Videos

This article, based on an OpenAI presentation, highlights the critical importance of AI model evaluation. It introduces OpenAI's internal 'GDP Eval' framework, designed to assess frontier models' performance on economically valuable, real-world tasks, moving beyond traditional academic benchmarks. GDP Eval employs expert pairwise grading to compare model outputs against human performance across diverse industries and professions, demonstrating significant progress in models like GPT-5. It also serves as a proactive measure to track AI's impact on the workforce and acts as a 'North Star' metric for internal research. However, it acknowledges limitations, primarily measuring performance on clearly defined tasks rather than the full complexity of real-world jobs involving prioritization or iteration. The second segment focuses on OpenAI's 'Evals product,' a suite of tools for developers to rigorously evaluate their AI applications and agents. Key new features include Datasets for building evaluations, Traces for debugging multi-agent systems, Automated Prompt Optimization to accelerate iteration, support for Third-Party Models, and enterprise-grade capabilities. The presentation underscores that robust evaluation is crucial for building high-performing AI applications, particularly in sensitive domains, by addressing challenges such as LLM non-determinism and compounding errors in agent systems. It concludes with best practices for developers, advocating for early and continuous evaluation using real human data and expert-guided automation.

This conversation between Sam Altman of OpenAI and Jony Ive of LoveFrom delves into their partnership aimed at creating new AI-powered devices. Jony Ive recounts how ChatGPT clarified his team's mission to build exceptional creative teams, leading to their collaboration with OpenAI. They explore the iterative design process, highlighting the importance of deep motivation and 'craft and care'—a commitment to unseen details driven by a belief in humanity's deserving of better tools. Both emphasize the need to move beyond existing device paradigms (like the smartphone) to truly harness AI's capabilities, envisioning interfaces that evoke delight and reduce anxiety, and fundamentally rethinking the nature of operating systems and user interfaces. They acknowledge the challenge posed by AI's rapid development, which generates a multitude of compelling product ideas, making focus difficult. The discussion concludes with a shared hope that AI tools will ultimately lead to more fulfilling, peaceful, and less alienating human experiences, rejecting the notion that current tech interactions are the immutable norm.

This OpenAI DevDay presentation details the significant advancements and new tools for developers across OpenAI's ecosystem. It traces OpenAI's journey from foundational research in reinforcement and unsupervised learning to the creation of powerful models like GPT-3 and the current GPT-5. Key announcements include the release of GPT-5, optimized for agentic tasks and advanced coding, along with principles for its effective use. The video introduces Sora 2 API for high-quality video generation and smaller, more cost-effective speech and image generation models. A major highlight is GPT-OSS, an open-source initiative aimed at democratizing AI. The presentation extensively covers enhancements to Codex, including GPT-5 Codex for agentic coding, Slack integration, MCP support for tools like Figma and Chrome DevTools, GitHub code review, and the new Codex SDK for embedding coding intelligence into custom workflows and applications. Furthermore, the Agent Kit, built on the Responses API, is introduced as a robust framework for building sophisticated AI agents, showcased through RAMP's procurement agent. Finally, the Apps SDK for ChatGPT is unveiled, allowing developers to create fully interactive, natural language-responsive applications directly within ChatGPT, demonstrated with examples like controlling lights, creating music, and personalized learning experiences. The overall message emphasizes empowering developers to shape the future of AI and software engineering.

This AMA features founders of Decagon (AI customer support agents) and Clay (AI-driven GTM platform) alongside an Andreessen Horowitz investor. They delve into critical aspects of scaling AI applications for enterprise, including methodologies for evaluating novel AI models and ensuring infrastructure flexibility in a rapidly evolving market. The discussion also covers strategies for balancing AI experimentation with essential enterprise safety guardrails, overcoming common deployment failures by focusing on quantifiable ROI and iterative launches, and achieving product differentiation in a crowded AI landscape through unique market philosophies and empowering non-technical users. Finally, they offer advice on resource prioritization and key considerations for new enterprise AI ventures, emphasizing self-awareness and following genuine curiosity.

The OpenAI "Build Hour" session, led by Christine and Steve, provides a comprehensive overview of the new Responses API. It traces the API's evolution from early completions to the current agent-centric, multimodal design, necessitated by the emergence of more agentic and highly multimodal models like GPT-5. Key distinctions of the Responses API include its "agentic loop" for executing multi-step tasks within a single request, the "items in and items out" paradigm for unified handling of messages, tool calls, and other model actions, native support for reasoning models to preserve the chain of thought across requests, enhanced multimodal workflows (e.g., image and PDF processing), and a fundamentally redesigned streaming mechanism for simpler event handling. Steve demonstrates migrating an existing chat application to the Responses API using a "migration pack" powered by Codex, and building an agent game that leverages features like the reasoning summarizer for transparent model thinking and the Machine-to-Machine Communication Protocol (MCP) for integrating external services. A preview of the Agent Kit and Agent Builder, a visual workflow tool for constructing complex agent flows, is also provided. The session concludes with a Q&A addressing common developer concerns, such as few-shot prompting for structured JSON, performance differences between APIs, context management across requests, prompt caching strategies, and common pitfalls, emphasizing the API's role in advancing agent development.