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Use Identical Prompts
When testing coding models, give each one the same prompt, constraints, and starting conditions. Do not tweak the second prompt after seeing the first result, because that turns the test into prompt iteration instead of model comparison. For small projects, ask for a single runnable artifact so evaluation is fast and visible.
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Judge The Browser, Not The Benchmark
Open the outputs and use them like a customer would. Check whether the app launches, responds to controls, handles failure states, and feels usable after the first minute. A model that scores well but ships a broken interface is not the better choice for your workflow.
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Pick Smoke Tests With Visible Failure
Small HTML games are useful because broken logic shows up immediately: dead buttons, bad controls, lag, missing loops, or unplayable mechanics. For your business, build equivalent smoke tests around your actual work, such as a booking form, invoice parser, product page, dashboard widget, or support macro. The best test is one where you can spot failure without reading every line of code.
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Run Agents In Parallel
Do not spend an hour arguing which model is best before the work starts. Run two or three agents on the same task at the same time, then keep the strongest artifact or merge the best parts. This is especially useful overnight, where wall-clock time matters more than squeezing every token from one model.
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Track Capability Gaps
A model can be strong at one task and fail because the surrounding tool path lacks a feature, such as image input support. Record those gaps as workflow constraints, not as vague opinions about intelligence. Your model menu should say what each option is good for, what it cannot handle, and when to route work elsewhere.
Why it matters
Small businesses do not need abstract model debates. They need a cheap way to find which AI setup can produce usable work for their actual jobs. A repeatable bake-off turns model choice into an operating habit: same task, parallel runs, real output, quick decision.