At Magenizr, we're always looking for tools to make life easier for developers and merchants. Not too long ago, we found ourselves in a bind. We were scouring the Adobe Commerce Marketplace and Github repositories for an error monitoring app tailored for e-commerce shops but came up empty-handed. New Relic was overkill for shops that just needed to pinpoint where errors were cropping up.

In this article, we'll explore how we developed an Aspire component that spins up an Ollama container and downloads a Large Language Model, ready for use. If you're new to any of these technologies, you can continue reading, otherwise feel free to skip to the technical walkthrough. As a quick bit of background, we recently released an Aspire component that brings a free, lightweight Raygun app into your local development environment to help debug exceptions. We then subsequently enhanced this with AI Error Resolution capabilities which runs entirely on your local machine.

Smart engineering teams are working out how to use Large Language Models (LLMs) to solve real business problems. At Raygun, we're no exception, and we're committing our time and effort to developing AI software applications that bring value to our customers. Our first AI-powered release is AI Error Resolution (AIER), a novel Crash Reporting feature that takes debugging with ChatGPT to the next level. We know that LLMs have already dramatically increased software engineers' productivity.