The useful distinction is not how many model calls a system makes, but whether it decides its own control flow. That difference changes testing, failure modes and security.
Memory capacity decides whether you can run a model at all, bandwidth decides how it feels, and compute is usually third. A practical sizing guide.
Quantum processors will live in data centres behind an API, not on your desk. Where the hardware really stands in mid-2026, and which skills transfer.
An AI model halved HAWK’s effective key strength in 60 hours. But the finding that runs end to end on a desktop, against a deployed ISO/IEC cipher, is the one worth reading twice.
Blackwell, Rubin, Feynman — a new architecture every year. Why research labs should buy for memory and facility fit rather than trying to time the roadmap.
Detection tools lose half their accuracy in the wild. A practical deepfake verification workflow for newsrooms, and the EU labelling rules landing on 2 August 2026.
Jensen Huang’s first X post shared the Open Weights and American AI Leadership letter. What open weights mean, why Washington is arguing, and who gets priced out.
QPUs are being colocated with GPU clusters not to accelerate AI, but because quantum error correction has a microsecond deadline only AI-class silicon can meet.
Post-quantum migration deadlines are set by regulation and data shelf life, not by qubit counts. Here is why the two clocks decoupled, and what to do in the next twelve months.
News traffic is a step function, not a curve. Notes on caching, images, archives and content protection from running digital operations at a national television network.