ROSGPT_Vision is a new robotic framework designed to command robots using only two prompts: a Visual Prompt for visual semantic features and an LLM Prompt to regulate robotic reactions. It is based on the Prompting Robotic Modalities (PRM) design pattern and is used to develop CarMate, a robotic application for monitoring driver distractions and providing real-time vocal notifications. The framework leverages state-of-the-art language models to facilitate advanced reasoning about image data and offers a unified platform for robots to perceive, interpret, and interact with visual data through natural language. LangChain is used for easy customization of prompts, and the implementation includes the CarMate application for driver monitoring and assistance.

Open WebUI Tools Collection is a set of tools for structured planning, arXiv paper search, Hugging Face text-to-image generation, prompt enhancement, and multi-model conversations. It enhances LLM interactions with academic research, image generation, and conversation management. Tools include arXiv Search Tool and Hugging Face Image Generator. Function Pipes like Planner Agent offer autonomous plan generation and execution. Filters like Prompt Enhancer improve prompt quality. Installation and configuration instructions are provided for each tool and pipe.

AgentForge is a low-code framework tailored for the rapid development, testing, and iteration of AI-powered autonomous agents and Cognitive Architectures. It is compatible with a range of LLM models and offers flexibility to run different models for different agents based on specific needs. The framework is designed for seamless extensibility and database-flexibility, making it an ideal playground for various AI projects. AgentForge is a beta-testing ground and future-proof hub for crafting intelligent, model-agnostic autonomous agents.

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.

Greenmask is a powerful open-source utility designed for logical database backup dumping, anonymization, synthetic data generation, and restoration. It is highly customizable, stateless, and backward-compatible with existing PostgreSQL utilities. Greenmask supports advanced subset systems, deterministic transformers, dynamic parameters, transformation conditions, and more. It is cross-platform, database type safe, extensible, and supports parallel execution and various storage options. Ideal for backup and restoration tasks, anonymization, transformation, and data masking.

Notes on AI state of the art, with a focus on generative and large language models. These are the "raw materials" for the https://lspace.swyx.io/ newsletter. This repo used to be called https://github.com/sw-yx/prompt-eng, but was renamed because Prompt Engineering is Overhyped. This is now an AI Engineering notes repo.

Agent Zero is a personal and organic AI framework designed to be dynamic, organically growing, and learning as you use it. It is fully transparent, readable, comprehensible, customizable, and interactive. The framework uses the computer as a tool to accomplish tasks, with no single-purpose tools pre-programmed. It emphasizes multi-agent cooperation, complete customization, and extensibility. Communication is key in this framework, allowing users to give proper system prompts and instructions to achieve desired outcomes. Agent Zero is capable of dangerous actions and should be run in an isolated environment. The framework is prompt-based, highly customizable, and requires a specific environment to run effectively.

This repository contains the code and instructions needed to build a sophisticated answer engine that leverages the capabilities of Groq, Mistral AI's Mixtral, Langchain.JS, Brave Search, Serper API, and OpenAI. Designed to efficiently return sources, answers, images, videos, and follow-up questions based on user queries, this project is an ideal starting point for developers interested in natural language processing and search technologies.

The Persian License Plate Recognition (PLPR) system is a state-of-the-art solution designed for detecting and recognizing Persian license plates in images and video streams. Leveraging advanced deep learning models and a user-friendly interface, it ensures reliable performance across different scenarios. The system offers advanced detection using YOLOv5 models, precise recognition of Persian characters, real-time processing capabilities, and a user-friendly GUI. It is well-suited for applications in traffic monitoring, automated vehicle identification, and similar fields. The system's architecture includes modules for resident management, entrance management, and a detailed flowchart explaining the process from system initialization to displaying results in the GUI. Hardware requirements include an Intel Core i5 processor, 8 GB RAM, a dedicated GPU with at least 4 GB VRAM, and an SSD with 20 GB of free space. The system can be installed by cloning the repository and installing required Python packages. Users can customize the video source for processing and run the application to upload and process images or video streams. The system's GUI allows for parameter adjustments to optimize performance, and the Wiki provides in-depth information on the system's architecture and model training.

Gemini Android is a repository showcasing Google's Generative AI on Android using Stream Chat SDK for Compose. It demonstrates the Gemini API for Android, implements UI elements with Jetpack Compose, utilizes Android architecture components like Hilt and AppStartup, performs background tasks with Kotlin Coroutines, and integrates chat systems with Stream Chat Compose SDK for real-time event handling. The project also provides technical content, instructions on building the project, tech stack details, architecture overview, modularization strategies, and a contribution guideline. It follows Google's official architecture guidance and offers a real-world example of app architecture implementation.

PeriDyno is a CUDA-based, highly parallel physics engine targeted at providing real-time simulation of physical environments for intelligent agents. It is designed to be easy to use and integrate into existing projects, and it provides a wide range of features for simulating a variety of physical phenomena. PeriDyno is open source and available under the Apache 2.0 license.

Kollektiv is a Retrieval-Augmented Generation (RAG) system designed to enable users to chat with their favorite documentation easily. It aims to provide LLMs with access to the most up-to-date knowledge, reducing inaccuracies and improving productivity. The system utilizes intelligent web crawling, advanced document processing, vector search, multi-query expansion, smart re-ranking, AI-powered responses, and dynamic system prompts. The technical stack includes Python/FastAPI for backend, Supabase, ChromaDB, and Redis for storage, OpenAI and Anthropic Claude 3.5 Sonnet for AI/ML, and Chainlit for UI. Kollektiv is licensed under a modified version of the Apache License 2.0, allowing free use for non-commercial purposes.

Inngest is a platform that offers durable functions to replace queues, state management, and scheduling for developers. It allows writing reliable step functions faster without dealing with infrastructure. Developers can create durable functions using various language SDKs, run a local development server, deploy functions to their infrastructure, sync functions with the Inngest Platform, and securely trigger functions via HTTPS. Inngest Functions support retrying, scheduling, and coordinating operations through triggers, flow control, and steps, enabling developers to build reliable workflows with robust support for various operations.

This repository contains code and demo for Knowledge Graph Prompting for Multi-Document Question Answering. It includes modules for data collection, training DPR and MDR models, fine-tuning T5 and LLaMA, and reproducing KGP-LLM algorithm. The workflow involves document collection, knowledge graph construction, fine-tuning models, and reproducing main table results. The repository provides instructions for environment setup, folder architecture, and running different modules.

TypeChat.NET is a framework that provides cross-platform libraries for building natural language interfaces with language models using strong types, type validation, and simple type-safe programs. It translates user intent into strongly typed objects and JSON programs, with support for schema export, extensibility, and common scenarios. The framework is actively developed with frequent updates, evolving based on exploration and feedback. It consists of assemblies for translating user intent, synthesizing JSON programs, and integrating with Microsoft Semantic Kernel. TypeChat.NET requires familiarity with and access to OpenAI language models for its examples and scenarios.

Pocket Flow is a 100-line minimalist LLM framework designed for (Multi-)Agents, Workflow, RAG, etc. It provides a core abstraction for LLM projects by focusing on computation and communication through a graph structure and shared store. The framework aims to support the development of LLM Agents, such as Cursor AI, by offering a minimal and low-level approach that is well-suited for understanding and usage. Users can install Pocket Flow via pip or by copying the source code, and detailed documentation is available on the project website.

ROSGPT_Vision is a new robotic framework designed to command robots using only two prompts: a Visual Prompt for visual semantic features and an LLM Prompt to regulate robotic reactions. It is based on the Prompting Robotic Modalities (PRM) design pattern and is used to develop CarMate, a robotic application for monitoring driver distractions and providing real-time vocal notifications. The framework leverages state-of-the-art language models to facilitate advanced reasoning about image data and offers a unified platform for robots to perceive, interpret, and interact with visual data through natural language. LangChain is used for easy customization of prompts, and the implementation includes the CarMate application for driver monitoring and assistance.

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.

This tool is designed to detect and classify Alzheimer's Disease using Deep Learning and Machine Learning algorithms on an early basis, which is further optimized using the Crow Search Algorithm (CSA). Alzheimer's is a fatal disease, and early detection is crucial for patients to predetermine their condition and prevent its progression. By analyzing MRI scanned images using Artificial Intelligence technology, this tool can classify patients who may or may not develop AD in the future. The CSA algorithm, combined with ML algorithms, has proven to be the most effective approach for this purpose.

This repository contains code for building cooperative embodied agents modularly with large language models. The agents are trained to perform tasks in two different environments: ThreeDWorld Multi-Agent Transport (TDW-MAT) and Communicative Watch-And-Help (C-WAH). TDW-MAT is a multi-agent environment where agents must transport objects to a goal position using containers. C-WAH is an extension of the Watch-And-Help challenge, which enables agents to send messages to each other. The code in this repository can be used to train agents to perform tasks in both of these environments.

This repository focuses on organizing resources for building synthetic datasets using large language models. It covers important datasets, libraries, tools, tutorials, and papers related to synthetic data generation. The goal is to provide pragmatic and practical resources for individuals interested in creating synthetic datasets for machine learning applications.

AI Devices Template is a project that serves as an AI-powered voice assistant utilizing various AI models and services to provide intelligent responses to user queries. It supports voice input, transcription, text-to-speech, image processing, and function calling with conditionally rendered UI components. The project includes customizable UI settings, optional rate limiting using Upstash, and optional tracing with Langchain's LangSmith for function execution. Users can clone the repository, install dependencies, add API keys, start the development server, and deploy the application. Configuration settings can be modified in `app/config.tsx` to adjust settings and configurations for the AI-powered voice assistant.

ROSGPT_Vision is a new robotic framework designed to command robots using only two prompts: a Visual Prompt for visual semantic features and an LLM Prompt to regulate robotic reactions. It is based on the Prompting Robotic Modalities (PRM) design pattern and is used to develop CarMate, a robotic application for monitoring driver distractions and providing real-time vocal notifications. The framework leverages state-of-the-art language models to facilitate advanced reasoning about image data and offers a unified platform for robots to perceive, interpret, and interact with visual data through natural language. LangChain is used for easy customization of prompts, and the implementation includes the CarMate application for driver monitoring and assistance.

AIBotPublic is an open-source version of AIBotPro, a comprehensive AI tool that provides various features such as knowledge base construction, AI drawing, API hosting, and more. It supports custom plugins and parallel processing of multiple files. The tool is built using bootstrap4 for the frontend, .NET6.0 for the backend, and utilizes technologies like SqlServer, Redis, and Milvus for database and vector database functionalities. It integrates third-party dependencies like Baidu AI OCR, Milvus C# SDK, Google Search, and more to enhance its capabilities.

LLMGA (Multimodal Large Language Model-based Generation Assistant) is a tool that leverages Large Language Models (LLMs) to assist users in image generation and editing. It provides detailed language generation prompts for precise control over Stable Diffusion (SD), resulting in more intricate and precise content in generated images. The tool curates a dataset for prompt refinement, similar image generation, inpainting & outpainting, and visual question answering. It offers a two-stage training scheme to optimize SD alignment and a reference-based restoration network to alleviate texture, brightness, and contrast disparities in image editing. LLMGA shows promising generative capabilities and enables wider applications in an interactive manner.

MetaAgent is a multi-agent collaboration platform designed to build, manage, and deploy multi-modal AI agents without the need for coding. Users can easily create AI agents by editing a yml file or using the provided UI. The platform supports features such as building LLM-based AI agents, multi-modal interactions with users using texts, audios, images, and videos, creating a company of agents for complex tasks like drawing comics, vector database and knowledge embeddings, and upcoming features like UI for creating and using AI agents, fine-tuning, and RLHF. The tool simplifies the process of creating and deploying AI agents for various tasks.