Showing 2 open source projects for "component"

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    Cloud tools for web scraping and data extraction

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    Automate web data collection with cloud tools that handle anti-bot measures, browser rendering, and data transformation out of the box. Extract content from any website, push to vector databases for RAG workflows, or pipe directly into your apps via API. Schedule runs, set up webhooks, and connect to your existing stack. Free tier available, then scale as you need to.
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  • 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.
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    PokeeResearch-7B

    PokeeResearch-7B

    Pokee Deep Research Model Open Source Repo

    ...The repository includes evaluation results on multi-step QA and research benchmarks, illustrating how web-time context boosts accuracy. Because the system is modular, you can swap the search component, reader, or policy to fit private deployments or different data domains. It’s aimed at developers who want a transparent, hackable research agent they can run locally or wire into existing workflows.
    Downloads: 3 This Week
    Last Update:
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  • 2
    DeepSeek VL2

    DeepSeek VL2

    Mixture-of-Experts Vision-Language Models for Advanced Multimodal

    ...“What’s going on in this scene?” or “Generate a caption appropriate to context”). The model supports both image understanding (vision tasks) and multimodal reasoning, and is likely used as a component in agent systems to process visual inputs as context for downstream tasks. The repository includes evaluation results (e.g. image/text alignment scores, common VL benchmarks), configuration files, and model weights (where permitted). While the internal architecture details are not fully documented publicly, the repo suggests that VL2 introduces enhancements over prior vision-language models (e.g. better scaling, cross-modal attention, more robust alignment) to improve grounding and multimodal understanding.
    Downloads: 2 This Week
    Last Update:
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