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Stanford Online

This lecture establishes that training data—not architecture—is the most critical and secretive component of modern language models, examining the technical impossibility of crawling the 'entire internet,' the three-stage data pipeline from raw web to specialized post-training, and the tightening legal constraints of copyright law and terms of service that increasingly restrict what can be legally used for training.

Stanford Online

This lecture explores how to evaluate language models, examining different definitions of "good" from benchmark scores to economic usage, diving deep into perplexity as a foundational metric and its limitations, and tracing the evolution of exam-based benchmarks like MMLU from novel challenges to saturated metrics.

Stanford Online

This lecture explores practical implementations of scaling laws for language models, focusing on the MiniCPM paper's techniques for stabilizing training across scales, including Maximal Update Parameterization (MUP) for consistent learning rates and Warm-up Stable Decay (WSD) schedules for efficient training continuation.

Stanford Online

This lecture covers practical training of text-to-image diffusion models, detailing the evolution from UNet to Diffusion Transformer architectures, the three-stage training pipeline (pre-training, post-training, and tuning), and critical optimizations including flow matching loss functions, logit-normal time step sampling, and resolution-aware noise scheduling.

Stripe

Stripe Sessions 2026 marked the company's most ambitious product launch day in history, centered on building economic infrastructure for the AI era. The keynote revealed a parabolic spike in new business formation since January 2026 and introduced tools including the Machine Payment Protocol, Link wallet for agents, and Stripe Projects to enable autonomous agent-to-agent commerce.

Stripe

Sam Altman discusses the recent 'parabolic' inflection in AI capabilities, particularly for coding with GPT 5.5 and Codex, while outlining OpenAI's evolution into a massive-scale 'intelligence utility' provider focused on automating general computer work through agents like OpenClaw.

Stripe

Nat Friedman and Daniel Gross describe the current era as the 'slow part' of the singularity, predicting that AI will drive massive economic shifts, force continuous security hardening, and enable a new golden age of personal hardware tinkering where AI agents reverse engineer proprietary systems in hours.

Stripe

Stripe co-founder John Collison and Head of Data Emily Sans analyze 2026 economic data revealing that AI is driving unprecedented business formation and global scaling among lean solopreneurs, while commerce rapidly evolves toward autonomous agent-to-agent transactions using stablecoin micropayments.

The David Lin Report

The traditional Thucydides Trap doesn't apply to US-China relations because economic interdependence makes war catastrophic and meaningless, marking a shift from military-based to economics-based geopolitics.

The Pomp Podcast

Arch Public's Andrew Parish and Tilman Holloway argue that infrastructure spending and asset tokenization will drive unprecedented money printing and 24/7 market volatility, making automated trading tools essential for investors to navigate the shift from human-driven to AI-dominated markets.

Joe Rogan Experience

Marc Andreessen discusses how cities like Austin and Chicago disabled AI surveillance systems (Flock and Shot Spotter) due to political concerns, severely hampering law enforcement's ability to solve violent crimes, while crime statistics are increasingly unreliable due to underreporting and outright fabrication.

Jordan Peterson

Jordan Peterson argues that meaningful growth requires voluntarily confronting painful but necessary lessons rather than avoiding them, while explaining that focus and authenticity depend on physiological regulation, incremental habit formation, and integrating one's symbolic 'inner child' through cultural and historical awareness.