Category: Programming

OpenCiv3 is an open-source, cross-platform project aiming to modernize and reimagine *Civilization III* using the Godot Engine and C#. Currently in early alpha development, this project offers a mod-oriented experience with ambitions to address limitations of the original game and expand mod capabilities. The latest release, v0.3 “Dutch” Preview 1, features standalone mode with placeholder graphics, but a local *Civilization III* installation is recommended for a polished experience.

R3 FORTH

r3 is a programming language and environment drawing heavily from the ColorForth philosophy, prioritizing extreme minimalism and creative power. The project centers around a lightweight, highly portable VM (r3evm) designed for both Windows and Linux, currently around 40kb in size. Core functionality includes a zero-bloat approach, focusing solely on the VM and stack, allowing developers to build complex systems with limited dependencies. Notably, r3 supports dynamic library loading (.DLL / .SO) and incorporates a self-hosted compiler (Windows) aiming for native execution. The system features a growing ecosystem with libraries for graphics, 3D engine development, and a TUI/Immediate Mode GUI, making it a compelling option for those seeking a highly customizable and performant coding experience.

Bunqueue is a blazing-fast job queue written in Bun and powered by SQLite for persistent storage. Designed for single-server deployments and prototyping, it offers a BullMQ-compatible API with zero external dependencies – no Redis required! It boasts impressive performance, up to 286K operations per second, and includes features such as DLQ, cron jobs, and S3 backups. It’s a great alternative to BullMQ when you want to avoid the operational overhead of a separate Redis instance, especially for smaller projects and embedded use cases.

github.com

The WordPress MCP Adapter facilitates seamless integration between WordPress and the Model Context Protocol (MCP). It acts as a crucial bridge, converting native WordPress abilities—like plugins, themes, and core functionality—into standardized MCP tools, resources, and prompts, enabling AI agents to intuitively interact with your WordPress site. The adapter boasts a flexible architecture with HTTP and STDIO transport options, comprehensive error handling, and built-in observability features—ideal for developers building advanced AI-powered workflows within WordPress.

Elysia – Ergonomic Framework for Humans
ElysiaJS presents itself as an ergonomic and human-centered JavaScript framework, designed to minimize complexity and allow developers to focus on building applications. The framework leverages TypeScript and provides sample code illustrating fundamental features like routing, file serving, and real-time communication. Benchmark results showcase Elysia’s significantly faster performance compared to popular frameworks like Express, Fastify, and others, demonstrating a speed increase of up to 21x against Express and 6x against Fastify, based on TechEmpower benchmarks. A key aspect is its ‘Single Source of Truth’ approach, using schemas for request validation and type inference, ensuring data integrity throughout the application.

UGBasic is an open-source, isomorphic BASIC compiler designed to create portable and efficient programs for a wide range of 8-bit retro-computers.

It allows developers to write a single source code that can be compiled for multiple vintage platforms—including Commodore, Atari, Sinclair, Tandy, and more—without sacrificing performance. ugBASIC supports advanced features like graphics rendering, sprite manipulation, multitasking via protothreads, memory bank management, and inline assembly. Its integrated IDE streamlines development, and the language includes modern conveniences such as conditional compilation, structured types, and support for various CPUs like the MOS 6502, Zilog Z80, and Motorola 6809. With ugBASIC, users can rapidly build games and applications that leverage the unique capabilities of classic hardware


Home – Welcome to MLBox’s official documentation — MLBox Documentation

MLBox is a powerful Automated Machine Learning python library. It provides the following features:

Fast reading and distributed data preprocessing/cleaning/formatting.
Highly robust feature selection and leak detection.
Accurate hyper-parameter optimization in high-dimensional space.
State-of-the art predictive models for classification and regression (Deep Learning, Stacking, LightGBM,…).
Prediction with models interpretation.


ryanjay0/miles-deep: Deep Learning Porn Video Classifier/Editor with Caffe

Using a deep convolutional neural network with residual connections, Miles Deep quickly classifies each second of a pornographic video into 6 categories based on sexual act with 95% accuracy. Then it uses that classification to automatically edit the video. It can remove all the scenes not containing sexual contact, or edit out just a specific act.


xgboost-node

XGBoost-Node is the first port of XGBoost to run existing XGBoost model with Node.js.

XGBoost is a library from DMLC. It is designed and optimized for boosted trees. The underlying algorithm of XGBoost is an extension of the classic gbm algorithm. With multi-threads and regularization, XGBoost is able to utilize more computational power and get a more accurate prediction.


SmoothGrad

When a machine learning model makes a prediction, often times we would like to determine which features of the input (pixels, for images) were important for the prediction. If the model makes a misprediction, we might want to know which features contributed to the misclassification. We can visualize the feature importance mask as a grayscale image with the same dimensions as the original image with brightness corresponding to importance of the pixel.


Auto-Keras

Auto-Keras is an open source software library for automated machine learning (AutoML). It is developed by DATA Lab at Texas A&M University and community contributors. The ultimate goal of AutoML is to provide easily accessible deep learning tools to domain experts with limited data science or machine learning background. Auto-Keras provides functions to automatically search for architecture and hyperparameters of deep learning models.

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