Open Source ChromeOS Software - Page 57
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Software for ChromeOS
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Hacks
A collection of hacks and one-off scripts
Hacks is a collection of experimental scripts, utilities, and one-off tools created to solve specific problems in security research, data processing, and automation. Rather than being a single cohesive application, it serves as a repository of practical command-line tools that can be used independently or combined into workflows. The scripts cover a wide range of tasks, including URL manipulation, parameter replacement, data extraction, and reconnaissance automation. Many of the tools in the repository are designed for efficiency and simplicity, enabling users to perform complex operations with minimal overhead. It is particularly popular among security researchers and developers who need quick, flexible solutions for niche problems. -
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Harbor LLM
Run a full local LLM stack with one command using Docker
Harbor is an open source, containerized toolkit designed to simplify running local large language model (LLM) environments. It combines a CLI and companion app to launch backends, frontends, and supporting services with minimal setup. With a single command, users can start preconfigured tools like Ollama and Open WebUI, enabling chat, workflows, and integrations immediately. Harbor supports multiple inference engines, including llama.cpp and vLLM, and connects them seamlessly to user interfaces. It also includes tools for web retrieval, image generation, voice interaction, and workflow automation. Built on Docker, Harbor allows services to run in isolated containers while communicating over a local network. It is intended for local development and experimentation rather than production deployment, giving developers a flexible way to explore AI systems, test configurations, and manage complex LLM stacks without manual wiring or setup overhead. -
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Harpoon
Command line OSINT and threat intelligence automation tool
Harpoon is a command line tool designed to assist with open source intelligence (OSINT) and threat intelligence investigations. It helps security professionals and researchers collect and analyze publicly available information from a wide range of online sources. Harpoon is written in Python and organized around a modular plugin system, where each plugin is responsible for querying a specific platform, API, or intelligence service. This design allows users to automate many reconnaissance and intelligence gathering tasks directly from the terminal. Harpoon integrates with numerous security and data services such as Shodan, VirusTotal, AlienVault OTX, and many other intelligence providers to retrieve information about domains, IP addresses, emails, and other indicators. Many commands rely on API keys that can be configured through a central configuration file, allowing users to connect their own intelligence accounts and data sources. -
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Heritrix
Internet Archive's open-source, web-scale, web crawler project
Heritrix is the Internet Archive's open-source, extensible, web-scale, archival-quality web crawler project. Heritrix (sometimes spelled heretrix, or misspelled or missaid as heratrix/heritix/heretix/heratix) is an archaic word for heiress (woman who inherits). Since our crawler seeks to collect and preserve the digital artifacts of our culture for the benefit of future researchers and generations, this name seemed apt. Heritrix is designed to respect the robots.txt exclusion directives† and META nofollow tags. Please consider the load your crawl will place on seed sites and set politeness policies accordingly. Also, always identify your crawl with contact information in the User-Agent so sites that may be adversely affected by your crawl can contact you or adapt their server behavior accordingly. -
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Hunyuan3D-1
A Unified Framework for Text-to-3D and Image-to-3D Generation
Hunyuan3D-1 is an earlier version in the same 3D generation line (the unified framework for text-to-3D and image-to-3D tasks) by Tencent Hunyuan. It provides a framework combining shape generation and texture synthesis, enabling users to create 3D assets from images or text conditions. While less advanced than version 2.1, it laid the foundations for the later PBR, higher resolution, and open-source enhancements. (Note: less detailed public documentation was found for Hunyuan3D-1 compared to 2.1.). Community and ecosystem support (e.g. usage via Blender addon for geometry/texture). Integration into user-friendly tools/platforms. -
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HunyuanCustom
Multimodal-Driven Architecture for Customized Video Generation
HunyuanCustom is a multimodal video customization framework by Tencent Hunyuan, aimed at generating customized videos featuring particular subjects (people, characters) under flexible conditions, while maintaining subject/identity consistency. It supports conditioning via image, audio, video, and text, and can perform subject replacement in videos, generate avatars speaking given audio, or combine multiple subject images. The architecture builds on HunyuanVideo, with added modules for identity reinforcement and modality-specific condition injection. Text-image fusion module based on LLaVA for improved multimodal understanding. Applicable to single- and multi-subject scenarios, video editing/replacement, singing avatars etc. -
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HunyuanWorld-Voyager
RGBD video generation model conditioned on camera input
HunyuanWorld-Voyager is a next-generation video diffusion framework developed by Tencent-Hunyuan for generating world-consistent 3D scene videos from a single input image. By leveraging user-defined camera paths, it enables immersive scene exploration and supports controllable video synthesis with high realism. The system jointly produces aligned RGB and depth video sequences, making it directly applicable to 3D reconstruction tasks. At its core, Voyager integrates a world-consistent video diffusion model with an efficient long-range world exploration engine powered by auto-regressive inference. To support training, the team built a scalable data engine that automatically curates large video datasets with camera pose estimation and metric depth prediction. As a result, Voyager delivers state-of-the-art performance on world exploration benchmarks while maintaining photometric, style, and 3D consistency. -
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Install Nothing
A terminal application that simulates installing things
Install Nothing is a Rust-based project is a command-line tool that simulates the output of an installation process without actually doing any real installation, letting users watch amusing fake progress screens as if packages, kernels, or desktops were being compiled and configured. Rather than running real tasks, it produces convincing terminal output that mimics the steps, logs, and scrolling messages of installation scripts, making it entertaining for demonstrations, jokes, or screensaver-style displays in terminal sessions. It’s designed for simplicity and safety, doing nothing destructive or permanent on your system while still delivering a satisfying illusion of intense computing activity. Users can configure its behavior to include or exclude specific fake stages, so the output can be tailored to the experience they want. -
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JEPA
PyTorch code and models for V-JEPA self-supervised learning from video
JEPA (Joint-Embedding Predictive Architecture) captures the idea of predicting missing high-level representations rather than reconstructing pixels, aiming for robust, scalable self-supervised learning. A context encoder ingests visible regions and predicts target embeddings for masked regions produced by a separate target encoder, avoiding low-level reconstruction losses that can overfit to texture. This makes learning focus on semantics and structure, yielding features that transfer well with simple linear probes and minimal fine-tuning. The repository provides training recipes, data pipelines, and evaluation utilities for image JEPA variants and often includes ablations that illuminate which masking and architectural choices matter. Because the objective is non-autoregressive and operates in embedding space, JEPA tends to be compute-efficient and stable at scale. The approach has become a strong alternative to contrastive or pixel-reconstruction methods for representation learning. -
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JMX Exporter
A process for exposing JMX Beans via HTTP for Prometheus consumption
JMX to Prometheus exporter: a collector that can configurable scrape and expose mBeans of a JMX target. This exporter is intended to be run as a Java Agent, exposing a HTTP server and serving metrics of the local JVM. It can be also run as a standalone HTTP server and scrape remote JMX targets, but this has various disadvantages, such as being harder to configure and being unable to expose process metrics (e.g., memory and CPU usage). Running the exporter as a Java agent is strongly encouraged. -
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JSON to Go
Translates JSON into a Go type in your browser instantly (original)
JSON to Go is a browser-based developer tool that converts JSON samples into Go struct definitions. It is designed to save Go developers time when working with APIs, configuration files, or external JSON payloads. Users paste JSON into the tool, and it generates a matching Go type that can be copied into a project. The tool makes reasonable assumptions about field names, types, nested objects, arrays, and struct tags, but it still expects users to review the output before using it in production. It is related to curl-to-go and fits the same lightweight, practical workflow for developers translating common web data into Go code. Its main value is speed, especially when prototyping clients for JSON-based APIs. -
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Janus
Unified Multimodal Understanding and Generation Models
Janus is a sophisticated open-source project from DeepSeek AI that aims to unify both visual understanding and image generation in a single model architecture. Rather than having separate systems for “look and describe” and “prompt and generate”, Janus uses an autoregressive transformer framework with a decoupled visual encoder—allowing it to ingest images for comprehension and to produce images from text prompts with shared internal representations. The design tackles long-standing conflicts in multimodal models: namely that the visual encoder has to serve both analysis (understanding) and synthesis (generation) roles. By splitting those pathways but keeping one unified core transformer, Janus maintains flexibility and achieves strong performance across tasks previously requiring distinct architectures. The repository includes pretrained checkpoints (for example 1.3B and 7B parameter versions), a Gradio demo, and guidance for local deployment. -
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Java and Spring Tutorials
Getting Started with Spring Boot 3
Java and Spring Tutorials is a large-scale educational codebase that provides practical examples and tutorials covering a wide range of software development topics, primarily focused on Java and related ecosystems. It serves as a companion resource for Baeldung articles, offering real-world code implementations that demonstrate concepts such as Spring Boot, persistence frameworks, REST APIs, security, testing, and more. The repository is organized into multiple modules, each targeting specific technologies or frameworks, making it easy for developers to explore topics independently. It emphasizes best practices, clean code structure, and production-ready patterns, allowing learners to not only understand theoretical concepts but also apply them in real applications. The project is continuously updated to reflect modern development trends and evolving technologies, ensuring relevance for both beginners and experienced developers. -
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JimuReport
Open source drag-and-drop reporting and dashboard builder platform
JimuReport is an open source data visualization and reporting platform designed to help developers and organizations build reports, dashboards, and large screen data displays through a visual interface. It provides an online report designer that uses an Excel-like editing experience, allowing users to construct reports with drag-and-drop components and cell-based layouts. It focuses on simplifying complex report development by enabling visual configuration instead of manual coding. JimuReport supports traditional report generation, print templates, and modern dashboard visualizations for business intelligence scenarios. JimuReport also includes components for building interactive charts, data tables, and analytical displays that can be used in enterprise applications. It can connect to multiple data sources and retrieve data through SQL queries, APIs, or other structured formats. It can be embedded into Java applications using Spring Boot integration modules. -
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Kindle_download_helper
Download all your kindle books script
Kindle_download_helper is a Python script for downloading Kindle books and personal documents from a user’s Amazon content library. It supports several Amazon regions, including Amazon, Amazon.cn, Amazon.de, Amazon.co.uk, and Amazon.co.jp. The workflow requires the user to log in through the browser, retrieve a CSRF token, and then run the script with the correct regional option. It can download purchased Kindle content and, depending on the flag used, personal document files as well. The repository is archived, so it should be treated as a historical utility rather than an actively maintained tool. It is best understood as a convenience script for users who need bulk access to their own Kindle library files. -
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Kreuzberg
Polyglot document intelligence framework
Kreuzberg is a flexible task orchestration and agent workflow platform designed to help developers build, coordinate, and scale intelligent agents or automation pipelines that interact with external services, runtime environments, and multi-step business workflows. It emphasizes modular design so that developers can define discrete tasks or “actions” and then compose them into complex flows where dependencies, parallel steps, and error handling are declaratively managed. This structure makes it simpler to build resilient automation pipelines that span multiple technologies or APIs without needing to glue everything together manually. The platform also supports observability and monitoring, helping teams trace the progression of workflows, catch errors early, and evaluate performance trends in orchestrated systems. -
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LIFELINES
Survival analysis in Python
LIFELINES is a pure Python library for survival analysis, a statistical field focused on modeling time until an event occurs. It can be used for traditional cases like medical survival time, but also for business and product questions such as churn, subscription length, equipment failure, and customer retention. The library includes estimators such as Kaplan-Meier, Nelson-Aalen, and regression-based survival models. It is designed to be accessible to Python users and works well with common scientific computing workflows. Built-in plotting methods and datasets help users explore survival curves and compare groups visually. It is a practical tool for analysts, researchers, and data scientists who need event-time modeling without leaving Python. -
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LIFETIMES
Lifetime value in Python
LIFETIMES is a Python library for customer lifetime value and repeat purchase behavior modeling. It helps analysts estimate how frequently customers may return, how long they may remain active, and how much value they may generate over time. The library is built around probabilistic models commonly used in customer analytics, including transaction frequency and monetary value modeling. It is useful for ecommerce, subscription-adjacent businesses, retail analytics, and retention analysis. The repository is now archived, so it should be treated as a stable historical project rather than an actively developed package. For current work, its ideas remain valuable, but teams may want to consider newer successor tools for ongoing production use. -
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LLM101n
LLM101n: Let's build a Storyteller
LLM101n is an educational repository that walks you through building and understanding large language models from first principles. It emphasizes intuition and hands-on implementation, guiding you from tokenization and embeddings to attention, transformer blocks, and sampling. The materials favor compact, readable code and incremental steps, so learners can verify each concept before moving on. You’ll see how data pipelines, batching, masking, and positional encodings fit together to train a small GPT-style model end to end. The repo often complements explanations with runnable notebooks or scripts, encouraging experimentation and modification. By the end, the focus is less on polishing a production system and more on internalizing how LLM components interact to produce coherent text. -
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LandPPT
An LLM-based presentation generation platform
LandPPT is an open-source AI platform that automatically generates professional presentation slides using large language models. The system allows users to create complete PowerPoint presentations simply by entering a topic or uploading source documents such as PDFs, Word files, or Markdown notes. Using natural language processing and structured content generation, the platform produces presentation outlines and converts them into fully formatted slide decks. The application integrates multiple AI models from providers such as OpenAI, Anthropic, Google, and locally hosted models to generate text, images, and structured presentation layouts. It also includes template systems and style options that allow presentations to be customized for different industries, visual themes, or storytelling formats. -
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Laravel Data
Powerful data objects for Laravel
This package enables the creation of rich data objects which can be used in various ways. Using this package you only need to describe your data once. -
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LiveKit Agents
Framework for building realtime multimodal voice AI agents apps
LiveKit Agents is an open source framework designed for building realtime AI agents that can participate as programmable entities within communication sessions. It enables developers to create conversational and multimodal agents capable of processing voice, audio, and other inputs in realtime environments. These agents can join LiveKit rooms as participants and interact with users or systems through speech, text, and other modalities. LiveKit Agents provides libraries and tooling that allow developers to combine speech-to-text, large language models, and text-to-speech services to build interactive AI experiences. It is designed to run server-side and can integrate with various AI model providers and realtime APIs to support different application requirements. LiveKit Agents also includes tools for scheduling and managing agent tasks, making it easier to connect users to automated assistants in live communication scenarios. -
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Lobe UI
An open-source UI component library for building AIGC web apps
Lobe UI is an open source UI component library specifically designed for building AI-generated content (AIGC) web applications, offering a modern toolkit tailored to chatbots, AI interfaces, and intelligent systems. It is built with React and TypeScript and integrates closely with Ant Design, allowing developers to leverage familiar component patterns while extending them for AI-centric use cases. The library includes specialized components and configurations for handling features like motion, theming, and internationalization, which are essential in dynamic AI-driven interfaces. It is optimized for modern frameworks such as Next.js and supports server-side rendering with proper configuration, making it suitable for production-grade applications. Lobe UI is part of a broader ecosystem that includes tools for charts, icons, and AI-related utilities, enabling developers to build complete AI products with consistent design. -
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Lossless Claw
LCM (Lossless Context Management) plugin for OpenClaw
Lossless Claw is an advanced context management plugin for the OpenClaw agent ecosystem that redefines how conversational memory is handled in large language model systems. Instead of relying on traditional sliding-window truncation or lossy summarization, it introduces a lossless architecture that preserves all historical messages while maintaining usable context within token limits. The system stores every interaction in a persistent database and incrementally summarizes older content into a hierarchical directed acyclic graph, allowing efficient compression without discarding information. This structure enables agents to dynamically reconstruct detailed context by expanding summaries when needed, effectively simulating perfect long-term memory. -
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Luna AI
Virtual AI anchor that combines state-of-the-art technology
Luna AI is a virtual AI streamer framework designed to power an interactive VTuber that can go live on major platforms and chat with viewers in real time. It is built around a core assistant persona called “Luna AI,” which can be driven by a wide range of large language models and platforms, including GPT-style APIs, Claude, LangChain-based backends, ChatGLM, Kimi, Ollama, and many others. The project supports multiple rendering backends for the avatar, such as Live2D, Unreal Engine (UE), and “xuniren,” and can output to streaming platforms like Bilibili, Douyin, Kuaishou, WeChat Channels, Pinduoduo, Douyu, YouTube, Twitch, and TikTok. For voice, it integrates with numerous TTS engines (Edge-TTS, VITS-Fast, ElevenLabs, VALL-E-X, OpenVoice, GPT-SoVITS, Azure TTS, fish-speech, ChatTTS, CosyVoice, F5-TTS, MultiTTS, MeloTTS, and others), and can optionally pass the output through voice conversion systems like so-vits-svc or DDSP-SVC to change timbre.
