Where it shows up in this material In the captured conversations, the code interpreter appears as one of the tools a custom GPT can reach for alongside things like image generation and web browsing — and the discussion centers on a specific, practical use: introducing randomness into an otherwise deterministic model. When Logan Kilpatrick and Dan Shipper playtested their custom GPT strategy game, Allocator, they found it was always positive — every scenario resolved favorably, which made it feel unlike a real game. Their identified fix was to add a dice roll via the code interpreter, letting a probability distribution create stakes, failure states, and genuine decision tension. In other words, the code interpreter is treated as the lever you pull when a GPT's behavior is too flat, too agreeable, or too predictable: because a language model by itself won't produce true randomness or computed probabilities, running actual code is how you get it. The takeaway for your own custom GPT builds is concrete — if playtesting reveals outcomes that never go wrong, the code interpreter is the natural mechanism for injecting dice rolls, probability, and computed randomness that the instructions alone can't supply.