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The tilt- and height-adjustable stand on Studio Display XDR.
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The threat extends beyond accidental errors. When AI writes the software, the attack surface shifts: an adversary who can poison training data or compromise the model’s API can inject subtle vulnerabilities into every system that AI touches. These are not hypothetical risks. Supply chain attacks are already among the most damaging in cybersecurity, and AI-generated code creates a new supply chain at a scale that did not previously exist. Traditional code review cannot reliably detect deliberately subtle vulnerabilities, and a determined adversary can study the test suite and plant bugs specifically designed to evade it. A formal specification is the defense: it defines what “correct” means independently of the AI that produced the code. When something breaks, you know exactly which assumption failed, and so does the auditor.
This then falls through into the tail of the IRQ handler. Our first task is to set up the next interrupt, which we do by reloading the original IRQ phase value, wrapping around if necessary, loading the corresponding scanline to register with the IRQ mechanism in $D012, and store the new phase back for the next time: