Resources and Industry Insights

Discover how to build a simple MCP server in Python to manage a to-do list example using the Model Context Protocol (MCP). This step-by-step guide walks you through creating the server, connecting it to Claude for Desktop, and enabling seamless interaction between AI models and real-world tools.

Bring real-time voice to your applications with Voice Agent! Built for low-latency, natural conversations, Voice Agent supports interruption, local processing, and full customization—perfect for embedded systems, desktop apps, or web interfaces. Discover how RidgeRun’s modular design makes building voice-powered experiences seamless and flexible.

Easily switch between OpenAI and local LLMs like Ollama using the same API! This post shows how to wrap the OpenAI Python (and JavaScript) client to talk to Ollama with minimal changes, letting you develop and test locally without sacrificing the familiar OpenAI interface. Learn how to install Ollama, pull models like llama3.1, and build a unified interface that works across both platforms.

Meta AI presents an improvement to the current LLMs: Byte Latent Transformers. They achieve same performance while being more efficeficient.

A tutorial on how to train LLMs using Python, Hugging Face and Unsloth.

This year RidgeRun.ai was fortunate enough to be a part of the Ai4. Here's some of our favorite moments!

In the very first DevDay, OpenAI announce more than a dozen of new features.