Search Results for "structure" - Page 2
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Showing 338 open source projects for "structure"
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Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
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Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
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chat
chat web app for teams, sass with user management and ratelimit
...The repository structure also points to both web and Flutter-based mobile components, suggesting a broader product surface than a simple browser-only interface. -
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OpenMLSys-ZH
Machine Learning Systems: Design and Implementation
...Its aim is to make the technical content, tutorials, architecture descriptions, and user guides of the OpenMLSys system more accessible to Chinese-speaking users. The repo mirrors the structure of the original OpenMLSys docs: sections on system design, API references, deployment instructions, module overviews, and example workflows. It helps bridge language barriers in open machine learning systems by providing side-by-side translation or localized explanations. The repository includes scripts or tooling to keep translation synchronized with upstream changes, versioning, and possibly translation metadata (contributors, timestamp). ... -
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VisualDL
Deep Learning Visualization Toolkit
VisualDL, a visualization analysis tool of PaddlePaddle, provides a variety of charts to show the trends of parameters and visualizes model structures, data samples, histograms of tensors, PR curves , ROC curves and high-dimensional data distributions. It enables users to understand the training process and the model structure more clearly and intuitively so as to optimize models efficiently. VisualDL provides various visualization functions, including tracking metrics in real-time, visualizing the model structure, displaying the data sample, visualizing the relationship between hyperparameters and model metrics, presenting the changes of distributions of tensors, showing the pr curves, projecting high-dimensional data to a lower dimensional space and more. ... -
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Talk to Figma MCP
AI bridge enabling Cursor agents to read and modify Figma designs
...It allows AI tools such as Cursor or other compatible agents to directly communicate with Figma documents and interact with design elements programmatically. Through this integration, an AI assistant can read the structure of a design, retrieve information about nodes or selections, and perform modifications to the layout or content. cursor-talk-to-figma-mcp includes an MCP server and a Figma plugin that communicate through a WebSocket connection, enabling real-time interaction between the AI environment and the design canvas. Developers can automate tasks such as creating UI elements, updating text, organizing layout structures, or managing annotations inside a design file. ... -
Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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Gitingest
Create prompt-friendly codebase digests from any Git repository URL
...In addition to producing the code digest, Gitingest also calculates statistics about the extracted content such as repository structure, total size of the extract, and token count. Gitingest can be used as a command line utility or integrated directly into Python applications. -
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GitAgent
A framework-agnostic, git-native standard for defining AI agents
...The repository typically includes a manifest file that describes the agent’s configuration, along with additional files that define behavior, skills, and integrations with external tools. This structure allows organizations to treat agents similarly to software projects, with version control, branching, auditing, and collaboration handled through Git. -
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LearnOpenCV
C++ and Python Examples
LearnOpenCV is a large educational repository that provides practical computer vision and deep learning examples in both Python and C++. The project accompanies the LearnOpenCV blog and contains hundreds of hands-on tutorials covering topics such as object detection, image processing, pose estimation, and neural networks. It is structured as a learning resource where each directory corresponds to a specific article or technical walkthrough. The repository supports beginners and advanced... -
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Prompt Engineering Techniques
Collection of tutorials for Prompt Engineering techniques
...The repository is framed as a “techniques library” that complements a more detailed book, which expands on the same topics with deeper explanations and exercises. It is intended for a wide audience, from beginners learning how to structure their first prompts to advanced practitioners optimizing stability, controllability, and reliability in production systems. -
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OpenHome Abilities
Open-source abilities for OpenHome agents
OpenHome Abilities is an open-source repository of modular voice AI plugins created for OpenHome agents, giving developers a lightweight way to extend what an agent can do through spoken triggers. Each ability is intentionally simple in structure, centering on a single main.py file that contains the core Python logic, which lowers the barrier to building and sharing custom behaviors. The system is meant to support a wide range of voice-driven actions, from API calls and media playback to quiz flows, device control, and multi-turn conversations, so it functions as a practical extension framework rather than a narrow template library. ... -
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POML
Prompt Orchestration Markup Language
...POML addresses this issue by introducing an HTML-like syntax that allows developers to organize prompts into structured components such as roles, tasks, and examples. This structure enables prompts to be reused, modified, and versioned more easily within complex AI applications. The language also supports integration of multiple data types including documents, tables, and other external inputs that must be incorporated into prompts dynamically. By separating prompt content from presentation logic, POML enables developers to maintain cleaner and more maintainable prompt pipelines. -
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ncnn
High-performance neural network inference framework for mobile
ncnn is a high-performance neural network inference computing framework designed specifically for mobile platforms. It brings artificial intelligence right at your fingertips with no third-party dependencies, and speeds faster than all other known open source frameworks for mobile phone cpu. ncnn allows developers to easily deploy deep learning algorithm models to the mobile platform and create intelligent APPs. It is cross-platform and supports most commonly used CNN networks, including... -
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System Prompts Leaks
Collection of extracted System Prompts from popular chatbots
System Prompts Leaks is a curated repository that collects known leaked or publicly exposed system prompts used by large language models, organized so researchers, developers, and AI safety advocates can analyze them in one place. The project highlights how system prompts — instructions that strongly influence model behavior — have been inadvertently shared in forums, datasets, and open repositories, illustrating common patterns and potential vulnerabilities in prompt design and deployment.... -
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FL4Health
Library to facilitate federated learning research
FL4Health is a Vector Institute toolkit for building modular, clinically-focused FL pipelines. Tailored for healthcare, it supports privacy-preserving FL, heterogeneous data settings, integrated reporting, and clear API design. -
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Bear Stone Smart Home
Custom Home Assistant configuration with automations and scripts setup
...It includes configuration files that manage entities such as lights, sensors, switches, and media devices, enabling centralized automation and monitoring. It demonstrates how to structure Home Assistant YAML files for scalability and maintainability in a real-world deployment. Bear Stone Smart Home also showcases custom automations and scripts designed to improve convenience, energy efficiency, and overall smart home behavior. Additionally, it may include examples of dashboards and user interface customization to enhance usability and visualization of home data. ... -
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Start Machine Learning in 2026
A complete guide to start and improve in machine learning
...The project organizes a large collection of learning resources, including online courses, books, tutorials, research articles, and video lectures that explain fundamental AI concepts. Its structure functions as a learning roadmap that gradually introduces essential topics such as programming, mathematics, statistics, neural networks, and modern deep learning techniques. The repository emphasizes flexibility by allowing learners to choose their own path through the material depending on their interests, preferred learning style, and level of prior knowledge. ... -
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Fractals
Fractals is a recursive task orchestrator for agent swarm
Fractals is an experimental open-source framework designed to orchestrate complex tasks using swarms of AI agents organized in a recursive structure. The system takes a high-level goal and decomposes it into a hierarchy of smaller subtasks, forming a self-similar tree that resembles a fractal structure. Each leaf node of this tree represents a specific executable task that can be processed independently by an AI agent. The framework runs these subtasks in isolated Git worktrees so agents can work on separate components of a project without interfering with one another. ... -
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OpenClaw CN
The Chinese version of OpenClaw
...It includes translated documentation, localized examples, and language-specific nuances so that developers in the Chinese ecosystem can adopt and contribute without a language barrier. The repository mirrors the structure of the upstream project but adds Chinese translations of core workflows, prompts, guidelines, and best practices for building multi-agent systems or AI applications. Beyond simple translation, the project often curates region-specific integrations or tooling recommendations that resonate with local developer environments and platforms. ... -
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AI-DLC
AI-Driven Life Cycle (AI-DLC) adaptive workflow steering rules for AI
AI-DLC is an open-source workflow framework from AWS Labs designed to structure software development around AI-assisted engineering processes. The project promotes an “AI-Driven Life Cycle” methodology where coding assistants, IDE agents, and automation systems participate directly in planning, implementation, testing, and operational workflows. Rather than focusing on a single model or IDE, the framework provides reusable rules, templates, and orchestration patterns compatible with tools such as Amazon Q Developer, Claude Code, Cursor, GitHub Copilot, and Cline. ... -
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npcpy
The AI toolkit for the AI developer
...Welcome to npcpy, the core library of the NPC Toolkit that supercharges natural language processing pipelines and agent tooling. npcpy is a flexible framework for building state-of-the-art applications and conducting novel research with LLMs. The structure of npcpy also allows one to pass an npc to get_llm_response in addition to using the NPC's wrapped method, allowing you to be flexible in your implementation and testing. -
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Claw Code
AI agent harness for AI coding agents
Claw Code is an open-source AI agent harness project focused on building better tools for orchestrating and managing autonomous coding agents. It originated as a clean-room reimplementation inspired by the architecture of Claude Code, aiming to replicate core concepts without using proprietary code. The project provides a Python-based foundation for experimenting with agent workflows, tool integration, and task execution pipelines. It emphasizes harness engineering—how agents are structured,... -
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Devgen
The AI codebase research assistant for Github
DevGen is a development tool that automates the creation of boilerplate code, providing templates and scaffolding for various programming languages and frameworks. -
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OpenAGI
When LLM Meets Domain Experts
...It provides a structured Python framework, pyopenagi, for defining agents as modular units that encapsulate execution logic, configuration, and dependency metadata. Agents are organized in a well-defined folder structure that includes code (agent.py), configuration (config.json), and extra requirements (meta_requirements.txt), which makes them easy to package, share, and reuse. The project includes tooling for registering agents with AIOS by uploading them via a command-line interface, enforcing a consistent naming scheme that matches the local folder layout. ... -
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Large Concept Model
Language modeling in a sentence representation space
Large Concept Model is a research codebase centered on concept-centric representation learning at scale, aiming to capture shared structure across many categories and modalities. It organizes training around concepts (rather than just raw labels), encouraging models to understand attributes, relations, and compositional structure that transfer across tasks. The repository provides training loops, data tooling, and evaluation routines to learn and probe these concept embeddings, typically from large image–text or weakly supervised corpora. ... -
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MiniMax-M2.5
State of the art LLM and coding model
...It delivers leading performance in coding, agentic tool use, search, and complex office workflows, achieving top benchmark scores such as 80.2% on SWE-Bench Verified and 76.3% on BrowseComp. Designed to reason efficiently and decompose tasks like an experienced architect, M2.5 plans features, structure, and system design before generating code. The model supports full-stack development across web, mobile, and desktop platforms, covering the entire lifecycle from system design to testing and code review. With native serving speeds of up to 100 tokens per second, it completes complex agentic tasks significantly faster than previous versions while maintaining high token efficiency. ... -
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The Operator Splitting QP Solver
The Operator Splitting QP Solver
OSQP uses a specialized ADMM-based first-order method with custom sparse linear algebra routines that exploit structure in problem data. The algorithm is absolutely division-free after the setup and it requires no assumptions on problem data (the problem only needs to be convex). It just works. OSQP has an easy interface to generate customized embeddable C code with no memory manager required. OSQP supports many interfaces including C/C++, Fortran, Matlab, Python, R, Julia, Rust.
