Showing 2 open source projects for "mx linux 32"

View related business solutions
  • 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.
    Try for free
  • 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
    OpenSIMPLY

    OpenSIMPLY

    Discrete-event simulation modeling software for science and education

    ...Some application of this modeling software are: - traffic simulation - network simulation, - emergency and evacuation ways simulation and much more. This simulation tool runs in Windows and Linux, on 32-bit and 64-bit platforms, as a GUI or console (terminal) application. Write the model only once, simulate anywhere. Videos on installing OpenSIMPLY for different IDEs and operating systems : https://www.youtube.com/playlist?list=PLnyWoktGqA
    Downloads: 1 This Week
    Last Update:
    See Project
  • 2
    riboshape

    riboshape

    Predicting ribosome footprint profile shapes from transcript sequences

    Riboshape is a suite of algorithms to predict ribosome footprint profile shapes from transcript sequences. It applies kernel smoothing to codon sequences to build predictive features, and uses these features to builds a sparse regression model to predict the ribosome footprint profile shapes. Reference: Liu, T.-Y. and Song, Y.S. Prediction of ribosome footprint profile shapes from transcript sequences. Proceedings of ISMB 2016, Bioinformatics, Vol. 32 No. 12 (2016) i183-i191.
    Downloads: 0 This Week
    Last Update:
    See Project
  • Previous
  • You're on page 1
  • Next