Showing 4 open source projects for "node-red"

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    PentAGI

    PentAGI

    Perform penetration testing tasks

    ...It leverages agent-based architecture and AI reasoning to chain together tools and strategies in a way that mimics experienced human testers. The project is built to be modular and extensible so researchers and red teams can customize behavior or integrate additional tools as needed. By focusing on autonomous decision-making in cybersecurity contexts, PentAGI represents part of the broader trend toward AI-assisted offensive security automation.
    Downloads: 11 This Week
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  • 2
    xiaohongshu-mcp

    xiaohongshu-mcp

    MCP for xiaohongshu.com

    xiaohongshu-mcp is a Model Context Protocol (MCP) server that equips AI assistants with first-class tools for working on Xiaohongshu (Little Red Book), focusing on day-to-day creator and operator workflows rather than generic browsing. The project centers on authenticated actions and data access that matter to content operations, such as checking login state, publishing or scheduling content, fetching recommendations and search results, reading post details, and acting on comments. ...
    Downloads: 22 This Week
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  • 3
    Memobase

    Memobase

    Fast backend for long-term AI user memory via structured profiles

    ...The system focuses on three principal performance metrics: high search performance, reduced large language model (LLM) costs through batch processing techniques, and low latency with minimal SQL operations. Memobase supports integration with existing LLM workflows via APIs and SDKs (including Python, Node, and Go), making it easy to adopt within diverse application stacks.
    Downloads: 2 This Week
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  • 4
    Kubeflow Trainer

    Kubeflow Trainer

    Distributed AI Model Training and LLM Fine-Tuning on Kubernetes

    Kubeflow Trainer is a Kubernetes-native platform designed for scalable, distributed training and fine-tuning of machine learning models, particularly large language models, across multi-node and multi-GPU environments. It extends the Kubeflow ecosystem by providing a unified framework for orchestrating training workloads using Kubernetes primitives, enabling seamless scaling from single-machine experiments to large production clusters. The platform supports a wide range of machine learning frameworks, including PyTorch, JAX, Hugging Face, DeepSpeed, and XGBoost, making it highly flexible for different AI use cases. ...
    Downloads: 0 This Week
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