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."
The actual problem: evaluation debt
Most developers evaluate AI coding tools the way they evaluate a new keyboard — vibes. You paste one prompt, the output looks plausible, and you either adopt the tool or dismiss it based on a sample size of one. That's evaluation debt, and it compounds: you end up trusting a model on tasks it's bad at, or abandoning one that would have saved you hours on the tasks it's good at.
