The recent OpenAI Build Hour focused intensely on the next frontier of application development: creating "Apps in ChatGPT." Corey Ching, from the Developer Experience team, alongside Christine Jones from Startup Marketing, walked developers, founders, and technical leaders through the core architecture, tools, and best practices required to leverage this conversational interface. The session highlighted the newly launched Apps SDK, the revolutionary Model Context Protocol (MCP) server, and the integral role of Codex in accelerating this AI-first development cycle. For sophisticated builders, the key takeaway is that the platform demands a fundamental re-evaluation of application design, prioritizing contextual utility over traditional navigation.
The platform is designed around a compelling architectural separation, decoupling the application logic from the conversational interface itself. Ching detailed this structure, explaining that the system relies on two main components: a standard web component (HTML/CSS/JS) rendered in an iframe inside ChatGPT, and the MCP Server. This server is where an app's capabilities are defined as atomic actions and metadata, allowing the large language model (LLM) to intelligently decide when and how to call external resources. Crucially, Ching emphasized that this foundational structure is built on an "Open Protocol." This design choice is critical for the ecosystem, ensuring that developers retain control over their proprietary backend logic and data while allowing the multimodal capabilities of the LLM to handle user intent, conversational routing, and response formatting. The initial demonstrations showcasing integrations like AllTrails (for trail finding) and Adobe Express (for graphic creation) immediately proved the power of this integration, moving beyond mere text generation to deliver map views, specialized data filtering, and direct creative workflow initiation, all driven purely by natural language prompts.
For the target audience of builders, the most compelling segment was the demonstration of building a real-time, multiplayer Ping-Pong app. The traditional application development process—reading extensive documentation, downloading boilerplate code, starting a repository, and coding for days—is drastically streamlined. OpenAI has introduced the Docs MCP Server, which integrates all of OpenAI’s developer documentation (API, Codex, Apps SDK, etc.) directly into the coding environment. This allows the AI to act as a deeply informed coding partner. When a developer uses the Codex CLI and issues a natural language command—such as, “Create me a ChatGPT app with a simple UI and MCP server to play a ping pong game. I’d like to be able to drag my cursor to control a paddle that I can play against the computer.”—the AI agent automatically references the relevant documentation, scaffolds the necessary files (server.js, package.json, HTML/CSS/JS for the widget), and even provides run instructions. The system’s internal planning steps reveal this process: "I'm clarifying the need to build a simple ChatGPT app with a canvas-based Ping Pong game front end and an MCP server backend, likely using the ChatGPT Apps SDK and MCP framework." This immediate transition from conversational intent to runnable code fundamentally changes the velocity of development, allowing builders to jump straight into iteration rather than wrestling with initial setup and configuration management.
Beyond mere functionality, the success of an App in ChatGPT hinges on delivering unique value that the native conversational interface cannot provide alone. Corey Ching laid out clear principles for maximizing this value. The core principle is "Extract, don't port"—developers should not simply mirror their existing website or app within the chat window, but expose atomic tools that are instantly useful based on conversational context. Furthermore, successful apps must be "Better than native ChatGPT," which means offering "something new to know, something new to do, or something new to show." The multiplayer Ping-Pong demo illustrated this perfectly: the app provided a canvas-based game (something new to do/show) but also integrated a post-game analysis tool (something new to know). By leveraging the model’s ability to interpret game statistics passed via the MCP server, the app provided personalized coaching tips, such as prioritizing "first return defense" or focusing on "surviving the first 2–3 hits of each rally." This multi-layered experience—combining real-time interaction with deep, contextual AI analysis—is where the platform’s true commercial potential lies. The flexibility of display modes (Inline, Picture-in-Picture, Fullscreen) further allows developers to tailor the user experience to the specific task, whether it's a quick data lookup or an immersive session.
The platform provides robust tools for managing state and user interactions, allowing for rich, transactional experiences previously limited to external applications. The Apps SDK exposes the `window.openai` API, granting developers granular control over the UI, including the ability to read conversational context, receive tool results, and push updates. This facility ensures that an app's UI is not static, but dynamically linked to the ongoing conversation and the model's reasoning process. This conversational integration minimizes the user's cognitive load, making the application feel native to the chat environment. The focus remains on optimizing for conversation, allowing the LLM to handle complex state management and routing, thereby simplifying the front-end development burden significantly. This is a deliberate design choice that encourages developers to focus their efforts on creating truly valuable atomic actions rather than extensive navigational structures. The OpenAI Apps Platform, powered by the Apps SDK and Codex, represents a mature toolset enabling rapid, AI-native application development, shifting the center of gravity for utility directly into the conversational interface.
