AI-lectures is a repository containing educational materials on various topics related to Artificial Intelligence, including Machine Learning, Robotics, and Optimization. It provides full scripts, slides, and exercises with solutions for different lectures. Users can compile the materials into PDFs for easy access and reference. The repository aims to offer comprehensive resources for individuals interested in learning about AI and its applications in intelligent systems.

PC Agent introduces a novel framework to empower autonomous digital agents through human cognition transfer. It consists of PC Tracker for data collection, Cognition Completion for transforming raw data, and a multi-agent system for decision-making and visual grounding. Users can set up the tool in Python environment, customize data collection with PC Tracker, process data into cognitive trajectories, and run the multi-agent system. The tool aims to enable AI to work autonomously while users sleep, providing a cognitive journey into the digital world.

BALROG is a benchmark tool designed to evaluate agentic Long-Longitudinal Memory (LLM) and Vision-Language Memory (VLM) capabilities using reinforcement learning environments. It provides a comprehensive assessment of agentic abilities, supports both language and vision-language models, integrates with popular AI APIs, and allows for easy integration of custom agents, new environments, and models.

eShopSupport is a sample .NET application showcasing common use cases and development practices for building AI solutions in .NET, specifically Generative AI. It demonstrates a customer support application for an e-commerce website using a services-based architecture with .NET Aspire. The application includes support for text classification, sentiment analysis, text summarization, synthetic data generation, and chat bot interactions. It also showcases development practices such as developing solutions locally, evaluating AI responses, leveraging Python projects, and deploying applications to the Cloud.

AI-Driven Research Assistant is an advanced AI-powered system utilizing specialized agents for data analysis, visualization, and report generation. It integrates LangChain, OpenAI's GPT models, and LangGraph for complex research processes. Key features include hypothesis generation, data processing, web search, code generation, and report writing. The system's unique Note Taker agent maintains project state, reducing overhead and improving context retention. System requirements include Python 3.10+ and Jupyter Notebook environment. Installation involves cloning the repository, setting up a Conda virtual environment, installing dependencies, and configuring environment variables. Usage instructions include setting data, running Jupyter Notebook, customizing research tasks, and viewing results. Main components include agents for hypothesis generation, process supervision, visualization, code writing, search, report writing, quality review, and note-taking. Workflow involves hypothesis generation, processing, quality review, and revision. Customization is possible by modifying agent creation and workflow definition. Current issues include OpenAI errors, NoteTaker efficiency, runtime optimization, and refiner improvement. Contributions via pull requests are welcome under the MIT License.

CrewAI is a cutting-edge framework designed to orchestrate role-playing autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks. It enables AI agents to assume roles, share goals, and operate in a cohesive unit, much like a well-oiled crew. Whether you're building a smart assistant platform, an automated customer service ensemble, or a multi-agent research team, CrewAI provides the backbone for sophisticated multi-agent interactions. With features like role-based agent design, autonomous inter-agent delegation, flexible task management, and support for various LLMs, CrewAI offers a dynamic and adaptable solution for both development and production workflows.

ToolMate AI is an advanced AI companion that integrates agents, tools, and plugins to excel in conversations, generative work, and task execution. It supports multi-step actions, allowing users to customize workflows for tackling complex projects with ease. The tool offers a wide range of AI backends and models, including Ollama, Llama.cpp, Groq Cloud API, OpenAI API, and Google Gemini via Vertex AI. Users can easily switch between backends and leverage AI models like wizardlm2 and mixtral. ToolMate AI stands out for its distinctive features such as tool calling for any LLMs, running multiple tools in one go, highly customizable plugins, and integration with popular AI tools. It also supports quick tool calling using '@' notation and enables the execution of computing tasks on demand. With features like multiple tools in one go, customizable plugins, system command and fabric integration, GPU offloading support, real-time data access, and device information retrieval, ToolMate AI offers a comprehensive solution for various tasks and content creation.

Dataformer is a robust framework for creating high-quality synthetic datasets for AI, offering speed, reliability, and scalability. It empowers engineers to rapidly generate diverse datasets grounded in proven research methodologies, enabling them to prioritize data excellence and achieve superior standards for AI models. Dataformer integrates with multiple LLM providers using one unified API, allowing parallel async API calls and caching responses to minimize redundant calls and reduce operational expenses. Leveraging state-of-the-art research papers, Dataformer enables users to generate synthetic data with adaptability, scalability, and resilience, shifting their focus from infrastructure concerns to refining data and enhancing models.

The Expected Parrot Domain-Specific Language (EDSL) package enables users to conduct computational social science and market research with AI. It facilitates designing surveys and experiments, simulating responses using large language models, and performing data labeling and other research tasks. EDSL includes built-in methods for analyzing, visualizing, and sharing research results. It is compatible with Python 3.9 - 3.11 and requires API keys for LLMs stored in a `.env` file.

hugegraph-ai aims to explore the integration of HugeGraph with artificial intelligence (AI) and provide comprehensive support for developers to leverage HugeGraph's AI capabilities in their projects. It includes modules for large language models, graph machine learning, and a Python client for HugeGraph. The project aims to address challenges like timeliness, hallucination, and cost-related issues by integrating graph systems with AI technologies.

BitMat is a Python package designed to optimize matrix multiplication operations by utilizing custom kernels written in Triton. It leverages the principles outlined in the "1bit-LLM Era" paper, specifically utilizing packed int8 data to enhance computational efficiency and performance in deep learning and numerical computing tasks.

The Codebase Context Specification (CCS) project aims to standardize embedding contextual information within codebases to enhance understanding for both AI and human developers. It introduces a convention similar to `.env` and `.editorconfig` files but focused on documenting code for both AI and humans. By providing structured contextual metadata, collaborative documentation guidelines, and standardized context files, developers can improve code comprehension, collaboration, and development efficiency. The project includes a linter for validating context files and provides guidelines for using the specification with AI assistants. Tooling recommendations suggest creating memory systems, IDE plugins, AI model integrations, and agents for context creation and utilization. Future directions include integration with existing documentation systems, dynamic context generation, and support for explicit context overriding.

ControlFlow is a Python framework designed for building agentic AI workflows. It provides a structured approach for defining tasks, assigning specialized AI agents, and orchestrating complex behaviors. By balancing AI autonomy with precise oversight, users can create sophisticated AI-powered applications with confidence. ControlFlow offers a task-centric architecture, structured results with type-safe outputs, specialized agents for efficient problem-solving, ecosystem integration with LangChain models, flexible control over workflows, multi-agent orchestration, and native observability and debugging capabilities.

FreeGenius AI is an ambitious project offering a comprehensive suite of AI solutions that mirror the capabilities of LetMeDoIt AI. It is designed to engage in intuitive conversations, execute codes, provide up-to-date information, and perform various tasks. The tool is free, customizable, and provides access to real-time data and device information. It aims to support offline and online backends, open-source large language models, and optional API keys. Users can use FreeGenius AI for tasks like generating tweets, analyzing audio, searching financial data, checking weather, and creating maps.

LeetCode Solver Bot is an automated tool designed to solve LeetCode problems using AI-powered code generation. It interacts with the LeetCode platform to fetch problems, generate solutions, submit them, and handle debugging if necessary. The tool supports automated login using GitHub authentication, fetching unsolved problems, AI-powered solution generation with GPT-4, automated solution submission and testing, debugging capabilities for failed submissions, and currently focuses on Python programming language.

LLM-on-Ray is a comprehensive solution for building, customizing, and deploying Large Language Models (LLMs). It simplifies complex processes into manageable steps by leveraging the power of Ray for distributed computing. The tool supports pretraining, finetuning, and serving LLMs across various hardware setups, incorporating industry and Intel optimizations for performance. It offers modular workflows with intuitive configurations, robust fault tolerance, and scalability. Additionally, it provides an Interactive Web UI for enhanced usability, including a chatbot application for testing and refining models.

AI-lectures is a repository containing educational materials on various topics related to Artificial Intelligence, including Machine Learning, Robotics, and Optimization. It provides full scripts, slides, and exercises with solutions for different lectures. Users can compile the materials into PDFs for easy access and reference. The repository aims to offer comprehensive resources for individuals interested in learning about AI and its applications in intelligent systems.

Start Machine Learning in 2024 is a comprehensive guide for beginners to advance in machine learning and artificial intelligence without any prior background. The guide covers various resources such as free online courses, articles, books, and practical tips to become an expert in the field. It emphasizes self-paced learning and provides recommendations for learning paths, including videos, podcasts, and online communities. The guide also includes information on building language models and applications, practicing through Kaggle competitions, and staying updated with the latest news and developments in AI. The goal is to empower individuals with the knowledge and resources to excel in machine learning and AI.

This repository hosts the Machine Learning for Games Course, providing mdx files and notebooks for learning. The course covers various topics related to applying machine learning techniques in game development. It offers a syllabus and resources for users to sign up and access the content for free. The project is maintained by Thomas Simonini and is available on GitHub for citation in publications.

H2O.ai's AI Tutorials aim to democratize open source, distributed machine learning by providing step-by-step tutorials for individuals of all skill levels. These tutorials are developed and maintained on Github and published on the H2O.ai Self-Paced Courses Landing Page. Users can begin their AI journey by exploring the tutorials available on the landing page and can contribute by fixing issues, updating tutorials, or creating new ones.

This repository contains course materials for the 'Introduction to Machine Learning' course at Sharif University of Technology. It includes slides, Jupyter notebooks, and exercises for the Fall 2024 semester. The content is continuously updated throughout the semester. Previous semester materials are also accessible. Visit www.SharifML.ir for class videos and additional information.

This repository provides a comprehensive collection of resources, open-source tools, and knowledge related to quantitative analysis. It serves as a valuable knowledge base and navigation guide for individuals interested in various aspects of quantitative investing, including platforms, programming languages, mathematical foundations, machine learning, deep learning, and practical applications. The repository is well-structured and organized, with clear sections covering different topics. It includes resources on system platforms, programming codes, mathematical foundations, algorithm principles, machine learning, deep learning, reinforcement learning, graph networks, model deployment, and practical applications. Additionally, there are dedicated sections on quantitative trading and investment, as well as large models. The repository is actively maintained and updated, ensuring that users have access to the latest information and resources.

The Sandbox AutoClicker is a bot designed for the crypto game The Sandbox, allowing users to automate various processes within the game. The tool offers features such as auto tuning, multi-account auto clicker, multi-threading, a convenient menu, and free proxies. It provides full optimization through a simple menu and is guaranteed to be safe for Windows systems, supporting versions 7/8/8.1/10/11 (x32/64).

momoai-AutoClicker is a bot designed for the crypto game MomoAI, allowing users to automate various processes within the game. The tool offers features such as auto tuning, auto clicker for multiple accounts, multi-threading capabilities, a convenient menu, and free proxies. It provides full optimization through a simple menu interface and ensures the safety of the Windows system. Compatible with Windows 7/8/8.1/10/11 (x32/64).

Ocademy is an AI learning community dedicated to Python, Data Science, Machine Learning, Deep Learning, and MLOps. They promote equal opportunities for everyone to access AI through open-source educational resources. The repository contains curated AI courses, tutorials, books, tools, and resources for learning and creating Generative AI. It also offers an interactive book to help adults transition into AI. Contributors are welcome to join and contribute to the community by following guidelines. The project follows a code of conduct to ensure inclusivity and welcomes contributions from those passionate about Data Science and AI.

The go2coding.github.io repository is a collection of resources for AI enthusiasts, providing information on AI products, open-source projects, AI learning websites, and AI learning frameworks. It aims to help users stay updated on industry trends, learn from community projects, access learning resources, and understand and choose AI frameworks. The repository also includes instructions for local and external deployment of the project as a static website, with details on domain registration, hosting services, uploading static web pages, configuring domain resolution, and a visual guide to the AI tool navigation website. Additionally, it offers a platform for AI knowledge exchange through a QQ group and promotes AI tools through a WeChat public account.

The 'step_into_llm' repository is dedicated to the 昇思MindSpore technology open class, which focuses on exploring cutting-edge technologies, combining theory with practical applications, expert interpretations, open sharing, and empowering competitions. The repository contains course materials, including slides and code, for the ongoing second phase of the course. It covers various topics related to large language models (LLMs) such as Transformer, BERT, GPT, GPT2, and more. The course aims to guide developers interested in LLMs from theory to practical implementation, with a special emphasis on the development and application of large models.

AI-lectures is a repository containing educational materials on various topics related to Artificial Intelligence, including Machine Learning, Robotics, and Optimization. It provides full scripts, slides, and exercises with solutions for different lectures. Users can compile the materials into PDFs for easy access and reference. The repository aims to offer comprehensive resources for individuals interested in learning about AI and its applications in intelligent systems.

This repository serves as a resource for the Introduction to Large Language Models (LLMs) course, providing Jupyter notebooks with hands-on examples and exercises to help users learn the basics of Large Language Models. It includes information on installed packages, updates, and setting up a virtual environment for managing packages and running Jupyter notebooks.

Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.

LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.

The VisionCraft API is a free API for using over 100 different AI models. From images to sound.