Showing 3 open source projects for "iphone"

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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.
    Try for free
  • Lightspeed golf course management software Icon
    Lightspeed golf course management software

    Lightspeed Golf is all-in-one golf course management software to help courses simplify operations, drive revenue and deliver amazing golf experiences.

    From tee sheet management, point of sale and payment processing to marketing, automation, reporting and more—Lightspeed is built for the pro shop, restaurant, back office, beverage cart and beyond.
    Learn More
  • 1

    Emgu CV

    Emgu CV is a cross platform .Net wrapper for OpenCV

    ...Allowing OpenCV functions to be called from .NET compatible languages such as C#, VB, VC++, IronPython etc. The wrapper can be compiled in Mono and run on Windows, Linux, Mac OS X, iPhone, iPad and Android devices.
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    Downloads: 92 This Week
    Last Update:
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  • 2
    AppIcon

    AppIcon

    AppIcon generates appiconset contains each resolution image for iOS

    AppIcon generates *.appiconset contains each resolution image for iOS, MacOS. AppIcon needs path of base image(.png). The size of base image is 1024x1024 pixel preferably.
    Downloads: 0 This Week
    Last Update:
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  • 3
    GPUImage 2

    GPUImage 2

    Framework for GPU-accelerated video and image processing

    ...By relying on the GPU to run these operations, performance improvements of 100X or more over CPU-bound code can be realized. This is particularly noticeable in mobile or embedded devices. On an iPhone 4S, this framework can easily process 1080p video at over 60 FPS. On a Raspberry Pi 3, it can perform Sobel edge detection on live 720p video at over 20 FPS.
    Downloads: 0 This Week
    Last Update:
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