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Teaching GPT-5 to Use a Computer
A copilot for your computer
Unlike previous models that might hallucinate or lose track of the current state, GPT-5's chain-of-thought reasoning allows it to break down "start playing this game" into discrete, executable steps while adapting to unexpected UI changes. We run a 3MB saliency scorer to identify interactive regions (buttons, text fields, links) and grab ~20 patches from those areas while throwing away dead space. Today we want to keep a planner in the loop for rare edge cases and safety; as the executor absorbs those patterns (via streaming, macros, distillation), the system becomes simpler and end-to-end.
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