Intro to Large Language Models
A one-hour, general-audience tour of what LLMs are, how they're trained, where they're going, and the new class of security problems they create.

Software Is Changing (Again)
From Software 1.0 (code) to 2.0 (weights) to 3.0 (prompts): Karpathy's case that natural language is becoming the new programming interface, with LLMs as the new computer.

Let's build GPT: from scratch, in code, spelled out
Karpathy builds and trains a decoder-only Transformer from first principles, following Attention Is All You Need, ending at the core of nanoGPT.

State of GPT
The pretraining → SFT → reward modeling → RLHF pipeline behind ChatGPT-class assistants, plus practical mental models for prompting and using them well.

Building the Software 2.0 Stack
Karpathy's argument that large chunks of conventional code are being replaced by learned weights — and what that means for the tools, infrastructure, and skills around them.

Objective-Driven AI
LeCun's case against autoregressive LLMs as a path to general intelligence, and his alternative agenda built around world models, JEPA, and energy-based inference.