Open Source ChromeOS Software - Page 100
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Software for ChromeOS
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MaterialFX
A library of material components for JavaFX
MaterialFX is an open-source Java library that provides material design components for JavaFX. JavaFX is a software platform intended to replace Swing in creating and delivering rich client applications that operate consistently across diverse platforms. With the release of JDK 11 in 2018, Oracle has made JavaFX part of the OpenJDK under the OpenJFX project in order to increase the pace of its development. -
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MathModel
Mathematical Modeling for Graduate Students, Mathematical Modeling
MathModel is a repository dedicated to collecting and organizing resources around mathematical modeling, especially for university students preparing for modeling competitions (e.g. undergraduate and graduate levels). The content spans exemplary competition papers, algorithmic solutions, LaTeX templates, presentation slides, MATLAB tutorials, and mind maps of modeling approaches. It acts as a centralized archive where learners can access high-quality past solutions as references, borrow formatting for their own reports, and study algorithmic strategies that performed well in contests. The repository is structured by topic and resource type so that users can more easily find templates, solved problems, and methodological notes. It also includes auxiliary educational materials like references, recommended textbooks, and guidebooks on mathematical modeling theory. -
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MathModelAgent
An Agent Designed for Mathematical Modeling
MathModelAgent is an AI agent system designed specifically for assisting with mathematical modeling tasks and academic problem solving. The platform automates the process of analyzing mathematical problems, constructing models, generating code for simulations or computations, and producing a complete research-style report. The project uses a multi-agent architecture where different specialized agents handle tasks such as problem interpretation, modeling design, programming implementation, and paper writing. Through integration with multiple large language models, the system can coordinate these components to generate structured modeling solutions and formatted research papers suitable for submission. The platform also includes a code execution environment that allows generated programs to be tested, corrected, and refined during the modeling workflow. -
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Matroska Server Mk2
The streaming solution to end all streaming problems
Matroska Server Mk2 is a media streaming server designed to serve MKV video files over a network with minimal configuration. It enables on-demand streaming of video content, making it suitable for personal media servers or lightweight distribution systems. The project focuses on efficient file serving and compatibility with common media players. It supports handling large video files while maintaining smooth playback performance. The implementation emphasizes simplicity and ease of deployment. It is particularly useful for users who want a straightforward way to stream local video libraries. The server architecture is optimized for reliability and low overhead. -
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Matter.js
A 2D rigid body physics engine for the web
Matter.js is a flexible 2D rigid body physics engine written in JavaScript, designed for use in web applications to simulate real-world physical interactions within the browser. It offers a comprehensive set of physics features like gravity, collisions, restitution (bounces), friction, sleeping bodies, and constraint systems that enable developers to build interacting objects with realistic motion. This engine can power everything from dynamic interactive animations and educational simulations to fully fledged physics-based games, and it works across major browsers and devices. Through modules such as Matter.Body, Matter.Composite, and Matter.Constraint, developers can create complex scenes with multiple bodies, jointed structures, and collision responses. Matter.js is popular in the web development community due to its simplicity, extensibility, and rich documentation, allowing both beginners and experienced programmers to leverage physics without deep expertise. -
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MaxKB
Open-source platform for building enterprise-grade agents
MaxKB (Max Knowledge Brain) is an open-source platform for building enterprise-grade AI agents with strong knowledge retrieval, RAG pipelines, and workflow orchestration. It focuses on practical deployments such as customer support, internal knowledge bases, research assistants, and education, bundling tools for data ingestion, chunking, embedding, retrieval, and answer synthesis. The system exposes flexible tool-use (including MCP), supports multi-model backends, and provides dashboards for dataset management and evaluation. It’s backed by an active org that also builds adjacent ops tooling, and there’s a dedicated documentation repo for configuration and contribution. Community posts describe “self-host your ChatGPT-style assistant” positioning, with integrations and workflows to move from demo to production. Security advisories are tracked publicly, with upgrade guidance when issues arise. -
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Mckay’s App Template
This is the template I use to start new full-stack projects
Mckay’s App Template is a full-stack starter template maintained by Mckay Wrigley, intended to help developers quickly bootstrap modern web applications without spending time on boilerplate setup. The template includes a frontend built with Next.js, Tailwind CSS, and often UI components/frameworks like Shadcn + Framer Motion; a backend stack using PostgreSQL, Supabase, and Drizzle ORM for database operations; authentication via Clerk; and optional payment integration using Stripe. By providing a ready-to-go configuration (with example folder structure, database setup, auth, payment hooks, and environment templates), it lets developers skip the tedious initial setup and get straight to building product features. The template is ideal for startups, prototypes, or side-projects where you want a robust, production-grade starting point — with user accounts, auth, DB, and payments already wired up. -
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MediaManager
A modern selfhosted media management system for your media library
MediaManager is a modern, self-hosted media management system that unifies and replaces the traditional “ARR” stack with a single, cohesive platform for discovering, organizing, and automating TV and movie libraries. Rather than relying on separate tools patched together, MediaManager offers a streamlined interface and workflow where media metadata, collection insights, and automation policies live side-by-side in one system. It is designed for ease of deployment with Docker, supports standardized metadata sources such as TMDB and TVDB, and integrates OAuth/OIDC for secure authentication. Users can browse, search, and manage their media with a responsive web frontend while developers benefit from a clean codebase that uses Python and modern web technologies. Its holistic approach toward acquisition, tracking, and library maintenance reduces duplication, improves media discovery workflows, and simplifies long-term management of large media collections. -
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MediaPipe Solutions
Cross-platform, customizable ML solutions
MediaPipe is an open-source framework developed by Google for building cross-platform machine learning pipelines that process audio, video, and other streaming data in real time. The system provides developers with tools and reusable components that allow them to combine multiple machine learning models with preprocessing and postprocessing logic into efficient perception pipelines. These pipelines can run on a wide variety of platforms including mobile devices, desktop systems, web browsers, and embedded edge devices. MediaPipe is widely used in computer vision and multimedia applications such as hand tracking, face detection, pose estimation, object recognition, and gesture analysis. The framework includes prebuilt solutions that developers can quickly integrate into applications as well as lower-level APIs that allow custom pipeline construction. -
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Megatron
Ongoing research training transformer models at scale
Megatron is a large, powerful transformer developed by the Applied Deep Learning Research team at NVIDIA. This repository is for ongoing research on training large transformer language models at scale. We developed efficient, model-parallel (tensor, sequence, and pipeline), and multi-node pre-training of transformer based models such as GPT, BERT, and T5 using mixed precision. Megatron is also used in NeMo Megatron, a framework to help enterprises overcome the challenges of building and training sophisticated natural language processing models with billions and trillions of parameters. Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. -
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Melody
Minimalist websocket framework for Go
Melody is a minimalist WebSocket framework for Go that wraps the standard WebSocket workflow in a simple, developer-friendly API. It is designed to make real-time server features easier to build without requiring a large framework or complex setup. Developers can use it for chat systems, live notifications, dashboards, multiplayer coordination, collaborative tools, and other bidirectional communication features. Melody provides connection lifecycle hooks, message handlers, broadcasting, session management, and grouped communication patterns. It is built to feel familiar to Go developers and can be integrated into existing HTTP applications. The project is especially useful when a team needs straightforward WebSocket behavior without building connection handling and broadcast logic from scratch. -
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Membrane Core
The core of Membrane Framework, multimedia processing framework
membrane_core is the foundation of the Membrane multimedia framework for Elixir, providing the abstractions and runtime needed to build real-time audio and video pipelines. It models media processing as a graph of lightweight, supervised OTP processes—elements connected by links—so work is isolated, fault-tolerant, and easy to scale or reconfigure at runtime. The core defines a clear lifecycle and callback API for elements, plus concepts like buffers, events, and capabilities/format negotiation to keep components interoperable and type-safe. Back-pressure, scheduling, and time synchronization are handled by the framework, enabling low-latency streaming and precise playback control without ad-hoc concurrency code. Developers compose pipelines from reusable building blocks and can dynamically add, remove, or switch elements while the system is running. -
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Metarget
Framework for automatic construction of vulnerable infrastructures
Metarget = meta- + target, a framework providing automatic constructions of vulnerable infrastructures, used to deploy simple or complicated vulnerable cloud native targets swiftly and automatically. During security research, we might find that the deployment of a vulnerable environment often takes much time, while the time spent on testing PoC or ExP is comparatively short. In the field of cloud-native security, thanks to the complexity of cloud-native systems, this issue is more terrible. There are already some excellent security projects like Vulhub, and VulApps in the open-source community, which pack vulnerable scenes into container images so that researchers could utilize them and deploy scenes quickly. Hence, we develop Metarget and hope to solve the deployment issue above to some extent. Furthermore, we also expect that Metarget could help to construct multilayer vulnerable cloud native scenes automatically. -
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Meteor
Meteor, the JavaScript App Platform
Meteor is a full-stack JavaScript framework that streamlines building real-time web and mobile applications from a single codebase. It couples a Node.js backend with a reactive data system so UI updates propagate automatically when data changes. The framework popularized DDP (a lightweight pub/sub protocol) and a client cache (Minimongo) to mirror server collections on the client for latency-compensated interactions. A batteries-included build system, package ecosystem, and opinionated project structure reduce setup time for accounts, methods, publications, and UI integration. Historically paired with Blaze, Meteor also works well with modern front-end libraries like React, Vue, or Svelte for rendering. Its dev experience focuses on hot reloads, rapid prototyping, and predictable data flow, making it attractive for teams that want real-time features without stitching together many separate tools. -
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Meteroid
Open-source pricing and billing infrastructure
Meteroid is an open-source, cloud-native pricing and billing infrastructure designed to support usage-based billing, subscription management, invoicing, and cost-tracking — particularly for SaaS or product-led businesses that need flexible billing models beyond flat subscriptions. It provides an API-driven billing engine: you feed Meteroid usage data from your application (e.g. API calls, storage usage, seats, feature usage), define custom pricing rules or plans, and Meteroid calculates charges, generates invoices, tracks usage, and supports cost limits or quotas. The system is written in Rust, emphasizing performance, reliability, and safety, making it suitable for production use in small startups up to large enterprises. Meteroid handles complexities like plan versioning, usage-based billing, re-invoicing, grandfathering of plans, experiments, and cost limiting — giving teams a robust billing backend without building one from scratch. -
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Micro Agent
AI CLI agent that writes code by iterating until tests pass
Micro Agent is a command-line tool designed to generate and refine code using a test-driven approach powered by large language models. Instead of producing one-shot code outputs, it creates or uses test cases and repeatedly iterates on the generated code until those tests pass successfully. This workflow emphasizes reliability by using structured feedback from failing tests to guide improvements, reducing the need for manual debugging and iteration. Micro Agent intentionally limits its scope to a focused task, avoiding complex multi-file operations or full project automation in order to minimize compounding errors. It supports multiple model providers, allowing users to configure different backends depending on their needs and environment. It can operate interactively, asking users questions to refine results, or run in a more automated mode tied to test scripts. -
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Milton
An infinite-canvas paint program
Milton is an open-source, infinite-canvas paint program that allows artists to sketch, draw, and paint without the constraints of a fixed canvas size. It combines the intuitive feel of raster-based painting with the scalability of vector graphics, enabling users to focus on creativity without worrying about resolution or canvas boundaries. -
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MimiClaw
Run OpenClaw on a $5 chip
MimiClaw (from the mimiclaw project) is an edge-AI personal assistant that runs directly on extremely low-cost hardware like an ESP32-S3 microcontroller without a full operating system, Node.js, or cloud backend. By running pure C on a bare-metal chip, MimiClaw brings AI interactions and persistent memory to a tiny USB-powered device you can carry in your pocket. You connect the device to Wi-Fi and chat with it using Telegram, making it a convenient always-on assistant for tasks like reminders, quick lookups, or custom AI interactions. Even though it’s running on minimal hardware, MimiClaw maintains local memory that persists across power cycles, enabling context continuity over time without relying on cloud services. Its architecture emphasizes privacy, low power, and portability, ideal for personal or hobbyist use cases where privacy and local control matter. -
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Minify php
CSS & JavaScript minifier, in PHP
Matthias Mullie Minify is a PHP library that minifies CSS and JavaScript files by removing whitespace, comments, and other unnecessary characters. It helps reduce file size and load time, making it ideal for optimizing web assets in production environments. The library is fast, reliable, and can process both strings and file-based input. -
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Mobile Detect
Mobile_Detect is a lightweight PHP class for detecting mobile devices
Mobile-Detect is a lightweight PHP library that inspects the HTTP user agent string and HTTP headers to detect whether a request comes from a mobile, tablet, or desktop device. It includes a rich set of detection rules covering device brands, operating systems, and browser engines so developers can tailor responses (views, assets, or layouts) based on client form factor. Its API is simple: isMobile(), isTablet(), or deviceFamily() methods make branching logic intuitive. The library is used in server-rendered PHP apps, CMS themes, and frameworks to serve mobile-friendly templates or redirect users to mobile versions. Because it is pattern-driven and optimized for fast matching, it adds minimal runtime overhead to request handling. By abstracting away the complexity of UA sniffing, Mobile-Detect helps teams gracefully support multi-device clients without duplicating detection logic. -
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Modern Unix
A collection of modern/faster/saner alternatives to unix commands
The Modern Unix project is a curated list of modern command-line tools that serve as replacements or enhancements for traditional Unix utilities. It highlights tools that improve usability, performance, and developer experience while maintaining compatibility with existing workflows. The repository organizes tools by category, such as file management, search, networking, and system monitoring, making it easy to discover alternatives. It emphasizes better defaults, improved interfaces, and more intuitive behavior compared to legacy commands. The project is especially valuable for developers who spend significant time in the terminal and want to modernize their toolkit. It also provides links to documentation and installation instructions, facilitating quick adoption. Overall, modern-unix acts as a comprehensive guide to upgrading the command-line experience with contemporary tools. -
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ModernBERT
Bringing BERT into modernity via both architecture changes and scaling
ModernBERT is an open-source research project that modernizes the classic BERT encoder architecture by incorporating recent advances in transformer design, training techniques, and efficiency improvements. The goal of the project is to bring BERT-style models up to date with the capabilities of modern large language models while preserving the strengths of bidirectional encoder architectures used for tasks such as classification, retrieval, and semantic search. ModernBERT introduces architectural improvements that enhance both training efficiency and inference performance, making the model more suitable for modern large-scale machine learning pipelines. The repository also includes FlexBERT, a modular framework that allows developers to experiment with different encoder building blocks and configurations when constructing new models. -
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Mole
CLI application to create ssh tunnels focused on resiliency
Mole is a command-line application for creating SSH tunnels with an emphasis on resilience and user experience. It is designed to help users access or expose services that are behind firewalls, private networks, or otherwise unreachable from the current machine. Mole uses SSH access to a jump server as the bridge between the user and the target service. It simplifies common tunneling workflows that would otherwise require remembering long SSH commands and port-forwarding flags. The project is useful for developers, operators, and administrators who frequently connect to internal databases, dashboards, or web services through secure tunnels. Its focus is not to replace SSH, but to make SSH tunneling more reliable, readable, and easier to manage. -
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MongoDB JVM Driver
The MongoDB drivers for Java, Kotlin, and Scala
Welcome to the documentation site for the Java Driver, the MongoDB driver for synchronous Java applications. Download it using Maven or Gradle, or set up a runnable project by following our Quick Start guide. For tutorials on how to use the MongoDB JVM Drivers, please reference MongoDB University. Additional tutorials, videos, and code examples using both the Java Driver and the Kotlin Driver can also be found in the MongoDB Developer Center. -
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Monty
A minimal, secure Python interpreter written in Rust for use by AI
Monty is an experimental, security-focused Python interpreter implemented in Rust and intended for running AI-generated Python safely under strict constraints. The project’s core goal is to enable code execution in environments where untrusted or model-produced code must be tightly sandboxed to reduce risk. Rather than offering a full “general-purpose Python runtime with everything enabled,” Monty is designed to be minimal and controlled, making it easier to reason about what code can do and what it cannot. It prioritizes guardrails like resource limits and restricted capabilities, which is especially useful for agentic workflows that need to execute small pieces of Python for data transforms, validation, or tool-like computations. Because it’s written in Rust, it’s positioned to deliver a compact, portable runtime that can be embedded into larger systems that need dependable isolation.
