Hyperpocket is a modular inference engine that allows developers to import pre-trained large language models, convert them into optimized formats, and run them locally with minimal dependencies. It supports quantization techniques to reduce model size and accelerate performance on CPUs and ARM-based devices. The framework exposes both C++ and Python interfaces, enabling seamless integration into existing applications and pipelines. Hyperpocket automatically manages memory allocation, tokenization, and batching to deliver consistent low-latency responses. Its cross-platform design means the same model can run on Windows, Linux, macOS, and embedded systems without modification. This makes Hyperpocket ideal for implementing privacy-focused chatbots, offline data analysis, and custom AI-powered tools on edge hardware.

SegAgent is a Python framework that orchestrates AI agents to perform semantic image segmentation through natural language interaction. By combining GPT-based language understanding with the Segment Anything Model (SAM), it converts user prompts—such as “segment the tumor region” or “refine around the edges”—into accurate masks. The agent retains conversational context, supports iterative refinement of segmentation results, and can integrate custom models or post-processing steps. It provides an extensible API, command-line tools, and Jupyter notebook examples. SegAgent accelerates annotation workflows, reduces manual tracing effort, and allows developers to embed conversational segmentation capabilities into broader pipelines or applications.