ai

Fackel is a multi-agent pentest framework where LLMs decide strategy, not hardcoded pipelines. A walkthrough of the architecture, the design decisions, and the lessons learned.

A practical overview of modern AI agent systems: tool use, retrieval, memory, verification, multi-agent patterns, evaluation, and security.

Autoregressive models are just the probability chain rule plus a conditional model. Here’s the mental model, the math, and what training is really doing.

A rigorous analysis of how probabilistic reasoning in generative models shapes security risk, failure modes, and robustness.

A highly technical article on Amazon Bedrock with mathematical foundations and numerical examples.

A quick, code-backed refresher on gradients, Jacobians, and the linear algebra that drives modern ML.

Argues that probabilistic behavior, distributional risk, and system composability invalidate core assumptions of classical threat modeling for generative AI.