Best AI tools for< Open Source Advocate >

DVC is an open-source version control system for machine learning projects. It allows users to track and manage their data, models, and code in a single place. DVC also provides a number of features that make it easy to collaborate on machine learning projects, such as experiment tracking, model registration, and pipeline management.

SoraWebui is an open-source web platform that simplifies video creation by allowing users to generate videos from text using OpenAI's Sora model. It provides an easy-to-use interface and one-click website deployment, making it accessible to both professionals and enthusiasts in video production and AI technology. SoraWebui also includes a simulated version of the Sora API called FakeSoraAPI, which allows developers to start developing and testing their projects in a mock environment.

Clarity Write is an open-source SaaS script that provides a comprehensive suite of AI-powered tools to transform content creation. With its powerful AI capabilities, users can effortlessly generate high-quality content, create stunning visuals, automate coding tasks, transcribe audio and video files, and engage with AI experts via chatbots. Clarity Write also offers a vast library of over 500 professionally designed templates, a feature-rich editor for refining content, and robust admin tools for streamlined management. By leveraging the capabilities of OpenAI APIs, Clarity Write empowers users to enhance their content creation process, unlock endless creativity, and simplify their operations.

Comfy Org is an open-source AI tooling platform dedicated to advancing and democratizing AI technology. The platform offers tools like node manager, node registry, CLI, automated testing, and public documentation to support the ComfyUI ecosystem. Comfy Org aims to make state-of-the-art AI models accessible to a wider audience by fostering an open-source and community-driven approach. The team behind Comfy Org consists of individuals passionate about developing and maintaining various components of the platform, ensuring a reliable and secure environment for users to explore and contribute to AI tooling.

karaok-AI is an open-source karaoke Player / Editor with automatic clip creation from any song file using vocals and lyrics extraction (Speech-to-Text). It uses WhisperHallu and WhisperTimeSync to extract vocals and lyrics. karaok-AI also includes kaiDJ, a minimalist and easy-to-use DJ Party Player with multi-sound cards support, two players with auto-mix between songs, and a pre-listen player. It can index thousands of songs in a single efficient database and allows for direct search and selection over all songs. Additionally, it offers playlist management with nested groups and the ability to open and save m3u and m3u8 playlists while keeping group definitions.

AIExh is a platform dedicated to discovering and following the hottest open-source AI projects. It serves as the #1 database for open-source AI, providing daily updates and recommendations. With a user base of over 1000 humans and 1000+ subscribers, AIExh covers a wide range of AI applications such as image identification, speech recognition, machine translation, and more. Users can explore various AI projects, submit their own projects, and stay updated on the latest advancements in artificial intelligence.

Evidently AI is an open-source machine learning (ML) monitoring and observability platform that helps data scientists and ML engineers evaluate, test, and monitor ML models from validation to production. It provides a centralized hub for ML in production, including data quality monitoring, data drift monitoring, ML model performance monitoring, and NLP and LLM monitoring. Evidently AI's features include customizable reports, structured checks for data and models, and a Python library for ML monitoring. It is designed to be easy to use, with a simple setup process and a user-friendly interface. Evidently AI is used by over 2,500 data scientists and ML engineers worldwide, and it has been featured in publications such as Forbes, VentureBeat, and TechCrunch.

OpenCV is a library of programming functions mainly aimed at real-time computer vision. Originally developed by Intel, it was later supported by Willow Garage and is now maintained by Itseez. OpenCV is cross-platform and free for use under the open-source BSD license.

Keep is an open-source AIOps platform designed for those dealing with alerts in complex environments. It leverages AI for IT Operations, offering high-quality integrations with monitoring systems, IRM, ticketing, source control, change management, and CMDB. Keep provides a bidirectional integration system to keep alerts and signals in sync. It also offers advanced querying, slicing, and data analysis capabilities, noise reduction, and workflow automation based on YAML. For enterprises, Keep provides alert correlation based on past incidents and AI technology for performance enhancement.

Ubdroid AI Answer Engine is an AI-powered tool that utilizes various open-source LLMs to provide answers to user queries. It works by processing user queries and fetching relevant information from these LLMs. The accuracy of the answers depends on the quality and relevance of the data provided by the LLMs. The free version of the tool has a request limit of 10 requests per minute. If a model is not working, users can select another model.

EnergeticAI is an open-source AI library that can be used in Node.js applications. It is optimized for serverless environments and provides fast cold-start, small module size, and pre-trained models. EnergeticAI can be used for a variety of tasks, including building recommendations, classifying text, and performing semantic search.

OpenVoiceOS is a community-driven, open-source voice AI platform for creating custom voice-controlled interfaces across devices with NLP, a customizable UI, and a focus on privacy and security. OpenVoiceOS is designed to provide users with a seamless and intuitive voice interface for controlling their smart home devices, playing music, setting reminders, and much more. OpenVoiceOS is open to all developers and contributors wanting to support a specific device or a platform. OpenVoiceOS is the platform to throw your ideas at if you have an experimental feature you want users to experience before landing them into any of the Linux-based open-source voice assistant projects upstream.

Civitai is an open-source generative AI platform that allows users to create images, videos, posts, articles, and bounties. It also provides a virtual canvas where users can unleash their creativity or get inspired by others. Civitai is a community-driven platform that offers a variety of resources for users, including tutorials, guides, and analysis on particular topics.

Aim is an open-source, self-hosted AI Metadata tracking tool designed to handle 100,000s of tracked metadata sequences. Two most famous AI metadata applications are: experiment tracking and prompt engineering. Aim provides a performant and beautiful UI for exploring and comparing training runs, prompt sessions.

Helix AI is a private GenAI platform that enables users to build AI applications using open source models. The platform offers tools for RAG (Retrieval-Augmented Generation) and fine-tuning, allowing deployment on-premises or in a Virtual Private Cloud (VPC). Users can access curated models, utilize Helix API tools to connect internal and external APIs, embed Helix Assistants into websites/apps for chatbot functionality, write AI application logic in natural language, and benefit from the innovative RAG system for Q&A generation. Additionally, users can fine-tune models for domain-specific needs and deploy securely on Kubernetes or Docker in any cloud environment. Helix Cloud offers free and premium tiers with GPU priority, catering to individuals, students, educators, and companies of varying sizes.

Aim is an open-source experiment tracker that logs your training runs, enables a beautiful UI to compare them, and an API to query them programmatically. It integrates seamlessly with your favorite tools.

Tune Chat is a chat application that utilizes open-source Large Language Models (LLMs) to provide users with a conversational and informative experience. It is designed to understand and respond to a wide range of user queries, offering assistance with various tasks and engaging in natural language conversations.

Qubinets is a cloud data environment solutions platform that provides building blocks for building big data, AI, web, and mobile environments. It is an open-source, no lock-in, secured, and private platform that can be used on any cloud, including AWS, Digital Ocean, Google Cloud, and Microsoft Azure. Qubinets makes it easy to plan, build, and run data environments, and it streamlines and saves time and money by reducing the grunt work in setup and provisioning.

Activepieces is an open-source no-code business automation tool that allows users to securely deploy automation for various departments such as marketing, sales, operations, HR, finance, and IT teams. It offers a customizable and self-hosted solution, enabling users to put their work on autopilot. Activepieces stands out for its user experience, ease of integration, and control over hosting. The tool leverages AI automation to streamline tasks like content strategy, security compliance, sales outreach, and customer support. With a community-driven approach, Activepieces aims to make the automation world more open and accessible.

Jan is an open-source ChatGPT-alternative that runs 100% offline. It allows users to chat with AI, download and run powerful models, connect to cloud AIs, set up a local API server, and chat with files. Highly customizable, Jan also offers features like creating personalized AI assistants, memory, and extensions. The application prioritizes local-first AI, user-owned data, and full customization, making it a versatile tool for AI enthusiasts and developers.

This repository contains the official implementation of RPG, a powerful training-free paradigm for text-to-image generation and editing. RPG utilizes proprietary or open-source MLLMs as prompt recaptioner and region planner with complementary regional diffusion. It achieves state-of-the-art results and can generate high-resolution images. The codebase supports diffusers and various diffusion backbones, including SDXL and SD v1.4/1.5. Users can reproduce results with GPT-4, Gemini-Pro, or local MLLMs like miniGPT-4. The repository provides tools for quick start, regional diffusion with GPT-4, and regional diffusion with local LLMs.

This course has 18 lessons. Each lesson covers its own topic so start wherever you like! Lessons are labeled either "Learn" lessons explaining a Generative AI concept or "Build" lessons that explain a concept and code examples in both **Python** and **TypeScript** when possible. Each lesson also includes a "Keep Learning" section with additional learning tools. **What You Need** * Access to the Azure OpenAI Service **OR** OpenAI API - _Only required to complete coding lessons_ * Basic knowledge of Python or Typescript is helpful - *For absolute beginners check out these Python and TypeScript courses. * A Github account to fork this entire repo to your own GitHub account We have created a **Course Setup** lesson to help you with setting up your development environment. Don't forget to star (🌟) this repo to find it easier later. ## 🧠 Ready to Deploy? If you are looking for more advanced code samples, check out our collection of Generative AI Code Samples in both **Python** and **TypeScript**. ## 🗣️ Meet Other Learners, Get Support Join our official AI Discord server to meet and network with other learners taking this course and get support. ## 🚀 Building a Startup? Sign up for Microsoft for Startups Founders Hub to receive **free OpenAI credits** and up to **$150k towards Azure credits to access OpenAI models through Azure OpenAI Services**. ## 🙏 Want to help? Do you have suggestions or found spelling or code errors? Raise an issue or Create a pull request ## 📂 Each lesson includes: * A short video introduction to the topic * A written lesson located in the README * Python and TypeScript code samples supporting Azure OpenAI and OpenAI API * Links to extra resources to continue your learning ## 🗃️ Lessons | | Lesson Link | Description | Additional Learning | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | | 00 | Course Setup | **Learn:** How to Setup Your Development Environment | Learn More | | 01 | Introduction to Generative AI and LLMs | **Learn:** Understanding what Generative AI is and how Large Language Models (LLMs) work. | Learn More | | 02 | Exploring and comparing different LLMs | **Learn:** How to select the right model for your use case | Learn More | | 03 | Using Generative AI Responsibly | **Learn:** How to build Generative AI Applications responsibly | Learn More | | 04 | Understanding Prompt Engineering Fundamentals | **Learn:** Hands-on Prompt Engineering Best Practices | Learn More | | 05 | Creating Advanced Prompts | **Learn:** How to apply prompt engineering techniques that improve the outcome of your prompts. | Learn More | | 06 | Building Text Generation Applications | **Build:** A text generation app using Azure OpenAI | Learn More | | 07 | Building Chat Applications | **Build:** Techniques for efficiently building and integrating chat applications. | Learn More | | 08 | Building Search Apps Vector Databases | **Build:** A search application that uses Embeddings to search for data. | Learn More | | 09 | Building Image Generation Applications | **Build:** A image generation application | Learn More | | 10 | Building Low Code AI Applications | **Build:** A Generative AI application using Low Code tools | Learn More | | 11 | Integrating External Applications with Function Calling | **Build:** What is function calling and its use cases for applications | Learn More | | 12 | Designing UX for AI Applications | **Learn:** How to apply UX design principles when developing Generative AI Applications | Learn More | | 13 | Securing Your Generative AI Applications | **Learn:** The threats and risks to AI systems and methods to secure these systems. | Learn More | | 14 | The Generative AI Application Lifecycle | **Learn:** The tools and metrics to manage the LLM Lifecycle and LLMOps | Learn More | | 15 | Retrieval Augmented Generation (RAG) and Vector Databases | **Build:** An application using a RAG Framework to retrieve embeddings from a Vector Databases | Learn More | | 16 | Open Source Models and Hugging Face | **Build:** An application using open source models available on Hugging Face | Learn More | | 17 | AI Agents | **Build:** An application using an AI Agent Framework | Learn More | | 18 | Fine-Tuning LLMs | **Learn:** The what, why and how of fine-tuning LLMs | Learn More |

Devon is an open-source pair programmer tool designed to facilitate collaborative coding sessions. It provides features such as multi-file editing, codebase exploration, test writing, bug fixing, and architecture exploration. The tool supports Anthropic, OpenAI, and Groq APIs, with plans to add more models in the future. Devon is community-driven, with ongoing development goals including multi-model support, plugin system for tool builders, self-hostable Electron app, and setting SOTA on SWE-bench Lite. Users can contribute to the project by developing core functionality, conducting research on agent performance, providing feedback, and testing the tool.

LSP-AI is an open source language server designed to enhance software engineers' productivity by integrating AI-powered functionality into various text editors. It serves as a backend for completion with large language models and offers features like unified AI capabilities, simplified plugin development, enhanced collaboration, broad compatibility with editors supporting Language Server Protocol, flexible LLM backend support, and commitment to staying updated with the latest advancements in LLM-driven software development. The tool aims to centralize open-source development work, provide a collaborative platform for developers, and offer a future-ready solution for AI-powered assistants in text editors.

StarCoder2-Instruct is an open-source pipeline that introduces StarCoder2-15B-Instruct-v0.1, a self-aligned code Large Language Model (LLM) trained with a fully permissive and transparent pipeline. It generates instruction-response pairs to fine-tune StarCoder-15B without human annotations or data from proprietary LLMs. The tool is primarily finetuned for Python code generation tasks that can be verified through execution, with potential biases and limitations. Users can provide response prefixes or one-shot examples to guide the model's output. The model may have limitations with other programming languages and out-of-domain coding tasks.

Vocode is an open source library that enables users to easily build voice-based LLM (Large Language Model) apps. With Vocode, users can create real-time streaming conversations with LLMs and deploy them for phone calls, Zoom meetings, and more. The library offers abstractions and integrations for transcription services, LLMs, and synthesis services, making it a comprehensive tool for voice-based applications.

ASTRA is an open-source platform designed for developing applications utilizing large language models. It merges the ideas of Backend-as-a-Service and LLM operations, allowing developers to swiftly create production-ready generative AI applications. Additionally, it empowers non-technical users to engage in defining and managing data operations for AI applications. With ASTRA, you can easily create real-time, multi-modal AI applications with low latency, even without any coding knowledge.

Whisper is an open-source library by Open AI that converts/extracts text from audio. It is a cross-platform tool that supports real-time transcription of various types of audio/video without manual conversion to WAV format. The library is designed to run on Linux and Android platforms, with plans for expansion to other platforms. Whisper utilizes three frameworks to function: DART for CLI execution, Flutter for mobile app integration, and web/WASM for web application deployment. The tool aims to provide a flexible and easy-to-use solution for transcription tasks across different programs and platforms.

The repository 'aise' is an open-source electronic book focusing on AI-assisted software engineering. It covers the latest AI-assisted software engineering practices, implementation details from AI models to IDE plugins, and practical experiences in coding intelligent agents. The book aims to provide insights on leveraging AI to enhance software development efficiency and effectiveness across the entire software development lifecycle.

LLM-Geo is an AI-powered geographic information system (GIS) that leverages Large Language Models (LLMs) for automatic spatial data collection, analysis, and visualization. By adopting LLM as the reasoning core, it addresses spatial problems with self-generating, self-organizing, self-verifying, self-executing, and self-growing capabilities. The tool aims to make spatial analysis easier, faster, and more accessible by reducing manual operation time and delivering accurate results through case studies. It uses GPT-4 API in a Python environment and advocates for further research and development in autonomous GIS.

Hal9 is a tool that allows users to create and deploy generative applications such as chatbots and APIs quickly. It is open, intuitive, scalable, and powerful, enabling users to use various models and libraries without the need to learn complex app frameworks. With a focus on AI tasks like RAG, fine-tuning, alignment, and training, Hal9 simplifies the development process by skipping engineering tasks like frontend development, backend integration, deployment, and operations.

Convoscope is a suite of smart glasses and web tools designed to augment conversations by providing live proactive agents that answer questions, offer definitions, insights, and alternative viewpoints. It includes features like 'Mira' AI Assistant, Convoscope Proactive AI Agents, Language Learning app, Screen Mirror functionality, and upcoming features such as Live Captions, ADHD Glasses, and Live Language Translation. The tool supports various smart glasses models and Android 12+ phones, offering a unique experience for real-life conversations, meetings, and video calls.

This repository contains a collection of papers and resources related to Large Language Models (LLMs), including their applications in various domains such as text generation, translation, question answering, and dialogue systems. The repository also includes discussions on the ethical and societal implications of LLMs. **Description** This repository is a collection of papers and resources related to Large Language Models (LLMs). LLMs are a type of artificial intelligence (AI) that can understand and generate human-like text. They have a wide range of applications, including text generation, translation, question answering, and dialogue systems. **For Jobs** - **Content Writer** - **Copywriter** - **Editor** - **Journalist** - **Marketer** **AI Keywords** - **Large Language Models** - **Natural Language Processing** - **Machine Learning** - **Artificial Intelligence** - **Deep Learning** **For Tasks** - **Generate text** - **Translate text** - **Answer questions** - **Engage in dialogue** - **Summarize text**

LLM Agents Papers is a repository containing must-read papers on Large Language Model Agents. It covers a wide range of topics related to language model agents, including interactive natural language processing, large language model-based autonomous agents, personality traits in large language models, memory enhancements, planning capabilities, tool use, multi-agent communication, and more. The repository also provides resources such as benchmarks, types of tools, and a tool list for building and evaluating language model agents. Contributors are encouraged to add important works to the repository.

This repository hosts the official code and data artifact for the paper 'Monitor-Guided Decoding of Code LMs with Static Analysis of Repository Context'. It introduces Monitor-Guided Decoding (MGD) for code generation using Language Models, where a monitor uses static analysis to guide the decoding. The repository contains datasets, evaluation scripts, inference results, a language server client 'multilspy' for static analyses, and implementation of various monitors monitoring for different properties in 3 programming languages. The monitors guide Language Models to adhere to properties like valid identifier dereferences, correct number of arguments to method calls, typestate validity of method call sequences, and more.

This repository contains the code and dataset for LDB, a novel debugging framework that enables Large Language Models (LLMs) to refine their generated programs by tracking the values of intermediate variables throughout the runtime execution. LDB segments programs into basic blocks, allowing LLMs to concentrate on simpler code units, verify correctness block by block, and pinpoint errors efficiently. The tool provides APIs for debugging and generating code with debugging messages, mimicking how human developers debug programs.

Functionary is a language model that interprets and executes functions/plugins. It determines when to execute functions, whether in parallel or serially, and understands their outputs. Function definitions are given as JSON Schema Objects, similar to OpenAI GPT function calls. It offers documentation and examples on functionary.meetkai.com. The newest model, meetkai/functionary-medium-v3.1, is ranked 2nd in the Berkeley Function-Calling Leaderboard. Functionary supports models with different context lengths and capabilities for function calling and code interpretation. It also provides grammar sampling for accurate function and parameter names. Users can deploy Functionary models serverlessly using Modal.com.

HybridAGI is the first Programmable LLM-based Autonomous Agent that lets you program its behavior using a **graph-based prompt programming** approach. This state-of-the-art feature allows the AGI to efficiently use any tool while controlling the long-term behavior of the agent. Become the _first Prompt Programmers in history_ ; be a part of the AI revolution one node at a time! **Disclaimer: We are currently in the process of upgrading the codebase to integrate DSPy**

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