Open Source ChromeOS Software - Page 58
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Software for ChromeOS
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Mako
An extremely fast, production-grade web bundler based on Rust
Mako is a new web bundler for web apps, libraries, and frameworks. It's designed to be fast, reliable, and easy to use. It has been used in hundreds of projects in production by Ant Group, and other companies. If you are looking for a modern web bundler, Mako is the right choice. -
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Medeo Video Generator
AI-powered video generation skill for OpenClaw
Medeo Video Generator is an AI-driven project designed to enable advanced video processing and generation capabilities within agent-based or automation systems. It provides a “skill” module that can be integrated into AI agents, allowing them to create, edit, and manipulate video content programmatically. The project focuses on bridging the gap between language-based AI systems and multimedia outputs by enabling models to produce structured video content as part of their workflows. It supports tasks such as video generation, editing, and transformation, making it useful for applications in content creation, marketing, and automated media production. The framework is designed to be modular, allowing developers to plug video capabilities into larger AI pipelines or agent systems. It emphasizes ease of integration and scalability, enabling both simple use cases and more complex multimedia workflows. -
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MemU
MemU is an open-source memory framework for AI companions
MemU is an agentic memory layer for LLM applications, specifically designed for AI companions. Transform your memory into an intelligent file system that automatically organizes, connects, and evolves with your memories. Simple, fast, and reliable memory infrastructure for AI applications. Powerful tools and dedicated support to scale your AI applications with confidence. Full proprietary features, commercial usage rights, and white-labeling options for your enterprise needs. SSO/RBAC integration and a dedicated algorithm team for scenario-specific optimization. User behavior analysis, real-time monitoring, and automated agent optimization tools. 24/7 dedicated support team, custom SLAs, and professional implementation services. -
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MetaVoice-1B
Foundational model for human-like, expressive TTS
MetaVoice — in the form of its source repository “metavoice-src” — is a large-scale text-to-speech (TTS) model. Specifically, the base model (MetaVoice-1B) uses around 1.2 billion parameters and has been trained on a massive dataset — reportedly around 100,000 hours of speech data. The goal is to provide human-like, expressive, and flexible TTS: able to generate natural-sounding speech that can handle diverse inputs and likely generalize over voice styles, intonation, prosody, and perhaps multiple languages or accents. With that scale and dataset volume, MetaVoice aims to push the boundary of what open-source TTS models can achieve: high fidelity, natural prosody, and robustness even for edge cases. As a foundational model, it can serve as the backbone for downstream tasks — such as voice generation, voice cloning, speech generation for virtual agents, or even audio production pipelines. -
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Multica
The open-source managed agents platform
Multica is an open-source platform designed to manage and orchestrate AI coding agents as if they were real team members within a development workflow. It introduces a paradigm where agents can be assigned tasks, participate in discussions, and autonomously execute work while reporting progress and blockers in real time. The system integrates with multiple AI coding tools and provides a unified interface for managing tasks, compute environments, and agent execution pipelines. It includes both a web interface and a CLI that connects local or cloud-based runtimes to the platform, enabling flexible deployment and scaling. Multica emphasizes collaboration between humans and AI by allowing agents to operate alongside developers in shared workspaces. It also supports reusable skill accumulation, meaning that solutions generated by agents can be reused across projects to improve efficiency over time. -
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MusicFree
Plug-in, customized, ad-free free music player
The MusicFree project is an open-source, plugin-based music player designed for mobile platforms such as Android and HarmonyOS, emphasizing flexibility, customization, and privacy. Unlike traditional music apps, it does not include built-in audio sources but instead relies entirely on plugins to fetch and manage music content. This modular architecture allows users to integrate multiple sources and extend functionality without modifying the core application. The project supports features such as searching, playback, playlist management, and lyric retrieval through plugin-defined interfaces. It also prioritizes user privacy by storing all data locally and avoiding any form of tracking or data collection. The app includes customization options such as themes and interface adjustments, enhancing user experience. Overall, MusicFree provides a flexible and extensible music platform that adapts to user needs through its plugin ecosystem. -
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MusicFreePlugins
MusicFreePlayPlugin
The MusicFreePluginsc project is a collection and framework for plugins that extend the functionality of the MusicFree ecosystem by providing access to various music sources and features. It defines a standardized interface for plugin development, allowing contributors to implement features such as search, playback, and metadata retrieval. The system is designed to be modular, enabling users to install, update, and manage plugins independently of the core application. It supports multiple types of plugins, including those for streaming, playlists, and lyric synchronization. The project encourages community contributions, making it possible to expand the available functionality over time. It also includes mechanisms for handling pagination, caching, and data formatting within plugins. Overall, MusicFreePluginsc acts as the backbone of the MusicFree ecosystem, enabling its extensibility and adaptability. -
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NanoBoyAdvance
A cycle-accurate Nintendo Game Boy Advance emulator
NanoBoyAdvance is a cycle-accurate Game Boy Advance emulator that prioritizes precision and correctness in replicating original hardware behavior. It is designed to emulate the GBA at a very low level, including CPU timing, DMA operations, graphics processing, and memory behavior, ensuring that even edge cases and obscure hardware quirks are faithfully reproduced. The emulator achieves extremely high compatibility, passing multiple hardware test suites and accurately running games that rely on precise timing conditions. In addition to accuracy, it introduces enhancements such as a high-quality audio mixer that improves sound output without altering internal emulation behavior. The project also includes modern usability features like save states, controller remapping, and support for loading ROMs from compressed archives. -
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NiL.JS
JavaScript engine for .NET written in C#
NiL.JS is a JavaScript engine written in C#, capable of executing ECMAScript 6.0 (ES2015) code. It allows embedding JavaScript execution within .NET applications, providing a bridge between C# and JavaScript. -
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NiceGUI
Create web-based user interfaces with Python
NiceGUI is a Python-based UI framework that enables developers to create interactive web applications using only Python code. It abstracts away the complexities of HTML, CSS, and JavaScript, allowing for rapid development of web interfaces directly from Python scripts. NiceGUI is suitable for building dashboards, control panels, and other web-based tools, especially in contexts like robotics and data visualization. -
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OPAL
Policy and data administration, distribution, and real-time updates
OPAL is an open-source administration layer for keeping authorization policies and authorization data synchronized in real time. It works with policy engines such as OPA and Cedar so applications can make decisions using current policy and data without constantly rebuilding custom sync logic. The project aggregates policy and data from external sources, watches for changes, and distributes updates to connected policy agents. It is built for cloud-native and microservice environments where authorization rules change frequently and need to propagate quickly. OPAL separates policy decision-making from policy distribution, which helps teams build more maintainable and consistent permission systems. It is especially useful for engineering teams implementing fine-grained authorization, relationship-based access control, feature access rules, and multi-service policy governance. -
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OWASP ModSecurity CRS
OWASP ModSecurity Core Rule Set (CRS) Project
The OWASP ModSecurity Core Rule Set (CRS) is a curated, generic Web Application Firewall rule set that detects and blocks common attack categories across most web apps. It focuses on broad protection—SQL injection, cross-site scripting, local/remote file inclusion, command injection, and protocol violations—without requiring app-specific knowledge. Rules are organized into paranoia levels so operators can tune detection aggressiveness and balance false positives against coverage. An anomaly-scoring model accumulates rule hits per request, enabling nuanced blocking thresholds and easier incident triage. The project ships with extensive documentation, exclusion packages, and testing tools to help tailor deployment for frameworks, CDNs, and APIs. Deployed on engines such as ModSecurity or compatible WAFs, CRS is a widely used baseline for HTTP security in reverse proxies and gateways. -
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Open Wearables
Self-hosted platform to unify wearable health data
Open Wearables is an open-source initiative that aims to provide a community-driven ecosystem for wearable device software and interoperability by connecting sensor data, activity tracking, and health insights across multiple platforms and devices. Instead of relying on closed vendor ecosystems, the project provides standardized data models and APIs that let developers and hobbyists collect, sync, and analyze biometric and environmental data from wearables, DIY sensors, and open hardware projects. This approach allows users to break free from manufacturer lock-in while enabling richer, customizable dashboards, real-time visualizations, and personalized health analytics that match real-world needs rather than a one-size-fits-all model. It provides building blocks for federated data storage, modular device drivers, and plugin frameworks so contributions from different communities can extend capabilities without rewriting core logic. -
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OpenAI Privacy Filter
Bidirectional token-classification model for identifiable info
OpenAI Privacy Filter is an open-weight machine learning model designed to detect and mask personally identifiable information in text with high efficiency and contextual awareness. It operates as a bidirectional token classification system that labels sensitive data in a single forward pass rather than generating text sequentially, enabling fast processing for large datasets. The model supports long-context inputs, allowing it to analyze extensive documents without chunking, which improves consistency in redaction tasks. It can run locally on standard hardware, ensuring that sensitive information never leaves the user’s environment and supporting privacy-first workflows. The system is fine-tunable, enabling adaptation to specific datasets or compliance requirements across industries. It identifies multiple categories of sensitive data such as names, emails, and credentials, replacing them with placeholders to preserve structure. -
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OpenChronicle
Open-source, local-first memory for any tool-capable LLM agent
OpenChronicle is a knowledge management and storytelling platform designed to organize information into structured timelines and interconnected narratives. It allows users to create chronological records that link events, ideas, and entities in a cohesive format. The system emphasizes visualization and organization of complex information over time. It can be used for research, writing, or personal knowledge tracking. OpenChronicle supports extensibility, enabling customization of how data is structured and displayed. It encourages users to build rich, interconnected knowledge systems. Overall, it transforms static notes into dynamic, timeline-driven narratives. -
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OpenClaw Medical Skills
The largest open-source medical AI skills library for OpenClaw
OpenClaw-Medical-Skills is an open-source library that provides a large collection of specialized medical capabilities designed for the OpenClaw AI agent ecosystem. The project organizes domain-specific “skills” that enable autonomous agents to perform tasks related to biomedical research, healthcare analysis, and clinical data interpretation. Each skill is packaged as a modular component that can be integrated into an OpenClaw-based AI assistant, allowing the agent to perform expert-level reasoning and workflows in medical contexts. Instead of relying on general-purpose language model responses, the repository equips AI agents with structured instructions and tools tailored to medical knowledge and datasets. This modular design allows developers and researchers to build AI systems that can access specialized medical reasoning processes, retrieve relevant biomedical information, and generate structured outputs suitable for analysis or downstream processing. -
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OpenShort.link
All-in-one, open-source, serverless URL shortener
OpenShortLink (OpenShort.link) is an open-source, serverless URL shortener designed to run entirely on Cloudflare’s edge stack, aiming to deliver fast redirects with minimal infrastructure management. It’s built for branded link ownership, letting you use your own domain for short links instead of relying on a third-party shortening service. Beyond basic shortening, it adds “smart routing” capabilities so a single short link can send different users to different destinations depending on factors like country or device type. The project also treats analytics as a first-class feature, capturing click activity and breaking it down by referrer, geography, device, browser, and campaign parameters to support marketing and operational reporting. For teams, it includes an account and permission model so multiple users can collaborate while keeping control over who can create, edit, or view links. -
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OpenTelemetry Collector distributions
OpenTelemetry Collector Official Releases
High-quality, ubiquitous, and portable telemetry to enable effective observability. OpenTelemetry is a collection of APIs, SDKs, and tools. Use it to instrument, generate, collect, and export telemetry data (metrics, logs, and traces) to help you analyze your software’s performance and behavior. Create and collect telemetry from your services and software, then forward it to a variety of analysis tools. OpenTelemetry integrates with many popular libraries and frameworks, and supports code-based and zero-code instrumentation. -
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Paperless-AI
AI-powered document analysis and tagging for Paperless-ngx
Paperless-AI is an AI-powered extension designed to enhance document management within Paperless-ngx by automating analysis, classification, and organization tasks. It continuously monitors incoming documents and processes them using various AI backends, enabling automatic assignment of titles, tags, document types, and correspondents. It integrates with multiple OpenAI-compatible services as well as local models, giving users flexibility in how document intelligence is handled. A key capability is its use of retrieval-augmented generation, which enables semantic search and natural language interaction across an entire document archive. Users can ask contextual questions about their files and receive precise answers based on full document understanding rather than simple keyword matching. Paperless-AI also includes a web interface for manual review and tagging, allowing greater control when handling sensitive or complex documents. -
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Plaso
Super timeline all the things
Plaso (Plaso Langar Að Safna Öllu), or "super timeline all the things," is a Python-based engine designed for automatic creation of timelines in digital forensic investigations. It processes various log files and artifacts to generate a chronological sequence of events, aiding analysts in understanding system activities. -
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PokeeResearch-7B
Pokee Deep Research Model Open Source Repo
PokeeResearchOSS provides an open-source, agentic “deep research” model centered on a 7B backbone that can browse, read, and synthesize current information from the web. Instead of relying only on static training data, the agent performs searches, visits pages, and extracts evidence before forming answers to complex queries. It is built to operate end-to-end: planning a research strategy, gathering sources, reasoning over conflicting claims, and writing a grounded response. The repository includes evaluation results on multi-step QA and research benchmarks, illustrating how web-time context boosts accuracy. Because the system is modular, you can swap the search component, reader, or policy to fit private deployments or different data domains. It’s aimed at developers who want a transparent, hackable research agent they can run locally or wire into existing workflows. -
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PromptPapers
Curated list of prompt-learning papers for NLP research use
PromptPapers is a curated repository that collects and organizes influential research papers focused on prompt-based tuning for large-scale pre-trained language models. It serves as a centralized reading list for researchers and practitioners interested in modern prompt learning techniques, which differ from traditional fine-tuning by leveraging pre-trained tasks directly for prediction. PromptPapers categorizes papers into multiple sections such as overview, pilot work, basics, analysis, improvements, and specializations, helping users navigate the evolution of the field systematically. It also includes keyword conventions to describe each paper’s methods, tasks, and properties, making it easier to compare different approaches. PromptPapers emphasizes community contribution, encouraging researchers to submit updates and add new papers to keep the list current. -
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Proxy
Proxy: Next Generation Polymorphism in C++
For decades, object-based virtual tables have been a de facto implementation of runtime polymorphism in many (compiled) programming languages. There are many drawbacks in this mechanism, including life management (because each object may have a different size and ownership) and reflection (because it is hard to balance between usability and memory allocation). To workaround these drawbacks, some languages like Java or C# choose to sacrifice performance by introducing GC to facilitate lifetime management, and JIT-compile the source code at runtime to generate full metadata. We improved the theory and implemented it as a C++ library without sacrificing performance, proposing to merge it into the C++ standard. -
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Python Machine Learning 3rd Ed.
The "Python Machine Learning (3rd edition)" book code repository
Python Machine Learning 3rd Ed. repository contains the complete source code that accompanies the book Python Machine Learning by Sebastian Raschka and collaborators. The project provides implementations of machine learning algorithms and data science workflows described in the book, enabling readers to experiment with real code while studying theoretical concepts. The repository includes Python notebooks and scripts demonstrating techniques such as data preprocessing, classification, regression, clustering, neural networks, and model evaluation. These examples are designed to illustrate how machine learning algorithms operate internally and how they can be applied to real datasets. Many examples rely on widely used libraries such as NumPy, scikit-learn, and deep learning frameworks to demonstrate modern machine learning workflows. -
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RAGs
Build ChatGPT over your data, all with natural language
RAGs is an open-source application designed to simplify the creation of retrieval-augmented generation pipelines through an interactive interface. Built with Streamlit and powered by the LlamaIndex ecosystem, the tool allows users to construct AI assistants that answer questions using their own data sources. Instead of requiring extensive programming knowledge, the application allows users to configure and build a RAG system using natural language instructions. The system automatically generates pipeline configurations that control how documents are retrieved, processed, and summarized before being used by a language model to generate responses. Users can also inspect and adjust parameters such as the number of retrieved documents, summarization strategies, and query settings through a configuration interface. Once the pipeline is created, the system enables conversational queries over the connected data sources, effectively creating a personalized knowledge assistant.
