Rakis is a decentralized verifiable AI network in the browser where nodes can accept AI inference requests, run local models, verify results, and arrive at consensus without servers. It is open-source, functional, multi-model, multi-chain, and browser-first, allowing anyone to participate in the network. The project implements an embedding-based consensus mechanism for verifiable inference. Users can run their own node on rakis.ai or use the compiled version hosted on Huggingface. The project is meant for educational purposes and is a work in progress.

HelixML is a private GenAI platform that allows users to deploy the best of open AI in their own data center or VPC while retaining complete data security and control. It includes support for fine-tuning models with drag-and-drop functionality. HelixML brings the best of open source AI to businesses in an ergonomic and scalable way, optimizing the tradeoff between GPU memory and latency.

FoundationaLLM is a platform designed for deploying, scaling, securing, and governing generative AI in enterprises. It allows users to create AI agents grounded in enterprise data, integrate REST APIs, experiment with large language models, centrally manage AI agents and assets, deploy scalable vectorization data pipelines, enable non-developer users to create their own AI agents, control access with role-based access controls, and harness capabilities from Azure AI and Azure OpenAI. The platform simplifies integration with enterprise data sources, provides fine-grain security controls, load balances across multiple endpoints, and is extensible to new data sources and orchestrators. FoundationaLLM addresses the need for customized copilots or AI agents that are secure, licensed, flexible, and suitable for enterprise-scale production.

Greenplum Database (GPDB) is an advanced, fully featured, open source data warehouse, based on PostgreSQL. It provides powerful and rapid analytics on petabyte scale data volumes. Uniquely geared toward big data analytics, Greenplum Database is powered by the world’s most advanced cost-based query optimizer delivering high analytical query performance on large data volumes.

Llama-github is a powerful tool that helps retrieve relevant code snippets, issues, and repository information from GitHub based on queries. It empowers AI agents and developers to solve coding tasks efficiently. With features like intelligent GitHub retrieval, repository pool caching, LLM-powered question analysis, and comprehensive context generation, llama-github excels at providing valuable knowledge context for development needs. It supports asynchronous processing, flexible LLM integration, robust authentication options, and logging/error handling for smooth operations and troubleshooting. The vision is to seamlessly integrate with GitHub for AI-driven development solutions, while the roadmap focuses on empowering LLMs to automatically resolve complex coding tasks.

Psychic is a tool that provides a platform for users to access psychic readings and services. It offers a range of features such as tarot card readings, astrology consultations, and spiritual guidance. Users can connect with experienced psychics and receive personalized insights and advice on various aspects of their lives. The platform is designed to be user-friendly and intuitive, making it easy for users to navigate and explore the different services available. Whether you're looking for guidance on love, career, or personal growth, Psychic has you covered.

Tiledesk is an Open Source Live Chat platform with integrated Chatbots written in NodeJs and Express. It provides a multi-channel platform for Web, Android, and iOS, offering out-of-the-box chatbots that work alongside humans. Users can automate conversations using native chatbot technology powered by AI, connect applications via APIs or Webhooks, deploy visual applications within conversations, and enable applications to interact with chatbots or end-users. Tiledesk is multichannel, allowing chatbot scripts with images and buttons to run on various channels like Whatsapp, Facebook Messenger, and Telegram. The project includes Tiledesk Server, Dashboard, Design Studio, Chat21 ionic, Web Widget, Server, Http Server, MongoDB, and a proxy. It offers Helm charts for Kubernetes deployment, but customization is recommended for production environments, such as integrating with external MongoDB or monitoring/logging tools. Enterprise customers can request private Docker images by contacting [email protected].

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.

An artificial intelligence framework entirely written in Java for game development with libGDX. It is a high-performance framework providing common AI techniques used in the game industry, covering movement AI, pathfinding, decision making, and infrastructure. The framework is designed to be used with libGDX but can be used independently. Current features include steering behaviors, formation motion, A* pathfinding, hierarchical pathfinding, behavior trees, state machine, message handling, and scheduling.

PearAI is an AI-powered code editor designed to enhance development by reducing the amount of coding required. It is a fork of VSCode and the main functionality lies within the 'extension/pearai' submodule. Users can contribute to the project by fixing issues, submitting bugs and feature requests, reviewing source code changes, and improving documentation. The tool aims to streamline the coding process and provide an efficient environment for developers to work in.

This is a development tutorial for eBPF based on CO-RE (Compile Once, Run Everywhere). It provides practical eBPF development practices from beginner to advanced, including basic concepts, code examples, and real-world applications. The tutorial focuses on eBPF examples in observability, networking, security, and more. It aims to help eBPF application developers quickly grasp eBPF development methods and techniques through examples in languages such as C, Go, and Rust. The tutorial is structured with independent eBPF tool examples in each directory, covering topics like kprobes, fentry, opensnoop, uprobe, sigsnoop, execsnoop, exitsnoop, runqlat, hardirqs, and more. The project is based on libbpf and frameworks like libbpf, Cilium, libbpf-rs, and eunomia-bpf for development.

Claudine is an AI agent designed to reason and act autonomously, leveraging the Anthropic API, Unix command line tools, HTTP, local hard drive data, and internet data. It can administer computers, analyze files, implement features in source code, create new tools, and gather contextual information from the internet. Users can easily add specialized tools. Claudine serves as a blueprint for implementing complex autonomous systems, with potential for customization based on organization-specific needs. The tool is based on the anthropic-kotlin-sdk and aims to evolve into a versatile command line tool similar to 'git', enabling branching sessions for different tasks.

TagUI is an open-source RPA tool that allows users to automate repetitive tasks on their computer, including tasks on websites, desktop apps, and the command line. It supports multiple languages and offers features like interacting with identifiers, automating data collection, moving data between TagUI and Excel, and sending Telegram notifications. Users can create RPA robots using MS Office Plug-ins or text editors, run TagUI on the cloud, and integrate with other RPA tools. TagUI prioritizes enterprise security by running on users' computers and not storing data. It offers detailed logs, enterprise installation guides, and support for centralised reporting.

Talk-To-ChatGPT is a Google Chrome and Microsoft Edge extension that enables users to interact with the ChatGPT AI using voice commands for speech recognition and text-to-speech responses. The tool enhances the conversational experience by allowing users to speak to the AI and receive spoken responses, making interactions more natural and engaging. It also supports ElevenLabs API integration for creating custom voices for text-to-speech. The extension provides settings for voice, language, and more, and can be installed from the Chrome and Edge web stores or manually. While the project has been discontinued due to upcoming desktop apps from OpenAI, it has been used to assist individuals with disabilities and the elderly in interacting with ChatGPT.

MediaAI is a repository containing lectures and materials for Aalto University's AI for Media, Art & Design course. The course is a hands-on, project-based crash course focusing on deep learning and AI techniques for artists and designers. It covers common AI algorithms & tools, their applications in art, media, and design, and provides hands-on practice in designing, implementing, and using these tools. The course includes lectures, exercises, and a final project based on students' interests. Students can complete the course without programming by creatively utilizing existing tools like ChatGPT and DALL-E. The course emphasizes collaboration, peer-to-peer tutoring, and project-based learning. It covers topics such as text generation, image generation, optimization, and game AI.

Rakis is a decentralized verifiable AI network in the browser where nodes can accept AI inference requests, run local models, verify results, and arrive at consensus without servers. It is open-source, functional, multi-model, multi-chain, and browser-first, allowing anyone to participate in the network. The project implements an embedding-based consensus mechanism for verifiable inference. Users can run their own node on rakis.ai or use the compiled version hosted on Huggingface. The project is meant for educational purposes and is a work in progress.

Deep Java Library (DJL) is an open-source, high-level, engine-agnostic Java framework for deep learning. It is designed to be easy to get started with and simple to use for Java developers. DJL provides a native Java development experience and allows users to integrate machine learning and deep learning models with their Java applications. The framework is deep learning engine agnostic, enabling users to switch engines at any point for optimal performance. DJL's ergonomic API interface guides users with best practices to accomplish deep learning tasks, such as running inference and training neural networks.

Caikit is an AI toolkit that enables users to manage models through a set of developer friendly APIs. It provides a consistent format for creating and using AI models against a wide variety of data domains and tasks.

The LiveKit Agent Framework is designed for building real-time, programmable participants that run on servers. Easily tap into LiveKit WebRTC sessions and process or generate audio, video, and data streams. The framework includes plugins for common workflows, such as voice activity detection and speech-to-text. Agents integrates seamlessly with LiveKit server, offloading job queuing and scheduling responsibilities to it. This eliminates the need for additional queuing infrastructure. Agent code developed on your local machine can scale to support thousands of concurrent sessions when deployed to a server in production.

llm-finetuning is a repository that provides a serverless twist to the popular axolotl fine-tuning library using Modal's serverless infrastructure. It allows users to quickly fine-tune any LLM model with state-of-the-art optimizations like Deepspeed ZeRO, LoRA adapters, Flash attention, and Gradient checkpointing. The repository simplifies the fine-tuning process by not exposing all CLI arguments, instead allowing users to specify options in a config file. It supports efficient training and scaling across multiple GPUs, making it suitable for production-ready fine-tuning jobs.

Lean Copilot is a tool that enables the use of large language models (LLMs) in Lean for proof automation. It provides features such as suggesting tactics/premises, searching for proofs, and running inference of LLMs. Users can utilize built-in models from LeanDojo or bring their own models to run locally or on the cloud. The tool supports platforms like Linux, macOS, and Windows WSL, with optional CUDA and cuDNN for GPU acceleration. Advanced users can customize behavior using Tactic APIs and Model APIs. Lean Copilot also allows users to bring their own models through ExternalGenerator or ExternalEncoder. The tool comes with caveats such as occasional crashes and issues with premise selection and proof search. Users can get in touch through GitHub Discussions for questions, bug reports, feature requests, and suggestions. The tool is designed to enhance theorem proving in Lean using LLMs.

The 'awesome-local-llms' repository is a curated list of open-source tools for local Large Language Model (LLM) inference, covering both proprietary and open weights LLMs. The repository categorizes these tools into LLM inference backend engines, LLM front end UIs, and all-in-one desktop applications. It collects GitHub repository metrics as proxies for popularity and active maintenance. Contributions are encouraged, and users can suggest additional open-source repositories through the Issues section or by running a provided script to update the README and make a pull request. The repository aims to provide a comprehensive resource for exploring and utilizing local LLM tools.

AiOS is a tool for human pose and shape estimation, performing human localization and SMPL-X estimation in a progressive manner. It consists of body localization, body refinement, and whole-body refinement stages. Users can download datasets for evaluation, SMPL-X body models, and AiOS checkpoint. Installation involves creating a conda virtual environment, installing PyTorch, torchvision, Pytorch3D, MMCV, and other dependencies. Inference requires placing the video for inference and pretrained models in specific directories. Test results are provided for NMVE, NMJE, MVE, and MPJPE on datasets like BEDLAM and AGORA. Users can run scripts for AGORA validation, AGORA test leaderboard, and BEDLAM leaderboard. The tool acknowledges codes from MMHuman3D, ED-Pose, and SMPLer-X.

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.

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.

Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features: * Self-contained, with no need for a DBMS or cloud service. * OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE). * Supports consumer-grade GPUs.

SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.

Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.