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Chaturanga: Ancient Indian Chess, Reborn in Glowing 3D

Chaturanga is the ancient Indian game of the four divisions — played with modern chess rules but authentic piece identities, carved in real-time glowing 3D. A free, offline chess tutor with a five-level teaching AI, a coach, and an openings trainer.

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Part of the #AI4Good series — one app a day, each built for good.

Chaturanga — ancient Indian chess, reborn in glowing 3D

Chess was born in India as Chaturanga — the “game of the four divisions.” Chaturanga the app plays by modern chess rules but keeps the ancient identities — Raja, Mantri, Gaja, Ashva, Ratha, Padati — and lets you actually learn the game instead of just losing it.

What it is

Chaturanga is ancient chess in real-time glowing 3D, with a full teach-and-play layer:

  • Play the Guru — an alpha-beta chess AI with five difficulty levels (Padati → Mantri), running in a Web Worker so the board stays smooth on phones.
  • A coach — a Hint that names the best move and why, plus a blunder review that gently flags mistakes and shows the stronger move.
  • Openings trainer — six classic openings (Italian, Ruy López, Sicilian, French, Queen’s Gambit, King’s Indian) walked move-by-move with a narrated lesson.
  • Piece inspector — tap a piece for a rotating 3D render and a diagram of how it moves and captures; a Warrior’s Eye camera looks across the board from a piece’s point of view.
  • Four themed worlds, each with its own army, board art, teachings and a portrait cinematic intro. Local hotseat, undo, captured-pieces tray, under-promotion, read-aloud narration — no backend, works offline.

How it was built

The web app is deliberately buildless — vanilla ES modules, chess.js for the rules, three.js for rendering, Capacitor for Android. The interesting parts:

  • A real chess engine, in the browser. The AI is alpha-beta negamax with quiescence search, MVV-LVA move ordering, and piece-square evaluation over chess.js, exposed as analyze() / bestMove() / classifyMove(). It runs in a Web Worker (with a main-thread fallback) so search never janks the render loop, and the five levels scale depth, blunder-rate and time cap. Root moves are searched full-window so every move gets an exact score — which is what makes the coach’s blunder detection possible.
  • The pieces are AI-reconstructed, not hand-modelled. Each one starts as a themed gpt-image-2 concept, becomes a mesh via the free Hunyuan3D-2 Hugging Face Space, gets its concept projected back on as texture in headless Blender, and ships as a small web GLB. Portrait intros are generated with Azure Sora-2. The full image-to-3D pipeline is its own post here.
  • Tested with node:test (rules + engine + coach/openings + all-worlds validation) and shipped as a debug-signed Capacitor APK (npm run apk, JDK 21).

The good

A patient, free, offline chess tutor — no sign-in, no subscription — that also carries heritage in every piece. Learning and culture on one board. That’s #AI4Good.

Play it

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