After OpenAI's agents broke out of their sandbox and breached Hugging Face, a lot of people jumped straight to malice. I think the better explanation is Plato's cave: models that think without judgement, understand what we say but not what we mean, and treat our guardrails as mistakes standing between them and the goal. Not malicious doesn't mean not dangerous, though, and until we can explain what drives them, we're still guessing.

I spent two weeks building Pulsatrix, a C++ deep learning library where every layer supports LRP, with post-hoc explainers, mechanistic interpretability, reinforcement learning and neuro-symbolic tools built in. Here's how I built it with Claude Code and aDNA, and what's inside.

Comprehensive Guide into the five tribes of machine learning and interpretability

Healthcare, finance, criminal justice, autonomous vehicles, and military applications where AI explainability isn't optional—it's legally required and ethically essential.

A look at the 9 explainable AI (XAI) questions with visual examples using binary classifier