AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work presents file-backed weight adoption: a framework-independent producer maps each tensor with MAP_SHARED, wraps the pages as a no-copy GPU buffer, and exports a DLPack capsule that PyTorch or MLX imports as ordinary storage.
This work instantiates budgeted oracle-to-hint compression in online judge (OJ) style algorithmic programming as a modular interactive agent that couples an LLM core with a sandboxed judger, a feedback-to-hint prompt constructor, and trajectory memory.
The resulting design principle is simple: in this regime, let the kernel own eviction, while model-specific knowledge is best spent on admission and advice.
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