AI (Artificial Inteligence) refers to the development of computer systems that can perform tasks typically requiring human intelligence, such as learning, problem-solving, and decision-making. AI utilizes algorithms and data to enable machines to process information, identify patterns, and adapt their actions, leading to improved performance and autonomous behaviour.
On April 16th, we brought together over 150 API leaders for a live conversation on how AI is reshaping API architecture. Missed it? Here’s a recap of the biggest insights—plus how to watch the replay and download the new eBook co-authored with Mike Amundsen.
Most AI projects fail—and it’s not because of the models. Hidden API issues like poor structure, security gaps, and technical debt silently kill AI initiatives. Here’s why fixing your APIs is the real key to AI success.
Tired of tweaking AI prompts just to get decent results? I was too—so Rahul built AI Prompt Enhancer, a free tool that turns rough instructions into clear, structured prompts. Here’s how it works, why he built it, and how tools like API Insights and Treblle helped him along the way.
The Model Context Protocol (MCP) is changing how AI connects with tools, data, and systems. This article explores how it works, why it matters, the security risks it introduces—and how platforms like Treblle can help you build safer, smarter AI integrations.
Managing API versions and deprecations can be complex, but AI is changing the game. From automating documentation to predicting impact, this article explores how AI-powered tools like Treblle help teams handle versioning with less friction and more confidence.
AI-first API design puts intelligent systems—not humans—at the center of how APIs are built. In this article, we explore what that means, why it matters, and how developers can start designing APIs that speak AI.
Traditional APIs like REST and GraphQL have powered integrations for years, but modern systems demand more flexibility. This article explores how MCP compares to traditional APIs—covering use cases, pros, cons, and how to choose the right fit.
Machine learning APIs make it easy to add AI capabilities like speech recognition, image analysis, and predictive insights to applications—without building models from scratch. In this guide, we’ll explore the best ML APIs, their benefits, and how they simplify AI integration for developers.
Connecting AI models to data sources is tough—custom integrations are time-consuming. Model Context Protocol (MCP) simplifies this by standardizing how models interact with tools. This guide explores how MCP works and why it could be a game-changer for developers.
