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Build the board. Claim the paths. Outthink the fluffle.

Easy to learn young, deep enough to grow into.

Trigame is a geometric strategy game built from triangular tiles, colored paths, chained token placement, sub-area capture, and a full ladder of rabbit-minded opponents ranging from Kit to Tu’er Ye.

Play now

Jump directly into the current game build and try your luck against Rusty Rabbit’s fluffle or against other humans.

Open current build →

Learn the rules

Read the illustrated instructions, notation, socket numbering, scoring, and examples of the Game Record language.

Open help / instructions →

Study the puzzle

The single-tile Kit problem treats part of Trigame like a small combinatorial game: finite, strange, and possibly solvable.

Open the problem page →

What Trigame became

Trigame began as a hand-drawn terrain game idea and evolved into something more abstract, more constructive, and more strategically alive. What started as tiles on paper became a living board: tokens create paths, paths create tokens, subs are captured, full tiles are won, and every move changes the shape of the next problem.

Along the way, the project picked up a whole rabbit ladder of opponents, a symbolic move language, a Game Record system, AI training tools, and a help/manual structure fit for curious players who want to look under the hood.

Trigame did not arrive all at once. It was burrowed out, one careful scrape at a time. Some tunnels collapsed. Some led nowhere. Some turned out to be hidden passages into better ideas than the ones first imagined.
Historic Trigame tile artwork

From paper terrain to digital strategy

Earlier versions focused on colored terrain tiles and physical placement. As the game moved into the browser, coding "accidents" sometimes revealed better rules than the originals. Tile overlap became a feature. Logging became infrastructure. The AI stopped being a difficulty setting and became a cast of personalities.

The current design keeps the spirit of the old board experiments while leaning into the strengths of a digital game: score tracking, AI ladders, move notation, automatic path effects, training matches, and archives of the project's long development trail.

Meet the fluffle

The built-in opponents are not merely "easy" through "hard." Each of Rusty Rabbit's kin can favor a different habit: Kit loves tiny fireworks, Newb-Bble likes more tiles, Lop-Sided scrambles for one neighborhood, Burrow Scout notices lanes, and stronger rabbits will push deeper into denial, conversion, and endgame control.

The long-term goal is not just stronger AI, but more memorable AI.

To this end, these personalities are handled by individual JavaScript files. We are considering the option of allowing players to creat theri own AI game characters so have included what we are doing now in case anyone wants to see in advance.

Fluffle character personaities JavaScript info..

Development archive

Trigame has a long visible history of versions, from SVG and early Canvas boards to scoring, sound, Game Record notation, Rusty Rabbit, and the current UI. That archive is part of the project's charm and may eventually grow into a structured lesson series.

Read more on the about page →
Historic Trigame tile artwork

Rusty Rabbit is the name ChatGPT chose from working on another related project in which he will be creating a serries of lessons on a variety of subjects including writing code. The process of developing this game step by step will serve as examples for his classes. ...Stay tuned!

Part of this ongoing educational aspet of Rusty's is this problem we call the Kit problem. Looking for the best possible moves for a single tile game. See more here: The Kit roblem for graduate level math heads.

Comparative Complexity of Trigame

Trigame combines two distinct layers of complexity: a finite local problem and an expanding global problem. A single tile may be treated as a closed combinatorial puzzle with fixed internal structure, while the game as a whole behaves like an open recursive network in which each placement can create additional future placements.

In the discussion that informed this summary, the internal logic of a single tile was described as having approximately 39,916,800 possible permutations. That figure refers only to the bounded logic of one tile or one local sub-problem. The wider game becomes far more complex because each tile placement opens additional sockets, allowing the board to continue expanding outward rather than remaining confined to a fixed grid.

Finite Tile, Divergent Game

Layer Type Description
Single Tile Closed / Finite A bounded combinatorial puzzle governed by fixed internal elements and a finite state space.
Whole Game Recursive / Expanding A growing network of placements in which each move can reshape the future board and enlarge the move tree.

In the same discussion, the game was described as gaining a net of 6 new sockets per tile placement when one socket is consumed and seven new ones are opened. Under that model, the number of available future placements grows rather than shrinks, making the overall game tree increasingly wide and deep as play continues.

Comparison with Chess and Go

Game Board Bounds Termination Complexity Character
Chess Fixed 8×8 board Yes Extremely large but finite game tree; often represented by the Shannon estimate of about 10120.
Go Fixed 19×19 board Yes Vast positional and game-tree complexity, often estimated around 10360 for 19×19 Go.
Trigame Expanding field Not fixed by board size alone Locally finite but globally divergent: complexity grows through recursive placement and expansion.

Illustrative Scale Estimates

The discussion also proposed rough comparison points suggesting that:

  • after about 14 turns, the branching complexity of a Trigame session could exceed the classical Chess estimate;
  • after about 38 turns, it could exceed the classical Go estimate;
  • by about 40 turns, the number of possible continuations would already be astronomically large.

These figures should be read as illustrative comparisons, not as formal proofs, but they communicate an important point: Trigame does not merely have a large number of possible positions; it also has a growth mechanism that can continue enlarging the space of possible play.

Why This Matters for AI

In bounded games such as Chess, strong engines can rely heavily on deep search because the board is fixed. In Go, Monte Carlo Tree Search and neural evaluation work well because, although the state space is vast, the board is still finite. Trigame places a different burden on intelligence: the challenge is not only to calculate well, but to manage the growth of the board itself.

For that reason, Trigame naturally favors:

  • heuristic pruning,
  • pattern recognition,
  • socket prioritization,
  • growth control,
  • and interference with an opponent's future options.

Plain Summary

Chess and Go are deep games played inside fences. Trigame is a deep game in which the field itself can continue to grow. That makes it locally finite, but globally explosive.

Trigame at fjd1.com - An original strategy game in continuing development.
Curious players can start with the help page, the single-tile problem (graduate level math), or the current playable build.