7 posts tagged with "AI Integration"

AI agents are getting smarter — and more dangerous.

Not because they reason better, but because they act without boundaries.

Agent Skills exist to fix that.

What's the lesson from Brian Casel's experience about the future of SDKs, no-code workflows, and automation?

In short: SDKs are dead - well, at least for many use cases in modern application development.

Transcripts from podcasts are treasure troves of insights, ideas, and stories. However, accessing and utilizing this wealth of information can be challenging for marketers, product managers, and content creators—especially those without deep technical expertise.

Lenny Rachitsky posted on LinkedIn about sharing transcripts of his podcast episodes, I decided to format the transcripts for easy consumption by language models and AI tools.

AI is everywhere. Too everywhere. From answering "hi" to deciding how your production platform runs sanity checks, we've quietly crossed a line. Not because AI can't do these things, but because it shouldn't do all of them.

You want your API to be instantly usable inside AI assistants like ChatGPT, and Web UIs. The OpenAI UI SDK plus the Model Context Protocol (MCP) lets you expose your API as structured tools that large language models can call directly.

For years, OpenAPI (OAS) has been the cornerstone of how we describe, document, and integrate APIs. It standardized how systems talk to each other — and in many ways, it made the modern internet possible.

So when the Model Context Protocol (MCP) appeared, some developers were skeptical. "Another spec?" they asked. "Why do we need this when we already have OAS?"

This guide demonstrates how to use the OpenAI Response API to integrate with the Model Context Protocol (MCP) for seamless, AI-driven API interactions. You will learn how to generate a REST API specification with Postman's AI Agent, deploy it as an MCP server using HAPI Server, and connect it through OpenAI's Response API for testing.