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Resources and Industry Insights
This resource and insight hub provides education, breaking news, our research, and more to benefit developers, corporate leadership, academics, marketing and business development professionals, and even those who are new to the concept of AI.



How to Run the Axelera Metis M.2 on Raspberry Pi 5
Learn how to set up and run AI models using the Axelera Metis M.2 on Raspberry Pi 5.

Daniela Brenes
Jul 165 min read
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NVIDIA DGX Spark: AI Supercomputing Power on Your Desktop
Bring AI Supercomputing to Your Desk!
The NVIDIA DGX Spark delivers up to 1 PetaFLOP of AI power in a compact, desktop-ready form. With 128 GB of unified memory and Grace-Blackwell architecture, it enables local development of massive models without cloud dependency. Explore how RidgeRun.ai can help you accelerate your AI projects with DGX Spark.

Marco Madrigal
Jul 149 min read
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Improving AI Model Performance Through Smart Parameter Tuning
AI models get smarter with the right training, but how well they perform hinges heavily on the fine-tuning process behind the scenes. Even the most advanced model can fall flat if its setup isn't adjusted to match its task properly. That’s where smart parameter tuning steps in. It helps you get the most out of your model without starting from scratch every time something goes off track. Think of it like making tweaks to a car before a race. If the tire pressure, fuel levels,

ridgerun
Jul 106 min read
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Steps To Fix Common AI Model Deployment Errors In Production
Deploying an AI model isn’t always smooth. Even after the development phase is complete, one major hurdle still remains—getting the model to function properly once it’s live. It may run slower than expected, deliver inconsistent outputs, or even break on certain types of inputs. These issues can be frustrating, especially when everything worked fine during testing. Dealing with AI model deployment errors quickly helps prevent downtime and keeps systems running the way they’re

ridgerun
Jul 16 min read
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Troubleshooting Visual Recognition Issues In Computer Vision Systems
Visual recognition systems play a big role in how machines understand what they see through computer vision AI. From identifying objects in live security footage to tracking goods in manufacturing, these systems process visual data to interpret their surroundings. But they’re not perfect. Problems like incorrect object detection, poor tracking, or inconsistent outputs can mess with operations and slow down performance when speed and accuracy matter most. When things start to

ridgerun
Jul 15 min read
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NVIDIA Isaac Sim, Omniverse, and Cosmos – The Robotics & AI Simulation Ecosystem Explained
NVIDIA's ecosystem of Omniverse, Isaac Sim, and Cosmos forms a powerful trio for robotics and AI development. Omniverse provides the collaborative 3D simulation platform, Isaac Sim enables realistic robotics testing, and Cosmos uses generative AI to accelerate synthetic data creation. This blog explains how these tools work together, their strengths, and when to use each to streamline your development workflow.

Adrian Araya
Jun 610 min read
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Introducing Juniper: How We Fine-Tuned a Small and Local Model for Function Calling
This post will take you through our journey on how we fine-tuned a model for function calling.

Daniela Brenes
Jun 312 min read
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Edge AI Model Optimization for Smarter Inspection
Discover how the optimization of an edge AI model improves real-time manufacturing inspection, increasing accuracy and minimizing downtime.

ridgerun
Jun 15 min read
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UV Tutorial: A Fast Python Package and Project Manager
This tutorial will show you how to get started with UV: a fast Python manager for packages and projects.
Michael Gruner
May 135 min read
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How to Write Your MCP Server in Python
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.

Adrian Araya
May 127 min read
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