learning theory to deep learning techniques and model evaluation, enabling learners to build and experiment with models step by step.
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