Open Source JavaScript Software Development Software for ChromeOS

LLM Datasets curates and standardizes datasets commonly used to train and fine-tune large language models, reducing the overhead of hunting down sources and normalizing formats. The repository aims to make datasets easy to inspect and transform, with scripts for downloading, deduping, cleaning, and converting to formats like JSONL that slot into training pipelines. It highlights instruction-tuning and conversation-style corpora while also pointing to code, math, or domain-specific sets for targeted capabilities. Quality is a recurring theme: examples and utilities help filter low-value samples, enforce length limits, and split train/validation consistently so results are comparable. Licensing and provenance are surfaced to encourage compliant usage and to guide dataset selection in commercial settings. For practitioners, the repo is a practical “starting pantry” that accelerates experimentation and helps keep data wrangling from dominating the project timeline.

Gamoliyas is an open source John Conway's Game of Life game totally written in DHTML (JavaScript, CSS and HTML). Uses mouse and keyboard. Very configurable. This cross-platform and cross-browser game was tested under BeOS, Linux, *BSD, Windows and other

JEOPS is a Java based forward chaining RULE ENGINE. This Rule Engine is used to power up the business process by rules in Java Application Servers, client applications, and Servlets.

JLog is a Prolog interpreter written in Java. It is fast and perfectly suited for education. It includes source editor, query panels, online help, animation primitives, and a GUI debugger. JScriptLog is Prolog in JavaScript.

Turn-based strategy game for developers/technical experienced users, based on neural networks, algorithmic programming languages and other techniques.

Proposed is an algorithm that uses computer vision, combined with a modified Rubine classifier, to allow arbitrary N-sided polygons as accepted sketches in real-time.

Thank you for your interest in Speedy Composer. Speedy Composer is an automated application for composing melodies for Speedy Net members. We recently made changes to the source code of Speedy Net, and converted it into the Python language and Django framework. Since Speedy Composer was originally written in PHP, it is not adapted to work with Speedy Net in its current form. So unfortunately we were forced to temporarily close the app Speedy Composer. But don't worry, we kept backups of all the tunes composed by Speedy Composer, and when the website is reopened we will upload them to the new site. Speedy Composer and Speedy Net are open source applications, free software. We are currently looking for volunteers to help us convert Speedy Composer to Python. If you are interested in volunteering, please contact me by email. Thank you and good luck, Uri Rodberg Founder and Director of Speedy Net and Speedy Composer, Speedy Paz Technologies Ltd. uri@speedy.net

The supervised-reptile repository contains code associated with the paper “On First-Order Meta-Learning Algorithms”, which introduces Reptile, a meta-learning algorithm for learning model parameter initializations that adapt quickly to new tasks. The implementation here is aimed at supervised few-shot learning settings (e.g. Omniglot, Mini-ImageNet), not reinforcement learning, and includes scripts to run training and evaluation for few-shot classification. The fundamental idea is: sample a task, train on that task (inner loop), and then move the initialization parameters toward the adapted parameters (outer loop). Because Reptile is a first-order algorithm, it avoids computing second derivatives or full meta-gradients, making it computationally simpler while retaining good performance. The repo includes training scripts, dataset fetchers (Omniglot, Mini-ImageNet), and modules for defining the Reptile update logic, variables, and hyperparameters.

ajile: Advanced JavaScript Importing & Loading Extension allows developers to easily create unique namespaces for JavaScript modules and quickly define dependencies that allow scripts to automatically load and import each other as needed.

Just download the above zip file, extract it and then open the index.html file on internet browsers like Firefox ( preferable ) or Google Chrome.

Pipeless is an open-source computer vision framework to create and deploy applications without the complexity of building and maintaining multimedia pipelines. It ships everything you need to create and deploy efficient computer vision applications that work in real-time in just minutes. Pipeless is inspired by modern serverless technologies. It provides the development experience of serverless frameworks applied to computer vision. You provide some functions that are executed for new video frames and Pipeless takes care of everything else. You can easily use industry-standard models, such as YOLO, or load your custom model in one of the supported inference runtimes. Pipeless ships some of the most popular inference runtimes, such as the ONNX Runtime, allowing you to run inference with high performance on CPU or GPU out-of-the-box. You can deploy your Pipeless application with a single command to edge and IoT devices or the cloud.