Minesweeper is often considered a solved problem for algorithms, but what happens when you introduce Persona-based Reasoning ? In this post, we explore the architecture and implementation of Neural Sweeper . 1. The Core Challenge: LLM as a Tactical Player Unlike standard algorithms (like backtracking or constraint satisfaction), an LLM doesn't "see" the board. It processes text. We had to transform the 2D grid into a structured string format that retains spatial context. Serialization Strategy We used a simple but effective grid serialization: ? for hidden cells F for flags 0-8 for revealed numbers const serialized = grid . map ( row => row . map ( cell => { if ( cell . state === ' hidden ' ) return ' ? ' ; if ( cell . state === ' flagged ' ) return ' F ' ; return cell . neighborMines . toString (); }). join ( ' ' ) ). join ( ' \n ' ); Enter fullscreen mode Exit fullscreen mode 2. Persona Prompt Engineering The "soul" of the app lies in the personas.…