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A practical guide to embedded AI systems covering model footprint, timing, watchdogs, update paths, and the engineering choices that keep device behavior reliable.
A buyer-focused guide to securing tool-using agents with scoped permissions, approval layers, audit trails, and deployable runtime controls.
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.
