Open Source ChromeOS Software - Page 98
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Software for ChromeOS
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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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LISA
LISA: Reasoning Segmentation via Large Language Model
LISA is an open-source multimodal AI system designed to enable language models to perform pixel-level reasoning and segmentation tasks on images. The project introduces a framework where a large language model can interpret natural language instructions and produce segmentation masks that highlight relevant regions in an image. Instead of relying solely on predefined object categories, the model is capable of reasoning about complex textual queries and translating them into visual segmentation outputs. This approach allows the system to identify objects or regions in images based on semantic descriptions, contextual reasoning, and world knowledge. The model integrates multimodal capabilities by combining language understanding with visual perception so that text instructions guide the segmentation process. Researchers created a specialized task called reasoning segmentation, where the model must generate a mask for regions described in natural language instructions. -
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LLM Vision
Visual intelligence for your home.
LLM Vision is an open-source integration for Home Assistant that adds multimodal large language model capabilities to smart home environments. The project enables Home Assistant to analyze images, video files, and live camera feeds using vision-capable AI models. Instead of relying only on traditional object detection pipelines, it allows users to send prompts about visual content and receive contextual descriptions or answers about what is happening in camera footage. The system can process events from surveillance platforms such as Frigate and convert them into meaningful summaries, notifications, or structured data for automation workflows. It also maintains a timeline of analyzed camera events that can be displayed in dashboards or queried through the assistant interface. -
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LLaMA-MoE
Building Mixture-of-Experts from LLaMA with Continual Pre-training
LLaMA-MoE is an open-source project that builds mixture-of-experts language models from LLaMA through expert partitioning and continual pre-training. The repository is centered on making MoE research more accessible by offering smaller and more affordable models with only about 3.0 to 3.5 billion activated parameters, which helps reduce deployment and experimentation costs. Its architecture works by splitting LLaMA feed-forward networks into sparse experts and adding gating mechanisms so that only selected experts are activated during inference and training. The project is not just a model release, but also a research framework that includes multiple expert construction methods, several gating strategies, and tooling for continual pre-training on filtered SlimPajama-based datasets. It also emphasizes training efficiency through features such as FlashAttention-v2 integration and fast streaming dataset loading, which are important for large-scale experimentation. -
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LabClaw
Operating Layer for LabOS (Stanford-Princeton AI Co-Scientists)
LabClaw is an open-source AI experimentation and agent orchestration platform designed to help developers build, test, and iterate on complex autonomous workflows in a controlled and modular environment. It provides a framework for composing multiple tools, prompts, and execution steps into structured pipelines that can be reused and evaluated across different scenarios. The system emphasizes experimentation, allowing users to run multiple variations of agent workflows, compare outputs, and refine performance over time. LabClaw is designed to integrate with various large language models and external tools, enabling flexible configurations that adapt to different use cases such as automation, research, and product prototyping. It also includes logging and observability features that help developers track agent decisions and debug behavior during execution. -
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Lagent
A lightweight framework for building LLM-based agents
Lagent is a lightweight open-source framework designed to help developers build autonomous agents powered by large language models. The framework provides tools and abstractions that allow language models to interact with external tools, execute tasks, and perform multi-step reasoning processes. Instead of using LLMs only for text generation, Lagent enables developers to transform models into agents capable of performing actions such as retrieving data, executing code, or interacting with APIs. The system includes modular components that allow developers to connect different models and tools within the same agent architecture. Its design emphasizes simplicity and flexibility so that developers can experiment with different agent workflows without needing a complex infrastructure setup. Lagent can also be deployed as a web service to support distributed or multi-agent applications. -
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Lagom
Reactive Microservices for the JVM
The opinionated microservices framework for moving away from the monolith. Lagom helps you decompose your legacy monolith and build, test, and deploy entire systems of Reactive microservices. Lagom is an open source framework for building systems of Reactive microservices in Java or Scala. Lagom builds on Akka and Play, proven technologies that are in production in some of the most demanding applications today. Lagom's integrated development environment allows you to focus on solving business problems instead of wiring services together. A single command builds the project, starts supporting components and your microservices, as well as the Lagom infrastructure. The build hot-reloads when it detects changes to source code. -
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Laravel Backpack
Build custom admin panels
It's never been easier to build and customize admin panels using Laravel. See why thousands of professionals have been using Backpack for Laravel, every day. Similar to how you'd build it without Backpack. To create a management page (aka CRUD), you create a CrudController. In there, configure or overwrite whatever you want. Load our views, or create your own. Laravel, Bootstrap & jQuery. Those are all the technologies you need to know, to customize anything in Backpack. You can totally use Vue, React, WebPack, Mix, Less, Sass, NPM etc. If you want to. But you don't have to. Backpack comes with support for multiple Bootstrap themes. If you need to build a custom page, dashboard, or component, you just copy-paste HTML blocks from its website. No designer is required. No pixel pushing. -
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Laravel Cashier
Laravel Cashier provides an expressive, fluent interface to Stripe
Laravel Cashier provides an expressive, fluent interface to Stripe's subscription billing services. It handles almost all of the boilerplate subscription billing code you are dreading writing. In addition to basic subscription management, Cashier can handle coupons, swapping subscriptions, subscription "quantities", cancellation grace periods, and even generate invoice PDFs. -
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Laravel Dusk
Laravel Dusk provides simple end-to-end testing and browser automation
Laravel Dusk provides an expressive, easy-to-use browser automation and testing API. By default, Dusk does not require you to install JDK or Selenium on your machine. Instead, Dusk uses a standalone Chromedriver. However, you are free to utilize any other Selenium driver you wish. -
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Laravel Jetstream
Tailwind scaffolding for the Laravel framework.
Laravel Jetstream is a beautifully designed application scaffolding for Laravel. Jetstream provides the perfect starting point for your next Laravel application and includes login, registration, email verification, two-factor authentication, session management, API support via Laravel Sanctum, and optional team management. Jetstream is designed using Tailwind CSS and offers your choice of Livewire or Inertia scaffolding. -
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Laravel Multitenancy
Make your Laravel app usable by multiple tenants
This package can make a Laravel app tenant aware. The philosophy of this package is that it should only provide the bare essentials to enable multi-tenancy. The package can determine which tenant should be the current tenant for the request. It also allows you to define what should happen when switching the current tenant to another one. It works for multitenancy projects that need to use one or multiple databases. The package contains a lot of niceties such as making queued jobs tenants aware, making an artisan command run for each tenant, an easy way to set a connection on a model, and much more. -
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Laravel Sentinel
A framework agnostic authentication & authorization system
Sentinel is a PHP 8.1+ framework agnostic fully-featured authentication & authorization system. It also provides additional features such as user roles and additional security features. -
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Laravel Valet
A more enjoyable local development experience for Mac
Valet is a Laravel development environment for Mac minimalists. No Vagrant, no /etc/hosts file. You can even share your sites publicly using local tunnels. Yeah, we like it too. Laravel Valet configures your Mac to always run Nginx in the background when your machine starts. Then, using DnsMasq, Valet proxies all requests on the *.test domain to point to sites installed on your local machine. In other words, a blazing-fast Laravel development environment that uses roughly 7 MB of RAM. Valet isn't a complete replacement for Vagrant or Homestead, but provides a great alternative if you want flexible basics, prefer extreme speed, or are working on a machine with a limited amount of RAM. -
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Laravel Webpush
Webpush notifications channel for Laravel
This package makes it easy to send web push notifications with Laravel. Web push notifications channel for Laravel. Now you can use the channel in your via() method inside the notification as well as send a web push notification. -
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Laravel wrapper for NativePHP
Laravel wrapper for the NativePHP framework
NativePHP is a new way to build native applications, using the tools you already know. NativePHP is not an especially opinionated way to build native apps. Right now, we only support a Laravel driver, but we're already working on making it work whatever framework you're using - and even if you're not using a framework at all. NativePHP is not a GUI framework. We don't want to tell you how to build your app. You can choose whatever UI toolset makes you and your team feel most productive. NativePHP is not some new custom fork of PHP. This is the good old PHP you know and love. We believe NativePHP is going to empower thousands of developers to build all kinds of applications. The only limit is your imagination. You could build a menubar app that lets you manage your cron jobs, or a cool new launcher app, or a screen recorder that puts cowboy hats on every smiley-face emoji it sees. -
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Learn AI Engineering
Learn AI and LLMs from scratch using free resources
Learn AI Engineering is a learning path for AI engineering that consolidates high-quality, free resources across the full stack: math, Python foundations, machine learning, deep learning, LLMs, agents, tooling, and deployment. Rather than a loose bookmark list, it organizes topics into a progression so learners can start from fundamentals and move toward practical, production-oriented skills. It mixes courses, articles, code labs, and videos, emphasizing materials that teach both concepts and hands-on implementation. The curation recognizes modern AI realities, including data pipelines, evaluation, prompt engineering, retrieval-augmented generation, and cost/performance trade-offs. It’s equally useful for refreshers—dipping into a specific module before a project—as it is for a full, self-directed curriculum. By centralizing the best references in one place, the repo reduces the overhead of finding, filtering, and sequencing resources, letting you focus on learning and building. -
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Learn Claude Code
Bash is all you need, write a claude code with only 16 line code
Learn Claude Code is an educational repository that teaches how modern AI coding agents work by walking learners through a sequence of progressively more complex agent implementations, starting with a minimal Bash-based agent and culminating in agents with explicit planning, subagents, and skills. It emphasizes a hands-on learning path where each version (from v0 to v4) adds conceptual building blocks like the core agent loop, todo planning, task decomposition, and domain knowledge skills, illuminating the patterns behind what makes a true AI agent tick. The goal is to demystify agent architectures like Claude Code by having learners build simplified versions themselves and observe how tools, memory management, planning constraints, and context isolation contribute to reliable agent behavior. Along the way, the project teaches fundamentals such as how to let models call external tools, maintain clean memory for long tasks, and inject domain expertise without retraining the model. -
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Learn PyTorch for Deep Learning
Materials for the Learn PyTorch for Deep Learning
Learn PyTorch for Deep Learning is an open-source educational repository that provides the full learning materials for the “Learn PyTorch for Deep Learning: Zero to Mastery” course created by Daniel Bourke. The project is designed to teach beginners how to build deep learning models using PyTorch through a hands-on, code-first learning approach. Instead of focusing heavily on theory alone, the repository encourages learners to experiment with code and develop practical machine learning skills through guided examples and exercises. The materials include Jupyter notebooks, explanations of core deep learning concepts, and step-by-step demonstrations of building and training neural networks. Throughout the lessons, users learn how to work with tensors, create neural network architectures, manage training workflows, and evaluate model performance. -
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Learn Regex The Easy Way
Learn regex the easy way
Learn Regex The Easy Way is a hands-on, interactive resource for learning regular expressions (regex) in a step-by-step, incremental way. Rather than just being a reference sheet, it is designed to help you build understanding gradually: you start with the basics like literal matching, then advance through character classes, quantifiers, groups, alternation, lookaheads/lookbehinds, and more advanced regex features. Each lesson is accompanied by examples and clear explanations, so learners can experiment and immediately see results without needing external tools. The educational nature of the repository makes it ideal for people who want to master regex not through passive reading, but active experimentation. Because regex tends to be a feature that many developers struggle with — especially when dealing with edge cases — having a structured learning path helps build intuition gradually. -
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LibChecker
An app to view libraries used in apps in your device
This app is used to view the third-party libraries used by applications in your device. It can view the ABI architecture of the application's native library (in general, whether the application is 64-bit or 32-bit). It can also view well-known libraries marked by The Rule Repository, and can even sort and view them according to the number of libraries references. -
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Light-4J
A fast, lightweight and more productive microservices framework
Light means lightweight, lightning-fast and shedding light on how to program with modern Java SE for cloud-native deployment. I had been working on the Java EE platforms since early 2000 and suffered performance and productivity issues. In 2014, I realized that the IT industry was moving from Monolithic to Microservices and from on-premise data centers to the public clouds. To reduce the production cost for my applications, I need to find a lightweight platform that has a small memory footprint, high throughput, and low latency in Java. Java EE and Spring/Spring Boot are too heavy to be considered. Other lightweight Java platforms all have different issues on the separation between the business logic and technical cross-cutting concerns. Moreover, they are not cloud-native or designed for container/cloud. -
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LinuxTimeline
Linux Distributions Timeline
Linux Distributions Timeline. -
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LiteBox
A security-focused library OS supporting kernel execution
LiteBox is a security-focused “library OS” sandboxing project that aims to shrink the interface between an application and its host environment to reduce attack surface. Instead of relying solely on broad OS-level permissions, it focuses on isolating workloads by tightly controlling the boundary where code interacts with host services and system resources. The design emphasizes interoperability across different integration layers, describing a separation between “North” shims (how apps or runtimes plug in) and “South” platforms (where the sandbox runs), which helps the system adapt to multiple deployment contexts. A key aspect of the project is that it targets both kernel-mode and user-mode scenarios, enabling experimentation with different trust and performance tradeoffs. The repository positions LiteBox as a foundation for building hardened execution environments where untrusted or semi-trusted components can run with reduced privileges and a minimized host interface. -
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Live Agent Studio
Open source AI Agents hosted on the oTTomator Live Agent Studio
Live Agent Studio is a curated repository of open-source AI agents associated with the oTTomator Live Agent Studio platform, showcasing a variety of agent implementations that illustrate how autonomous and semi-autonomous tools can be constructed using modern AI frameworks. Each agent in the collection is designed for a specific use case — such as content summarization, task automation, travel planning, or RAG workflows — and is provided with the code or configuration needed to explore and extend it on your own, making the repository both a learning resource and a practical starting point for real projects. The repository is community focused, with sample agents like tweet generators, smart selectors, research assistants, and multi-tool workflows that show how agents can integrate with tools like n8n or custom Python code. Because it’s tied to the broader Live Agent Studio ecosystem, users can experiment with deploying and using these agents in a hosted environment.
