This repository provides a comprehensive guide to understanding and applying artificial intelligence (AI) theory, including basic knowledge, machine learning, deep learning, and natural language processing (BERT). It features detailed explanations, annotated code, and datasets to help users grasp the concepts and implement them in practice. The repository is continuously updated to ensure the latest information and best practices are covered.

This repository contains a significant portion of the FULL official v0, Manus, and Cursor system prompts and AI models. It includes over 5,000+ lines of insights into their structure and functionality. The available files include FULL v0, v0 model.txt, v0 tools.txt, Cursor (with cursor agent.txt, cursor ask.txt, cursor edit.txt), and Manus Folder with multiple files inside.

This repository provides a comprehensive guide to modern AI for beginners, covering both theoretical foundations and practical implementation. It emphasizes the importance of understanding both the mathematical principles and the code implementation of AI models. The repository includes resources on PyTorch, deep learning fundamentals, mathematical foundations, transformer-based LLMs, diffusion models, software engineering, and full-stack development. It also features tutorials on natural language processing with transformers, reinforcement learning, and practical deep learning for coders.

Lidar AI Solution is a highly optimized repository for self-driving 3D lidar, providing solutions for sparse convolution, BEVFusion, CenterPoint, OSD, and Conversion. It includes CUDA and TensorRT implementations for various tasks such as 3D sparse convolution, BEVFusion, CenterPoint, PointPillars, V2XFusion, cuOSD, cuPCL, and YUV to RGB conversion. The repository offers easy-to-use solutions, high accuracy, low memory usage, and quantization options for different tasks related to self-driving technology.

Chipper provides a web interface, CLI, and architecture for pipelines, document chunking, web scraping, and query workflows. It is built with Haystack, Ollama, Hugging Face, Docker, Tailwind, and ElasticSearch, running locally or as a Dockerized service. Originally created to assist in creative writing, it now offers features like local Ollama and Hugging Face API, ElasticSearch embeddings, document splitting, web scraping, audio transcription, user-friendly CLI, and Docker deployment. The project aims to be educational, beginner-friendly, and a playground for AI exploration and innovation.

The Data Scientist Roadmap2024 provides a comprehensive guide to mastering essential tools for data science success. It includes programming languages, machine learning libraries, cloud platforms, and concepts categorized by difficulty. The roadmap covers a wide range of topics from programming languages to machine learning techniques, data visualization tools, and DevOps/MLOps tools. It also includes web development frameworks and specific concepts like supervised and unsupervised learning, NLP, deep learning, reinforcement learning, and statistics. Additionally, it delves into DevOps tools like Airflow and MLFlow, data visualization tools like Tableau and Matplotlib, and other topics such as ETL processes, optimization algorithms, and financial modeling.

LawGlance is an AI-powered legal assistant that aims to bridge the gap between people and legal access. It is a free, open-source initiative designed to provide quick and accurate legal support tailored to individual needs. The project covers various laws, with plans for international expansion in the future. LawGlance utilizes AI-powered Retriever-Augmented Generation (RAG) to deliver legal guidance accessible to both laypersons and professionals. The tool is developed with support from mentors and experts at Data Science Academy and Curvelogics.

LLMChat is an all-in-one AI chat interface that supports multiple language models, offers a plugin library for enhanced functionality, enables web search capabilities, allows customization of AI assistants, provides text-to-speech conversion, ensures secure local data storage, and facilitates data import/export. It also includes features like knowledge spaces, prompt library, personalization, and can be installed as a Progressive Web App (PWA). The tech stack includes Next.js, TypeScript, Pglite, LangChain, Zustand, React Query, Supabase, Tailwind CSS, Framer Motion, Shadcn, and Tiptap. The roadmap includes upcoming features like speech-to-text and knowledge spaces.

TensorZero is an open-source platform that helps LLM applications graduate from API wrappers into defensible AI products. It enables a data & learning flywheel for LLMs by unifying inference, observability, optimization, and experimentation. The platform includes a high-performance model gateway, structured schema-based inference, observability, experimentation, and data warehouse for analytics. TensorZero Recipes optimize prompts and models, and the platform supports experimentation features and GitOps orchestration for deployment.

ComfyUI-Ollama-Describer is an extension for ComfyUI that enables the use of LLM models provided by Ollama, such as Gemma, Llava (multimodal), Llama2, Llama3, or Mistral. It requires the Ollama library for interacting with large-scale language models, supporting GPUs using CUDA and AMD GPUs on Windows, Linux, and Mac. The extension allows users to run Ollama through Docker and utilize NVIDIA GPUs for faster processing. It provides nodes for image description, text description, image captioning, and text transformation, with various customizable parameters for model selection, API communication, response generation, and model memory management.

Fueling-Ambitions-Via-Book-Discoveries is an Advanced Machine Learning & AI Course designed for students, professionals, and AI researchers. The course integrates rigorous theoretical foundations with practical coding exercises, ensuring learners develop a deep understanding of AI algorithms and their applications in finance, healthcare, robotics, NLP, cybersecurity, and more. Inspired by MIT, Stanford, and Harvard’s AI programs, it combines academic research rigor with industry-standard practices used by AI engineers at companies like Google, OpenAI, Facebook AI, DeepMind, and Tesla. Learners can learn 50+ AI techniques from top Machine Learning & Deep Learning books, code from scratch with real-world datasets, projects, and case studies, and focus on ML Engineering & AI Deployment using Django & Streamlit. The course also offers industry-relevant projects to build a strong AI portfolio.

KitchenAI is an open-source toolkit designed to simplify AI development by serving as an AI backend and LLMOps solution. It aims to empower developers to focus on delivering results without being bogged down by AI infrastructure complexities. With features like simplifying AI integration, providing an AI backend, and empowering developers, KitchenAI streamlines the process of turning AI experiments into production-ready APIs. It offers built-in LLMOps features, is framework-agnostic and extensible, and enables faster time-to-production. KitchenAI is suitable for application developers, AI developers & data scientists, and platform & infra engineers, allowing them to seamlessly integrate AI into apps, deploy custom AI techniques, and optimize AI services with a modular framework. The toolkit eliminates the need to build APIs and infrastructure from scratch, making it easier to deploy AI code as production-ready APIs in minutes. KitchenAI also provides observability, tracing, and evaluation tools, and offers a Docker-first deployment approach for scalability and confidence.

Agent Hub is a platform for AI Agent solutions, containing three different projects aimed at transforming enterprise workflows, enhancing personalized language learning experiences, and enriching multimodal interactions. The projects include GitHub Sentinel for project management and automatic updates, LanguageMentor for personalized language learning support, and ChatPPT for multimodal AI-driven insights and PowerPoint automation in enterprise settings. The future vision of agent-hub is to serve as a launchpad for more AI Agents catering to different industries and pushing the boundaries of AI technology. Users are encouraged to explore, clone the repository, and contribute to the development of transformative AI agents.

ComfyUI_Yvann-Nodes is a pack of custom nodes that enable audio reactivity within ComfyUI, allowing users to create AI-driven animations that sync with music. Users can generate audio reactive AI videos, control AI generation styles, content, and composition with any audio input. The tool is simple to use by dropping workflows in ComfyUI and specifying audio and visual inputs. It is flexible and works with existing ComfyUI AI tech and nodes like IPAdapter, AnimateDiff, and ControlNet. Users can pick workflows for Images → Video or Video → Video, download the corresponding .json file, drop it into ComfyUI, install missing custom nodes, set inputs, and generate audio-reactive animations.

The 'summarize' tool is designed to transcribe and summarize videos from various sources using AI models. It helps users efficiently summarize lengthy videos, take notes, and extract key insights by providing timestamps, original transcripts, and support for auto-generated captions. Users can utilize different AI models via Groq, OpenAI, or custom local models to generate grammatically correct video transcripts and extract wisdom from video content. The tool simplifies the process of summarizing video content, making it easier to remember and reference important information.

LLM-Navigation is a repository dedicated to documenting learning records related to large models, including basic knowledge, prompt engineering, building effective agents, model expansion capabilities, security measures against prompt injection, and applications in various fields such as AI agent control, browser automation, financial analysis, 3D modeling, and tool navigation using MCP servers. The repository aims to organize and collect information for personal learning and self-improvement through AI exploration.

FalkorDB is the first queryable Property Graph database to use sparse matrices to represent the adjacency matrix in graphs and linear algebra to query the graph. Primary features: * Adopting the Property Graph Model * Nodes (vertices) and Relationships (edges) that may have attributes * Nodes can have multiple labels * Relationships have a relationship type * Graphs represented as sparse adjacency matrices * OpenCypher with proprietary extensions as a query language * Queries are translated into linear algebra expressions

This repository provides a comprehensive guide to understanding and applying artificial intelligence (AI) theory, including basic knowledge, machine learning, deep learning, and natural language processing (BERT). It features detailed explanations, annotated code, and datasets to help users grasp the concepts and implement them in practice. The repository is continuously updated to ensure the latest information and best practices are covered.

AWS AI/ML Workshop & example collection in Korean. The example codes in this repository are divided into 4 categories: AI services, Applied AI, SageMaker, Integration, Generative AI, and AWS Neuron. Each directory has its own Readme file. This repository also provides useful information for self-studying SageMaker.

AI 0x0 is a versatile AI query generation desktop floating assistant application that supports MacOS and Windows. It allows users to utilize AI capabilities in any desktop software to query and generate text, images, audio, and video data, helping them work more efficiently. The application features a dynamic desktop floating ball, floating dialogue bubbles, customizable presets, conversation bookmarking, preset packages, network acceleration, query mode, input mode, mouse navigation, deep customization of ChatGPT Next Web, support for full-format libraries, online search, voice broadcasting, voice recognition, voice assistant, application plugins, multi-model support, online text and image generation, image recognition, frosted glass interface, light and dark theme adaptation for each language model, and free access to all language models except Chat0x0 with a key.

AIAS is a comprehensive AI training platform that offers courses and practical examples in various AI fields such as traditional image processing, deep learning algorithms, JavaAI applications, NLP, web development, image generation, and desktop application development. The platform also provides SDKs for tasks like image recognition, OCR, natural language processing, audio processing, video analysis, and big data analysis. Users can access training materials, source code, and tools for developing AI applications across different domains.

Monadic Chat is a locally hosted web application designed to create and utilize intelligent chatbots. It provides a Linux environment on Docker to GPT and other LLMs, enabling the execution of advanced tasks that require external tools. The tool supports voice interaction, image and video recognition and generation, and AI-to-AI chat, making it useful for using AI and developing various applications. It is available for Mac, Windows, and Linux (Debian/Ubuntu) with easy-to-use installers.

Deeppowers is a powerful Python library for deep learning applications. It provides a wide range of tools and utilities to simplify the process of building and training deep neural networks. With Deeppowers, users can easily create complex neural network architectures, perform efficient training and optimization, and deploy models for various tasks. The library is designed to be user-friendly and flexible, making it suitable for both beginners and experienced deep learning practitioners.

This repository contains projects and notes on common practical techniques for fine-tuning Large Language Models (LLMs). It includes fine-tuning LLM notebooks, Colab links, LLM techniques and utils, and other smaller language models. The repository also provides links to YouTube videos explaining the concepts and techniques discussed in the 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.

Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.

PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.

Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features: * Self-contained, with no need for a DBMS or cloud service. * OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE). * Supports consumer-grade GPUs.

SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.

Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.