OpenAI's Greg Brockman and Thibault Sottiaux explore the evolution of AI-powered software development, from the first coding sparks in GPT-3 to today's GPT-5 Codex. They detail the concept of the "harness," the rise of agentic coding, breakthroughs in automated code review, and how AI agents capable of running for hours on complex refactoring will reshape the future of engineering.
Jay Alammar, Director at Cohere, discusses the practical adoption of Large Language Models in the enterprise. He covers the evolution of Retrieval-Augmented Generation (RAG) from a simple anti-hallucination tool to complex, agentic systems, the critical role of evaluation as intellectual property, and future trends like text diffusion and the increasing capability of smaller models for specialized business tasks.
Birgitta Böckeler from Thoughtworks provides a comprehensive overview of the current AI tooling landscape for software delivery. The talk focuses on the architecture and capabilities of modern coding assistants, differentiating between the underlying model and the crucial role of tooling in providing context. It explores what works today, the promising but challenging frontiers like testing and refactoring, and offers a practical guide to the major tools in the ecosystem.
OpenAI's Build Hour provides a deep dive into GPT-5, showcasing its advanced coding and agentic capabilities. The session covers the new Responses API, critical for leveraging the model's reasoning, along with new parameters for steerability and practical prompting techniques for building complex, reliable applications.
In this analysis, experts Bryan Casey, Mihai Criveti, and Chris Hay dissect the OpenAI GPT-5 release, comparing its capabilities against Anthropic's Claude Opus 4.1. While GPT-5 introduces significant improvements in accessibility, agentic capabilities, and reliability, the consensus is that it does not yet dethrone Claude as the daily driver for developers due to key differences in user experience and workflow management.
Itamar Friedman of Qodo argues that the future of AI in software development lies in moving beyond simple code generation to 'vibe coding with confidence.' This is achieved through multi-agent workflows, grounded in team standards and orchestrated via the Command Line Interface (CLI), enabling a holistic AI-driven approach across the entire SDLC.