Rónán!

Stateless Probabilistic Minesweeper A relatively easy random challenge (or not so easy, I 'm actually curious): Prize: Live X podcast between @themoveguy (creator of Move book) and myself to the first person who will implement the following: In this Minesweeper variant: The board’s mines are not fixed upfront and hidden cells have no remembered state between moves. - Every time you reveal a cell or query a hidden cell, its state (mine or safe) is sampled fresh, based only on the currently revealed visible state. - The number shown on any revealed safe cell reflects the actual number of adjacent mines consistent with the visible board and the current random sampling. - The algorithm must ensure every sampled board state satisfies all revealed clues, without contradiction. - No persistent assumptions or stored states exist for hidden cells; all sampling and consistency checks happen dynamically on demand. - On each user's move, sample (roll) the mine/safe state of the chosen cell and any relevant neighbors freshly, constrained only by the currently revealed board. - Ensure the sampled assignment of mines and safes across the board is globally consistent with all visible clues (numbers on revealed cells). - Maintain logical consistency without remembering any hidden cell’s prior assignments. - Support configurable per-cell mine probabilities and their conditional neighbor dependencies. - Automatically deduce forced mines or safe cells when only a single consistent choice exists. - Implement flood fill to reveal safe neighbors of zero-valued cells. - Guarantee the first click is always safe, so we can have a game. - Provide interactive gameplay in a website. - Explain the algorithm clearly and provide a Python or JS script. Bonus - Integrate efficient algorithms to prune impossible assignments early during sampling. - Explore approaches like constraint satisfaction problem (CSP) solvers or SAT solvers adapted for stateless sampling. - Provide a solver assistant suggesting moves with minimal risk based on fresh sampling. - Implement as a Sui smart contract, using Sui's Native Randomness and No Nautilus, MPC or ZK (no cheats). - For math nerds: if that game is not possible mathematically, provide a proof.

@MattiSchroder @levelsio He knows what state is. It’s a joke

Stateless Probabilistic Minesweeper A relatively easy random challenge (or not so easy, I 'm actually curious): Prize: Live X podcast between @themoveguy (creator of Move book) and myself to the first person who will implement the following: In this Minesweeper variant: The board’s mines are not fixed upfront and hidden cells have no remembered state between moves. - Every time you reveal a cell or query a hidden cell, its state (mine or safe) is sampled fresh, based only on the currently revealed visible state. - The number shown on any revealed safe cell reflects the actual number of adjacent mines consistent with the visible board and the current random sampling. - The algorithm must ensure every sampled board state satisfies all revealed clues, without contradiction. - No persistent assumptions or stored states exist for hidden cells; all sampling and consistency checks happen dynamically on demand. - On each user's move, sample (roll) the mine/safe state of the chosen cell and any relevant neighbors freshly, constrained only by the currently revealed board. - Ensure the sampled assignment of mines and safes across the board is globally consistent with all visible clues (numbers on revealed cells). - Maintain logical consistency without remembering any hidden cell’s prior assignments. - Support configurable per-cell mine probabilities and their conditional neighbor dependencies. - Automatically deduce forced mines or safe cells when only a single consistent choice exists. - Implement flood fill to reveal safe neighbors of zero-valued cells. - Guarantee the first click is always safe, so we can have a game. - Provide interactive gameplay in a website. - Explain the algorithm clearly and provide a Python or JS script. Bonus - Integrate efficient algorithms to prune impossible assignments early during sampling. - Explore approaches like constraint satisfaction problem (CSP) solvers or SAT solvers adapted for stateless sampling. - Provide a solver assistant suggesting moves with minimal risk based on fresh sampling. - Implement as a Sui smart contract, using Sui's Native Randomness and No Nautilus, MPC or ZK (no cheats). - For math nerds: if that game is not possible mathematically, provide a proof.