The persistent gap between theoretical quantum computational superiority and the practical reality of machine learning on modern hardware has recently been illuminated by a massive empirical study. Siavash Kakavand and his research team spearheaded an exhaustive investigation that scrutinized the
Security reviews were piling up, a compliance audit loomed, and the team’s lead asked a quietly radical question that has spread across engineering floors: if an open-weight agent can ship working code on a single consumer GPU at near-frontier quality, why keep core development inside opaque clouds
Traders watched a little-known photonics maker rocket nearly fourfold in Hong Kong, a jolt that turned modest sales into a momentary US$10 billion story and thrust a hardware bottleneck into the limelight. The spectacle was not only about a ticker symbol; it was a referendum on how AI
Procurement teams want verifiable code, analysts want airtight math, and risk officers want schema guarantees, yet most enterprise stacks still pay frontier-scale prices to coax small models into brittle reasoning that falters without a heavyweight teacher or weeks of finely tuned reinforcement, a
Physical AI in South Korea has shifted from lab demos to city services that answer for uptime, safety, and integration. The center of gravity is constrained autonomy that works within tight geofences when conditions are known. Kakao Mobility’s strategy converts ride-hailing muscle into Level 4
Laurent Giraid has spent years building AI systems that move beyond raw data into meaningful representations—first with hand-tuned encodings, then with neural features, and now with multimodal encoders that read text, see images, and interpret context at once. In this conversation with Dustin
