llm

OpenAI's annual developer conference — where new models, APIs, and platform features are announced to the global developer community building on GPT, DALL-E, Whisper, and Sora.

AWS and DeepLearning.ai's hands-on course on building with LLMs — covering transformer architecture, pre-training, fine-tuning, RLHF, and deploying generative AI applications at scale.

Andrew Ng's non-technical course on Generative AI — covering how LLMs work, practical applications, prompt engineering, building AI workflows, and responsible deployment.

A concise, hands-on introduction to LangChain — the most popular framework for building LLM-powered applications and AI agents in Python.

Wharton professor Ethan Mollick's practical guide to working alongside AI. Explores how to use LLMs as collaborative partners rather than tools, with concrete principles for integrating AI into your work and thinking without losing your own judgment.

Karpathy shares his personal, practical workflow for using LLMs day-to-day — which models he uses for which tasks, how he prompts them, and the mental model he applies to get consistently better results from tools like ChatGPT and Claude.

A crisp 1-hour introduction to LLMs for a general technical audience. Karpathy covers what language models are, how they work at a high level, the emerging 'System 2' reasoning capabilities, and the security challenges they introduce — without requiring a machine learning background.

A 3.5-hour end-to-end walkthrough of how large language models like ChatGPT are actually built — from tokenization and pretraining through supervised fine-tuning and RLHF. Karpathy explains everything in plain engineer-to-engineer language with no hype.

AI infrastructure for agent builders with a reasoning LLM that outperforms leading models at 1/10th the cost.

AI cloud infrastructure with NVIDIA GPU clusters for training and deploying models at scale.