This review explores Physical AI as a closed-loop system integrating perception, world models, decision-making, and multi-timescale feedback, distinguishing it from mere automation by its ability to verify actions against physical consequences and adapt continuously through real-world deployment data.
This article examines the ethical and practical limits of risk-benefit analysis in engineering, using the Ford Pinto case to show how monetizing human life leads to dangerous decisions, and proposes three critical questions engineers must ask to guard unquantifiable safety boundaries.
This article explains why "zero accidents" is an unworkable safety goal and introduces the ALARP principle—As Low As Reasonably Practicable—as a framework for making defensible risk trade-offs in system design, using examples like emergency stops and input validation to show how safety measures can introduce new risks.
AgentiLoop Agent is a native Swift macOS application that enables AI models to control the entire system — editing code, running Xcode builds, operating any app via accessibility APIs, executing shell commands, and sending iMessage reports — while enforcing safety through goal-state verification, read-before-edit enforcement, snapshots with rollback, dangerous command blocking, and 273 automated tests.
