Search Results for "horizon"
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Showing 101 open source projects for "horizon"
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Laravel Horizon
Dashboard and code-driven configuration for Laravel queues
Horizon provides a beautiful dashboard and code-driven configuration for your Laravel-powered Redis queues. Horizon allows you to easily monitor key metrics of your queue system such as job throughput, runtime, and job failures. All of your worker configuration is stored in a single, simple configuration file, allowing your configuration to stay in source control where your entire team can collaborate. -
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RabbitMQ Queue driver for Laravel
RabbitMQ driver for Laravel Queue. Supports Laravel Horizon
RabbitMQ driver for Laravel Queue. Supports Laravel Horizon. Optionally add queue options to the config of a connection. -
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Shadcn UI NextJS Boilerplate
Shadcn UI NextJS Boilerplate. Free Open-source ChatGPT UI Admin
Launch your SaaS startup within a few days with the first Admin Dashboard Shadcn UI NextJS boilerplate. Get started with Horizon AI Boilerplate today. Horizon AI Boilerplate is the first open-source Admin Dashboard OpenAI ChatGPT AI Template made for Shadcn UI, NextJS, and Tailwind CSS! Start creating outstanding Chat AI SaaS Apps faster. It comes with over 30+ dark/light frontend individual elements, like buttons, inputs, navbars, nav tabs, cards, or alerts, giving you the freedom of choosing and combining. -
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Log Viewer
Fast and beautiful Log Viewer for Laravel
Beautiful, fast, and open-source viewer for your app logs. Forget scrolling through raw log files to find the errors or debug logs you're looking for. Forget about SSH'ing onto the production server just to read the logs. Log Viewer helps you quickly and clearly see individual log entries, to search, filter, and make sense of your Laravel logs fast. It is free and easy to install. -
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libctru
Homebrew development library for Nintendo 3DS/Horizon OS user mode
Library for writing user mode ARM11 code for the 3DS (CTR) This library aims to provide the foundations necessary to write 3DS Homebrew, and straightforwardly access the different functionalities provided by the 3DS operating system. It is not meant to provide higher-level functions; to put things in perspective, the purpose of libctru would be to sit between the OS and a possible port of SDL rather than replace it. libctru is just a library and needs a toolchain to function. devkitARM... -
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Beads
A memory upgrade for your coding agent
...Instead of storing plans as unstructured Markdown or ephemeral notes, Beads organizes agent state, task artifacts, and relationships as nodes and edges in a version-controlled graph so that long-horizon projects don’t lose context or coherence as the agent proceeds. This approach helps coding agents — and human collaborators — track which tasks depend on others, what has been done, and where workflows branch or reunify without losing important data. By leveraging Git as the storage backbone, the project ensures that memory is persistent, diffable, and sharable, with the ability to roll back, branch, or merge memory states just like source code. -
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Agent Skills for Context Engineering
A comprehensive collection of Agent Skills for context engineering
Agent Skills for Context Engineering is a curated collection of reusable “agent skills” focused on helping AI agents perform better on long-horizon, multi-step work by managing context deliberately. Rather than being a single application, it packages practical guidance into skill modules that agents can load to improve planning, retrieval, memory usage, and overall reliability in real workflows. The repository emphasizes context engineering as a discipline, covering why agents fail when context gets too large, too noisy, or poorly structured, and how to mitigate those failure modes with repeatable patterns. ... -
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Sapiens
High-resolution models for human tasks
...It integrates sensory inputs such as vision, audio, and proprioception into a unified learning architecture that allows agents to understand and adapt to their surroundings dynamically. The project emphasizes long-horizon reasoning and cross-modal grounding—connecting language, perception, and action into a single agentic model capable of following abstract goals. It includes simulation environments, datasets, and benchmarks for testing grounded understanding, imitation learning, and decision-making. The system’s modular pipeline supports both imitation-based and reinforcement-based training strategies, allowing flexible experimentation with different embodiments and tasks. -
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MiniMax-M2.1
MiniMax M2.1, a SOTA model for real-world dev & agents.
MiniMax-M2.1 is an open-source, state-of-the-art agentic language model released to democratize high-performance AI capabilities. It goes beyond a simple parameter upgrade, delivering major gains in coding, tool use, instruction following, and long-horizon planning. The model is designed to be transparent, controllable, and accessible, enabling developers to build autonomous systems without relying on closed platforms. MiniMax-M2.1 excels in real-world software engineering tasks, including multilingual development and complex workflow automation. It demonstrates strong generalization across agent frameworks and consistently improves upon its predecessor, MiniMax-M2. ... -
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verl-agent
Designed for training LLM/VLM agents via RL
...The framework supports multi-turn interactions between agents and their environments, allowing the system to receive feedback after each step and adjust its strategy accordingly. This step-wise interaction model makes it possible to train agents to operate in long-horizon scenarios where decisions depend on cumulative context and previous outcomes. Developers can configure memory modules that determine how historical information is stored and incorporated into each step of the reasoning process. -
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GLM-5
From Vibe Coding to Agentic Engineering
GLM-5 is a next-generation open-source large language model (LLM) developed by the Z .ai team under the zai-org organization that pushes the boundaries of reasoning, coding, and long-horizon agentic intelligence. Building on earlier GLM series models, GLM-5 dramatically scales the parameter count (to roughly 744 billion) and expands pre-training data to significantly improve performance on complex tasks such as multi-step reasoning, software engineering workflows, and agent orchestration compared to its predecessors like GLM-4.5. ... -
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Deep Research Web UI
AI-powered research assistant that performs iterative, deep research
...It operates as a front-end system for deep research agents that iteratively refine queries, retrieve information from multiple sources, and synthesize structured outputs into coherent reports. The platform emphasizes long-horizon reasoning, allowing users to explore topics in depth rather than receiving shallow, single-response answers. Built with modern web technologies such as Vue and TypeScript, it provides a responsive interface for managing research sessions, tracking intermediate steps, and reviewing collected data. The system supports integration with advanced models like DeepSeek R1, enabling more sophisticated reasoning and contextual understanding across multiple sources. -
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Jolt Physics
A multi core friendly rigid body physics and collision detection lib
A multi core friendly rigid body physics and collision detection library suitable for games and VR applications, used by Horizon Forbidden West. So why create yet another physics engine? First of all, this has been a personal learning project and secondly I wanted to address some issues that I had with existing physics engines. In games we usually need to do many more things than to simulate the physics world and we need to do this across multiple threads. -
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Meta Agents Research Environments (ARE)
Meta Agents Research Environments is a comprehensive platform
Meta Agents Research Environments (ARE) is a simulation and benchmarking platform. It is designed to evaluate AI agents in dynamic, evolving, multi-step tasks. Unlike static benchmarks, ARE supports environments where agents must adapt to changes over time and reason over sequences of actions. It interacts with applications and faces uncertainty. The included Gaia2 benchmark offers 800 scenarios across multiple “universes”. It can test reasoning, memory, tool use, and adaptability.... -
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Cradle framework
The Cradle framework is a first attempt at General Computer Control
Cradle is an open-source framework designed to enable AI agents to perform complex computer tasks by interacting with software environments in a way similar to human users. The system introduces the concept of General Computer Control, where AI agents receive screenshots as input and perform actions through simulated keyboard and mouse operations. This approach allows agents to interact with any software interface without relying on specialized APIs or predefined automation scripts. The... -
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EverMemOS
Long-term memory OS for AI with structured recall and context awarenes
...EverMemOS goes beyond simple retrieval by actively applying stored knowledge to current tasks, improving personalization and consistency. EverMemOS uses a multi-stage memory lifecycle to convert raw dialogue into structured semantic data, supporting long-horizon reasoning and adaptive behavior across sessions. -
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MiroThinker
MiroThinker is an open source deep research agent
MiroThinker is an open-source deep research AI agent designed to perform complex reasoning, information gathering, and predictive analysis tasks. The system focuses on enabling long-horizon research workflows by allowing the agent to interact repeatedly with external tools, search systems, and data sources while refining its reasoning through iterative steps. Rather than simply generating responses from a single prompt, the agent performs structured multi-step reasoning processes that involve searching for information, analyzing evidence, and synthesizing conclusions. ... -
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CUDA Agent
Large-Scale Agentic RL for High-Performance CUDA Kernel Generation
...The project addresses the long-standing challenge that efficient CUDA programming typically requires deep hardware expertise by training an autonomous coding agent capable of iterative improvement through execution feedback. Its architecture combines large-scale data synthesis, a skill-augmented CUDA development environment, and long-horizon reinforcement learning to build intrinsic optimization capability rather than relying on simple post-hoc tuning. The system operates in a ReAct-style loop where the agent profiles baseline implementations, writes CUDA code, compiles it in a sandbox, and iteratively refines performance. CUDA-Agent has demonstrated strong benchmark results, achieving high pass rates and significant speedups compared with compiler baselines such as torch.compile. -
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CausalImpact
An R package for causal inference in time series
...The package requires as input a response time series plus one or more control (covariate) time series that are assumed unaffected by the intervention, and it divides the time horizon into “pre-intervention” and “post-intervention” periods. It uses Bayesian modeling to fit a structural time series to the pre-period and extrapolate a counterfactual prediction for the post period, then compares observed vs predicted to infer the causal effect. The package supports plotting, summary tables, and verbal narratives for interpretive reports. -
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Laravel Workflow
Durable workflow engine that allows users to track job status
Laravel Workflow is a package for the Laravel web framework that provides tools for defining and managing workflows and activities. A workflow is a series of interconnected activities that are executed in a specific order to achieve a desired result. Activities are individual tasks or pieces of logic that are executed as part of a workflow. Laravel Workflow can be used to automate and manage complex processes, such as financial transactions, data analysis, data pipelines, micro-services, job... -
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PyTorch Forecasting
Time series forecasting with PyTorch
PyTorch Forecasting aims to ease state-of-the-art time series forecasting with neural networks for both real-world cases and research alike. The goal is to provide a high-level API with maximum flexibility for professionals and reasonable defaults for beginners. A time series dataset class that abstracts handling variable transformations, missing values, randomized subsampling, multiple history lengths, etc. A base model class that provides basic training of time series models along with...
