aiocoap is a Python library that implements the Constrained Application Protocol (CoAP) using native asyncio methods in Python 3. It supports various CoAP standards such as RFC7252, RFC7641, RFC7959, RFC8323, RFC7967, RFC8132, RFC9176, RFC8613, and draft-ietf-core-oscore-groupcomm-17. The library provides features for clients and servers, including multicast support, blockwise transfer, CoAP over TCP, TLS, and WebSockets, No-Response, PATCH/FETCH, OSCORE, and Group OSCORE. It offers an easy-to-use interface for concurrent operations and is suitable for IoT applications.

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.

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.

LLMeBench is a flexible framework designed for accelerating benchmarking of Large Language Models (LLMs) in the field of Natural Language Processing (NLP). It supports evaluation of various NLP tasks using model providers like OpenAI, HuggingFace Inference API, and Petals. The framework is customizable for different NLP tasks, LLM models, and datasets across multiple languages. It features extensive caching capabilities, supports zero- and few-shot learning paradigms, and allows on-the-fly dataset download and caching. LLMeBench is open-source and continuously expanding to support new models accessible through APIs.

Apache cTAKES is a clinical Text Analysis and Knowledge Extraction System that focuses on extracting knowledge from clinical text through Natural Language Processing (NLP) techniques. It is modular and employs rule-based and machine learning methods to extract concepts such as symptoms, procedures, diagnoses, medications, and anatomy with attributes and standard codes. cTAKES can identify temporal events, dates, and times, placing events in a patient timeline. It supports various biomedical text processing tasks and can handle different types of clinical and health-related narratives using multiple data standards. cTAKES is widely used in research initiatives and encourages contributions from professionals, researchers, doctors, and students from diverse backgrounds.

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.

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.

Craftium is an open-source platform based on the Minetest voxel game engine and the Gymnasium and PettingZoo APIs, designed for creating fast, rich, and diverse single and multi-agent environments. It allows for connecting to Craftium's Python process, executing actions as keyboard and mouse controls, extending the Lua API for creating RL environments and tasks, and supporting client/server synchronization for slow agents. Craftium is fully extensible, extensively documented, modern RL API compatible, fully open source, and eliminates the need for Java. It offers a variety of environments for research and development in reinforcement learning.

LangChain is a framework for developing Elixir applications powered by language models. It enables applications to connect language models to other data sources and interact with the environment. The library provides components for working with language models and off-the-shelf chains for specific tasks. It aims to assist in building applications that combine large language models with other sources of computation or knowledge. LangChain is written in Elixir and is not aimed for parity with the JavaScript and Python versions due to differences in programming paradigms and design choices. The library is designed to make it easy to integrate language models into applications and expose features, data, and functionality to the models.

This repository provides a Python implementation of a method that leverages pre-trained large language models to construct and utilize world models for model-based task planning. It includes scripts to generate domain models using natural language descriptions, correct domain models based on feedback, and support plan generation for tasks in different domains. The code has been refactored for better readability and includes tools for validating PDDL syntax and handling corrective feedback.

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.

RAG-FiT is a library designed to improve Language Models' ability to use external information by fine-tuning models on specially created RAG-augmented datasets. The library assists in creating training data, training models using parameter-efficient finetuning (PEFT), and evaluating performance using RAG-specific metrics. It is modular, customizable via configuration files, and facilitates fast prototyping and experimentation with various RAG settings and configurations.

RAG Foundry is a library designed to enhance Large Language Models (LLMs) by fine-tuning models on RAG-augmented datasets. It helps create training data, train models using parameter-efficient finetuning (PEFT), and measure performance using RAG-specific metrics. The library is modular, customizable using configuration files, and facilitates prototyping with various RAG settings and configurations for tasks like data processing, retrieval, training, inference, and evaluation.

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.

DLIO is an I/O benchmark tool designed for Deep Learning applications. It emulates modern deep learning applications using Benchmark Runner, Data Generator, Format Handler, and I/O Profiler modules. Users can configure various I/O patterns, data loaders, data formats, datasets, and parameters. The tool is aimed at emulating the I/O behavior of deep learning applications and provides a modular design for flexibility and customization.

The Artificial Intelligence Controller Interface (AICI) lets you build Controllers that constrain and direct output of a Large Language Model (LLM) in real time. Controllers are flexible programs capable of implementing constrained decoding, dynamic editing of prompts and generated text, and coordinating execution across multiple, parallel generations. Controllers incorporate custom logic during the token-by-token decoding and maintain state during an LLM request. This allows diverse Controller strategies, from programmatic or query-based decoding to multi-agent conversations to execute efficiently in tight integration with the LLM itself.

aiocoap is a Python library that implements the Constrained Application Protocol (CoAP) using native asyncio methods in Python 3. It supports various CoAP standards such as RFC7252, RFC7641, RFC7959, RFC8323, RFC7967, RFC8132, RFC9176, RFC8613, and draft-ietf-core-oscore-groupcomm-17. The library provides features for clients and servers, including multicast support, blockwise transfer, CoAP over TCP, TLS, and WebSockets, No-Response, PATCH/FETCH, OSCORE, and Group OSCORE. It offers an easy-to-use interface for concurrent operations and is suitable for IoT applications.

Aino is an experimental HTTP framework for Elixir that uses elli instead of Cowboy like Phoenix and Plug. It focuses on writing handlers to process requests through middleware functions. Aino works on a token instead of a conn, allowing flexibility in adding custom keys. It includes built-in middleware for common tasks and a routing layer for defining routes. Handlers in Aino must return a token with specific keys for response rendering.

Envoy AI Gateway is an open source project that utilizes Envoy Gateway to manage request traffic from application clients to Generative AI services. The project aims to provide a seamless and efficient solution for handling communication between clients and AI services. It is designed to enhance the performance and scalability of AI applications by leveraging the capabilities of Envoy Gateway. The project welcomes contributions from the community and encourages collaboration to further develop and improve the functionality of the AI Gateway.

Tinystruct is a simple Java framework designed for easy development with better performance. It offers a modern approach with features like CLI and web integration, built-in lightweight HTTP server, minimal configuration philosophy, annotation-based routing, and performance-first architecture. Developers can focus on real business logic without dealing with unnecessary complexities, making it transparent, predictable, and extensible.

Hashbrown is a lightweight and efficient hashing library for Python, designed to provide easy-to-use cryptographic hashing functions for secure data storage and transmission. It supports a variety of hashing algorithms, including MD5, SHA-1, SHA-256, and SHA-512, allowing users to generate hash values for strings, files, and other data types. With Hashbrown, developers can quickly implement data integrity checks, password hashing, digital signatures, and other security features in their Python applications.

Olares is an open-source sovereign cloud OS designed for local AI, enabling users to build their own AI assistants, sync data across devices, self-host their workspace, stream media, and more within a sovereign cloud environment. Users can effortlessly run leading AI models, deploy open-source AI apps, access AI apps and models anywhere, and benefit from integrated AI for personalized interactions. Olares offers features like edge AI, personal data repository, self-hosted workspace, private media server, smart home hub, and user-owned decentralized social media. The platform provides enterprise-grade security, secure application ecosystem, unified file system and database, single sign-on, AI capabilities, built-in applications, seamless access, and development tools. Olares is compatible with Linux, Raspberry Pi, Mac, and Windows, and offers a wide range of system-level applications, third-party components and services, and additional libraries and components.

zig-aio is a library that provides an io_uring-like asynchronous API and coroutine-powered IO tasks for the Zig programming language. It offers support for different operating systems and backends, such as io_uring, iocp, and posix. The library aims to provide efficient IO operations by leveraging coroutines and async IO mechanisms. Users can create servers and clients with ease using the provided API functions for socket operations, sending and receiving data, and managing connections.

ExecuTorch is an end-to-end solution for enabling on-device inference capabilities across mobile and edge devices including wearables, embedded devices and microcontrollers. It is part of the PyTorch Edge ecosystem and enables efficient deployment of PyTorch models to edge devices. Key value propositions of ExecuTorch are: * **Portability:** Compatibility with a wide variety of computing platforms, from high-end mobile phones to highly constrained embedded systems and microcontrollers. * **Productivity:** Enabling developers to use the same toolchains and SDK from PyTorch model authoring and conversion, to debugging and deployment to a wide variety of platforms. * **Performance:** Providing end users with a seamless and high-performance experience due to a lightweight runtime and utilizing full hardware capabilities such as CPUs, NPUs, and DSPs.

The Holoscan SDK is part of NVIDIA Holoscan, the AI sensor processing platform that combines hardware systems for low-latency sensor and network connectivity, optimized libraries for data processing and AI, and core microservices to run streaming, imaging, and other applications, from embedded to edge to cloud. It can be used to build streaming AI pipelines for a variety of domains, including Medical Devices, High Performance Computing at the Edge, Industrial Inspection and more.

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.

aiocoap is a Python library that implements the Constrained Application Protocol (CoAP) using native asyncio methods in Python 3. It supports various CoAP standards such as RFC7252, RFC7641, RFC7959, RFC8323, RFC7967, RFC8132, RFC9176, RFC8613, and draft-ietf-core-oscore-groupcomm-17. The library provides features for clients and servers, including multicast support, blockwise transfer, CoAP over TCP, TLS, and WebSockets, No-Response, PATCH/FETCH, OSCORE, and Group OSCORE. It offers an easy-to-use interface for concurrent operations and is suitable for IoT applications.

CPP-Notes is a comprehensive repository providing detailed insights into the history, evolution, and modern development of the C++ programming language. It covers the foundational concepts of C++ and its transition from C, highlighting key features such as object-oriented programming, generic programming, and modern enhancements introduced in C++11/14/17. The repository delves into the significance of C++ in system programming, library development, and its role as a versatile and efficient language. It explores the historical milestones of C++ development, from its inception in 1979 by Bjarne Stroustrup to the latest C++20 standard, showcasing major advancements like Concepts, Ranges library, Coroutines, Modules, and enhanced concurrency features.

AI-on-the-edge-device is a project that enables users to digitize analog water, gas, power, and other meters using an ESP32 board with a supported camera. It integrates Tensorflow Lite for AI processing, offers a small and affordable device with integrated camera and illumination, provides a web interface for administration and control, supports Homeassistant, Influx DB, MQTT, and REST API. The device captures meter images, extracts Regions of Interest (ROIs), runs them through AI for digitization, and allows users to send data to MQTT, InfluxDb, or access it via REST API. The project also includes 3D-printable housing options and tools for logfile management.

RKLLM software stack is a toolkit designed to help users quickly deploy AI models to Rockchip chips. It consists of RKLLM-Toolkit for model conversion and quantization, RKLLM Runtime for deploying models on Rockchip NPU platform, and RKNPU kernel driver for hardware interaction. The toolkit supports RK3588 and RK3576 series chips and various models like TinyLLAMA, Qwen, Phi, ChatGLM3, Gemma, InternLM2, and MiniCPM. Users can download packages, docker images, examples, and docs from RKLLM_SDK. Additionally, RKNN-Toolkit2 SDK is available for deploying additional AI models.

RK3588 is a flagship 8K SoC chip by Rockchip, integrating Cortex-A76 and Cortex-A55 cores with NEON coprocessor for 8K video codec. This repository curates resources for developing with RK3588, including official resources, RKNN models, projects, development boards, documentation, tools, and sample code.