Building Orvel: what does training your own AI actually mean?
When people hear "train your own AI," they usually imagine a giant model being built from zero. That is one version of training, but it is not the version most products need. For a practical agent, training often means shaping how it remembers, what it can use, what corrections it keeps, and how it improves without losing the person it is meant to serve.
That is the idea behind Orvel. The goal is not to make another chat box with a different skin. The goal is to make an AI that can learn your working style, connect to the tools you actually use, run on local or hosted models, and keep its knowledge organized enough that you can inspect it, fix it, and trust it.
In that world, "training" becomes a product surface. You teach the agent by giving it memories, documents, examples, rules, tasks, failures, and feedback. You evaluate it by checking whether those lessons change future behavior in useful ways. The hard part is not only model quality; it is building the system around the model so learning feels understandable instead of magical.
That is what I care about with Orvel: an assistant that can be corrected, extended, and carried across models without starting over every time the backend changes.