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  • ChainStream enables streaming submodel chaining inference for large language models on mobile and desktop devices with cross-platform support.
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    What is ChainStream?
    ChainStream is a cross-platform mobile and desktop inference framework that streams partial outputs from large language models in real time. It breaks LLM inference into submodel chains, enabling incremental token delivery and reducing perceived latency. Developers can integrate ChainStream into their apps using a simple C++ API, select preferred backends like ONNX Runtime or TFLite, and customize pipeline stages. It runs on Android, iOS, Windows, Linux, and macOS, allowing for truly on-device AI-driven chat, translation, and assistant features without server dependencies.
    ChainStream Core Features
    • Real-time token streaming inference
    • Submodel chain execution
    • Cross-platform C++ SDK
    • Multi-backend support (ONNX, MNN, TFLite)
    • Low-latency on-device LLM
    ChainStream Pro & Cons

    The Cons

    Project is still a work in progress with evolving documentation
    May require advanced knowledge to fully utilize framework capabilities
    No direct pricing or commercial product details available yet

    The Pros

    Supports continuous context sensing and sharing for enhanced agent interaction
    Open-source with active community engagement and contributor participation
    Provides comprehensive documentation for multiple user roles
    Developed by a reputable AI research institute
    Demonstrated in academic and industry workshops and conferences
  • Java-Action-Shape offers agents within the LightJason MAS a suite of Java actions to generate, transform, and analyze geometric shapes.
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    What is Java-Action-Shape?
    Java-Action-Shape is a dedicated action library designed to extend the LightJason multi-agent framework with advanced geometric capabilities. It provides agents with out-of-the-box actions to instantiate common shapes (circle, rectangle, polygon), apply transformations (translate, rotate, scale), and perform analytical computations (area, perimeter, centroid). Each action is thread-safe and integrates with LightJason’s asynchronous execution model, ensuring efficient parallel processing. Developers can define custom shapes by specifying vertices and edges, register them within the agent’s action registry, and include them in plan definitions. By centralizing shape-related logic, Java-Action-Shape reduces boilerplate code, enforces consistent APIs, and accelerates the creation of geometry-driven agent applications, from simulations to educational tools.
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