Coding & Development

LittleD is a relational database specifically engineered for microprocessor devices and sensor nodes, requiring minimal RAM (as little as 1kB for most queries). It provides a SQL frontend to manage data, supporting core relational operations such as CREATE TABLE, INSERT statements, and SELECT-FROM-WHERE syntax, including inner joins, projections, and selections over arbitrary expressions. Currently, it supports integers and fixed-width strings. The system is highly configurable, allowing users to control the database's codespace footprint by dictating which features are compiled in via a simple configuration header file. LittleD is written in C and is suitable for devices with capabilities similar to the Arduino Mega2560, making it an ideal solution for embedded systems requiring robust data management.

awesome-prompts offers a curated collection of ChatGPT prompts, meticulously sourced from the highest-rated GPTs available in the GPTs Store. This resource is specifically designed for individuals interested in prompt engineering, prompt attack, and prompt protection. Beyond just providing prompts, the repository also features a selection of advanced prompt engineering papers, offering deeper insights into the field. Its primary goal is to assist users in discovering, exploring, and understanding effective prompt strategies for various applications.

SPIFFS (SPI Flash File System) is an open-source file system specifically engineered for SPI NOR flash devices in embedded systems. It prioritizes low RAM usage, operating with statically sized RAM buffers independent of the number of files. SPIFFS offers a POSIX-like API for file operations, including open, close, read, write, seek, and stat. It supports wear leveling to extend the lifespan of flash memory and includes built-in file system consistency checks. While highly configurable and capable of running multiple configurations on the same target, it currently does not support directories, creating a flat file structure. It is intended for small memory devices, typically up to 128MB, due to its design goal of minimal RAM consumption.

Lemon is an open-source, embeddable, and lightweight programming language designed for flexibility and integration. It comes with the complete source code for its compiler and virtual machine, allowing developers to understand and modify its core functionality. Additionally, the source code for the core Lemon library is provided, facilitating custom development and porting to various environments. It supports building on Windows via TDM-GCC and offers options for dynamic or static linking, and includes built-in libraries for POSIX OS and BSD Socket functionalities. This makes Lemon a versatile choice for developers looking for a customizable programming language.

R-FCN (Region-based Fully Convolutional Networks) is an open-source object detection framework designed for computer vision research and applications. It utilizes deep fully-convolutional networks to achieve accurate and efficient object detection. Unlike previous region-based detectors that apply costly per-region sub-networks, R-FCN shares almost all computation on the entire image, making it highly efficient. The framework can integrate powerful fully convolutional image classifier backbones, such as ResNets, for enhanced performance. It supports end-to-end training and inference for object detection and has been tested on Windows and Ubuntu platforms, requiring MATLAB and a Caffe build.

This repository offers a real-time 3D multi-person pose estimation demo built with PyTorch. It leverages the Lightweight OpenPose and Single-Shot Multi-Person 3D Pose Estimation From Monocular RGB papers to detect and track 2D and 3D coordinates of up to 18 keypoints, including ears, eyes, nose, neck, shoulders, elbows, wrists, hips, knees, and ankles. The model was trained on MS COCO and CMU Panoptic datasets, achieving 100 mm MPJPE on the CMU Panoptic subset. For enhanced performance, it supports Intel OpenVINO for fast inference on CPUs and NVIDIA TensorRT for accelerated inference on Jetson devices, offering significant speedups.

crawl4ai is an open-source web crawler and scraper specifically engineered to be LLM-friendly. This tool empowers users to efficiently extract structured and unstructured data from websites, making it readily available for integration into diverse AI applications. Its open-source nature fosters community contributions and allows for customization and extension by developers. The project is hosted on GitHub, encouraging collaboration and transparency in its development.

Gemini vs GPT vs Claude is a dedicated AI comparison tool designed for evaluating the performance of leading large language models. Users can input custom prompts and observe the responses generated by Gemini Pro, GPT-4, and Claude 3. This side-by-side comparison facilitates a detailed analysis of each model's strengths, weaknesses, and unique characteristics, helping users understand their respective capabilities and limitations for various tasks.

RaDe-GS, or Rasterizing Depth in Gaussian Splatting, is a cutting-edge Content & Design tool developed by HKUST-SAIL. It significantly enhances the performance and accuracy of 3D scene reconstruction and rendering by incorporating advanced techniques like multi-view regularization and refined densification strategies. The project provides updated code and formulations, enabling users to achieve superior results on challenging datasets such as DTU and Tanks and Temples. It also supports novel view synthesis and geometry evaluation, making it a powerful resource for researchers and developers working with 3D Gaussian Splatting. The tool is built upon the original 3D Gaussian Splatting implementation and integrates ideas from several recent works to offer a robust and efficient solution for 3D graphics tasks.

Checkie.AI, now operating as TabSense.AI, functions as a confidential report index. The platform requires users to enter a specific passcode to decrypt and access the encrypted report content directly within their browser. Based on the available information, it seems to be a specialized tool for accessing and reviewing sensitive reports, likely within the domain of testing and analysis, given its previous branding as Checkie.AI and Testers.AI. The website's primary function is to serve as a secure gateway to these encrypted reports, ensuring that only authorized individuals with the correct passcode can view the information.

Hubris is a microcontroller operating environment specifically engineered for deeply embedded systems that demand high reliability. Its design, initially proposed in RFD41, has undergone significant evolution to meet modern embedded system requirements. The kernel is lightweight, memory-protected, and utilizes a message-passing architecture for inter-task communication. It supports development on Linux and Windows platforms, with informal support for macOS and Illumos. Hubris provides a structured repository layout for applications, build systems, peripheral definitions, drivers, and system components like the kernel and ABI. It includes a custom build system using `cargo xtask` for complex multi-architecture builds and offers integration with `rust-analyzer` for an enhanced development experience.

img2pose is an open-source PyTorch implementation for real-time, six degrees of freedom (6DoF), 3D face pose estimation. This tool uniquely performs face alignment and detection without requiring preliminary face detection or facial landmark localization, simplifying the process. It leverages a Faster R-CNN-based model to regress 6DoF pose for all faces in a photo, even tiny ones. The system allows for visualization of detections, customization of projected bounding boxes, and cropping/aligning faces for further processing. Accepted at CVPR 2021, img2pose outperforms state-of-the-art face pose estimators and even surpasses comparable models on the WIDER FACE detection benchmark, despite not being optimized for bounding box labels.

nvim-luapad is an interactive real-time scratchpad designed for Neovim's embedded Lua engine, enabling developers to quickly test and debug Lua code directly within their editor. It evaluates code in real-time, displaying output as virtual text where it was called. The tool extends the native Lua command with deep function completion, streamlining the development of Neovim plugins. While powerful for small code chunks, users should exercise caution as it evaluates every typed character, which can lead to unintended side effects with system calls or UI manipulations. It offers configurable options like error indicators, preview windows, and evaluation triggers, making it a flexible tool for Neovim Lua development.

KAPAO (Keypoints and Poses as Objects) is an efficient single-stage multi-person human pose estimation method. It models keypoints and poses as objects within a dense anchor-based detection framework, simultaneously detecting both pose and keypoint objects and fusing them to predict human poses. This approach results in a model that is faster and more accurate than previous single-stage methods like DEKR and HigherHRNet, especially when not using test-time augmentation. The repository provides the official PyTorch implementation, including setup instructions, trained models, and various inference demos for static images, videos, and even depth video. It also details experiments on COCO and CrowdPose datasets, along with training commands for different model sizes (KAPAO-S, KAPAO-M, KAPAO-L).

context-portal is an open-source server designed to manage project context using a Model Context Protocol (MCP). It constructs a project-specific knowledge graph, which serves to enhance the capabilities of AI assistants. The tool facilitates Retrieval Augmented Generation (RAG), allowing for more context-aware development directly within Integrated Development Environments (IDEs). Essentially, context-portal functions as a memory bank specifically tailored for AI development tools, providing relevant information to improve their performance and understanding.

uber-apk-signer is a command-line interface (CLI) tool designed for Android developers to efficiently sign, zip align, and verify Android application packages (APKs). It supports all major Android signing schemes including v1, v2, v3, and v4, ensuring broad compatibility for modern Android development. A key feature is its embedded debug keystore, which simplifies the debug signing process. The tool automatically verifies signatures and zipalignment after each signing operation, providing immediate feedback on the integrity of the APKs. It can process single or multiple APKs, offering flexibility for various development workflows and includes options for custom keystores, password management, and integration with external zipalign executables.

UnrealEnginePython is an open-source plugin designed to embed a full Python VM (versions 3.x and 2.7) directly into Unreal Engine 4, supporting both the editor and runtime environments. This integration provides easy access to UE4's internal API and reflection system, allowing developers to write other plugins, automate tasks, create unit tests, and implement gameplay elements using Python. It's particularly useful for development pipelines already utilizing Python (e.g., Maya, Blender) to seamlessly integrate Unreal Engine. The plugin also exposes wrappers for third-party libraries like FbxSdk, enabling low-level interaction with FBX files. A unique feature is the ability to change Python code even after a project has been packaged, offering flexibility for modding or post-release updates. It supports Unreal Engine versions up to 4.23 and includes experimental Editor/IDE features.

tensorflow-face-detection is an open-source face detection tool built upon a MobileNet SSD architecture and integrated with the TensorFlow object detection API. It has been trained using the extensive WIDERFACE dataset, which contributes to its robustness in detecting faces across various poses and conditions. A key advantage of this tool is its efficiency, providing fast inference speeds while maintaining a low memory footprint, making it suitable for applications where resources are constrained. Its adaptability to different face orientations enhances its utility for a wide range of face detection tasks.

Turbo is a robust framework designed for LuaJIT 2, aimed at simplifying the development of fast and scalable network applications. It leverages an event-driven, non-blocking, and no-thread design to achieve excellent performance and a minimal footprint, making it suitable for high-load applications and embedded systems. The framework supports various network applications, including HTTP REST APIs, dynamic web pages via templating, and WebSockets. It provides generic building blocks like an I/O loop and IO Stream classes, along with customizable TCP (with SSL) server classes. Turbo is particularly optimized for the HTTP(S) protocol, catering to web and HTTP API developers, while also offering direct integration with existing C libraries for ultimate memory and CPU performance.

TorchRec is a PyTorch domain library specifically designed for large-scale recommendation systems (RecSys). It provides essential sparsity and parallelism primitives, enabling the training and inference of models with extensive embedding tables sharded across multiple GPUs. This library is crucial for Meta's production RecSys models and has been used to accelerate advancements in the field, including the latest version of Meta's DLRM. Key features include various sharding strategies, an automatic sharding plan planner, pipelined training for performance, optimized kernels powered by FBGEMM, and quantization support. It also offers common RecSys modules and datasets, along with end-to-end training examples.

Grayskull is a minimalist, dependency-free computer vision library written in C, specifically engineered for microcontrollers and other resource-constrained devices like drones and robotics. It focuses on grayscale image processing, providing a suite of modern and practical algorithms that fit within a few kilobytes of code. Key features include image operations such as copy, crop, resize (bilinear), and downsample, along with filtering capabilities like blur, Sobel edges, and various thresholding methods (global, Otsu, adaptive). The library also supports morphology operations (erosion, dilation), geometry functions like connected components and perspective warp, and advanced features like FAST/ORB keypoints for object tracking and LBP cascades for face and vehicle detection. Its single-header design, integer-based operations, and pure C99 implementation ensure no dynamic memory allocation or C++ dependencies, making it ideal for embedded vision projects.

Sming is an open-source, asynchronous embedded C++ framework designed to simplify the development of high-performance and network-enabled embedded applications. It offers broad compatibility, supporting popular architectures such as ESP8266, ESP32, and Raspberry Pi Pico, making it versatile for various IoT projects. The framework is modular, allowing developers to integrate specific functionalities as needed, which enhances efficiency in IoT development. Sming focuses on providing a robust and flexible environment for creating embedded systems, enabling developers to build complex applications with ease while maintaining high performance and efficient resource utilization.

continuous-eval is an open-source package designed for the data-driven evaluation of applications powered by Large Language Models (LLMs). It provides a modular approach to evaluation, allowing users to apply tailored metrics to each specific module within their LLM pipeline. The tool includes a comprehensive library of metrics to facilitate thorough assessment. It supports the evaluation of diverse LLM use cases, including Retrieval-Augmented Generation (RAG), code generation, and the utilization of agent tools.

blinker-library is a comprehensive IoT solution designed for embedded hardware, offering cross-hardware and cross-platform compatibility. It supports popular microcontrollers like Arduino R4, ESP32, and ESP8266, making it versatile for various IoT applications. The library provides full-stack support, including APP, device, and server components, and leverages public cloud services for efficient data transmission and storage. Inspired by the simplicity of the 'Blink' sketch, blinker aims to make IoT project development as straightforward as lighting an LED. It's ideal for smart home systems, data monitoring, and other IoT fields, helping users build projects more quickly and effectively.