The `repo-to-text` tool converts a directory's structure and contents into a single text file. It generates a formatted text representation that includes the directory tree and file contents, making it easy to share code with LLMs for development and debugging. Users can customize the tool's behavior with various options and settings, including output directory specification, debug logging, and file inclusion/exclusion rules. The tool supports Docker usage for containerized environments and provides detailed instructions for installation, usage, settings configuration, and contribution guidelines. It is a versatile tool for converting repository contents into text format for easy sharing and documentation.

ai-digest is a CLI tool designed to aggregate your codebase into a single Markdown file for use with Claude Projects or custom ChatGPTs. It aggregates all files in the specified directory and subdirectories, ignores common build artifacts and configuration files, and provides options for whitespace removal and custom ignore patterns. The tool is useful for preparing codebases for AI analysis and assistance.

code2prompt is a command-line tool that converts your codebase into a single LLM prompt with a source tree, prompt templating, and token counting. It automates generating LLM prompts from codebases of any size, customizing prompt generation with Handlebars templates, respecting .gitignore, filtering and excluding files using glob patterns, displaying token count, including Git diff output, copying prompt to clipboard, saving prompt to an output file, excluding files and folders, adding line numbers to source code blocks, and more. It helps streamline the process of creating LLM prompts for code analysis, generation, and other tasks.

Reader is a tool that converts any URL to an LLM-friendly input with a simple prefix `https://r.jina.ai/`. It improves the output for your agent and RAG systems at no cost. Reader supports image reading, captioning all images at the specified URL and adding `Image [idx]: [caption]` as an alt tag. This enables downstream LLMs to interact with the images in reasoning, summarizing, etc. Reader offers a streaming mode, useful when the standard mode provides an incomplete result. In streaming mode, Reader waits a bit longer until the page is fully rendered, providing more complete information. Reader also supports a JSON mode, which contains three fields: `url`, `title`, and `content`. Reader is backed by Jina AI and licensed under Apache-2.0.

The user interface (and the main server) of the simulator of aids and social benefits for young people. It is based on the free socio-fiscal simulator Openfisca.

Vectara Answer is a sample app for Vectara-powered Summarized Semantic Search (or question-answering) with advanced configuration options. For examples of what you can build with Vectara Answer, check out Ask News, LegalAid, or any of the other demo applications.

Renumics RAG is a retrieval-augmented generation assistant demo that utilizes LangChain and Streamlit. It provides a tool for indexing documents and answering questions based on the indexed data. Users can explore and visualize RAG data, configure OpenAI and Hugging Face models, and interactively explore questions and document snippets. The tool supports GPU and CPU setups, offers a command-line interface for retrieving and answering questions, and includes a web application for easy access. It also allows users to customize retrieval settings, embeddings models, and database creation. Renumics RAG is designed to enhance the question-answering process by leveraging indexed documents and providing detailed answers with sources.

The Slack Bot is a tool designed to enhance the workflow of development teams by integrating with Jenkins, GitHub, GitLab, and Jira. It allows for custom commands, macros, crons, and project-specific commands to be implemented easily. Users can interact with the bot through Slack messages, execute commands, and monitor job progress. The bot supports features like starting and monitoring Jenkins jobs, tracking pull requests, querying Jira information, creating buttons for interactions, generating images with DALL-E, playing quiz games, checking weather, defining custom commands, and more. Configuration is managed via YAML files, allowing users to set up credentials for external services, define custom commands, schedule cron jobs, and configure VCS systems like Bitbucket for automated branch lookup in Jenkins triggers.

ComfyUI Desktop is a packaged desktop application that allows users to easily use ComfyUI with bundled features like ComfyUI source code, ComfyUI-Manager, and uv. It automatically installs necessary Python dependencies and updates with stable releases. The app comes with Electron, Chromium binaries, and node modules. Users can store ComfyUI files in a specified location and manage model paths. The tool requires Python 3.12+ and Visual Studio with Desktop C++ workload for Windows. It uses nvm to manage node versions and yarn as the package manager. Users can install ComfyUI and dependencies using comfy-cli, download uv, and build/launch the code. Troubleshooting steps include rebuilding modules and installing missing libraries. The tool supports debugging in VSCode and provides utility scripts for cleanup. Crash reports can be sent to help debug issues, but no personal data is included.

gpt-cli is a command-line interface tool for interacting with various chat language models like ChatGPT, Claude, and others. It supports model customization, usage tracking, keyboard shortcuts, multi-line input, markdown support, predefined messages, and multiple assistants. Users can easily switch between different assistants, define custom assistants, and configure model parameters and API keys in a YAML file for easy customization and management.

This repository codifies the Airflow cluster that is deployed at workflow.telemetry.mozilla.org (behind SSO) and commonly referred to as "WTMO" or simply "Airflow". Some links relevant to users and developers of WTMO: * The `dags` directory in this repository contains some custom DAG definitions * Many of the DAGs registered with WTMO don't live in this repository, but are instead generated from ETL task definitions in bigquery-etl * The Data SRE team maintains a WTMO Developer Guide (behind SSO)

This Python Telegram bot utilizes Google's `gemini-pro` LLM API to generate creative text formats based on user input. It's designed to be an engaging and interactive way to explore the capabilities of large language models. Key features include generating various text formats like poems, code, scripts, and musical pieces. The bot supports real-time streaming of the generation process, allowing users to witness the text unfold. Additionally, it can respond to messages with Bard's creative output and handle image-based inputs for multimodal responses. User authentication is optional, and the bot can be easily integrated with Docker or installed via pipenv.

Dir-assistant is a tool that allows users to interact with their current directory's files using local or API Language Models (LLMs). It supports various platforms and provides API support for major LLM APIs. Users can configure and customize their local LLMs and API LLMs using the tool. Dir-assistant also supports model downloads and configurations for efficient usage. It is designed to enhance file interaction and retrieval using advanced language models.

OpenCommit is a tool that auto-generates meaningful commits using AI, allowing users to quickly create commit messages for their staged changes. It provides a CLI interface for easy usage and supports customization of commit descriptions, emojis, and AI models. Users can configure local and global settings, switch between different AI providers, and set up Git hooks for integration with IDE Source Control. Additionally, OpenCommit can be used as a GitHub Action to automatically improve commit messages on push events, ensuring all commits are meaningful and not generic. Payments for OpenAI API requests are handled by the user, with the tool storing API keys locally.

TypeGPT is a Python application that enables users to interact with ChatGPT or Google Gemini from any text field in their operating system using keyboard shortcuts. It provides global accessibility, keyboard shortcuts for communication, and clipboard integration for larger text inputs. Users need to have Python 3.x installed along with specific packages and API keys from OpenAI for ChatGPT access. The tool allows users to run the program normally or in the background, manage processes, and stop the program. Users can use keyboard shortcuts like `/ask`, `/see`, `/stop`, `/chatgpt`, `/gemini`, `/check`, and `Shift + Cmd + Enter` to interact with the application in any text field. Customization options are available by modifying files like `keys.txt` and `system_prompt.txt`. Contributions are welcome, and future plans include adding support for other APIs and a user-friendly GUI.

Windows Agent Arena (WAA) is a scalable Windows AI agent platform designed for testing and benchmarking multi-modal, desktop AI agents. It provides researchers and developers with a reproducible and realistic Windows OS environment for AI research, enabling testing of agentic AI workflows across various tasks. WAA supports deploying agents at scale using Azure ML cloud infrastructure, allowing parallel running of multiple agents and delivering quick benchmark results for hundreds of tasks in minutes.

The Ollama Conversation integration adds a conversation agent powered by Ollama in Home Assistant. This agent can be used in automations to query information provided by Home Assistant about your house, including areas, devices, and their states. Users can install the integration via HACS and configure settings such as API timeout, model selection, context size, maximum tokens, and other parameters to fine-tune the responses generated by the AI language model. Contributions to the project are welcome, and discussions can be held on the Home Assistant Community platform.

rclip is a command-line photo search tool powered by the OpenAI's CLIP neural network. It allows users to search for images using text queries, similar image search, and combining multiple queries. The tool extracts features from photos to enable searching and indexing, with options for previewing results in supported terminals or custom viewers. Users can install rclip on Linux, macOS, and Windows using different installation methods. The repository follows the Conventional Commits standard and welcomes contributions from the community.

Honcho is a platform for creating personalized AI agents and LLM powered applications for end users. The repository is a monorepo containing the server/API for managing database interactions and storing application state, along with a Python SDK. It utilizes FastAPI for user context management and Poetry for dependency management. The API can be run using Docker or manually by setting environment variables. The client SDK can be installed using pip or Poetry. The project is open source and welcomes contributions, following a fork and PR workflow. Honcho is licensed under the AGPL-3.0 License.

OpenSumi is a framework designed to help users quickly build AI Native IDE products. It provides a set of tools and templates for creating Cloud IDEs, Desktop IDEs based on Electron, CodeBlitz web IDE Framework, Lite Web IDE on the Browser, and Mini-App liked IDE. The framework also offers documentation for users to refer to and a detailed guide on contributing to the project. OpenSumi encourages contributions from the community and provides a platform for users to report bugs, contribute code, or improve documentation. The project is licensed under the MIT license and contains third-party code under other open source licenses.

The Ultralytics YOLO iOS App GitHub repository offers an advanced object detection tool leveraging YOLOv8 models for iOS devices. Users can transform their devices into intelligent detection tools to explore the world in a new and exciting way. The app provides real-time detection capabilities with multiple AI models to choose from, ranging from 'nano' to 'x-large'. Contributors are welcome to participate in this open-source project, and licensing options include AGPL-3.0 for open-source use and an Enterprise License for commercial integration. Users can easily set up the app by following the provided steps, including cloning the repository, adding YOLOv8 models, and running the app on their iOS devices.

PyAirbyte brings the power of Airbyte to every Python developer by providing a set of utilities to use Airbyte connectors in Python. It enables users to easily manage secrets, work with various connectors like GitHub, Shopify, and Postgres, and contribute to the project. PyAirbyte is not a replacement for Airbyte but complements it, supporting data orchestration frameworks like Airflow and Snowpark. Users can develop ETL pipelines and import connectors from local directories. The tool simplifies data integration tasks for Python developers.

MD-Agent is a LLM-agent based toolset for Molecular Dynamics. It uses Langchain and a collection of tools to set up and execute molecular dynamics simulations, particularly in OpenMM. The tool assists in environment setup, installation, and usage by providing detailed steps. It also requires API keys for certain functionalities, such as OpenAI and paper-qa for literature searches. Contributions to the project are welcome, with a detailed Contributor's Guide available for interested individuals.

FlowGen is a tool built for AutoGen, a great agent framework from Microsoft and a lot of contributors. It provides intuitive visual tools that streamline the construction and oversight of complex agent-based workflows, simplifying the process for creators and developers. Users can create Autoflows, chat with agents, and share flow templates. The tool is fully dockerized and supports deployment on Railway.app. Contributions to the project are welcome, and the platform uses semantic-release for versioning and releases.

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.

AgentCloud is an open-source platform that enables companies to build and deploy private LLM chat apps, empowering teams to securely interact with their data. It comprises three main components: Agent Backend, Webapp, and Vector Proxy. To run this project locally, clone the repository, install Docker, and start the services. The project is licensed under the GNU Affero General Public License, version 3 only. Contributions and feedback are welcome from the community.

This framework generates fuzz targets for real-world `C`/`C++` projects with various Large Language Models (LLM) and benchmarks them via the `OSS-Fuzz` platform. It manages to successfully leverage LLMs to generate valid fuzz targets (which generate non-zero coverage increase) for 160 C/C++ projects. The maximum line coverage increase is 29% from the existing human-written targets.

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.

The Azure-Analytics-and-AI-Engagement repository provides packaged Industry Scenario DREAM Demos with ARM templates (Containing a demo web application, Power BI reports, Synapse resources, AML Notebooks etc.) that can be deployed in a customer’s subscription using the CAPE tool within a matter of few hours. Partners can also deploy DREAM Demos in their own subscriptions using DPoC.