Mojo is a new programming language that combines Python syntax and ecosystem with systems programming and metaprogramming features. It aims to bridge the gap between research and production, designed to be the best way to extend Python over time. The repository includes source code for Mojo examples, documentation hosted at modular.com, and the Mojo standard library. It has two primary branches: 'main' for stable released versions and 'nightly' for the latest builds. To install Mojo, follow the guide for the last released build or use the nightly builds for a view of the development progress. Contributions are welcome on the 'nightly' branch, and the repository is licensed under the Apache License v2.0 with LLVM Exceptions.

Mojo is a new programming language that bridges the gap between research and production by combining Python syntax and ecosystem with systems programming and metaprogramming features. Mojo is still young, but it is designed to become a superset of Python over time.

Aisuite is a simple, unified interface to multiple Generative AI providers. It allows developers to easily interact with various Language Model (LLM) providers like OpenAI, Anthropic, Azure, Google, AWS, and more through a standardized interface. The library focuses on chat completions and provides a thin wrapper around python client libraries, enabling creators to test responses from different LLM providers without changing their code. Aisuite maximizes stability by using HTTP endpoints or SDKs for making calls to the providers. Users can install the base package or specific provider packages, set up API keys, and utilize the library to generate chat completion responses from different models.

Cohere's Build-Your-Own-Connector framework allows integration of Cohere's Command LLM via the Chat API endpoint to any datastore/software holding text information with a search endpoint. Enables user queries grounded in proprietary information. Use-cases include question/answering, knowledge working, comms summary, and research. Repository provides code for popular datastores and a template connector. Requires Python 3.11+ and Poetry. Connectors can be built and deployed using Docker. Environment variables set authorization values. Pre-commits for linting. Connectors tailored to integrate with Cohere's Chat API for creating chatbots. Connectors return documents as JSON objects for Cohere's API to generate answers with citations.

Bench is a tool for evaluating LLMs for production use cases. It provides a standardized workflow for LLM evaluation with a common interface across tasks and use cases. Bench can be used to test whether open source LLMs can do as well as the top closed-source LLM API providers on specific data, and to translate the rankings on LLM leaderboards and benchmarks into scores that are relevant for actual use cases.

This repository contains scripts and tools for evaluating a chat app that uses the RAG architecture. It provides parameters to assess the quality and style of answers generated by the chat app, including system prompt, search parameters, and GPT model parameters. The tools facilitate running evaluations, with examples of evaluations on a sample chat app. The repo also offers guidance on cost estimation, setting up the project, deploying a GPT-4 model, generating ground truth data, running evaluations, and measuring the app's ability to say 'I don't know'. Users can customize evaluations, view results, and compare runs using provided tools.

BentoDiffusion is a BentoML example project that demonstrates how to serve and deploy diffusion models in the Stable Diffusion (SD) family. These models are specialized in generating and manipulating images based on text prompts. The project provides a guide on using SDXL Turbo as an example, along with instructions on prerequisites, installing dependencies, running the BentoML service, and deploying to BentoCloud. Users can interact with the deployed service using Swagger UI or other methods. Additionally, the project offers the option to choose from various diffusion models available in the repository for deployment.

Stagehand is an AI web browsing framework that simplifies and extends web automation using three simple APIs: act, extract, and observe. It aims to provide a lightweight, configurable framework without complex abstractions, allowing users to automate web tasks reliably. The tool generates Playwright code based on atomic instructions provided by the user, enabling natural language-driven web automation. Stagehand is open source, maintained by the Browserbase team, and supports different models and model providers for flexibility in automation tasks.

Atomic Agents is a modular and extensible framework designed for creating powerful applications. It follows the principles of Atomic Design, emphasizing small and single-purpose components. Leveraging Pydantic for data validation and serialization, the framework offers a set of tools and agents that can be combined to build AI applications. It depends on the Instructor package and supports various APIs like OpenAI, Cohere, Anthropic, and Gemini. Atomic Agents is suitable for developers looking to create AI agents with a focus on modularity and flexibility.

RayLLM (formerly known as Aviary) is an LLM serving solution that makes it easy to deploy and manage a variety of open source LLMs, built on Ray Serve. It provides an extensive suite of pre-configured open source LLMs, with defaults that work out of the box. RayLLM supports Transformer models hosted on Hugging Face Hub or present on local disk. It simplifies the deployment of multiple LLMs, the addition of new LLMs, and offers unique autoscaling support, including scale-to-zero. RayLLM fully supports multi-GPU & multi-node model deployments and offers high performance features like continuous batching, quantization and streaming. It provides a REST API that is similar to OpenAI's to make it easy to migrate and cross test them. RayLLM supports multiple LLM backends out of the box, including vLLM and TensorRT-LLM.

llamafile is a tool that enables users to distribute and run Large Language Models (LLMs) with a single file. It combines llama.cpp with Cosmopolitan Libc to create a framework that simplifies the complexity of LLMs into a single-file executable called a 'llamafile'. Users can run these executable files locally on most computers without the need for installation, making open LLMs more accessible to developers and end users. llamafile also provides example llamafiles for various LLM models, allowing users to try out different LLMs locally. The tool supports multiple CPU microarchitectures, CPU architectures, and operating systems, making it versatile and easy to use.

The RAG Experiment Accelerator is a versatile tool that helps you conduct experiments and evaluations using Azure AI Search and RAG pattern. It offers a rich set of features, including experiment setup, integration with Azure AI Search, Azure Machine Learning, MLFlow, and Azure OpenAI, multiple document chunking strategies, query generation, multiple search types, sub-querying, re-ranking, metrics and evaluation, report generation, and multi-lingual support. The tool is designed to make it easier and faster to run experiments and evaluations of search queries and quality of response from OpenAI, and is useful for researchers, data scientists, and developers who want to test the performance of different search and OpenAI related hyperparameters, compare the effectiveness of various search strategies, fine-tune and optimize parameters, find the best combination of hyperparameters, and generate detailed reports and visualizations from experiment results.

FigStep is a black-box jailbreaking algorithm against large vision-language models (VLMs). It feeds harmful instructions through the image channel and uses benign text prompts to induce VLMs to output contents that violate common AI safety policies. The tool highlights the vulnerability of VLMs to jailbreaking attacks, emphasizing the need for safety alignments between visual and textual modalities.

ModernBERT is a repository focused on modernizing BERT through architecture changes and scaling. It introduces FlexBERT, a modular approach to encoder building blocks, and heavily relies on .yaml configuration files to build models. The codebase builds upon MosaicBERT and incorporates Flash Attention 2. The repository is used for pre-training and GLUE evaluations, with a focus on reproducibility and documentation. It provides a collaboration between Answer.AI, LightOn, and friends.

Call GPT is a voice application that utilizes Deepgram for Speech to Text, elevenlabs for Text to Speech, and OpenAI for GPT prompt completion. It allows users to chat with ChatGPT on the phone, providing better transcription, understanding, and speaking capabilities than traditional IVR systems. The app returns responses with low latency, allows user interruptions, maintains chat history, and enables GPT to call external tools. It coordinates data flow between Deepgram, OpenAI, ElevenLabs, and Twilio Media Streams, enhancing voice interactions.

RouteLLM is a framework for serving and evaluating LLM routers. It allows users to launch an OpenAI-compatible API that routes requests to the best model based on cost thresholds. Trained routers are provided to reduce costs while maintaining performance. Users can easily extend the framework, compare router performance, and calibrate cost thresholds. RouteLLM supports multiple routing strategies and benchmarks, offering a lightweight server and evaluation framework. It enables users to evaluate routers on benchmarks, calibrate thresholds, and modify model pairs. Contributions for adding new routers and benchmarks are welcome.

Generate React components on-the-fly at runtime using AI. Register your components, and let Hydra choose when to show them in your App. Hydra development is still early, and patterns for different types of components and apps are still being developed. Join the discord to chat with the developers. Expects to be used in a NextJS project. Components that have function props do not work.

Mojo is a new programming language that combines Python syntax and ecosystem with systems programming and metaprogramming features. It aims to bridge the gap between research and production, designed to be the best way to extend Python over time. The repository includes source code for Mojo examples, documentation hosted at modular.com, and the Mojo standard library. It has two primary branches: 'main' for stable released versions and 'nightly' for the latest builds. To install Mojo, follow the guide for the last released build or use the nightly builds for a view of the development progress. Contributions are welcome on the 'nightly' branch, and the repository is licensed under the Apache License v2.0 with LLVM Exceptions.

Horizon AI Boilerplate is an open-source Admin Dashboard template designed for Shadcn UI, NextJS, and Tailwind CSS. It provides over 30 dark/light frontend elements for creating Chat AI SaaS Apps quickly. The documentation is detailed and complex, guiding users through installation and usage. Users can start their local server with simple commands. The tool requires a valid OpenAI API key for ChatGPT functionality. Additionally, a Figma version is available for design purposes. The PRO version offers more components and pages. Users can report issues on GitHub and connect with the community via Discord. The tool credits open-source resources like Shadcn UI Library, NextJS Subscription Payments, and ChatBot UI by mckaywrigley.

ByteDance's Piano Transcription is a PyTorch implementation for transcribing piano recordings into MIDI files with pedals. This repository provides a simple GUI and packaging for Windows and Nix on Linux/macOS. It supports using GPU for inference and includes CLI usage. Users can upgrade the tool and report issues to the upstream project. The tool focuses on providing MIDI files, and any other improvements to transcription results should be directed to the original project.

AIA Podcast's AI Tools Catalog is a collection of AI-powered tools mentioned in the podcast. These tools can be beneficial for programming, content creation, and enhancing productivity. To contribute, users can add services by providing a brief description in the Telegram chat or suggest improvements by forking the repository and submitting a PR. Users can also report closed or inoperative tools through the creation of an Issue. The catalog is a valuable resource for discovering innovative AI tools and services.

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.