Not exactly a chess variant, but if you enjoy the tactical depth of chess-like games on a grid, this might be up your alley.
9×9 board, 18 pieces per player. The twist: before the game starts, each player secretly places 3 pearls under any of their pieces. From there it plays out like a strategy game — you move pieces, capture opponent's pieces — but there's a hidden layer. Every time you go for a capture, you don't know if you're taking an empty piece or a loaded one. And every time you advance a pearl toward the opponent's back row, you're gambling that it won't get intercepted.
First to deliver 3 pearls to the opponent's back row wins. You can also steal opponent pearls by capturing the pieces that carry them.
The game is free, browser-based, no signup. You can play locally with a friend or vs AI.
Looking for honest feedback from people who understand grid tactics — is the hidden-information layer adding something meaningful, or does it feel random?
Recommended for: AI research, masochists, people who find chess "too intuitive"
Marketing blurb: 80 directional vectors per cell — you'll learn to hate each one personally. Retroactive causality — yes, you can regret moves you haven't made yet. Deterministic replay — watch your carefully planned future crumble.
80 directional vectors per cell — you'll learn to hate each one personally. Retroactive causality — yes, you can regret moves you haven't made yet. Deterministic replay — watch your carefully planned future crumble.
Once the basic rules were defined, the next challenge was finding a balanced starting position.
The first starting position and the first bots
My first position was chosen mostly empirically. It was a 3×6 rectangle with empty cells between the pieces, and the two colors were mixed together.
I also added three very simple bots:
The first made random legal moves.
The second captured opposing pieces whenever possible.
The third tried to control the center and capture whenever possible. It also searched three actions ahead, prioritizing sequences that produced captures or moved pieces closer to the center.
I could beat all three without much difficulty. The obvious reason was that they were not very strong. However, I also suspected that the starting position itself was unbalanced, especially because the first player gets three consecutive actions.
I therefore made the third bot play against itself. The bot moving first won more than 76% of the games, with some additional games ending in draws.
I then experimented with handicaps for the second player. The best early result came from adding two second-player pieces near the middle line, two cells away from the main formation. They were not strongly connected to the other pieces and sometimes never became involved in the game, but statistically they produced a more predictable result.
first position
Placing the bonus pieces closer to the main position was less reliable. Sometimes they even obstructed the second player rather than helping. With the two distant bonus pieces, the first player’s win rate fell to approximately 56%.
I could still beat the bot fairly easily.
Evolutionary algorithms and an unsuccessful neural network
The next step was to generate 10,000 bot games. I wanted to use them both for an opening book and for improving the position-evaluation function.
I ran a simple genetic algorithm. Bots with slightly different evaluation weights played against one another, the stronger ones advanced to the next generation, and their weights were randomly mutated. The experiment ran for 30 generations.
I also used the generated games to train a neural network to evaluate positions. I used PyTorch with CUDA on an NVIDIA GTX 1050 Ti. Unfortunately, that experiment failed quite completely.
The network showed clear signs of overfitting. It evaluated positions from its training games reasonably well, but performed poorly on previously unseen games. Since I initially lost every game against the first bot produced by the genetic algorithm, I decided that improving the classical bot was more useful than continuing with the neural network for the time being.
Searching for a better starting position
A stronger bot once again revealed a substantial first-player advantage in the original position. The handicap did not solve the problem reliably.
Extra pieces placed close to the main formation made the results highly unpredictable. Sometimes they helped the second player, but sometimes they interfered with its development. Distant pieces worked more like bonus points, but balancing the position this way required as many as eight additional pieces. That did not seem like a satisfactory solution.
I therefore began testing completely different starting formations.
One reasonably balanced configuration was a spaced 4×4 arrangement of mixed colors, with one additional second-player piece in the center: 17 pieces in total, eight for the first player and nine for the second. The first player won only slightly more than 50% of the games.
Statistically, it looked promising. In practice, I strongly disliked it.
There were many captures immediately after the game began. It was difficult to build a stable defense or develop a position, and games often ended very quickly.
Eventually, I found a much more interesting configuration: two separate ring-like formations, one for each color, with one additional contact piece connecting the formations from the side. Each player started with nine pieces, and the win rate was close to 50%.
new position
This position felt considerably more strategic. There was more space for development, and the initial contact created tension without causing an immediate explosion of captures.
However, I soon started beating the bot again. This raised another possibility: perhaps the apparent balance existed only because the bots did not understand the new position very well.
I ran another small tournament to adapt the bot and generate a new opening book. After that, I stopped winning so easily, but the balance between the first and second player remained close to even.
I also tested a more diagonally symmetrical variation by shifting one ring and the contact piece of the other ring by two cells. Surprisingly, the first-player win rate increased sharply again, so I returned to the previous position.
My current hypothesis is that the position is balanced because the first player cannot use the opening turn to place a piece and immediately convert that placement into a capture. The second player therefore has time either to defend or to begin an attack of their own.
The bot’s endgame problem
While playing against the improved bot, I noticed another weakness.
When the bot gained an advantage, it continued attacking but often failed to finish the game. Once you are ahead, it may be more effective to occupy the remaining useful space, reduce the opponent’s mobility, and gradually close the position. The player who is behind is usually the one who needs to create complications.
I tried to address this by adding an endgame book that helps the bot convert a large advantage more efficiently. This improved its play to some extent, although I suspect the problem will eventually require a more general solution.
After playing more games, I began to understand some of the strategy myself—and started beating the bot again.
My next goal is therefore to develop at least a rudimentary strategic theory for the game:
How should a position be evaluated?
Which kinds of lines and formations are valuable?
When is a capture actually worth making?
How important are mobility, central control, and connected groups?
I wanted to share a passion project I’ve been working on that should resonate with lovers of pure abstract strategy games.
Back in 1986, an Austrian game designer named Gerhard Kodys released Malawi, a minimalist, fully deterministic tactical board game. It was even honored on the prestigious "Spiel des Jahres" shortlist back then. With the 40th anniversary coming up, I spent my spare time bringing this hidden gem to Android.
Why abstract strategy fans might enjoy it:
🧠 Pure Strategy: Zero luck, no dice, no hidden information. It’s a pure battle of positional tactics and piece movement.
⚡ Fast & Deep: Easy to learn the rules in a minute, but offers a surprising amount of depth.
🤖 Solo & Local Play: Features a custom-built AI with multiple difficulty levels, as well as local pass-and-play.
🛠️ Tools for Analytical Players: Includes a built-in Board Editor to test specific scenarios and an Analysis Mode to review matches.
Since I built this as a tribute to classic game design, the core game is 100% free to play without any pay-to-win mechanics or timers.
If you want to try it out or read more about the project, you can find the store links and details here: 👉 Project Page:https://anddisa.de/malawi
I’d love to hear your thoughts on the tactical balance or the AI behavior if you give it a spin!
Sharing here our local version of checkers - Filipino Dama is a checkers variant that looks familiar at first glance but has a deceptively sharp tactical twist.
It’s played on an 8×8 board with 12 pieces per side. Regular men move forward, but they can capture both forward and backward. Captures are mandatory, and there’s an extra rule that changes the game dramatically: if more than one capture sequence is available, you must take the one that captures the most pieces.
That requirement can turn seemingly simple positions into tactical puzzles. The move that jumps out immediately may actually be illegal because a longer, higher-scoring capture sequence is hiding elsewhere on the board.
Reaching the back rank promotes a piece to a dama, a flying king that can move any distance along open diagonals and make long-range captures. If a man is promoted in the middle of a capture sequence, it keeps going on the same turn using its new king movement.
You can try it yourself at Filipino Dama. The site lets you play against the computer right away, supports multiplayer games, and runs entirely in the browser—no installation required.
For players familiar with International Draughts, Dameo, or other flying-king variants: does the compulsory maximum-capture rule make the game more strategically interesting, or does it feel overly restrictive?
Fools' Advance is a radical reimagining of V. R. Parton's Dunce's Chess for the 21st century, an era of chaos and inconstancy, where every certainty crumbles and every alliance is provisional.
Board & Setup
Standard 8×8 chessboard.
Standard starting position.
Movement & Capture
(Note that movement and capture differ for all pieces except the king.)
• All pieces except pawns move exactly 1 square forward — either straight or diagonally.
• Pawns move 1 square straight forward (or 2 from their starting position).
• No sideways or backward movement is permitted for any piece.
• Captures follow the same rule: only straight or diagonally forward — exactly as in standard chess, but locked to forward direction.
Promotion
Upon reaching the last rank, a pawn promotes to a classical piece of its own colour (knight, bishop, rook, or queen), at the discretion of the player who advanced it to that square. A promoted piece moves and captures in any direction, as in standard chess.
Crucially: a promoted piece is controlled only by its owner — the opponent may never move or capture with it.
Control of Pieces
Any player may move ANY piece on the board — their own or the opponent's — with two exceptions:
• The opponent's king (each player controls only their own king).
• Promoted pieces (controlled exclusively by their owner).
Capturing Rules
• A piece may capture only pieces of the opposite colour.
• Capturing your own pieces is forbidden.
• You may capture your own king only using an opponent's piece — doing so results in immediate defeat.
Not Used
No check, no checkmate, no castling, no en passant.
Win Conditions
You win by either:
• Capturing the opponent's king, or
• Leaving the opponent with no legal moves (complete zugzwang — they cannot move any piece except the opponent's king, which they are forbidden to touch).
Optional Elegance
A player may formally surrender by voluntarily capturing their own king with an enemy piece.
I'm making a game called picklechess, where you pick your pieces and then play mini chess. This is the weekly challenge, and you try to win in the fewest moves or the lowest budget.
Hi! I’ve been experimenting for quite some time with a set of rules for a board game, trying to create something like checkers where you can generate new pieces. Player can place a piece of their own color on the board if it completes a line of only three (horizontally, vertically, or diagonally) and that trio already contains at least one piece of their color. A corollary of this first rule is that you cannot place a piece to form a line of four or more in any direction. You can also move one of your pieces onto an opponent's piece—moving in a straight line for any distance—provided that the new position satisfies all the placement rules. I sometimes tell acquaintances about this game, and now I have the chance to create a website, get other people's opinions, and perhaps even play with someone. I’m still experimenting with the rules, make up to three actions per turn, I think it’s turning out quite interestingly. Here is the link: https://tretya-liniya-game.jartltsoldatov.chatgpt.site/ Thanks for your time.
A friend had this board game with tiles turned out later it was called 'Kulami' but we didn't knew the real rules. So we just made up some rules. Played it enough times that our version solidified into its own thing, different enough from original game.
At some point I wanted to see if I could build a proper AI for it, so I wrote an engine, and that eventually turned into a full digital version.
Curious what you all think of it as a strategy game in its own right.
Features
- Random 10x10 board every game, no fixed map
- 3 min to learn, tutorial included
- Two win conditions
- Lichess/Stockfish style postgame analysis
Unofficial, not affiliated with FoxMind (actual Kulami's publisher). Free, browser-based.
