AiRabbit

Model-Context Protocol (MCP) is a game-changing technology that's transforming how AI agents interact with information. It allows AI agents to communicate with various systems and analyze data from different sources on the fly, even without the user knowing their exact structure beforehand. When dealing with large datasets, we

In today's world, many of us frequently interact with AI models like GPT, spending significant time prompting, reading responses, and refining our queries. Through this process, we gain valuable experience and start to understand how "our" AI works, learning the best ways to phrase our requests

In a previous post, we discussed how to build an MCP-powered voice agent with real-time capabilities. This allowed you to use your own data, applications, and services to create an AI assistant for yourself or your customers, all using LiveKit in a local environment with OpenAI and Deepgram. In this

AI agents and their supporting technologies (RAG, MCP, Long Term Memory and many more) are becoming the foundation of AI-powered systems across all industries. Equipped with advanced AI language models (LLMs) and vast amounts of data, these agents can handle many tasks much faster and more affordably than human labor.

Recently, I came across an intriguing post from an influencer demonstrating how to generate n8n workflows using Claude. This method involved creating and saving various files within Claude projects. While the idea of using AI to generate n8n workflows is certainly appealing, this particular approach has a significant drawback: the

When you're writing a blog post with a lot of text, adding variety can make it much more engaging for your readers. While Artificial Intelligence (AI) can certainly help with many aspects – like structuring your content, doing research, or even creating diagrams right within your prompt – there'