Panda is a car interface tool that speaks CAN and CAN FD, running on STM32F413 and STM32H725. It provides safety modes and controls_allowed feature for message handling. The tool ensures code rigor through CI regression tests, including static code analysis, MISRA C:2012 violations check, unit tests, and hardware-in-the-loop tests. The software interface supports Python library, C++ library, and socketcan in kernel. Panda is licensed under the MIT license.

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.

OntoGPT is a Python package for extracting structured information from text using large language models, instruction prompts, and ontology-based grounding. It provides a command line interface and a minimal web app for easy usage. The tool has been evaluated on test data and is used in related projects like TALISMAN for gene set analysis. OntoGPT enables users to extract information from text by specifying relevant terms and provides the extracted objects as output.

VisualWebArena is a benchmark for evaluating multimodal autonomous language agents through diverse and complex web-based visual tasks. It builds on the reproducible evaluation introduced in WebArena. The repository provides scripts for end-to-end training, demos to run multimodal agents on webpages, and tools for setting up environments for evaluation. It includes trajectories of the GPT-4V + SoM agent on VWA tasks, along with human evaluations on 233 tasks. The environment supports OpenAI models and Gemini models for evaluation.

VerifAI is a document-based question-answering system that addresses hallucinations in generative large language models and search engines. It retrieves relevant documents, generates answers with references, and verifies answers for accuracy. The engine uses generative search technology and a verification model to ensure no misinformation. VerifAI supports various document formats and offers user registration with a React.js interface. It is open-source and designed to be user-friendly, making it accessible for anyone to use.

FastTrackML is an experiment tracking server focused on speed and scalability, fully compatible with MLFlow. It provides a user-friendly interface to track and visualize your machine learning experiments, making it easy to compare different models and identify the best performing ones. FastTrackML is open source and can be easily installed and run with pip or Docker. It is also compatible with the MLFlow Python package, making it easy to integrate with your existing MLFlow workflows.

sdkit (stable diffusion kit) is an easy-to-use library for utilizing Stable Diffusion in AI Art projects. It includes features like ControlNets, LoRAs, Textual Inversion Embeddings, GFPGAN, CodeFormer for face restoration, RealESRGAN for upscaling, k-samplers, support for custom VAEs, NSFW filter, model-downloader, parallel GPU support, and more. It offers a model database, auto-scanning for malicious models, and various optimizations. The API consists of modules for loading models, generating images, filters, model merging, and utilities, all managed through the sdkit.Context object.

ScreenAgent is a project focused on creating an environment for Visual Language Model agents (VLM Agent) to interact with real computer screens. The project includes designing an automatic control process for agents to interact with the environment and complete multi-step tasks. It also involves building the ScreenAgent dataset, which collects screenshots and action sequences for various daily computer tasks. The project provides a controller client code, configuration files, and model training code to enable users to control a desktop with a large model.

J.A.I.son is a Python project designed for generating responses using various components and applications. It requires specific plugins like STT, T2T, TTSG, and TTSC to function properly. Users can customize responses, voice, and configurations. The project provides a Discord bot, Twitch events and chat integration, and VTube Studio Animation Hotkeyer. It also offers features for managing conversation history, training AI models, and monitoring conversations.

EasyDist is an automated parallelization system and infrastructure designed for multiple ecosystems. It offers usability by making parallelizing training or inference code effortless with just a single line of change. It ensures ecological compatibility by serving as a centralized source of truth for SPMD rules at the operator-level for various machine learning frameworks. EasyDist decouples auto-parallel algorithms from specific frameworks and IRs, allowing for the development and benchmarking of different auto-parallel algorithms in a flexible manner. The architecture includes MetaOp, MetaIR, and the ShardCombine Algorithm for SPMD sharding rules without manual annotations.

ScandEval is a framework for evaluating pretrained language models on mono- or multilingual language tasks. It provides a unified interface for benchmarking models on a variety of tasks, including sentiment analysis, question answering, and machine translation. ScandEval is designed to be easy to use and extensible, making it a valuable tool for researchers and practitioners alike.

Langfuse Docs is a repository for langfuse.com, built on Nextra. It provides guidelines for contributing to the documentation using GitHub Codespaces and local development setup. The repository includes Python cookbooks in Jupyter notebooks format, which are converted to markdown for rendering on the site. It also covers media management for images, videos, and gifs. The stack includes Nextra, Next.js, shadcn/ui, and Tailwind CSS. Additionally, there is a bundle analysis feature to analyze the production build bundle size using @next/bundle-analyzer.

2P-Kt is a Kotlin-based and multi-platform reboot of tuProlog (2P), a multi-paradigm logic programming framework written in Java. It consists of an open ecosystem for Symbolic Artificial Intelligence (AI) with modules supporting logic terms, unification, indexing, resolution of logic queries, probabilistic logic programming, binary decision diagrams, OR-concurrent resolution, DSL for logic programming, parsing modules, serialisation modules, command-line interface, and graphical user interface. The tool is designed to support knowledge representation and automatic reasoning through logic programming in an extensible and flexible way, encouraging extensions towards other symbolic AI systems than Prolog. It is a pure, multi-platform Kotlin project supporting JVM, JS, Android, and Native platforms, with a lightweight library leveraging the Kotlin common library.

HugeSCM is a cloud-based version control system designed to address R&D repository size issues. It effectively manages large repositories and individual large files by separating data storage and utilizing advanced algorithms and data structures. It aims for optimal performance in handling version control operations of large-scale repositories, making it suitable for single large library R&D, AI model development, and game or driver development.

The On-board Artificial Intelligence Research (OnAIR) Platform is a framework that enables AI algorithms written in Python to interact with NASA's cFS. It is intended to explore research concepts in autonomous operations in a simulated environment. The platform provides tools for generating environments, handling telemetry data through Redis, running unit tests, and contributing to the repository. Users can set up a conda environment, configure telemetry and Redis examples, run simulations, and conduct unit tests to ensure the functionality of their AI algorithms. The platform also includes guidelines for licensing, copyright, and contributions to the repository.

LeRobot is a state-of-the-art AI library for real-world robotics in PyTorch. It aims to provide models, datasets, and tools to lower the barrier to entry to robotics, focusing on imitation learning and reinforcement learning. LeRobot offers pretrained models, datasets with human-collected demonstrations, and simulation environments. It plans to support real-world robotics on affordable and capable robots. The library hosts pretrained models and datasets on the Hugging Face community page.

Panda is a car interface tool that speaks CAN and CAN FD, running on STM32F413 and STM32H725. It provides safety modes and controls_allowed feature for message handling. The tool ensures code rigor through CI regression tests, including static code analysis, MISRA C:2012 violations check, unit tests, and hardware-in-the-loop tests. The software interface supports Python library, C++ library, and socketcan in kernel. Panda is licensed under the MIT license.

This open hardware project aims to extend the life of Volvo P2 platform vehicles by updating them to current EU safety and emission standards. It involves designing and prototyping OEM hardware electronics that can replace existing electronics in these vehicles, using the existing wiring and without requiring reverse engineering or modifications. The project focuses on serviceability, maintenance, repairability, and personal ownership safety, and explores the advantages of using open solutions compared to conventional hardware electronics solutions.

Panda is a car interface tool that speaks CAN and CAN FD, running on STM32F413 and STM32H725. It provides safety modes and controls_allowed feature for message handling. The tool ensures code rigor through CI regression tests, including static code analysis, MISRA C:2012 violations check, unit tests, and hardware-in-the-loop tests. The software interface supports Python library, C++ library, and socketcan in kernel. Panda is licensed under the MIT license.

This Github repository contains practice session materials for the TUM course on Artificial Intelligence in Automotive Technology. It includes coding examples used in the lectures to teach the foundations of AI in automotive technology. The repository aims to provide hands-on experience and practical knowledge in applying AI concepts to the automotive industry.

LoLLMs WebUI (Lord of Large Language Multimodal Systems: One tool to rule them all) is a user-friendly interface to access and utilize various LLM (Large Language Models) and other AI models for a wide range of tasks. With over 500 AI expert conditionings across diverse domains and more than 2500 fine tuned models over multiple domains, LoLLMs WebUI provides an immediate resource for any problem, from car repair to coding assistance, legal matters, medical diagnosis, entertainment, and more. The easy-to-use UI with light and dark mode options, integration with GitHub repository, support for different personalities, and features like thumb up/down rating, copy, edit, and remove messages, local database storage, search, export, and delete multiple discussions, make LoLLMs WebUI a powerful and versatile tool.

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.

MinIO is a High Performance Object Storage released under GNU Affero General Public License v3.0. It is API compatible with Amazon S3 cloud storage service. Use MinIO to build high performance infrastructure for machine learning, analytics and application data workloads.

Mage is an open-source data pipeline tool for transforming and integrating data. It offers an easy developer experience, engineering best practices built-in, and data as a first-class citizen. Mage makes it easy to build, preview, and launch data pipelines, and provides observability and scaling capabilities. It supports data integrations, streaming pipelines, and dbt integration.

AiTreasureBox is a versatile AI tool that provides a collection of pre-trained models and algorithms for various machine learning tasks. It simplifies the process of implementing AI solutions by offering ready-to-use components that can be easily integrated into projects. With AiTreasureBox, users can quickly prototype and deploy AI applications without the need for extensive knowledge in machine learning or deep learning. The tool covers a wide range of tasks such as image classification, text generation, sentiment analysis, object detection, and more. It is designed to be user-friendly and accessible to both beginners and experienced developers, making AI development more efficient and accessible to a wider audience.