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A practical guide to shipping embedded AI on devices that live with real constraints. It covers model footprint, timing, watchdogs, updates, and operational reliability.
A buyer-focused guide to securing tool-using AI systems in production. It covers scoped permissions, approvals, audit trails, and runtime controls that support fast teams.
A practical guide to stopping sensitive data from leaking through AI systems. It covers prompts, RAG, memory, tool permissions, and runtime controls that keep boundaries clear.
A practical guide to the main C++ libraries for neural-network inference and deployment. It shows where ONNX Runtime, LibTorch, OpenVINO, TensorFlow Lite, and llama.cpp fit in production systems.
