#rust #embedded #embedded_hal #esp32 #rust

This tool helps you work with Espressif devices like ESP32, ESP32-C, ESP32-H, and ESP32-S series using Rust programming language. It provides a hardware abstraction layer (HAL) that makes it easier to control the hardware without needing the standard library. This is useful for creating efficient and low-level programs for these devices. If you have questions or need help, you can open an issue, start a discussion, or join the community on Matrix. The tool is free to use under MIT or Apache-2.0 licenses.

https://github.com/esp-rs/esp-hal

#cplusplus #cypher #database #embeddable #embedded #graph #graph_database #graphdb #neo4j #nosql #olap #wasm

Kuzu is a fast and scalable embedded graph database. It helps users handle complex data queries on large databases efficiently. Kuzu offers features like a flexible data model, fast join algorithms, and multi-core processing, making it ideal for analytical tasks. It integrates easily into applications without needing a separate server, reducing latency and complexity. This makes it beneficial for users who need to analyze large amounts of graph data quickly and effectively.

https://github.com/kuzudb/kuzu

#c_lang #embedded #filesystem #microcontroller

LittleFS is a file system designed for small devices like microcontrollers. It helps keep your data safe even if the power goes off suddenly. This is because it uses a "copy-on-write" system, which means it doesn't overwrite old data until the new data is safely stored. LittleFS also helps extend the life of your storage by spreading out writes across different areas, a process called wear leveling. This makes it very reliable and efficient for devices with limited memory and storage.

https://github.com/littlefs-project/littlefs

#cplusplus #arm #convolution #deep_learning #embedded_devices #llm #machine_learning #ml #mnn #transformer #vulkan #winograd_algorithm

MNN is a lightweight and efficient deep learning framework that helps run AI models on mobile devices and other small devices. It supports many types of AI models and can handle tasks like image recognition and language processing quickly and locally on your device. This means you can use AI features without needing to send data to the cloud, which improves privacy and speed. MNN is used in many apps, including those from Alibaba, and supports various platforms like Android and iOS. It also helps reduce the size of AI models, making them faster and more efficient.

https://github.com/alibaba/MNN

#cplusplus #arduino #cansat #csv #embedded #graph #ground_station #iot #microcontroller #network #projects #qt #serial #serial_studio

Serial Studio is a free, easy-to-use tool that lets you visualize real-time data from devices like microcontrollers via serial ports, Bluetooth, or network connections. It works on Windows, macOS, and Linux, and offers customizable dashboards with various widgets to monitor sensor data, debug info, or telemetry. You can quickly plot data, export it as CSV for analysis, and even use advanced features like checksum validation and JavaScript data processing. It supports hobbyists, educators, and professionals by simplifying data monitoring and debugging, saving you time and effort in understanding your device’s output. Pro versions add commercial use and extra features[1][4][5].

https://github.com/Serial-Studio/Serial-Studio

#c_lang #bluetooth #bluetooth_le #embedded #embedded_c #iot #mcu #microcontroller #real_time #rtos #zephyr #zephyr_rtos #zephyros

Zephyr is a free, open-source real-time operating system (RTOS) designed for small, resource-limited devices like sensors, wearables, and IoT gateways. It supports many hardware types such as ARM, Intel x86, and RISC-V, making it flexible for different projects. Zephyr is modular, so you can include only what you need, saving memory and power. It focuses on security with features like memory protection and secure boot. It also offers built-in networking and tools for easy development and testing. This helps you build reliable, fast, and secure embedded systems efficiently, especially for IoT and real-time applications[1][2][3].

https://github.com/zephyrproject-rtos/zephyr

#python #ai #context #embedded #faiss #knowledge_base #knowledge_graph #llm #machine_learning #memory #nlp #offline_first #opencv #python #rag #retrieval_augmented_generation #semantic_search #vector_database #video_processing

Memvid lets you store millions of text pieces inside a single MP4 video file using QR codes, making your data 50-100 times smaller than usual databases. You can search this video instantly in under 100 milliseconds without needing servers or internet after setup. It works offline, is easy to use with simple Python code, and supports PDFs and chat with your data. The upcoming version 2 will add features like continuous memory updates, shareable capsules, fast local caching, and better video compression, making your AI memory smarter, faster, and more flexible. This means you get a powerful, portable, and efficient way to manage and search huge knowledge bases quickly and easily.

https://github.com/Olow304/memvid

#verilog #cocotb #embedded #fpga #iss #risc_v #rtl #verilator #verilog #vpn #vproc #wireguard

This project creates an open-source, hardware-based WireGuard VPN using an affordable FPGA board, making fast and secure VPNs more accessible. Unlike slow software VPNs or costly proprietary hardware, this FPGA design runs WireGuard encryption and packet processing at near wire speed without needing a PC host. It uses common tools and languages (SystemVerilog, open-source FPGA tools) and includes a soft CPU for control tasks and hardware logic for data encryption and routing. This means you get a faster, more efficient, and customizable VPN solution that is open and affordable, ideal for learning, development, or deployment in cost-sensitive environments.

https://github.com/chili-chips-ba/wireguard-fpga

#python #artificial_intelligence #cloud_ml #computer_systems #courseware #deep_learning #edge_machine_learning #embedded_ml #machine_learning #machine_learning_systems #mobile_ml #textbook #tinyml

You can learn how to build real-world AI systems from start to finish with an open-source textbook originally from Harvard University. It teaches you not just how to train AI models but how to design scalable systems, manage data pipelines, deploy models in production, monitor them continuously, and optimize for devices like phones or IoT gadgets. This helps you become an engineer who can create efficient, reliable, and sustainable AI systems that work well in practice. The book offers hands-on labs, community support, and free online access, making it easier to gain practical skills in machine learning systems engineering.

https://github.com/harvard-edge/cs249r_book

#cplusplus #arm #baidu #deep_learning #embedded #fpga #mali #mdl #mobile #mobile_deep_learning #neural_network

Paddle Lite is a lightweight, high-performance deep learning inference framework designed to run AI models efficiently on mobile, embedded, and edge devices. It supports multiple platforms like Android, iOS, Linux, Windows, and macOS, and languages including C++, Java, and Python. You can easily convert models from other frameworks to PaddlePaddle format, optimize them for faster and smaller deployment, and run them with ready-made examples. This helps you deploy AI applications quickly on various devices with low memory use and fast speed, making it ideal for real-time, resource-limited environments. It also supports many hardware accelerators for better performance.

https://github.com/PaddlePaddle/Paddle-Lite