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 ' );

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2. Persona Prompt Engineering

The "soul" of the app lies in the personas.…