Conference talks worth your timeStanford 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.
Stanford Online
a16z co-founder Ben Horowitz traces how a16z scaled VC as a network business, then argues AI commoditizes code and UI by parallelizing engineering through GPUs and data. Covers what remains defensible (network effects, org integration), why culture is actions not beliefs, and why SaaS obituaries outrun the fundamentals.
MLOps Community
Pre-approves SQL statements at deploy time, binds user credentials server-side, and strips sensitive parameters from agent visibility entirely. Covers the four-stage hardening model and the buildtime-vs-runtime tool distinction, with a focus on stopping confused-deputy attacks in production.
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.
AI Engineer
Eric Allam argues replay-based durable execution breaks down for long-running agents that clone repos and hold in-memory state. Trigger.dev's Firecracker-based implementation uses an append-only context log for code compatibility and VM snapshots for execution state, hitting sub-second snapshots and 200ms restores at scale.