Simon Willison recently demonstrated the sophisticated reasoning capabilities of modern large language models by tasking Claude with deconstructing a classic piece of software history: Turbo Pascal 3.02A. Released in 1985, this legendary executable was famous for fitting a full text editor, IDE, and compiler into a tiny 39-kilobyte file. By providing the binary file—the raw, machine-readable data—to the AI, Willison showed how these models can bridge the gap between ancient machine code and human understanding.

The process involved a sequence of complex prompts where the AI explored the binary's structure and identified specific functional segments. It then performed a decompilation, which is the act of translating machine-level instructions back into a more readable format like assembly language. The AI did not just stop at translation; it reconstructed the logic into annotated code, providing a clear map of how the 40-year-old software operated under the hood.

To present these findings, Willison utilized the Artifacts feature to create an interactive visualization. This allowed for a side-by-side view of the original binary segments and their corresponding high-level code explanations. This experiment highlights a growing trend where AI serves as a powerful tool for digital archaeology, enabling developers and historians to preserve and study legacy software that might otherwise remain opaque to modern eyes.

Simon Willison recently demonstrated the sophisticated reasoning capabilities of modern large language models by tasking Claude with deconstructing a classic piece of software history: Turbo Pascal 3.02A. Released in 1985, this legendary executable was famous for fitting a full text editor, IDE, and compiler into a tiny 39-kilobyte file. By providing the binary file—the raw, machine-readable data—to the AI, Willison showed how these models can bridge the gap between ancient machine code and human understanding.

The process involved a sequence of complex prompts where the AI explored the binary's structure and identified specific functional segments. It then performed a decompilation, which is the act of translating machine-level instructions back into a more readable format like assembly language. The AI did not just stop at translation; it reconstructed the logic into annotated code, providing a clear map of how the 40-year-old software operated under the hood.

To present these findings, Willison utilized the Artifacts feature to create an interactive visualization. This allowed for a side-by-side view of the original binary segments and their corresponding high-level code explanations. This experiment highlights a growing trend where AI serves as a powerful tool for digital archaeology, enabling developers and historians to preserve and study legacy software that might otherwise remain opaque to modern eyes.