Apps SDK Preview: Plan, Build, and Deploy ChatGPT Apps

OpenAI’s Apps SDK page outlines how to design, build, and deploy ChatGPT-native apps. It stresses quality via design and developer guidelines and provides a clear path: research use cases, set up an MCP server, and deploy. Supporting guides help with metadata optimization, security and privacy, and troubleshooting, with the SDK currently in preview.

Key Points

Apps SDK is available in preview now for building and testing; app submissions will open later this year.
Quality is central: follow App design guidelines and App developer guidelines to align with ChatGPT-native UX, safety, and policy standards.
The recommended workflow is Plan (research use cases), Build (set up an MCP server), and Deploy (ship your app).
Guides cover Optimize Metadata, Security & Privacy, and Troubleshooting to improve discovery, safeguard users, and resolve issues.
Resources and references are provided to support end-to-end development and integration with ChatGPT.

Sentiment

Overall, the sentiment is **mixed to cautiously skeptical**. While there is recognition of the technical advancements and potential for a large user base, a dominant theme is developer fatigue and significant doubt regarding the user experience efficacy of chat-based interfaces. Many commenters express cynicism about OpenAI's strategic motivations, comparing the SDK to past 'app store' failures and viewing it as a move to build a 'walled garden' ecosystem.

In Agreement

The rapid pace of new tools and SDKs, while challenging, fosters a thriving ecosystem of competition and innovation.
Modern LLMs offer genuine language comprehension, making current chatbot initiatives fundamentally more capable than older, regex-based systems, and this comprehension is often underutilized.
The Model Context Protocol (MCP) is seen as a positive directional step, addressing protocol gaps and enabling the return of UI elements to the client, which expands possibilities for LLM-integrated applications.
The potential to reach a large user base (e.g., 700M ChatGPT users) presents a significant opportunity for developers, reminiscent of the early success of the iPhone App Store.
The concept of micro-scoped LLM instances coordinating tasks via workflows is effective for automation.
Similar 'super app' functionalities (like Tencent's WeChat) demonstrate a proven model for integrated platforms.

Opposed

Developers are experiencing significant fatigue due to the constant churn of new models, tools, and SDKs, feeling overwhelmed by the need to continuously learn new paradigms.
Conversational user interfaces are criticized as opaque, lacking 'affordances' for discoverability, and being brittle in real-world use, leading to user confusion and a preference for visual elements over language-heavy interactions.
This initiative is seen as primarily reinforcing OpenAI's 'walled garden' and moat, with developers effectively building OpenAI's platform and potentially cannibalizing their own opportunities, similar to past platform plays.
Many view this as a repeat of OpenAI's previous 'app store' attempts (e.g., GPTs) and other companies' chatbot platform flops, predicting a similar lack of widespread success.
Skepticism exists regarding the monetization strategy for developers (revenue sharing, potential for hidden ads, preferential discovery), and doubts are raised about the economic viability for small independent developers.
There is disappointment that OpenAI, having promised AGI, is instead delivering SDKs and 'TikTok clones,' seen as a distraction from their core mission.
Current MCP-UI implementations are considered too slow for practical use, and there's a desire for entirely generative UI solutions over hard-coded widget schemas to overcome brittleness.
A platform needs a strong user moat or unfair advantage beyond just a 'better quality model' to succeed long-term.