ai
- LangGraph, CrewAI, and Agno: getting started with AI agents in Python
A practical guide to getting started with AI agents. Three frameworks, the same problem, real examples, and an honest comparison.
- When AI Stops Being a Tool and Becomes an Attack Surface
When AI becomes an attack surface: prompt injection, end-to-end attack chains, at-risk architectures, and defensive actions.
- Fackel: an autonomous pentest framework powered by ReAct agents
Fackel: a multi-agent pentest framework where LLMs decide strategy. Architecture walkthrough, design decisions, and lessons learned.
- The State of the Art in AI Agents (2026): What ‘Modern’ Actually Means
A practical overview of modern AI agent systems: tool use, retrieval, memory, verification, multi-agent patterns, evaluation, and security.
- The chain rule behind autoregressive models
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.
- Security Implications of Probabilistic Reasoning in Generative AI
A rigorous analysis of how probabilistic reasoning in generative models shapes security risk, failure modes, and robustness.
- Amazon Bedrock: foundations, systems, and scaling
A highly technical article on Amazon Bedrock with mathematical foundations and numerical examples.
- Calculus, AI, and linear algebra: a compact field guide
A quick, code-backed refresher on gradients, Jacobians, and the linear algebra that drives modern ML.
- Why Traditional Threat Modeling Breaks Down in Generative AI Systems
Probabilistic behavior, distributional risk, and system composability invalidate core assumptions of classical threat modeling for generative AI.