Showing 3 open source projects for "red-files"

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  • Context for your AI agents Icon
    Context for your AI agents

    Crawl websites, sync to vector databases, and power RAG applications. Pre-built integrations for LLM pipelines and AI assistants.

    Build data pipelines that feed your AI models and agents without managing infrastructure. Crawl any website, transform content, and push directly to your preferred vector store. Use 10,000+ tools for RAG applications, AI assistants, and real-time knowledge bases. Monitor site changes, trigger workflows on new data, and keep your AIs fed with fresh, structured information. Cloud-native, API-first, and free to start until you need to scale.
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  • Run applications fast and securely in a fully managed environment Icon
    Run applications fast and securely in a fully managed environment

    Cloud Run is a fully-managed compute platform that lets you run your code in a container directly on top of scalable infrastructure.

    Run frontend and backend services, batch jobs, deploy websites and applications, and queue processing workloads without the need to manage infrastructure.
    Try for free
  • 1

    evotopo

    Evolving Realistic Topologies for Wireless Network Simulation

    ...It is released under the GPL v3 license. It comes with reference topology files in .csv format for mesh networks deployed in Berlin, Wien, Leipzig and MIT. See the README file for further information.
    Downloads: 0 This Week
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  • 2
    NS-2 Trace Statistics is a tool for easy generation of summary statistics from Network Simulator trace files, such as: total and network delay, packets generated, sent, received and dropped, run length histograms and MRU stack depth.
    Downloads: 0 This Week
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  • 3
    SNNSraster is a utility for quick ANN analysis of raster GIS maps with the use of Stuttgart Neural Network Simulator trained network files. It was developed to read and write binary raster files. SNNSraster is a project of the Geography Laboratory of the University of Siena. The code was developed by Giancarlo Macchi Jánica between 2006 and 2007. SNNSraster's fundamental objective is to improve the ability to integrate the use of artificial neural networks in GIS environments.
    Downloads: 0 This Week
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