Conference talks worth your timeStanford Online
Jensen Huang traces how jointly optimizing CPUs, GPUs, networking, storage, and software frameworks—rather than tuning each independently—delivered a million-fold performance gain over ten years versus roughly 100x from semiconductor advances alone. Covers Hopper, Grace Blackwell, and Vera Rubin architecture roles across pre-training, inference, and agentic workloads.
Stanford Online
Scott Nolan argues energy, not chips, caps AI scaling—and nuclear is the only viable baseload option. The US produces under 0.1% of global enrichment capacity after its last facility closed in 2013, creating a dependency on Russia and Europe that General Matter's enrichment rebuild targets directly.
BSides San Francisco
By capping outbound bandwidth to ~100 Mbps at perimeter routers with per-service token buckets, Anthropic forces full-weight exfiltration—terabytes—to take weeks under assumed full-cluster compromise. The rollout cut egress 98% while keeping research workflows intact, buying detection time until TEEs and confidential compute mature.
Stanford Online
Three strategies compound: a two-phase quality-aware curriculum, front-loading math and code before post-training (16-19% gains that survive SFT and RL), and RLP—which reframes pre-training as RL with dense information-gain rewards. RLP alone hits 35% improvement on a 12B model using 200B fewer tokens than baseline.
Android Makers
Surveys research showing GitHub data reveals copy-paste code rose from 8% to 12% post-AI adoption, refactoring dropped, and churn increased. DORA data confirms 90% adoption but post-release instability offsets delivery gains. Argues for spec-driven development and pair-programming with AI as navigator to preserve architectural judgment.