Prompt engineering

Jan 19, 2026

Coding with AI // Chip Huyen

Chip Huyen provides a deep dive into the evolving landscape of AI-powered coding. The talk covers the different interfaces for AI coding tools, introduces new metrics like "interruption rate" to measure productivity, and outlines a framework for the levels of coding automation. Huyen argues that the engineer's role is shifting from writing code to architecting systems and reviewing AI-generated output, emphasizing the rise of spec-driven development and the critical importance of system thinking.

Jan 12, 2026

AI on campus

A panel of university students from LSE, Princeton, Berkeley, and ASU discuss the real-world impact of AI on campus life. They cover how AI is used as both a powerful learning tool and a crutch, the innovative projects students are building, how universities are adapting, and the challenges of navigating cheating, job applications, and 'AI slop' in a rapidly changing educational landscape.

Jan 09, 2026

KDD '25 AI Reasoning Day keynote: Improving AI Reasoning through Intent, Interaction, and Inspection

A deep dive into practical strategies for improving AI reasoning in code and structured tasks. The talk covers capturing richer user intent through examples, enabling collaborative interaction, and using automated inspection for iterative refinement, illustrated with real-world applications from Microsoft.

Jan 08, 2026

DSPy: The End of Prompt Engineering - Kevin Madura, AlixPartners

An in-depth guide to DSPy, a framework for programming with language models, not just prompting them. Learn its core concepts—Signatures, Modules, Adapters, and Optimizers—and see real-world examples of building robust, testable, and transferable AI applications for the enterprise.

Dec 02, 2025

Shaping Model Behavior in GPT-5.1— the OpenAI Podcast Ep. 11

OpenAI's Christina Kim (Research) and Laurentia Romaniuk (Product) discuss the development of GPT-5.1, detailing the shift to universal "reasoning models" to enhance both IQ and EQ. They explore the nuances of "model personality," the technical challenges of balancing steerability with safety, and how features like Memory create a more personalized, context-aware user experience.

Nov 20, 2025

Inside the AI Black Box

Emmanuel Ameisen of Anthropic's interpretability team explains the inner workings of LLMs, drawing analogies to biology. He covers surprising findings on how models plan, represent concepts across languages, and the mechanistic causes of hallucinations, offering practical advice for developers on evaluation and post-training strategies.