You don't need a paid API or a production risk to find out whether an AI coding model is worth your time. This sandbox-first workflow uses a versioned prompt suite, a throwaway repo, and a free-tier endpoint to turn "does this model feel good?" into a rerunnable benchmark—all on a zero budget.
There's a conversation happening right now about what happens when we hand AI agents more tools and the boundaries fail. It's a good conversation, but it skips a step most of us hit first: before you worry about an agent escaping its sandbox, you have to pick a model, wire it into a workflow, and figure out whether it actually helps—ideally without putting a credit card behind an experiment that might go nowhere.
This article is about that earlier step. It's a repeatable workflow I've structured for evaluating AI coding assistance on side projects where the budget is literally zero, using a fixed prompt suite, a throwaway git repo, and free-tier tooling. The workflow doesn't depend on any single provider, but I'll show where free model access and a hosted free server slot fit naturally, because that combination removes the two most common blockers: API cost anxiety and "my laptop can't run this locally."
