feat: NCS-41 Bot Platform (#33 )

Co-authored-by: Janis <janis@nowchess.de> Reviewed-on: #33 Co-authored-by: Janis <janis.e.20@gmx.de> Co-committed-by: Janis <janis.e.20@gmx.de>
ci: bump version with Build-41
2026-04-19 15:52:08 +02:00 · 2026-04-16 16:55:00 +00:00
125 changed files with 8644 additions and 429 deletions
@@ -2,8 +2,8 @@

 > **Stack:** raw-http | none | unknown | scala

-> 0 routes | 0 models | 0 components | 35 lib files | 0 env vars | 0 middleware
-> **Token savings:** this file is ~3.700 tokens. Without it, AI exploration would cost ~18.200 tokens. **Saves ~14.500 tokens per conversation.**
+> 0 routes | 0 models | 0 components | 63 lib files | 1 env vars | 1 middleware
+> **Token savings:** this file is ~0 tokens. Without it, AI exploration would cost ~0 tokens. **Saves ~0 tokens per conversation.**

 ---

@@ -60,6 +60,113 @@
  - class ApiResponse
  - function error
  - function totalPages
+- `modules/bot/python/nnue.py`
+  - function get_weights_dir: ()
+  - function get_data_dir: ()
+  - function list_checkpoints: ()
+  - function migrate_legacy_data: ()
+  - function show_header: ()
+  - function show_checkpoints_table: ()
+  - _...10 more_
+- `modules/bot/python/src/dataset.py`
+  - function get_datasets_dir: () -> Path
+  - function next_dataset_version: () -> int
+  - function list_datasets: () -> List[Tuple[int, Dict]]
+  - function load_dataset_metadata: (version) -> Optional[Dict]
+  - function save_dataset_metadata: (version, metadata) -> None
+  - function create_dataset: (version, labeled_jsonl_path, sources, stockfish_depth) -> Path
+  - _...4 more_
+- `modules/bot/python/src/export.py` — function export_to_nbai: (weights_file, output_file, trained_by, train_loss)
+- `modules/bot/python/src/generate.py` — function play_random_game_and_collect_positions: (output_file, total_positions, samples_per_game, min_move, max_move, num_workers)
+- `modules/bot/python/src/label.py` — function normalize_evaluation: (cp_value, method, scale), function label_positions_with_stockfish: (positions_file, output_file, stockfish_path, batch_size, depth, verbose, normalize, num_workers)
+- `modules/bot/python/src/tactical_positions_extractor.py`
+  - function download_and_extract_puzzle_db: (url, output_dir)
+  - function extract_puzzle_positions: (puzzle_csv, max_puzzles) -> Set[str]
+  - function load_positions_from_file: (file_path) -> Set[str]
+  - function merge_positions: (tactical, other, output_file)
+  - function extract_tactical_only: (puzzle_csv, output_file, max_puzzles) -> int
+  - function interactive_merge_positions: (puzzle_csv, output_file, max_puzzles)
+- `modules/bot/python/src/train.py`
+  - function fen_to_features: (fen)
+  - function find_next_version: (base_name)
+  - function save_metadata: (weights_file, metadata)
+  - function train_nnue: (data_file, output_file, epochs, batch_size, lr, checkpoint, stockfish_depth, use_versioning, early_stopping_patience, weight_decay, subsample_ratio)
+  - function burst_train: (data_file, output_file, duration_minutes, epochs_per_season, early_stopping_patience, batch_size, lr, initial_checkpoint, stockfish_depth, use_versioning, weight_decay, subsample_ratio)
+  - class NNUEDataset
+  - _...1 more_
+- `modules/bot/src/main/scala/de/nowchess/bot/Bot.scala`
+  - class Bot
+  - function name
+  - function nextMove
+- `modules/bot/src/main/scala/de/nowchess/bot/BotController.scala`
+  - class BotController
+  - function getBot
+  - function listBots
+- `modules/bot/src/main/scala/de/nowchess/bot/BotMoveRepetition.scala`
+  - class BotMoveRepetition
+  - function blockedMoves
+  - function repeatedMove
+  - function filterAllowed
+- `modules/bot/src/main/scala/de/nowchess/bot/Config.scala` — class Config
+- `modules/bot/src/main/scala/de/nowchess/bot/ai/Evaluation.scala`
+  - class Evaluation
+  - class CHECKMATE_SCORE
+  - class DRAW_SCORE
+  - function evaluate
+  - function initAccumulator
+  - function copyAccumulator
+  - _...2 more_
+- `modules/bot/src/main/scala/de/nowchess/bot/bots/classic/EvaluationClassic.scala`
+  - class EvaluationClassic
+  - function evaluate
+  - function countRay
+- `modules/bot/src/main/scala/de/nowchess/bot/bots/nnue/EvaluationNNUE.scala` — class EvaluationNNUE, function evaluate
+- `modules/bot/src/main/scala/de/nowchess/bot/bots/nnue/NNUE.scala`
+  - class NNUE
+  - function initAccumulator
+  - function pushAccumulator
+  - function copyAccumulator
+  - function recomputeAccumulator
+  - function validateAccumulator
+  - _...4 more_
+- `modules/bot/src/main/scala/de/nowchess/bot/bots/nnue/NbaiLoader.scala`
+  - class NbaiLoader
+  - function load
+  - function loadDefault
+- `modules/bot/src/main/scala/de/nowchess/bot/bots/nnue/NbaiMigrator.scala` — class NbaiMigrator, function migrateFromBin
+- `modules/bot/src/main/scala/de/nowchess/bot/bots/nnue/NbaiModel.scala`
+  - function toJson
+  - class NbaiMetadata
+  - function fromJson
+  - function str
+  - function num
+- `modules/bot/src/main/scala/de/nowchess/bot/bots/nnue/NbaiWriter.scala` — class NbaiWriter, function write
+- `modules/bot/src/main/scala/de/nowchess/bot/logic/AlphaBetaSearch.scala`
+  - function bestMove
+  - function bestMove
+  - function bestMoveWithTime
+  - function bestMoveWithTime
+  - function loop
+  - function loop
+  - _...2 more_
+- `modules/bot/src/main/scala/de/nowchess/bot/logic/MoveOrdering.scala`
+  - class MoveOrdering
+  - class OrderingContext
+  - function addKillerMove
+  - function getKillerMoves
+  - function addHistory
+  - function getHistory
+  - _...3 more_
+- `modules/bot/src/main/scala/de/nowchess/bot/logic/TranspositionTable.scala`
+  - function probe
+  - function store
+  - function clear
+- `modules/bot/src/main/scala/de/nowchess/bot/util/PolyglotBook.scala` — function probe, function select
+- `modules/bot/src/main/scala/de/nowchess/bot/util/PolyglotHash.scala` — class PolyglotHash, function hash
+- `modules/bot/src/main/scala/de/nowchess/bot/util/ZobristHash.scala`
+  - class ZobristHash
+  - function hash
+  - function nextHash
 - `modules/core/src/main/scala/de/nowchess/chess/command/Command.scala`
  - class Command
  - function execute
@@ -82,7 +189,7 @@
  - function turn
  - function context
  - function canUndo
-  - _...10 more_
+  - _...11 more_
 - `modules/core/src/main/scala/de/nowchess/chess/observer/Observer.scala`
  - function context
  - class Observer
@@ -93,6 +200,13 @@
  - _...1 more_
 - `modules/io/src/main/scala/de/nowchess/io/GameContextExport.scala` — class GameContextExport, function exportGameContext
 - `modules/io/src/main/scala/de/nowchess/io/GameContextImport.scala` — class GameContextImport, function importGameContext
+- `modules/io/src/main/scala/de/nowchess/io/GameFileService.scala`
+  - class GameFileService
+  - function saveGameToFile
+  - function loadGameFromFile
+  - class FileSystemGameService
+  - function saveGameToFile
+  - function loadGameFromFile
 - `modules/io/src/main/scala/de/nowchess/io/fen/FenExporter.scala`
  - class FenExporter
  - function boardToFen
@@ -114,6 +228,8 @@
  - function parseBoard
  - function importGameContext
 - `modules/io/src/main/scala/de/nowchess/io/fen/FenParserSupport.scala` — function buildSquares
+- `modules/io/src/main/scala/de/nowchess/io/json/JsonExporter.scala` — class JsonExporter, function exportGameContext
+- `modules/io/src/main/scala/de/nowchess/io/json/JsonParser.scala` — class JsonParser, function importGameContext
 - `modules/io/src/main/scala/de/nowchess/io/pgn/PgnExporter.scala`
  - class PgnExporter
  - function exportGameContext
@@ -160,43 +276,58 @@

 ---

+# Config
+
+## Environment Variables
+
+- `STOCKFISH_PATH` **required** — modules/bot/python/nnue.py
+
+---
+
+# Middleware
+
+## custom
+- generate — `modules/bot/python/src/generate.py`
+
+---
+
 # Dependency Graph

 ## Most Imported Files (change these carefully)

- `modules/api/src/main/scala/de/nowchess/api/game/GameContext.scala` — imported by **28** files
- `modules/api/src/main/scala/de/nowchess/api/board/Square.scala` — imported by **21** files
- `modules/api/src/main/scala/de/nowchess/api/board/Color.scala` — imported by **19** files
- `modules/api/src/main/scala/de/nowchess/api/move/Move.scala` — imported by **14** files
- `modules/api/src/main/scala/de/nowchess/api/board/Board.scala` — imported by **13** files
- `modules/api/src/main/scala/de/nowchess/api/board/Piece.scala` — imported by **10** files
- `modules/api/src/main/scala/de/nowchess/api/board/PieceType.scala` — imported by **9** files
- `modules/io/src/main/scala/de/nowchess/io/fen/FenParser.scala` — imported by **9** files
- `modules/rule/src/main/scala/de/nowchess/rules/sets/DefaultRules.scala` — imported by **8** files
- `modules/io/src/main/scala/de/nowchess/io/GameContextImport.scala` — imported by **7** files
- `modules/api/src/main/scala/de/nowchess/api/board/CastlingRights.scala` — imported by **4** files
- `modules/io/src/main/scala/de/nowchess/io/GameContextExport.scala` — imported by **4** files
- `modules/rule/src/main/scala/de/nowchess/rules/RuleSet.scala` — imported by **4** files
+- `modules/api/src/main/scala/de/nowchess/api/game/GameContext.scala` — imported by **60** files
+- `modules/api/src/main/scala/de/nowchess/api/move/Move.scala` — imported by **40** files
+- `modules/api/src/main/scala/de/nowchess/api/board/Square.scala` — imported by **39** files
+- `modules/api/src/main/scala/de/nowchess/api/board/Color.scala` — imported by **36** files
+- `modules/api/src/main/scala/de/nowchess/api/board/Board.scala` — imported by **22** files
+- `modules/api/src/main/scala/de/nowchess/api/board/PieceType.scala` — imported by **21** files
+- `modules/api/src/main/scala/de/nowchess/api/board/Piece.scala` — imported by **21** files
+- `modules/rule/src/main/scala/de/nowchess/rules/sets/DefaultRules.scala` — imported by **17** files
+- `modules/rule/src/main/scala/de/nowchess/rules/RuleSet.scala` — imported by **10** files
+- `modules/io/src/main/scala/de/nowchess/io/fen/FenParser.scala` — imported by **10** files
+- `modules/api/src/main/scala/de/nowchess/api/board/CastlingRights.scala` — imported by **8** files
+- `modules/io/src/main/scala/de/nowchess/io/GameContextImport.scala` — imported by **8** files
+- `modules/bot/src/main/scala/de/nowchess/bot/util/PolyglotBook.scala` — imported by **5** files
+- `modules/bot/src/main/scala/de/nowchess/bot/BotDifficulty.scala` — imported by **5** files
+- `modules/io/src/main/scala/de/nowchess/io/GameContextExport.scala` — imported by **5** files
+- `modules/bot/src/main/scala/de/nowchess/bot/bots/ClassicalBot.scala` — imported by **4** files
+- `modules/bot/src/main/scala/de/nowchess/bot/bots/classic/EvaluationClassic.scala` — imported by **4** files
+- `modules/bot/src/main/scala/de/nowchess/bot/logic/AlphaBetaSearch.scala` — imported by **4** files
+- `modules/bot/src/main/scala/de/nowchess/bot/Bot.scala` — imported by **4** files
 - `modules/core/src/main/scala/de/nowchess/chess/observer/Observer.scala` — imported by **4** files
- `modules/core/src/main/scala/de/nowchess/chess/engine/GameEngine.scala` — imported by **4** files
- `modules/io/src/main/scala/de/nowchess/io/pgn/PgnParser.scala` — imported by **2** files
- `modules/io/src/main/scala/de/nowchess/io/pgn/PgnExporter.scala` — imported by **2** files
- `modules/io/src/main/scala/de/nowchess/io/fen/FenExporter.scala` — imported by **2** files
- `modules/io/src/main/scala/de/nowchess/io/fen/FenParserSupport.scala` — imported by **2** files
- `modules/core/src/main/scala/de/nowchess/chess/controller/Parser.scala` — imported by **1** files

 ## Import Map (who imports what)

- `modules/api/src/main/scala/de/nowchess/api/game/GameContext.scala` ← `modules/core/src/main/scala/de/nowchess/chess/command/Command.scala`, `modules/core/src/main/scala/de/nowchess/chess/engine/GameEngine.scala`, `modules/core/src/main/scala/de/nowchess/chess/observer/Observer.scala`, `modules/core/src/test/scala/de/nowchess/chess/command/CommandInvokerBranchTest.scala`, `modules/core/src/test/scala/de/nowchess/chess/command/CommandInvokerTest.scala` +23 more
- `modules/api/src/main/scala/de/nowchess/api/board/Square.scala` ← `modules/api/src/main/scala/de/nowchess/api/game/GameContext.scala`, `modules/api/src/main/scala/de/nowchess/api/move/Move.scala`, `modules/api/src/test/scala/de/nowchess/api/game/GameContextTest.scala`, `modules/api/src/test/scala/de/nowchess/api/move/MoveTest.scala`, `modules/core/src/main/scala/de/nowchess/chess/command/Command.scala` +16 more
- `modules/api/src/main/scala/de/nowchess/api/board/Color.scala` ← `modules/api/src/main/scala/de/nowchess/api/game/GameContext.scala`, `modules/api/src/test/scala/de/nowchess/api/game/GameContextTest.scala`, `modules/core/src/main/scala/de/nowchess/chess/engine/GameEngine.scala`, `modules/core/src/main/scala/de/nowchess/chess/observer/Observer.scala`, `modules/core/src/test/scala/de/nowchess/chess/engine/EngineTestHelpers.scala` +14 more
- `modules/api/src/main/scala/de/nowchess/api/move/Move.scala` ← `modules/api/src/main/scala/de/nowchess/api/game/GameContext.scala`, `modules/api/src/test/scala/de/nowchess/api/board/BoardTest.scala`, `modules/api/src/test/scala/de/nowchess/api/game/GameContextTest.scala`, `modules/core/src/main/scala/de/nowchess/chess/engine/GameEngine.scala`, `modules/core/src/test/scala/de/nowchess/chess/engine/GameEngineIntegrationTest.scala` +9 more
- `modules/api/src/main/scala/de/nowchess/api/board/Board.scala` ← `modules/api/src/main/scala/de/nowchess/api/game/GameContext.scala`, `modules/api/src/test/scala/de/nowchess/api/game/GameContextTest.scala`, `modules/core/src/main/scala/de/nowchess/chess/engine/GameEngine.scala`, `modules/core/src/test/scala/de/nowchess/chess/engine/EngineTestHelpers.scala`, `modules/core/src/test/scala/de/nowchess/chess/engine/GameEngineGameEndingTest.scala` +8 more
- `modules/api/src/main/scala/de/nowchess/api/board/Piece.scala` ← `modules/core/src/main/scala/de/nowchess/chess/command/Command.scala`, `modules/core/src/main/scala/de/nowchess/chess/engine/GameEngine.scala`, `modules/core/src/test/scala/de/nowchess/chess/engine/GameEnginePromotionTest.scala`, `modules/core/src/test/scala/de/nowchess/chess/engine/GameEngineScenarioTest.scala`, `modules/rule/src/test/scala/de/nowchess/rule/DefaultRulesStateTransitionsTest.scala` +5 more
- `modules/api/src/main/scala/de/nowchess/api/board/PieceType.scala` ← `modules/core/src/main/scala/de/nowchess/chess/engine/GameEngine.scala`, `modules/core/src/test/scala/de/nowchess/chess/engine/GameEngineIntegrationTest.scala`, `modules/core/src/test/scala/de/nowchess/chess/engine/GameEnginePromotionTest.scala`, `modules/rule/src/test/scala/de/nowchess/rule/DefaultRulesStateTransitionsTest.scala`, `modules/rule/src/test/scala/de/nowchess/rule/DefaultRulesTest.scala` +4 more
- `modules/io/src/main/scala/de/nowchess/io/fen/FenParser.scala` ← `modules/core/src/test/scala/de/nowchess/chess/engine/EngineTestHelpers.scala`, `modules/core/src/test/scala/de/nowchess/chess/engine/GameEngineLoadGameTest.scala`, `modules/core/src/test/scala/de/nowchess/chess/engine/GameEngineNotationTest.scala`, `modules/core/src/test/scala/de/nowchess/chess/engine/GameEnginePromotionTest.scala`, `modules/core/src/test/scala/de/nowchess/chess/engine/GameEngineScenarioTest.scala` +4 more
- `modules/rule/src/main/scala/de/nowchess/rules/sets/DefaultRules.scala` ← `modules/core/src/main/scala/de/nowchess/chess/engine/GameEngine.scala`, `modules/core/src/test/scala/de/nowchess/chess/engine/EngineTestHelpers.scala`, `modules/core/src/test/scala/de/nowchess/chess/engine/GameEngineIntegrationTest.scala`, `modules/core/src/test/scala/de/nowchess/chess/engine/GameEnginePromotionTest.scala`, `modules/io/src/main/scala/de/nowchess/io/pgn/PgnExporter.scala` +3 more
- `modules/io/src/main/scala/de/nowchess/io/GameContextImport.scala` ← `modules/core/src/main/scala/de/nowchess/chess/engine/GameEngine.scala`, `modules/core/src/test/scala/de/nowchess/chess/engine/GameEngineIntegrationTest.scala`, `modules/io/src/main/scala/de/nowchess/io/fen/FenParser.scala`, `modules/io/src/main/scala/de/nowchess/io/fen/FenParserCombinators.scala`, `modules/io/src/main/scala/de/nowchess/io/fen/FenParserFastParse.scala` +2 more
+- `modules/api/src/main/scala/de/nowchess/api/game/GameContext.scala` ← `modules/bot/src/main/scala/de/nowchess/bot/Bot.scala`, `modules/bot/src/main/scala/de/nowchess/bot/BotMoveRepetition.scala`, `modules/bot/src/main/scala/de/nowchess/bot/ai/Evaluation.scala`, `modules/bot/src/main/scala/de/nowchess/bot/bots/ClassicalBot.scala`, `modules/bot/src/main/scala/de/nowchess/bot/bots/HybridBot.scala` +55 more
+- `modules/api/src/main/scala/de/nowchess/api/move/Move.scala` ← `modules/api/src/main/scala/de/nowchess/api/game/GameContext.scala`, `modules/api/src/test/scala/de/nowchess/api/board/BoardTest.scala`, `modules/api/src/test/scala/de/nowchess/api/game/GameContextTest.scala`, `modules/bot/src/main/scala/de/nowchess/bot/Bot.scala`, `modules/bot/src/main/scala/de/nowchess/bot/BotMoveRepetition.scala` +35 more
+- `modules/api/src/main/scala/de/nowchess/api/board/Square.scala` ← `modules/api/src/main/scala/de/nowchess/api/game/GameContext.scala`, `modules/api/src/main/scala/de/nowchess/api/move/Move.scala`, `modules/api/src/test/scala/de/nowchess/api/game/GameContextTest.scala`, `modules/api/src/test/scala/de/nowchess/api/move/MoveTest.scala`, `modules/bot/src/main/scala/de/nowchess/bot/bots/classic/EvaluationClassic.scala` +34 more
+- `modules/api/src/main/scala/de/nowchess/api/board/Color.scala` ← `modules/api/src/main/scala/de/nowchess/api/game/GameContext.scala`, `modules/api/src/test/scala/de/nowchess/api/game/GameContextTest.scala`, `modules/bot/src/main/scala/de/nowchess/bot/bots/classic/EvaluationClassic.scala`, `modules/bot/src/main/scala/de/nowchess/bot/bots/nnue/NNUE.scala`, `modules/bot/src/main/scala/de/nowchess/bot/logic/MoveOrdering.scala` +31 more
+- `modules/api/src/main/scala/de/nowchess/api/board/Board.scala` ← `modules/api/src/main/scala/de/nowchess/api/game/GameContext.scala`, `modules/api/src/test/scala/de/nowchess/api/game/GameContextTest.scala`, `modules/bot/src/main/scala/de/nowchess/bot/bots/nnue/NNUE.scala`, `modules/bot/src/main/scala/de/nowchess/bot/logic/MoveOrdering.scala`, `modules/bot/src/test/scala/de/nowchess/bot/AlphaBetaSearchTest.scala` +17 more
+- `modules/api/src/main/scala/de/nowchess/api/board/PieceType.scala` ← `modules/bot/src/main/scala/de/nowchess/bot/bots/classic/EvaluationClassic.scala`, `modules/bot/src/main/scala/de/nowchess/bot/bots/nnue/NNUE.scala`, `modules/bot/src/main/scala/de/nowchess/bot/logic/AlphaBetaSearch.scala`, `modules/bot/src/main/scala/de/nowchess/bot/logic/MoveOrdering.scala`, `modules/bot/src/main/scala/de/nowchess/bot/util/PolyglotHash.scala` +16 more
+- `modules/api/src/main/scala/de/nowchess/api/board/Piece.scala` ← `modules/bot/src/main/scala/de/nowchess/bot/bots/nnue/NNUE.scala`, `modules/bot/src/main/scala/de/nowchess/bot/logic/MoveOrdering.scala`, `modules/bot/src/main/scala/de/nowchess/bot/util/PolyglotHash.scala`, `modules/bot/src/main/scala/de/nowchess/bot/util/ZobristHash.scala`, `modules/bot/src/test/scala/de/nowchess/bot/AlphaBetaSearchTest.scala` +16 more
+- `modules/rule/src/main/scala/de/nowchess/rules/sets/DefaultRules.scala` ← `modules/bot/src/main/scala/de/nowchess/bot/bots/ClassicalBot.scala`, `modules/bot/src/main/scala/de/nowchess/bot/bots/HybridBot.scala`, `modules/bot/src/main/scala/de/nowchess/bot/bots/NNUEBot.scala`, `modules/bot/src/main/scala/de/nowchess/bot/logic/AlphaBetaSearch.scala`, `modules/bot/src/test/scala/de/nowchess/bot/AlphaBetaSearchTest.scala` +12 more
+- `modules/rule/src/main/scala/de/nowchess/rules/RuleSet.scala` ← `modules/bot/src/main/scala/de/nowchess/bot/bots/ClassicalBot.scala`, `modules/bot/src/main/scala/de/nowchess/bot/bots/HybridBot.scala`, `modules/bot/src/main/scala/de/nowchess/bot/bots/NNUEBot.scala`, `modules/bot/src/main/scala/de/nowchess/bot/logic/AlphaBetaSearch.scala`, `modules/bot/src/test/scala/de/nowchess/bot/AlphaBetaSearchTest.scala` +5 more
+- `modules/io/src/main/scala/de/nowchess/io/fen/FenParser.scala` ← `modules/bot/src/test/scala/de/nowchess/bot/PolyglotHashTest.scala`, `modules/core/src/test/scala/de/nowchess/chess/engine/EngineTestHelpers.scala`, `modules/core/src/test/scala/de/nowchess/chess/engine/GameEngineLoadGameTest.scala`, `modules/core/src/test/scala/de/nowchess/chess/engine/GameEngineNotationTest.scala`, `modules/core/src/test/scala/de/nowchess/chess/engine/GameEnginePromotionTest.scala` +5 more

 ---

@@ -0,0 +1,5 @@
+# Config
+
+## Environment Variables
+
+- `STOCKFISH_PATH` **required** — modules/bot/python/nnue.py
@@ -2,36 +2,36 @@

 ## Most Imported Files (change these carefully)

- `modules/api/src/main/scala/de/nowchess/api/game/GameContext.scala` — imported by **28** files
- `modules/api/src/main/scala/de/nowchess/api/board/Square.scala` — imported by **21** files
- `modules/api/src/main/scala/de/nowchess/api/board/Color.scala` — imported by **19** files
- `modules/api/src/main/scala/de/nowchess/api/move/Move.scala` — imported by **14** files
- `modules/api/src/main/scala/de/nowchess/api/board/Board.scala` — imported by **13** files
- `modules/api/src/main/scala/de/nowchess/api/board/Piece.scala` — imported by **10** files
- `modules/api/src/main/scala/de/nowchess/api/board/PieceType.scala` — imported by **9** files
- `modules/io/src/main/scala/de/nowchess/io/fen/FenParser.scala` — imported by **9** files
- `modules/rule/src/main/scala/de/nowchess/rules/sets/DefaultRules.scala` — imported by **8** files
- `modules/io/src/main/scala/de/nowchess/io/GameContextImport.scala` — imported by **7** files
- `modules/api/src/main/scala/de/nowchess/api/board/CastlingRights.scala` — imported by **4** files
- `modules/io/src/main/scala/de/nowchess/io/GameContextExport.scala` — imported by **4** files
- `modules/rule/src/main/scala/de/nowchess/rules/RuleSet.scala` — imported by **4** files
+- `modules/api/src/main/scala/de/nowchess/api/game/GameContext.scala` — imported by **60** files
+- `modules/api/src/main/scala/de/nowchess/api/move/Move.scala` — imported by **40** files
+- `modules/api/src/main/scala/de/nowchess/api/board/Square.scala` — imported by **39** files
+- `modules/api/src/main/scala/de/nowchess/api/board/Color.scala` — imported by **36** files
+- `modules/api/src/main/scala/de/nowchess/api/board/Board.scala` — imported by **22** files
+- `modules/api/src/main/scala/de/nowchess/api/board/PieceType.scala` — imported by **21** files
+- `modules/api/src/main/scala/de/nowchess/api/board/Piece.scala` — imported by **21** files
+- `modules/rule/src/main/scala/de/nowchess/rules/sets/DefaultRules.scala` — imported by **17** files
+- `modules/rule/src/main/scala/de/nowchess/rules/RuleSet.scala` — imported by **10** files
+- `modules/io/src/main/scala/de/nowchess/io/fen/FenParser.scala` — imported by **10** files
+- `modules/api/src/main/scala/de/nowchess/api/board/CastlingRights.scala` — imported by **8** files
+- `modules/io/src/main/scala/de/nowchess/io/GameContextImport.scala` — imported by **8** files
+- `modules/bot/src/main/scala/de/nowchess/bot/util/PolyglotBook.scala` — imported by **5** files
+- `modules/bot/src/main/scala/de/nowchess/bot/BotDifficulty.scala` — imported by **5** files
+- `modules/io/src/main/scala/de/nowchess/io/GameContextExport.scala` — imported by **5** files
+- `modules/bot/src/main/scala/de/nowchess/bot/bots/ClassicalBot.scala` — imported by **4** files
+- `modules/bot/src/main/scala/de/nowchess/bot/bots/classic/EvaluationClassic.scala` — imported by **4** files
+- `modules/bot/src/main/scala/de/nowchess/bot/logic/AlphaBetaSearch.scala` — imported by **4** files
+- `modules/bot/src/main/scala/de/nowchess/bot/Bot.scala` — imported by **4** files
 - `modules/core/src/main/scala/de/nowchess/chess/observer/Observer.scala` — imported by **4** files
- `modules/core/src/main/scala/de/nowchess/chess/engine/GameEngine.scala` — imported by **4** files
- `modules/io/src/main/scala/de/nowchess/io/pgn/PgnParser.scala` — imported by **2** files
- `modules/io/src/main/scala/de/nowchess/io/pgn/PgnExporter.scala` — imported by **2** files
- `modules/io/src/main/scala/de/nowchess/io/fen/FenExporter.scala` — imported by **2** files
- `modules/io/src/main/scala/de/nowchess/io/fen/FenParserSupport.scala` — imported by **2** files
- `modules/core/src/main/scala/de/nowchess/chess/controller/Parser.scala` — imported by **1** files

 ## Import Map (who imports what)

- `modules/api/src/main/scala/de/nowchess/api/game/GameContext.scala` ← `modules/core/src/main/scala/de/nowchess/chess/command/Command.scala`, `modules/core/src/main/scala/de/nowchess/chess/engine/GameEngine.scala`, `modules/core/src/main/scala/de/nowchess/chess/observer/Observer.scala`, `modules/core/src/test/scala/de/nowchess/chess/command/CommandInvokerBranchTest.scala`, `modules/core/src/test/scala/de/nowchess/chess/command/CommandInvokerTest.scala` +23 more
- `modules/api/src/main/scala/de/nowchess/api/board/Square.scala` ← `modules/api/src/main/scala/de/nowchess/api/game/GameContext.scala`, `modules/api/src/main/scala/de/nowchess/api/move/Move.scala`, `modules/api/src/test/scala/de/nowchess/api/game/GameContextTest.scala`, `modules/api/src/test/scala/de/nowchess/api/move/MoveTest.scala`, `modules/core/src/main/scala/de/nowchess/chess/command/Command.scala` +16 more
- `modules/api/src/main/scala/de/nowchess/api/board/Color.scala` ← `modules/api/src/main/scala/de/nowchess/api/game/GameContext.scala`, `modules/api/src/test/scala/de/nowchess/api/game/GameContextTest.scala`, `modules/core/src/main/scala/de/nowchess/chess/engine/GameEngine.scala`, `modules/core/src/main/scala/de/nowchess/chess/observer/Observer.scala`, `modules/core/src/test/scala/de/nowchess/chess/engine/EngineTestHelpers.scala` +14 more
- `modules/api/src/main/scala/de/nowchess/api/move/Move.scala` ← `modules/api/src/main/scala/de/nowchess/api/game/GameContext.scala`, `modules/api/src/test/scala/de/nowchess/api/board/BoardTest.scala`, `modules/api/src/test/scala/de/nowchess/api/game/GameContextTest.scala`, `modules/core/src/main/scala/de/nowchess/chess/engine/GameEngine.scala`, `modules/core/src/test/scala/de/nowchess/chess/engine/GameEngineIntegrationTest.scala` +9 more
- `modules/api/src/main/scala/de/nowchess/api/board/Board.scala` ← `modules/api/src/main/scala/de/nowchess/api/game/GameContext.scala`, `modules/api/src/test/scala/de/nowchess/api/game/GameContextTest.scala`, `modules/core/src/main/scala/de/nowchess/chess/engine/GameEngine.scala`, `modules/core/src/test/scala/de/nowchess/chess/engine/EngineTestHelpers.scala`, `modules/core/src/test/scala/de/nowchess/chess/engine/GameEngineGameEndingTest.scala` +8 more
- `modules/api/src/main/scala/de/nowchess/api/board/Piece.scala` ← `modules/core/src/main/scala/de/nowchess/chess/command/Command.scala`, `modules/core/src/main/scala/de/nowchess/chess/engine/GameEngine.scala`, `modules/core/src/test/scala/de/nowchess/chess/engine/GameEnginePromotionTest.scala`, `modules/core/src/test/scala/de/nowchess/chess/engine/GameEngineScenarioTest.scala`, `modules/rule/src/test/scala/de/nowchess/rule/DefaultRulesStateTransitionsTest.scala` +5 more
- `modules/api/src/main/scala/de/nowchess/api/board/PieceType.scala` ← `modules/core/src/main/scala/de/nowchess/chess/engine/GameEngine.scala`, `modules/core/src/test/scala/de/nowchess/chess/engine/GameEngineIntegrationTest.scala`, `modules/core/src/test/scala/de/nowchess/chess/engine/GameEnginePromotionTest.scala`, `modules/rule/src/test/scala/de/nowchess/rule/DefaultRulesStateTransitionsTest.scala`, `modules/rule/src/test/scala/de/nowchess/rule/DefaultRulesTest.scala` +4 more
- `modules/io/src/main/scala/de/nowchess/io/fen/FenParser.scala` ← `modules/core/src/test/scala/de/nowchess/chess/engine/EngineTestHelpers.scala`, `modules/core/src/test/scala/de/nowchess/chess/engine/GameEngineLoadGameTest.scala`, `modules/core/src/test/scala/de/nowchess/chess/engine/GameEngineNotationTest.scala`, `modules/core/src/test/scala/de/nowchess/chess/engine/GameEnginePromotionTest.scala`, `modules/core/src/test/scala/de/nowchess/chess/engine/GameEngineScenarioTest.scala` +4 more
- `modules/rule/src/main/scala/de/nowchess/rules/sets/DefaultRules.scala` ← `modules/core/src/main/scala/de/nowchess/chess/engine/GameEngine.scala`, `modules/core/src/test/scala/de/nowchess/chess/engine/EngineTestHelpers.scala`, `modules/core/src/test/scala/de/nowchess/chess/engine/GameEngineIntegrationTest.scala`, `modules/core/src/test/scala/de/nowchess/chess/engine/GameEnginePromotionTest.scala`, `modules/io/src/main/scala/de/nowchess/io/pgn/PgnExporter.scala` +3 more
- `modules/io/src/main/scala/de/nowchess/io/GameContextImport.scala` ← `modules/core/src/main/scala/de/nowchess/chess/engine/GameEngine.scala`, `modules/core/src/test/scala/de/nowchess/chess/engine/GameEngineIntegrationTest.scala`, `modules/io/src/main/scala/de/nowchess/io/fen/FenParser.scala`, `modules/io/src/main/scala/de/nowchess/io/fen/FenParserCombinators.scala`, `modules/io/src/main/scala/de/nowchess/io/fen/FenParserFastParse.scala` +2 more
+- `modules/api/src/main/scala/de/nowchess/api/game/GameContext.scala` ← `modules/bot/src/main/scala/de/nowchess/bot/Bot.scala`, `modules/bot/src/main/scala/de/nowchess/bot/BotMoveRepetition.scala`, `modules/bot/src/main/scala/de/nowchess/bot/ai/Evaluation.scala`, `modules/bot/src/main/scala/de/nowchess/bot/bots/ClassicalBot.scala`, `modules/bot/src/main/scala/de/nowchess/bot/bots/HybridBot.scala` +55 more
+- `modules/api/src/main/scala/de/nowchess/api/move/Move.scala` ← `modules/api/src/main/scala/de/nowchess/api/game/GameContext.scala`, `modules/api/src/test/scala/de/nowchess/api/board/BoardTest.scala`, `modules/api/src/test/scala/de/nowchess/api/game/GameContextTest.scala`, `modules/bot/src/main/scala/de/nowchess/bot/Bot.scala`, `modules/bot/src/main/scala/de/nowchess/bot/BotMoveRepetition.scala` +35 more
+- `modules/api/src/main/scala/de/nowchess/api/board/Square.scala` ← `modules/api/src/main/scala/de/nowchess/api/game/GameContext.scala`, `modules/api/src/main/scala/de/nowchess/api/move/Move.scala`, `modules/api/src/test/scala/de/nowchess/api/game/GameContextTest.scala`, `modules/api/src/test/scala/de/nowchess/api/move/MoveTest.scala`, `modules/bot/src/main/scala/de/nowchess/bot/bots/classic/EvaluationClassic.scala` +34 more
+- `modules/api/src/main/scala/de/nowchess/api/board/Color.scala` ← `modules/api/src/main/scala/de/nowchess/api/game/GameContext.scala`, `modules/api/src/test/scala/de/nowchess/api/game/GameContextTest.scala`, `modules/bot/src/main/scala/de/nowchess/bot/bots/classic/EvaluationClassic.scala`, `modules/bot/src/main/scala/de/nowchess/bot/bots/nnue/NNUE.scala`, `modules/bot/src/main/scala/de/nowchess/bot/logic/MoveOrdering.scala` +31 more
+- `modules/api/src/main/scala/de/nowchess/api/board/Board.scala` ← `modules/api/src/main/scala/de/nowchess/api/game/GameContext.scala`, `modules/api/src/test/scala/de/nowchess/api/game/GameContextTest.scala`, `modules/bot/src/main/scala/de/nowchess/bot/bots/nnue/NNUE.scala`, `modules/bot/src/main/scala/de/nowchess/bot/logic/MoveOrdering.scala`, `modules/bot/src/test/scala/de/nowchess/bot/AlphaBetaSearchTest.scala` +17 more
+- `modules/api/src/main/scala/de/nowchess/api/board/PieceType.scala` ← `modules/bot/src/main/scala/de/nowchess/bot/bots/classic/EvaluationClassic.scala`, `modules/bot/src/main/scala/de/nowchess/bot/bots/nnue/NNUE.scala`, `modules/bot/src/main/scala/de/nowchess/bot/logic/AlphaBetaSearch.scala`, `modules/bot/src/main/scala/de/nowchess/bot/logic/MoveOrdering.scala`, `modules/bot/src/main/scala/de/nowchess/bot/util/PolyglotHash.scala` +16 more
+- `modules/api/src/main/scala/de/nowchess/api/board/Piece.scala` ← `modules/bot/src/main/scala/de/nowchess/bot/bots/nnue/NNUE.scala`, `modules/bot/src/main/scala/de/nowchess/bot/logic/MoveOrdering.scala`, `modules/bot/src/main/scala/de/nowchess/bot/util/PolyglotHash.scala`, `modules/bot/src/main/scala/de/nowchess/bot/util/ZobristHash.scala`, `modules/bot/src/test/scala/de/nowchess/bot/AlphaBetaSearchTest.scala` +16 more
+- `modules/rule/src/main/scala/de/nowchess/rules/sets/DefaultRules.scala` ← `modules/bot/src/main/scala/de/nowchess/bot/bots/ClassicalBot.scala`, `modules/bot/src/main/scala/de/nowchess/bot/bots/HybridBot.scala`, `modules/bot/src/main/scala/de/nowchess/bot/bots/NNUEBot.scala`, `modules/bot/src/main/scala/de/nowchess/bot/logic/AlphaBetaSearch.scala`, `modules/bot/src/test/scala/de/nowchess/bot/AlphaBetaSearchTest.scala` +12 more
+- `modules/rule/src/main/scala/de/nowchess/rules/RuleSet.scala` ← `modules/bot/src/main/scala/de/nowchess/bot/bots/ClassicalBot.scala`, `modules/bot/src/main/scala/de/nowchess/bot/bots/HybridBot.scala`, `modules/bot/src/main/scala/de/nowchess/bot/bots/NNUEBot.scala`, `modules/bot/src/main/scala/de/nowchess/bot/logic/AlphaBetaSearch.scala`, `modules/bot/src/test/scala/de/nowchess/bot/AlphaBetaSearchTest.scala` +5 more
+- `modules/io/src/main/scala/de/nowchess/io/fen/FenParser.scala` ← `modules/bot/src/test/scala/de/nowchess/bot/PolyglotHashTest.scala`, `modules/core/src/test/scala/de/nowchess/chess/engine/EngineTestHelpers.scala`, `modules/core/src/test/scala/de/nowchess/chess/engine/GameEngineLoadGameTest.scala`, `modules/core/src/test/scala/de/nowchess/chess/engine/GameEngineNotationTest.scala`, `modules/core/src/test/scala/de/nowchess/chess/engine/GameEnginePromotionTest.scala` +5 more
@@ -51,6 +51,113 @@
  - class ApiResponse
  - function error
  - function totalPages
+- `modules/bot/python/nnue.py`
+  - function get_weights_dir: ()
+  - function get_data_dir: ()
+  - function list_checkpoints: ()
+  - function migrate_legacy_data: ()
+  - function show_header: ()
+  - function show_checkpoints_table: ()
+  - _...10 more_
+- `modules/bot/python/src/dataset.py`
+  - function get_datasets_dir: () -> Path
+  - function next_dataset_version: () -> int
+  - function list_datasets: () -> List[Tuple[int, Dict]]
+  - function load_dataset_metadata: (version) -> Optional[Dict]
+  - function save_dataset_metadata: (version, metadata) -> None
+  - function create_dataset: (version, labeled_jsonl_path, sources, stockfish_depth) -> Path
+  - _...4 more_
+- `modules/bot/python/src/export.py` — function export_to_nbai: (weights_file, output_file, trained_by, train_loss)
+- `modules/bot/python/src/generate.py` — function play_random_game_and_collect_positions: (output_file, total_positions, samples_per_game, min_move, max_move, num_workers)
+- `modules/bot/python/src/label.py` — function normalize_evaluation: (cp_value, method, scale), function label_positions_with_stockfish: (positions_file, output_file, stockfish_path, batch_size, depth, verbose, normalize, num_workers)
+- `modules/bot/python/src/tactical_positions_extractor.py`
+  - function download_and_extract_puzzle_db: (url, output_dir)
+  - function extract_puzzle_positions: (puzzle_csv, max_puzzles) -> Set[str]
+  - function load_positions_from_file: (file_path) -> Set[str]
+  - function merge_positions: (tactical, other, output_file)
+  - function extract_tactical_only: (puzzle_csv, output_file, max_puzzles) -> int
+  - function interactive_merge_positions: (puzzle_csv, output_file, max_puzzles)
+- `modules/bot/python/src/train.py`
+  - function fen_to_features: (fen)
+  - function find_next_version: (base_name)
+  - function save_metadata: (weights_file, metadata)
+  - function train_nnue: (data_file, output_file, epochs, batch_size, lr, checkpoint, stockfish_depth, use_versioning, early_stopping_patience, weight_decay, subsample_ratio)
+  - function burst_train: (data_file, output_file, duration_minutes, epochs_per_season, early_stopping_patience, batch_size, lr, initial_checkpoint, stockfish_depth, use_versioning, weight_decay, subsample_ratio)
+  - class NNUEDataset
+  - _...1 more_
+- `modules/bot/src/main/scala/de/nowchess/bot/Bot.scala`
+  - class Bot
+  - function name
+  - function nextMove
+- `modules/bot/src/main/scala/de/nowchess/bot/BotController.scala`
+  - class BotController
+  - function getBot
+  - function listBots
+- `modules/bot/src/main/scala/de/nowchess/bot/BotMoveRepetition.scala`
+  - class BotMoveRepetition
+  - function blockedMoves
+  - function repeatedMove
+  - function filterAllowed
+- `modules/bot/src/main/scala/de/nowchess/bot/Config.scala` — class Config
+- `modules/bot/src/main/scala/de/nowchess/bot/ai/Evaluation.scala`
+  - class Evaluation
+  - class CHECKMATE_SCORE
+  - class DRAW_SCORE
+  - function evaluate
+  - function initAccumulator
+  - function copyAccumulator
+  - _...2 more_
+- `modules/bot/src/main/scala/de/nowchess/bot/bots/classic/EvaluationClassic.scala`
+  - class EvaluationClassic
+  - function evaluate
+  - function countRay
+- `modules/bot/src/main/scala/de/nowchess/bot/bots/nnue/EvaluationNNUE.scala` — class EvaluationNNUE, function evaluate
+- `modules/bot/src/main/scala/de/nowchess/bot/bots/nnue/NNUE.scala`
+  - class NNUE
+  - function initAccumulator
+  - function pushAccumulator
+  - function copyAccumulator
+  - function recomputeAccumulator
+  - function validateAccumulator
+  - _...4 more_
+- `modules/bot/src/main/scala/de/nowchess/bot/bots/nnue/NbaiLoader.scala`
+  - class NbaiLoader
+  - function load
+  - function loadDefault
+- `modules/bot/src/main/scala/de/nowchess/bot/bots/nnue/NbaiMigrator.scala` — class NbaiMigrator, function migrateFromBin
+- `modules/bot/src/main/scala/de/nowchess/bot/bots/nnue/NbaiModel.scala`
+  - function toJson
+  - class NbaiMetadata
+  - function fromJson
+  - function str
+  - function num
+- `modules/bot/src/main/scala/de/nowchess/bot/bots/nnue/NbaiWriter.scala` — class NbaiWriter, function write
+- `modules/bot/src/main/scala/de/nowchess/bot/logic/AlphaBetaSearch.scala`
+  - function bestMove
+  - function bestMove
+  - function bestMoveWithTime
+  - function bestMoveWithTime
+  - function loop
+  - function loop
+  - _...2 more_
+- `modules/bot/src/main/scala/de/nowchess/bot/logic/MoveOrdering.scala`
+  - class MoveOrdering
+  - class OrderingContext
+  - function addKillerMove
+  - function getKillerMoves
+  - function addHistory
+  - function getHistory
+  - _...3 more_
+- `modules/bot/src/main/scala/de/nowchess/bot/logic/TranspositionTable.scala`
+  - function probe
+  - function store
+  - function clear
+- `modules/bot/src/main/scala/de/nowchess/bot/util/PolyglotBook.scala` — function probe, function select
+- `modules/bot/src/main/scala/de/nowchess/bot/util/PolyglotHash.scala` — class PolyglotHash, function hash
+- `modules/bot/src/main/scala/de/nowchess/bot/util/ZobristHash.scala`
+  - class ZobristHash
+  - function hash
+  - function nextHash
 - `modules/core/src/main/scala/de/nowchess/chess/command/Command.scala`
  - class Command
  - function execute
@@ -73,7 +180,7 @@
  - function turn
  - function context
  - function canUndo
-  - _...10 more_
+  - _...11 more_
 - `modules/core/src/main/scala/de/nowchess/chess/observer/Observer.scala`
  - function context
  - class Observer
@@ -84,6 +191,13 @@
  - _...1 more_
 - `modules/io/src/main/scala/de/nowchess/io/GameContextExport.scala` — class GameContextExport, function exportGameContext
 - `modules/io/src/main/scala/de/nowchess/io/GameContextImport.scala` — class GameContextImport, function importGameContext
+- `modules/io/src/main/scala/de/nowchess/io/GameFileService.scala`
+  - class GameFileService
+  - function saveGameToFile
+  - function loadGameFromFile
+  - class FileSystemGameService
+  - function saveGameToFile
+  - function loadGameFromFile
 - `modules/io/src/main/scala/de/nowchess/io/fen/FenExporter.scala`
  - class FenExporter
  - function boardToFen
@@ -105,6 +219,8 @@
  - function parseBoard
  - function importGameContext
 - `modules/io/src/main/scala/de/nowchess/io/fen/FenParserSupport.scala` — function buildSquares
+- `modules/io/src/main/scala/de/nowchess/io/json/JsonExporter.scala` — class JsonExporter, function exportGameContext
+- `modules/io/src/main/scala/de/nowchess/io/json/JsonParser.scala` — class JsonParser, function importGameContext
 - `modules/io/src/main/scala/de/nowchess/io/pgn/PgnExporter.scala`
  - class PgnExporter
  - function exportGameContext
@@ -0,0 +1,4 @@
+# Middleware
+
+## custom
+- generate — `modules/bot/python/src/generate.py`
@@ -8,3 +8,5 @@
 /dataSources.local.xml
 # Editor-based HTTP Client requests
 /httpRequests/
+
+sonarlint.xml
@@ -0,0 +1,133 @@
+<component name="ProjectCodeStyleConfiguration">
+  <code_scheme name="Project" version="173">
+    <AndroidXmlCodeStyleSettings>
+      <option name="USE_CUSTOM_SETTINGS" value="true" />
+    </AndroidXmlCodeStyleSettings>
+    <JetCodeStyleSettings>
+      <option name="CODE_STYLE_DEFAULTS" value="KOTLIN_OFFICIAL" />
+    </JetCodeStyleSettings>
+    <ScalaCodeStyleSettings>
+      <option name="FORMATTER" value="1" />
+    </ScalaCodeStyleSettings>
+    <XML>
+      <option name="XML_KEEP_LINE_BREAKS" value="false" />
+      <option name="XML_ALIGN_ATTRIBUTES" value="false" />
+      <option name="XML_SPACE_INSIDE_EMPTY_TAG" value="true" />
+    </XML>
+    <codeStyleSettings language="XML">
+      <option name="FORCE_REARRANGE_MODE" value="1" />
+      <indentOptions>
+        <option name="CONTINUATION_INDENT_SIZE" value="4" />
+      </indentOptions>
+      <arrangement>
+        <rules>
+          <section>
+            <rule>
+              <match>
+                <AND>
+                  <NAME>xmlns:android</NAME>
+                  <XML_ATTRIBUTE />
+                  <XML_NAMESPACE>^$</XML_NAMESPACE>
+                </AND>
+              </match>
+            </rule>
+          </section>
+          <section>
+            <rule>
+              <match>
+                <AND>
+                  <NAME>xmlns:.*</NAME>
+                  <XML_ATTRIBUTE />
+                  <XML_NAMESPACE>^$</XML_NAMESPACE>
+                </AND>
+              </match>
+              <order>BY_NAME</order>
+            </rule>
+          </section>
+          <section>
+            <rule>
+              <match>
+                <AND>
+                  <NAME>.*:id</NAME>
+                  <XML_ATTRIBUTE />
+                  <XML_NAMESPACE>http://schemas.android.com/apk/res/android</XML_NAMESPACE>
+                </AND>
+              </match>
+            </rule>
+          </section>
+          <section>
+            <rule>
+              <match>
+                <AND>
+                  <NAME>.*:name</NAME>
+                  <XML_ATTRIBUTE />
+                  <XML_NAMESPACE>http://schemas.android.com/apk/res/android</XML_NAMESPACE>
+                </AND>
+              </match>
+            </rule>
+          </section>
+          <section>
+            <rule>
+              <match>
+                <AND>
+                  <NAME>name</NAME>
+                  <XML_ATTRIBUTE />
+                  <XML_NAMESPACE>^$</XML_NAMESPACE>
+                </AND>
+              </match>
+            </rule>
+          </section>
+          <section>
+            <rule>
+              <match>
+                <AND>
+                  <NAME>style</NAME>
+                  <XML_ATTRIBUTE />
+                  <XML_NAMESPACE>^$</XML_NAMESPACE>
+                </AND>
+              </match>
+            </rule>
+          </section>
+          <section>
+            <rule>
+              <match>
+                <AND>
+                  <NAME>.*</NAME>
+                  <XML_ATTRIBUTE />
+                  <XML_NAMESPACE>^$</XML_NAMESPACE>
+                </AND>
+              </match>
+              <order>BY_NAME</order>
+            </rule>
+          </section>
+          <section>
+            <rule>
+              <match>
+                <AND>
+                  <NAME>.*</NAME>
+                  <XML_ATTRIBUTE />
+                  <XML_NAMESPACE>http://schemas.android.com/apk/res/android</XML_NAMESPACE>
+                </AND>
+              </match>
+            </rule>
+          </section>
+          <section>
+            <rule>
+              <match>
+                <AND>
+                  <NAME>.*</NAME>
+                  <XML_ATTRIBUTE />
+                  <XML_NAMESPACE>.*</XML_NAMESPACE>
+                </AND>
+              </match>
+              <order>BY_NAME</order>
+            </rule>
+          </section>
+        </rules>
+      </arrangement>
+    </codeStyleSettings>
+    <codeStyleSettings language="kotlin">
+      <option name="CODE_STYLE_DEFAULTS" value="KOTLIN_OFFICIAL" />
+    </codeStyleSettings>
+  </code_scheme>
+</component>
@@ -1,5 +1,5 @@
 <component name="ProjectCodeStyleConfiguration">
  <state>
-    <option name="PREFERRED_PROJECT_CODE_STYLE" value="Default" />
+    <option name="USE_PER_PROJECT_SETTINGS" value="true" />
  </state>
 </component>
@@ -11,6 +11,7 @@
            <option value="$PROJECT_DIR$" />
            <option value="$PROJECT_DIR$/modules" />
            <option value="$PROJECT_DIR$/modules/api" />
+            <option value="$PROJECT_DIR$/modules/bot" />
            <option value="$PROJECT_DIR$/modules/core" />
            <option value="$PROJECT_DIR$/modules/io" />
            <option value="$PROJECT_DIR$/modules/rule" />
@@ -5,7 +5,7 @@
      <option name="deprecationWarnings" value="true" />
      <option name="uncheckedWarnings" value="true" />
    </profile>
-    <profile name="Gradle 2" modules="NowChessSystems.modules.core.main,NowChessSystems.modules.core.scoverage,NowChessSystems.modules.core.test,NowChessSystems.modules.io.main,NowChessSystems.modules.io.scoverage,NowChessSystems.modules.io.test,NowChessSystems.modules.rule.main,NowChessSystems.modules.rule.scoverage,NowChessSystems.modules.rule.test,NowChessSystems.modules.ui.main,NowChessSystems.modules.ui.scoverage,NowChessSystems.modules.ui.test">
+    <profile name="Gradle 2" modules="NowChessSystems.modules.bot.main,NowChessSystems.modules.bot.scoverage,NowChessSystems.modules.bot.test,NowChessSystems.modules.core.main,NowChessSystems.modules.core.scoverage,NowChessSystems.modules.core.test,NowChessSystems.modules.io.main,NowChessSystems.modules.io.scoverage,NowChessSystems.modules.io.test,NowChessSystems.modules.rule.main,NowChessSystems.modules.rule.scoverage,NowChessSystems.modules.rule.test,NowChessSystems.modules.ui.main,NowChessSystems.modules.ui.scoverage,NowChessSystems.modules.ui.test">
      <option name="deprecationWarnings" value="true" />
      <option name="uncheckedWarnings" value="true" />
      <parameters>
@@ -19,6 +19,7 @@ Try to stick to these commands for consistency.
 | `api`  | Model / shared types | (none) |
 | `core` | Primary business logic | api, rule |
 | `rule` | Game rules | api |
+| `bot`  | Bots and AI | api,rule,io |
 | `io`   | Export formats | api, core |
 | `ui`   | Entrypoint & UI | core, io |

@@ -47,6 +48,9 @@ Try to stick to these commands for consistency.
 - **Immutable state as primary model:** GameContext (api) holds board, history, player state — immutable, passed through the system. Each move creates a new GameContext, enabling undo/redo without side effects.
 - **Observer pattern for UI decoupling:** GameEngine publishes move/state events; CommandInvoker queues moves; UI listens to events, not polling. GameEngine never imports UI code.
 - **RuleSet trait encapsulates rules:** Move generation, check, castling, en passant all in RuleSet impl. GameEngine calls rules as a black box; rules don't know about the rest of core.
+- **Polyglot hash must follow spec index layout:** piece keys use interleaved mapping `(pieceType * 2 + colorBit)` with black=0/white=1, castling keys are `768..771`, en-passant file keys are `772..779` and are XORed only if side-to-move has a pawn that can capture en passant, side-to-move key is `780` for white.
+- **Alpha-beta uses sequential PV search by default:** parallel split was disabled because fixed-window futures removed pruning effectiveness; correctness and pruning quality take priority over speculative parallelism.
+- **Search hash is updated incrementally per move:** bot search now updates Zobrist keys from parent hash with move deltas instead of recomputing piece scans at every node.

 ## Rules

@@ -22,6 +22,26 @@ sonar {
        }.joinToString(",")

        property("sonar.scala.coverage.reportPaths", scoverageReports)
+        property(
+            "sonar.coverage.exclusions",
+            // UI renders JavaFX components; headless test environments cannot exercise rendering paths
+            "modules/ui/**," +
+            // FastParse macro-generated combinators produce synthetic branches that scoverage marks as uncovered
+            "modules/io/src/main/scala/de/nowchess/io/fen/FenParserFastParse*," +
+            // NNUE inference pipeline — coverage requires a trained model file not present in CI
+            "**/bot/**/NNUE.scala," +
+            "**/bot/**/NNUEBot.scala," +
+            "**/bot/**/EvaluationNNUE.scala," +
+            // NBAI binary format loader/writer — error paths require crafted corrupt files; migrator is a one-shot tool
+            "**/bot/**/NbaiLoader.scala," +
+            "**/bot/**/NbaiModel.scala," +
+            "**/bot/**/NbaiMigrator.scala," +
+            "**/bot/**/NbaiWriter.scala," +
+            // PolyglotBook — binary I/O and dead-code guards (bit-masked fields can never exceed valid range)
+            "**/bot/**/PolyglotBook.scala," +
+            "**/bot/**/MoveOrdering.scala," +
+            "**/bot/**/AlphaBetaSearch.scala"
+        )
    }
 }

@@ -35,6 +55,7 @@ val versions = mapOf(
    "SCALAFX"               to "21.0.0-R32",
    "JAVAFX"                to "21.0.1",
    "JUNIT_BOM"             to "5.13.4",
+    "ONNXRUNTIME"           to "1.19.2",
    "SCALA_PARSER_COMBINATORS" to "2.4.0",
    "FASTPARSE"             to "3.0.2",
    "JACKSON"               to "2.17.2",
@@ -1,5 +1,5 @@
 import glob,re
-mods=['api','core','io','rule','ui']
+mods=['api','core','io','rule','ui', 'bot']
 tot=0
 for m in mods:
 s=0
@@ -34,3 +34,11 @@
 * NCS-14 implemented insufficient moves rule ([#30](https://git.janis-eccarius.de/NowChess/NowChessSystems/issues/30)) ([b0399a4](https://git.janis-eccarius.de/NowChess/NowChessSystems/commit/b0399a4e489950083066c9538df9a84dcc7a4613))
 * NCS-21 Write Scripts to automate certain tasks ([#15](https://git.janis-eccarius.de/NowChess/NowChessSystems/issues/15)) ([8051871](https://git.janis-eccarius.de/NowChess/NowChessSystems/commit/80518719d536a087d339fe02530825dc07f8b388))
 * NCS-25 Add linters to keep quality up ([#27](https://git.janis-eccarius.de/NowChess/NowChessSystems/issues/27)) ([fd4e67d](https://git.janis-eccarius.de/NowChess/NowChessSystems/commit/fd4e67d4f782a7e955822d90cb909d0a81676fb2))
+##  (2026-04-16)
+
+### Features
+
+* NCS-13 Implement Threefold Repetition ([#31](https://git.janis-eccarius.de/NowChess/NowChessSystems/issues/31)) ([767d305](https://git.janis-eccarius.de/NowChess/NowChessSystems/commit/767d3051a76c266050b6335774d66e2db2273c16))
+* NCS-14 implemented insufficient moves rule ([#30](https://git.janis-eccarius.de/NowChess/NowChessSystems/issues/30)) ([b0399a4](https://git.janis-eccarius.de/NowChess/NowChessSystems/commit/b0399a4e489950083066c9538df9a84dcc7a4613))
+* NCS-21 Write Scripts to automate certain tasks ([#15](https://git.janis-eccarius.de/NowChess/NowChessSystems/issues/15)) ([8051871](https://git.janis-eccarius.de/NowChess/NowChessSystems/commit/80518719d536a087d339fe02530825dc07f8b388))
+* NCS-25 Add linters to keep quality up ([#27](https://git.janis-eccarius.de/NowChess/NowChessSystems/issues/27)) ([fd4e67d](https://git.janis-eccarius.de/NowChess/NowChessSystems/commit/fd4e67d4f782a7e955822d90cb909d0a81676fb2))
@@ -0,0 +1,11 @@
+package de.nowchess.api.bot
+
+import de.nowchess.api.game.GameContext
+import de.nowchess.api.move.Move
+
+trait Bot {
+
+  def name: String
+  def nextMove(context: GameContext): Option[Move]
+
+}
@@ -1,6 +1,6 @@
 package de.nowchess.api.game

-import de.nowchess.api.board.{Board, CastlingRights, Color, Square}
+import de.nowchess.api.board.{Board, CastlingRights, Color, PieceType, Square}
 import de.nowchess.api.move.Move

 /** Immutable bundle of complete game state. All state changes produce new GameContext instances.
@@ -15,6 +15,13 @@ case class GameContext(
    result: Option[GameResult] = None,
    initialBoard: Board = Board.initial,
 ):
+  private lazy val whiteKingSquare: Option[Square] =
+    board.pieces.find((_, p) => p.color == Color.White && p.pieceType == PieceType.King).map(_._1)
+  private lazy val blackKingSquare: Option[Square] =
+    board.pieces.find((_, p) => p.color == Color.Black && p.pieceType == PieceType.King).map(_._1)
+  def kingSquare(color: Color): Option[Square] =
+    if color == Color.White then whiteKingSquare else blackKingSquare
+
  /** Create new context with updated board. */
  def withBoard(newBoard: Board): GameContext = copy(board = newBoard)

@@ -0,0 +1,8 @@
+package de.nowchess.api.game
+
+import de.nowchess.api.bot.Bot
+import de.nowchess.api.player.PlayerInfo
+
+sealed trait Participant
+final case class Human(playerInfo: PlayerInfo) extends Participant
+final case class BotParticipant(bot: Bot)      extends Participant
@@ -71,3 +71,9 @@ class GameContextTest extends AnyFunSuite with Matchers:
  test("withResult clears result"):
    val ctx = GameContext.initial.withResult(Some(GameResult.Win(Color.Black)))
    ctx.withResult(None).result shouldBe None
+
+  test("kingSquare returns white king position"):
+    GameContext.initial.kingSquare(Color.White) shouldBe Some(Square(File.E, Rank.R1))
+
+  test("kingSquare returns black king position"):
+    GameContext.initial.kingSquare(Color.Black) shouldBe Some(Square(File.E, Rank.R8))
@@ -1,3 +1,3 @@
 MAJOR=0
-MINOR=5
+MINOR=6
 PATCH=0
@@ -0,0 +1,86 @@
+plugins {
+    id("scala")
+    id("org.scoverage")
+}
+
+group = "de.nowchess"
+version = "1.0-SNAPSHOT"
+
+@Suppress("UNCHECKED_CAST")
+val versions = rootProject.extra["VERSIONS"] as Map<String, String>
+
+repositories {
+    mavenCentral()
+}
+
+scala {
+    scalaVersion = versions["SCALA3"]!!
+}
+
+scoverage {
+    scoverageVersion.set(versions["SCOVERAGE"]!!)
+    excludedPackages.set(
+        listOf(
+            "de\\.nowchess\\.bot\\.bots\\.NNUEBot",
+            "de\\.nowchess\\.bot\\.bots\\.nnue\\..*",
+            "de\\.nowchess\\.bot\\.util\\.PolyglotBook",
+        )
+    )
+    excludedFiles.set(
+        listOf(
+            ".*NNUE\\.scala",
+            ".*NNUEBot\\.scala",
+            ".*NbaiLoader\\.scala",
+            ".*NbaiMigrator\\.scala",
+            ".*NbaiWriter\\.scala",
+            ".*PolyglotBook\\.scala",
+        )
+    )
+}
+
+tasks.withType<ScalaCompile> {
+    scalaCompileOptions.additionalParameters = listOf("-encoding", "UTF-8")
+}
+
+dependencies {
+
+    implementation("org.scala-lang:scala3-compiler_3") {
+        version {
+            strictly(versions["SCALA3"]!!)
+        }
+    }
+    implementation("org.scala-lang:scala3-library_3") {
+        version {
+            strictly(versions["SCALA3"]!!)
+        }
+    }
+
+    implementation(project(":modules:api"))
+    implementation(project(":modules:io"))
+    implementation(project(":modules:rule"))
+    implementation("com.microsoft.onnxruntime:onnxruntime:${versions["ONNXRUNTIME"]!!}")
+
+    testImplementation(platform("org.junit:junit-bom:${versions["JUNIT_BOM"]!!}"))
+    testImplementation("org.junit.jupiter:junit-jupiter")
+    testImplementation("org.scalatest:scalatest_3:${versions["SCALATEST"]!!}")
+    testImplementation("co.helmethair:scalatest-junit-runner:${versions["SCALATEST_JUNIT"]!!}")
+
+    testRuntimeOnly("org.junit.platform:junit-platform-launcher")
+}
+
+tasks.test {
+    useJUnitPlatform {
+        includeEngines("scalatest")
+        testLogging {
+            events("skipped", "failed")
+        }
+    }
+    finalizedBy(tasks.reportScoverage)
+}
+tasks.reportScoverage {
+    dependsOn(tasks.test)
+}
+
+tasks.jar {
+    duplicatesStrategy = DuplicatesStrategy.EXCLUDE
+}
@@ -0,0 +1,22 @@
+# Data and weights are local artifacts, not committed
+data/
+
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+env/
+venv/
+ENV/
+.venv
+
+# IDE
+.idea/
+.vscode/
+*.swp
+*.swo
+tactical_data/
+trainingdata/
+/datasets/
@@ -0,0 +1,173 @@
+# Training Dataset Management
+
+The NNUE training pipeline now features versioned dataset management, similar to model versioning. This prevents data loss and allows you to maintain multiple training configurations.
+
+## Directory Structure
+
+```
+datasets/
+  ds_v1/
+    labeled.jsonl       # Training data: {"fen": "...", "eval": 0.5, "eval_raw": 150}
+    metadata.json       # Version info and composition
+  ds_v2/
+    labeled.jsonl
+    metadata.json
+```
+
+## Metadata Schema
+
+Each dataset has a `metadata.json` file tracking its composition:
+
+```json
+{
+  "version": 1,
+  "created": "2026-04-13T15:30:45.123456",
+  "total_positions": 1000000,
+  "stockfish_depth": 12,
+  "sources": [
+    {
+      "type": "generated",
+      "count": 500000,
+      "params": {
+        "num_positions": 500000,
+        "min_move": 1,
+        "max_move": 50
+      }
+    },
+    {
+      "type": "tactical",
+      "count": 300000,
+      "max_puzzles": 300000
+    },
+    {
+      "type": "file_import",
+      "count": 200000,
+      "path": "/path/to/original_file.txt"
+    }
+  ]
+}
+```
+
+## TUI Workflow
+
+### Main Menu
+```
+1 - Manage Training Data
+2 - Train Model
+3 - Export Model
+4 - Exit
+```
+
+### Training Data Management Submenu
+```
+1 - Create new dataset
+2 - Extend existing dataset
+3 - View all datasets
+4 - Delete dataset
+5 - Back
+```
+
+## Creating a Dataset
+
+Use the "Create new dataset" option to add data from one or more sources:
+
+1. **Generate random positions** — Play random games and sample positions
+   - Number of positions
+   - Move range (min/max move number to sample from)
+   - Number of worker threads
+
+2. **Import from file** — Load positions from a FEN file
+   - File must contain one FEN string per line
+   - Duplicates are automatically removed
+
+3. **Extract tactical puzzles** — Download and extract Lichess puzzle database
+   - Maximum number of puzzles to include
+   - Automatically filters for tactical themes (forks, pins, mates, etc.)
+
+You can combine multiple sources in a single dataset creation session. All positions are:
+- Deduplicated (only unique FENs are kept)
+- Labeled with Stockfish evaluations
+- Saved to `datasets/ds_vN/labeled.jsonl`
+
+## Extending a Dataset
+
+Use "Extend existing dataset" to add more positions to an existing dataset:
+
+1. Select the dataset version to extend
+2. Choose data sources (same options as creation)
+3. Confirm labeling parameters
+4. New positions are:
+   - Labeled with Stockfish
+   - Deduplicated against the target dataset (preventing duplicates)
+   - Merged into the existing `labeled.jsonl`
+   - Metadata is updated with the new source entry
+
+## Training with a Dataset
+
+When you start training (Standard or Burst mode), you'll be prompted to select a dataset version. The TUI will display all available datasets with:
+- Version number
+- Total number of positions
+- Source types (generated, tactical, imported)
+- Stockfish depth used
+- Creation date
+
+## Legacy Data Migration
+
+If you have existing labeled data in `data/training_data.jsonl` from before this update:
+
+1. Open the "Manage Training Data" menu
+2. Choose "Create new dataset"
+3. Select "Import from file"
+4. Point to `data/training_data.jsonl`
+5. Complete the dataset creation
+
+Alternatively, you can manually copy the file to `datasets/ds_v1/labeled.jsonl` and create a `metadata.json` file.
+
+## Viewing Dataset Details
+
+Use "View all datasets" to see a table of all datasets with:
+- Version number
+- Position count
+- Source composition
+- Stockfish depth
+- Creation date
+
+## Deleting a Dataset
+
+Use "Delete dataset" to remove a dataset and free up disk space. **This action cannot be undone.**
+
+⚠️ The system does not prevent deleting datasets used by model checkpoints. Plan accordingly.
+
+## Technical Details
+
+### Deduplication Strategy
+
+When extending a dataset, positions are deduplicated **within that dataset only**. This allows different datasets to contain overlapping positions if desired.
+
+When creating a new dataset from multiple sources, all sources are combined and deduplicated before labeling.
+
+### Labeled Position Format
+
+Each line in `labeled.jsonl` is a JSON object:
+```json
+{
+  "fen": "rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1",
+  "eval": 0.0,
+  "eval_raw": 0
+}
+```
+
+- `fen`: The position in Forsyth-Edwards Notation
+- `eval`: Normalized evaluation ([-1, 1] range using tanh)
+- `eval_raw`: Raw Stockfish evaluation in centipawns
+
+### Storage Location
+
+Datasets are stored in the `datasets/` directory relative to the script location. The old `data/` directory is preserved for backward compatibility but is not actively used by the new system.
+
+## Performance Tips
+
+- **Smaller datasets train faster** — Start with 100k-500k positions
+- **Deduplication matters** — Use the extend functionality to build up your dataset without redundant data
+- **Stockfish depth** — Depth 12-14 balances accuracy and labeling speed
+- **Workers** — Use 4-8 workers for labeling if your machine supports it; more workers = faster but uses more CPU/memory
@@ -0,0 +1,129 @@
+# NNUE Python Pipeline
+
+Central CLI for training and exporting chess evaluation neural networks (NNUE).
+
+## Directory Structure
+
+```
+python/
+├── nnue.py              # Main CLI entry point
+├── src/                 # Python modules
+│   ├── generate.py      # Generate random chess positions
+│   ├── label.py         # Label positions with Stockfish
+│   ├── train.py         # Train NNUE model
+│   └── export.py        # Export weights to Scala
+├── data/                # Training data (gitignored)
+│   ├── positions.txt
+│   └── training_data.jsonl
+└── weights/             # Model weights (gitignored)
+    ├── nnue_weights_v1.pt
+    ├── nnue_weights_v1_metadata.json
+    └── ...
+```
+
+## Quick Start
+
+```bash
+# Train a new model (500k positions, auto-detect checkpoint)
+python nnue.py train
+
+# Train from specific checkpoint
+python nnue.py train --from-checkpoint 2
+
+# Train with custom games count
+python nnue.py train --games 200000
+
+# Train with custom positions file
+python nnue.py train --positions-file my_positions.txt
+
+# Export specific version to Scala
+python nnue.py export 2
+
+# List all checkpoints
+python nnue.py list
+```
+
+## CLI Commands
+
+### `train` - Train NNUE model
+
+```bash
+python nnue.py train [OPTIONS]
+```
+
+**Options:**
+- `--from-checkpoint N` - Resume from checkpoint version N (default: uses latest)
+- `--games N` - Number of games to generate (default: 500000)
+- `--positions-file FILE` - Use existing positions file instead of generating
+- `--stockfish PATH` - Path to Stockfish binary (default: `$STOCKFISH_PATH` or `/usr/games/stockfish`)
+
+**Examples:**
+```bash
+# Train with latest checkpoint
+python nnue.py train
+
+# Train from v2 with 100k games
+python nnue.py train --from-checkpoint 2 --games 100000
+
+# Train with custom positions
+python nnue.py train --positions-file my_games.txt --stockfish /opt/stockfish/sf15
+```
+
+### `export` - Export weights to Scala
+
+```bash
+python nnue.py export WEIGHTS [output_path]
+```
+
+**Arguments:**
+- `WEIGHTS` - Version number (e.g., `2`) or full filename (e.g., `nnue_weights_v2.pt`)
+
+**Examples:**
+```bash
+# Export version 2
+python nnue.py export 2
+
+# Export with full filename
+python nnue.py export nnue_weights_v3.pt
+```
+
+Output goes to `../src/main/scala/de/nowchess/bot/bots/nnue/NNUEWeights_vN.scala`
+
+### `list` - List available checkpoints
+
+```bash
+python nnue.py list
+```
+
+Shows all available model versions with file sizes.
+
+## Data Flow
+
+1. **Generate** → `data/positions.txt`
+   - Random chess positions from 8-20 move openings
+   - Filters out checks, game-over states, and captures
+
+2. **Label** → `data/training_data.jsonl`
+   - Evaluates each position with Stockfish at depth 12
+   - Stores FEN + evaluation in JSONL format
+
+3. **Train** → `weights/nnue_weights_vN.pt`
+   - Trains neural network on labeled positions
+   - Auto-versioning (v1, v2, v3, etc.)
+   - Saves metadata alongside weights
+
+4. **Export** → `NNUEWeights_vN.scala`
+   - Converts weights to Scala object
+   - Ready for integration into bot
+
+## Versioning
+
+- Models are automatically versioned (v1, v2, v3, etc.)
+- Each version gets a `_metadata.json` file with training info
+- Training from checkpoint uses latest version unless specified with `--from-checkpoint`
+
+## Files
+
+- `data/` and `weights/` are gitignored (local artifacts)
+- Documentation in `docs/` explains training, debugging, and incremental improvements
+- Source modules in `src/` are independent and can be imported for custom workflows
@@ -0,0 +1,951 @@
+#!/usr/bin/env python3
+"""Central NNUE pipeline TUI for training and exporting models."""
+
+import os
+import shutil
+import sys
+import tempfile
+from pathlib import Path
+from rich.console import Console
+from rich.table import Table
+from rich.panel import Panel
+from rich.prompt import Prompt, Confirm
+from rich import print as rprint
+
+# Add src directory to path so we can import modules
+sys.path.insert(0, str(Path(__file__).parent / "src"))
+
+from generate import play_random_game_and_collect_positions
+from label import label_positions_with_stockfish
+from train import train_nnue, burst_train, DEFAULT_HIDDEN_SIZES
+from export import export_to_nbai
+from tactical_positions_extractor import (
+    download_and_extract_puzzle_db,
+    extract_tactical_only
+)
+from lichess_importer import import_lichess_evals
+from dataset import (
+    get_datasets_dir,
+    list_datasets,
+    next_dataset_version,
+    load_dataset_metadata,
+    create_dataset,
+    extend_dataset,
+    get_dataset_labeled_path,
+    delete_dataset,
+    show_datasets_table
+)
+
+
+def get_weights_dir():
+    """Get/create weights directory."""
+    weights_dir = Path(__file__).parent / "weights"
+    weights_dir.mkdir(exist_ok=True)
+    return weights_dir
+
+
+def get_data_dir():
+    """Get/create legacy data directory (for migration)."""
+    data_dir = Path(__file__).parent / "data"
+    data_dir.mkdir(exist_ok=True)
+    return data_dir
+
+
+def list_checkpoints():
+    """List available checkpoint versions."""
+    weights_dir = get_weights_dir()
+    checkpoints = sorted(weights_dir.glob("nnue_weights_v*.pt"))
+    if not checkpoints:
+        return []
+    return [int(cp.stem.split("_v")[1]) for cp in checkpoints]
+
+
+def migrate_legacy_data():
+    """On first run, offer to import existing data/training_data.jsonl as ds_v1."""
+    console = Console()
+    data_dir = get_data_dir()
+    legacy_file = data_dir / "training_data.jsonl"
+    datasets = list_datasets()
+
+    # Only migrate if legacy data exists and no datasets exist yet
+    if legacy_file.exists() and not datasets:
+        console.print("\n[cyan]Legacy data detected: data/training_data.jsonl[/cyan]")
+        console.print("[dim]Tip: Use 'Manage Training Data' menu to import it as ds_v1[/dim]")
+
+
+def show_header():
+    """Display application header."""
+    console = Console()
+    console.clear()
+    console.print(
+        Panel(
+            "[bold cyan]🧠 NNUE Training Pipeline[/bold cyan]\n"
+            "[dim]Neural Network Utility Evaluation - Dataset & Model Management[/dim]",
+            border_style="cyan",
+            padding=(1, 2),
+        )
+    )
+
+
+def show_checkpoints_table():
+    """Display available checkpoints in a table."""
+    console = Console()
+    available = list_checkpoints()
+
+    if not available:
+        console.print("[yellow]ℹ No model checkpoints found yet[/yellow]")
+        return
+
+    table = Table(title="Available Model Checkpoints", show_header=True, header_style="bold cyan")
+    table.add_column("Version", style="dim")
+    table.add_column("File Size", justify="right")
+    table.add_column("Status", justify="center")
+
+    weights_dir = get_weights_dir()
+    for v in sorted(available):
+        weights_file = weights_dir / f"nnue_weights_v{v}.pt"
+        if weights_file.exists():
+            size = weights_file.stat().st_size / (1024**2)
+            table.add_row(f"v{v}", f"{size:.1f} MB", "✓ Ready")
+        else:
+            table.add_row(f"v{v}", "?", "[red]✗ Missing[/red]")
+
+    console.print(table)
+
+
+def show_main_menu():
+    """Display and handle main menu."""
+    console = Console()
+
+    # Migrate legacy data on first run
+    migrate_legacy_data()
+
+    while True:
+        show_header()
+        show_checkpoints_table()
+
+        console.print("\n[bold]What would you like to do?[/bold]")
+        console.print("[cyan]1[/cyan] - Manage Training Data")
+        console.print("[cyan]2[/cyan] - Train Model")
+        console.print("[cyan]3[/cyan] - Export Model")
+        console.print("[cyan]4[/cyan] - Exit")
+
+        choice = Prompt.ask("\nSelect option", choices=["1", "2", "3", "4"])
+
+        if choice == "1":
+            datasets_menu()
+        elif choice == "2":
+            training_menu()
+        elif choice == "3":
+            export_interactive()
+        elif choice == "4":
+            console.print("[yellow]👋 Goodbye![/yellow]")
+            return
+
+
+def datasets_menu():
+    """Dataset management submenu."""
+    console = Console()
+
+    while True:
+        show_header()
+        show_datasets_table(console)
+
+        console.print("\n[bold]Training Data Management[/bold]")
+        console.print("[cyan]1[/cyan] - Create new dataset")
+        console.print("[cyan]2[/cyan] - Extend existing dataset")
+        console.print("[cyan]3[/cyan] - View all datasets")
+        console.print("[cyan]4[/cyan] - Delete dataset")
+        console.print("[cyan]5[/cyan] - Back")
+
+        choice = Prompt.ask("\nSelect option", choices=["1", "2", "3", "4", "5"])
+
+        if choice == "1":
+            create_dataset_interactive()
+        elif choice == "2":
+            extend_dataset_interactive()
+        elif choice == "3":
+            show_header()
+            show_datasets_table(console)
+            Prompt.ask("\nPress Enter to continue")
+        elif choice == "4":
+            delete_dataset_interactive()
+        elif choice == "5":
+            return
+
+
+def create_dataset_interactive():
+    """Interactive dataset creation flow."""
+    console = Console()
+    show_header()
+
+    console.print("\n[bold cyan]📊 Create New Dataset[/bold cyan]")
+
+    sources = []
+    combined_count = 0
+
+    # Allow user to add multiple sources
+    while True:
+        console.print("\n[bold]Add data source (repeat until done):[/bold]")
+        console.print("[cyan]a[/cyan] - Generate random positions")
+        console.print("[cyan]b[/cyan] - Import from file")
+        console.print("[cyan]c[/cyan] - Extract Lichess tactical puzzles")
+        console.print("[cyan]d[/cyan] - Import Lichess eval database (.jsonl.zst)")
+        console.print("[cyan]e[/cyan] - Done adding sources")
+
+        choice = Prompt.ask("Select", choices=["a", "b", "c", "d", "e"])
+
+        if choice == "a":
+            num_positions = int(Prompt.ask("Number of positions to generate", default="100000"))
+            min_move = int(Prompt.ask("Minimum move number", default="1"))
+            max_move = int(Prompt.ask("Maximum move number", default="50"))
+            num_workers = int(Prompt.ask("Number of workers", default="8"))
+
+            console.print("[dim]Generating positions...[/dim]")
+            temp_file = Path(tempfile.gettempdir()) / "temp_positions.txt"
+            count = play_random_game_and_collect_positions(
+                str(temp_file),
+                total_positions=num_positions,
+                samples_per_game=1,
+                min_move=min_move,
+                max_move=max_move,
+                num_workers=num_workers
+            )
+            if count > 0:
+                sources.append({
+                    "type": "generated",
+                    "count": count,
+                    "params": {"num_positions": num_positions, "min_move": min_move, "max_move": max_move}
+                })
+                combined_count += count
+                console.print(f"[green]✓ {count:,} positions generated[/green]")
+            else:
+                console.print("[red]✗ Generation failed[/red]")
+
+        elif choice == "b":
+            file_path = Prompt.ask("Path to FEN file")
+            try:
+                with open(file_path, 'r') as f:
+                    count = sum(1 for _ in f)
+                sources.append({"type": "file_import", "count": count, "path": file_path})
+                combined_count += count
+                console.print(f"[green]✓ {count:,} positions from file[/green]")
+            except FileNotFoundError:
+                console.print(f"[red]✗ File not found: {file_path}[/red]")
+
+        elif choice == "c":
+            max_puzzles = int(Prompt.ask("Maximum puzzles to extract", default="300000"))
+            console.print("[dim]Extracting tactical positions...[/dim]")
+            temp_file = Path(tempfile.gettempdir()) / "temp_tactical.txt"
+            try:
+                csv_path = download_and_extract_puzzle_db(output_dir=str(Path(__file__).parent / "tactical_data"))
+                if csv_path:
+                    count = extract_tactical_only(csv_path, str(temp_file), max_puzzles)
+                    sources.append({"type": "tactical", "count": count, "max_puzzles": max_puzzles})
+                    combined_count += count
+                    console.print(f"[green]✓ {count:,} tactical positions extracted[/green]")
+            except Exception as e:
+                console.print(f"[red]✗ Tactical extraction failed: {e}[/red]")
+
+        elif choice == "d":
+            zst_path = Prompt.ask("Path to lichess_db_eval.jsonl.zst")
+            max_pos = Prompt.ask("Max positions to import (blank = no limit)", default="")
+            max_pos = int(max_pos) if max_pos.strip() else None
+            min_depth = int(Prompt.ask("Minimum eval depth to accept", default="20"))
+            console.print("[dim]Importing Lichess evals (this may take a while)...[/dim]")
+            temp_file = Path(tempfile.gettempdir()) / "temp_lichess.jsonl"
+            temp_file.unlink(missing_ok=True)
+            try:
+                count = import_lichess_evals(
+                    input_path=zst_path,
+                    output_file=str(temp_file),
+                    max_positions=max_pos,
+                    min_depth=min_depth,
+                )
+                if count > 0:
+                    sources.append({
+                        "type": "lichess",
+                        "count": count,
+                        "params": {"min_depth": min_depth, "max_positions": max_pos},
+                    })
+                    combined_count += count
+                    console.print(f"[green]✓ {count:,} positions imported from Lichess[/green]")
+                else:
+                    console.print("[red]✗ No positions imported[/red]")
+            except Exception as e:
+                console.print(f"[red]✗ Lichess import failed: {e}[/red]")
+
+        elif choice == "e":
+            if not sources:
+                console.print("[yellow]⚠ No sources added yet[/yellow]")
+                continue
+            break
+
+    if not sources:
+        console.print("[yellow]Dataset creation cancelled[/yellow]")
+        return
+
+    # Determine whether any sources still need Stockfish labeling.
+    # Lichess sources are already labeled; only generated/tactical/file sources need it.
+    needs_labeling = any(s["type"] != "lichess" for s in sources)
+
+    stockfish_depth = 12
+    if needs_labeling:
+        console.print("\n[bold cyan]🏷️  Labeling Parameters[/bold cyan]")
+        stockfish_path = Prompt.ask(
+            "Stockfish path",
+            default=os.environ.get("STOCKFISH_PATH") or shutil.which("stockfish") or "/usr/bin/stockfish"
+        )
+        stockfish_depth = int(Prompt.ask("Stockfish analysis depth", default="12"))
+        num_workers = int(Prompt.ask("Number of parallel workers", default="1"))
+
+    # Summary and confirm
+    console.print("\n[bold]Dataset Summary:[/bold]")
+    console.print(f"  Total positions: {combined_count:,}")
+    for source in sources:
+        console.print(f"  - {source['type']}: {source['count']:,}")
+    if needs_labeling:
+        console.print(f"  Stockfish depth: {stockfish_depth}")
+
+    if not Confirm.ask("\nProceed to create dataset?", default=True):
+        console.print("[yellow]Cancelled[/yellow]")
+        return
+
+    try:
+        labeled_file = Path(tempfile.gettempdir()) / "labeled.jsonl"
+        labeled_file.unlink(missing_ok=True)
+
+        # --- Step 1: Collect already-labeled data (Lichess source) ---
+        lichess_tmp = Path(tempfile.gettempdir()) / "temp_lichess.jsonl"
+        if lichess_tmp.exists():
+            import shutil as _shutil
+            _shutil.copy(lichess_tmp, labeled_file)
+            console.print(f"\n[bold cyan]Step 1: Pre-labeled data copied[/bold cyan]")
+            console.print(f"[green]✓ Lichess positions ready[/green]")
+
+        # --- Step 2: Combine unlabeled sources and run Stockfish (if any) ---
+        non_lichess = [s for s in sources if s["type"] != "lichess"]
+        if non_lichess:
+            console.print("\n[bold cyan]Step 2: Combining unlabeled sources[/bold cyan]")
+            combined_fen_file = Path(tempfile.gettempdir()) / "combined_positions.txt"
+            all_fens = set()
+
+            for source in non_lichess:
+                if source["type"] == "generated":
+                    temp_file = Path(tempfile.gettempdir()) / "temp_positions.txt"
+                elif source["type"] == "file_import":
+                    temp_file = Path(source["path"])
+                elif source["type"] == "tactical":
+                    temp_file = Path(tempfile.gettempdir()) / "temp_tactical.txt"
+                else:
+                    continue
+
+                if temp_file.exists():
+                    with open(temp_file, "r") as f:
+                        for line in f:
+                            fen = line.strip()
+                            if fen:
+                                all_fens.add(fen)
+
+            with open(combined_fen_file, "w") as f:
+                for fen in all_fens:
+                    f.write(fen + "\n")
+            console.print(f"[green]✓ Combined {len(all_fens):,} unique unlabeled positions[/green]")
+
+            console.print("\n[bold cyan]Step 2b: Labeling with Stockfish[/bold cyan]")
+            success = label_positions_with_stockfish(
+                str(combined_fen_file),
+                str(labeled_file),
+                stockfish_path,
+                depth=stockfish_depth,
+                num_workers=num_workers,
+            )
+            if not success:
+                console.print("[red]✗ Stockfish labeling failed[/red]")
+                return
+            console.print("[green]✓ Positions labeled[/green]")
+
+        # --- Step 3: Create dataset ---
+        console.print("\n[bold cyan]Step 3: Creating Dataset[/bold cyan]")
+        version = next_dataset_version()
+        create_dataset(
+            version=version,
+            labeled_jsonl_path=str(labeled_file),
+            sources=sources,
+            stockfish_depth=stockfish_depth,
+        )
+        console.print(f"[green]✓ Dataset created: ds_v{version}[/green]")
+        console.print(f"[bold]Location: {get_datasets_dir() / f'ds_v{version}'}[/bold]")
+
+        Prompt.ask("\nPress Enter to continue")
+
+    except Exception as e:
+        console.print(f"[red]✗ Error: {e}[/red]")
+        import traceback
+        traceback.print_exc()
+        Prompt.ask("Press Enter to continue")
+
+
+def extend_dataset_interactive():
+    """Interactive dataset extension flow."""
+    console = Console()
+    show_header()
+
+    console.print("\n[bold cyan]📊 Extend Existing Dataset[/bold cyan]")
+
+    datasets = list_datasets()
+    if not datasets:
+        console.print("[yellow]ℹ No datasets available to extend[/yellow]")
+        Prompt.ask("Press Enter to continue")
+        return
+
+    show_datasets_table(console)
+    version = int(Prompt.ask("\nEnter dataset version to extend (e.g., 1)"))
+
+    if not any(v == version for v, _ in datasets):
+        console.print("[red]✗ Dataset not found[/red]")
+        return
+
+    sources = []
+    combined_count = 0
+
+    # Allow user to add sources
+    while True:
+        console.print("\n[bold]Add data source:[/bold]")
+        console.print("[cyan]a[/cyan] - Generate random positions")
+        console.print("[cyan]b[/cyan] - Import from file")
+        console.print("[cyan]c[/cyan] - Extract Lichess tactical puzzles")
+        console.print("[cyan]d[/cyan] - Import Lichess eval database (.jsonl.zst)")
+        console.print("[cyan]e[/cyan] - Done adding sources")
+
+        choice = Prompt.ask("Select", choices=["a", "b", "c", "d", "e"])
+
+        if choice == "a":
+            num_positions = int(Prompt.ask("Number of positions to generate", default="100000"))
+            min_move = int(Prompt.ask("Minimum move number", default="1"))
+            max_move = int(Prompt.ask("Maximum move number", default="50"))
+            num_workers = int(Prompt.ask("Number of workers", default="8"))
+
+            console.print("[dim]Generating positions...[/dim]")
+            temp_file = Path(tempfile.gettempdir()) / "temp_positions.txt"
+            count = play_random_game_and_collect_positions(
+                str(temp_file),
+                total_positions=num_positions,
+                samples_per_game=1,
+                min_move=min_move,
+                max_move=max_move,
+                num_workers=num_workers
+            )
+            if count > 0:
+                sources.append({
+                    "type": "generated",
+                    "count": count,
+                    "params": {"num_positions": num_positions, "min_move": min_move, "max_move": max_move}
+                })
+                combined_count += count
+                console.print(f"[green]✓ {count:,} positions generated[/green]")
+
+        elif choice == "b":
+            file_path = Prompt.ask("Path to FEN file")
+            try:
+                with open(file_path, 'r') as f:
+                    count = sum(1 for _ in f)
+                sources.append({"type": "file_import", "count": count, "path": file_path})
+                combined_count += count
+                console.print(f"[green]✓ {count:,} positions from file[/green]")
+            except FileNotFoundError:
+                console.print(f"[red]✗ File not found: {file_path}[/red]")
+
+        elif choice == "c":
+            max_puzzles = int(Prompt.ask("Maximum puzzles to extract", default="300000"))
+            console.print("[dim]Extracting tactical positions...[/dim]")
+            temp_file = Path(tempfile.gettempdir()) / "temp_tactical.txt"
+            try:
+                csv_path = download_and_extract_puzzle_db(output_dir=str(Path(__file__).parent / "tactical_data"))
+                if csv_path:
+                    count = extract_tactical_only(csv_path, str(temp_file), max_puzzles)
+                    sources.append({"type": "tactical", "count": count, "max_puzzles": max_puzzles})
+                    combined_count += count
+                    console.print(f"[green]✓ {count:,} tactical positions extracted[/green]")
+            except Exception as e:
+                console.print(f"[red]✗ Extraction failed: {e}[/red]")
+
+        elif choice == "d":
+            zst_path = Prompt.ask("Path to lichess_db_eval.jsonl.zst")
+            max_pos = Prompt.ask("Max positions to import (blank = no limit)", default="")
+            max_pos = int(max_pos) if max_pos.strip() else None
+            min_depth = int(Prompt.ask("Minimum eval depth to accept", default="20"))
+            console.print("[dim]Importing Lichess evals (this may take a while)...[/dim]")
+            temp_file = Path(tempfile.gettempdir()) / "temp_lichess.jsonl"
+            temp_file.unlink(missing_ok=True)
+            try:
+                count = import_lichess_evals(
+                    input_path=zst_path,
+                    output_file=str(temp_file),
+                    max_positions=max_pos,
+                    min_depth=min_depth,
+                )
+                if count > 0:
+                    sources.append({
+                        "type": "lichess",
+                        "count": count,
+                        "params": {"min_depth": min_depth, "max_positions": max_pos},
+                    })
+                    combined_count += count
+                    console.print(f"[green]✓ {count:,} positions imported from Lichess[/green]")
+                else:
+                    console.print("[red]✗ No positions imported[/red]")
+            except Exception as e:
+                console.print(f"[red]✗ Lichess import failed: {e}[/red]")
+
+        elif choice == "e":
+            if not sources:
+                console.print("[yellow]⚠ No sources added yet[/yellow]")
+                continue
+            break
+
+    if not sources:
+        console.print("[yellow]Extension cancelled[/yellow]")
+        return
+
+    needs_labeling = any(s["type"] != "lichess" for s in sources)
+
+    stockfish_depth = 12
+    if needs_labeling:
+        console.print("\n[bold cyan]🏷️  Labeling Parameters[/bold cyan]")
+        stockfish_path = Prompt.ask(
+            "Stockfish path",
+            default=os.environ.get("STOCKFISH_PATH") or shutil.which("stockfish") or "/usr/bin/stockfish"
+        )
+        stockfish_depth = int(Prompt.ask("Stockfish analysis depth", default="12"))
+        num_workers = int(Prompt.ask("Number of parallel workers", default="1"))
+
+    # Summary and confirm
+    console.print("\n[bold]Extension Summary:[/bold]")
+    console.print(f"  Target dataset: ds_v{version}")
+    console.print(f"  New positions: {combined_count:,}")
+    for source in sources:
+        console.print(f"  - {source['type']}: {source['count']:,}")
+    if needs_labeling:
+        console.print(f"  Stockfish depth: {stockfish_depth}")
+
+    if not Confirm.ask("\nProceed to extend dataset?", default=True):
+        console.print("[yellow]Cancelled[/yellow]")
+        return
+
+    try:
+        labeled_file = Path(tempfile.gettempdir()) / "labeled.jsonl"
+        labeled_file.unlink(missing_ok=True)
+
+        # Copy pre-labeled Lichess data if present
+        lichess_tmp = Path(tempfile.gettempdir()) / "temp_lichess.jsonl"
+        if lichess_tmp.exists():
+            import shutil as _shutil
+            _shutil.copy(lichess_tmp, labeled_file)
+            console.print(f"\n[bold cyan]Step 1: Pre-labeled data copied[/bold cyan]")
+            console.print(f"[green]✓ Lichess positions ready[/green]")
+
+        # Combine and label remaining sources with Stockfish
+        non_lichess = [s for s in sources if s["type"] != "lichess"]
+        if non_lichess:
+            console.print("\n[bold cyan]Step 2: Combining unlabeled sources[/bold cyan]")
+            combined_fen_file = Path(tempfile.gettempdir()) / "combined_positions.txt"
+            all_fens = set()
+
+            for source in non_lichess:
+                if source["type"] == "generated":
+                    temp_file = Path(tempfile.gettempdir()) / "temp_positions.txt"
+                elif source["type"] == "file_import":
+                    temp_file = Path(source["path"])
+                elif source["type"] == "tactical":
+                    temp_file = Path(tempfile.gettempdir()) / "temp_tactical.txt"
+                else:
+                    continue
+                if temp_file.exists():
+                    with open(temp_file, "r") as f:
+                        for line in f:
+                            fen = line.strip()
+                            if fen:
+                                all_fens.add(fen)
+
+            with open(combined_fen_file, "w") as f:
+                for fen in all_fens:
+                    f.write(fen + "\n")
+            console.print(f"[green]✓ Combined {len(all_fens):,} unique unlabeled positions[/green]")
+
+            console.print("\n[bold cyan]Step 2b: Labeling with Stockfish[/bold cyan]")
+            success = label_positions_with_stockfish(
+                str(combined_fen_file),
+                str(labeled_file),
+                stockfish_path,
+                depth=stockfish_depth,
+                num_workers=num_workers,
+            )
+            if not success:
+                console.print("[red]✗ Stockfish labeling failed[/red]")
+                return
+            console.print("[green]✓ Positions labeled[/green]")
+
+        # Extend dataset
+        console.print("\n[bold cyan]Step 3: Extending Dataset[/bold cyan]")
+        success = extend_dataset(
+            version=version,
+            new_labeled_path=str(labeled_file),
+            new_source_entry={
+                "type": "merged_sources",
+                "count": combined_count,
+                "sources": sources,
+            }
+        )
+
+        if success:
+            metadata = load_dataset_metadata(version)
+            console.print(f"[green]✓ Dataset extended[/green]")
+            console.print(f"[bold]Total positions: {metadata['total_positions']:,}[/bold]")
+        else:
+            console.print("[red]✗ Extension failed[/red]")
+
+        Prompt.ask("\nPress Enter to continue")
+
+    except Exception as e:
+        console.print(f"[red]✗ Error: {e}[/red]")
+        import traceback
+        traceback.print_exc()
+        Prompt.ask("Press Enter to continue")
+
+
+def delete_dataset_interactive():
+    """Interactive dataset deletion."""
+    console = Console()
+    show_header()
+
+    console.print("\n[bold cyan]⚠️  Delete Dataset[/bold cyan]")
+
+    datasets = list_datasets()
+    if not datasets:
+        console.print("[yellow]ℹ No datasets to delete[/yellow]")
+        Prompt.ask("Press Enter to continue")
+        return
+
+    show_datasets_table(console)
+    version = int(Prompt.ask("\nEnter dataset version to delete (e.g., 1)"))
+
+    if not any(v == version for v, _ in datasets):
+        console.print("[red]✗ Dataset not found[/red]")
+        return
+
+    if Confirm.ask(f"Delete ds_v{version}? This cannot be undone.", default=False):
+        if delete_dataset(version):
+            console.print(f"[green]✓ Dataset ds_v{version} deleted[/green]")
+        else:
+            console.print("[red]✗ Deletion failed[/red]")
+
+    Prompt.ask("Press Enter to continue")
+
+
+def training_menu():
+    """Training submenu."""
+    console = Console()
+
+    while True:
+        show_header()
+
+        console.print("\n[bold]Training[/bold]")
+        console.print("[cyan]1[/cyan] - Standard Training")
+        console.print("[cyan]2[/cyan] - Burst Training")
+        console.print("[cyan]3[/cyan] - View Model Checkpoints")
+        console.print("[cyan]4[/cyan] - Back")
+
+        choice = Prompt.ask("\nSelect option", choices=["1", "2", "3", "4"])
+
+        if choice == "1":
+            train_interactive()
+        elif choice == "2":
+            burst_train_interactive()
+        elif choice == "3":
+            show_header()
+            show_checkpoints_table()
+            Prompt.ask("\nPress Enter to continue")
+        elif choice == "4":
+            return
+
+
+def train_interactive():
+    """Interactive training menu."""
+    console = Console()
+    show_header()
+
+    console.print("\n[bold cyan]📚 Standard Training Configuration[/bold cyan]")
+
+    # Dataset selection
+    datasets = list_datasets()
+    if not datasets:
+        console.print("[red]✗ No datasets available. Create one first.[/red]")
+        Prompt.ask("Press Enter to continue")
+        return
+
+    console.print("\n[bold]Available Datasets:[/bold]")
+    show_datasets_table(console)
+    dataset_version = int(Prompt.ask("\nEnter dataset version to train on (e.g., 1)"))
+
+    if not any(v == dataset_version for v, _ in datasets):
+        console.print("[red]✗ Dataset not found[/red]")
+        return
+
+    labeled_file = get_dataset_labeled_path(dataset_version)
+    if not labeled_file:
+        console.print("[red]✗ Dataset labeled.jsonl not found[/red]")
+        return
+
+    # Checkpoint selection
+    available = list_checkpoints()
+    use_checkpoint = False
+    checkpoint_version = None
+
+    if available:
+        console.print(f"\n[dim]Available checkpoints: {', '.join([f'v{v}' for v in sorted(available)])}[/dim]")
+        use_checkpoint = Confirm.ask("Start from an existing checkpoint?", default=False)
+        if use_checkpoint:
+            checkpoint_version = Prompt.ask(
+                "Enter checkpoint version",
+                default=str(max(available))
+            )
+
+    # Training parameters
+    epochs = int(Prompt.ask("Number of epochs", default="100"))
+    batch_size = int(Prompt.ask("Batch size", default="16384"))
+    subsample_ratio = float(Prompt.ask("Stochastic subsample ratio per epoch (1.0 = all data)", default="1.0"))
+    default_layers = ",".join(str(s) for s in DEFAULT_HIDDEN_SIZES)
+    hidden_layers_str = Prompt.ask(
+        "Hidden layer sizes (comma-separated, e.g. 1536,1024,512,256)",
+        default=default_layers
+    )
+    hidden_sizes = [int(x.strip()) for x in hidden_layers_str.split(",") if x.strip()]
+    early_stopping = None
+    if Confirm.ask("Enable early stopping?", default=False):
+        early_stopping = int(Prompt.ask("Patience (epochs)", default="5"))
+
+    arch_str = " → ".join(str(s) for s in [768] + hidden_sizes + [1])
+
+    # Confirm and start
+    console.print("\n[bold]Configuration Summary:[/bold]")
+    console.print(f"  Dataset: ds_v{dataset_version}")
+    console.print(f"  Architecture: {arch_str}")
+    console.print(f"  Epochs: {epochs}")
+    console.print(f"  Batch size: {batch_size}")
+    console.print(f"  Subsample ratio: {subsample_ratio:.0%}")
+    if early_stopping:
+        console.print(f"  Early stopping: Yes (patience: {early_stopping})")
+    else:
+        console.print(f"  Early stopping: No")
+    if use_checkpoint:
+        console.print(f"  Checkpoint: v{checkpoint_version}")
+    else:
+        console.print(f"  Checkpoint: None (training from scratch)")
+
+    if not Confirm.ask("\nStart training?", default=True):
+        console.print("[yellow]Training cancelled[/yellow]")
+        Prompt.ask("Press Enter to continue")
+        return
+
+    # Execute training
+    weights_dir = get_weights_dir()
+
+    try:
+        console.print("\n[bold cyan]Training Model[/bold cyan]")
+        checkpoint = None
+        if use_checkpoint:
+            checkpoint = str(weights_dir / f"nnue_weights_v{checkpoint_version}.pt")
+
+        train_nnue(
+            data_file=str(labeled_file),
+            output_file=str(weights_dir / "nnue_weights.pt"),
+            epochs=epochs,
+            batch_size=batch_size,
+            checkpoint=checkpoint,
+            use_versioning=True,
+            early_stopping_patience=early_stopping,
+            subsample_ratio=subsample_ratio,
+            hidden_sizes=hidden_sizes,
+        )
+        console.print("[green]✓ Training complete[/green]")
+
+        # Show result
+        available = list_checkpoints()
+        new_version = max(available) if available else 1
+        console.print(f"\n[bold green]✓ Training successful![/bold green]")
+        console.print(f"[bold]New checkpoint: v{new_version}[/bold]")
+        Prompt.ask("Press Enter to continue")
+
+    except Exception as e:
+        console.print(f"[red]✗ Error: {e}[/red]")
+        import traceback
+        traceback.print_exc()
+        Prompt.ask("Press Enter to continue")
+
+
+def burst_train_interactive():
+    """Interactive burst training menu."""
+    console = Console()
+    show_header()
+
+    console.print("\n[bold cyan]⚡ Burst Training Configuration[/bold cyan]")
+    console.print("[dim]Repeatedly restarts from the best checkpoint until the time budget expires.[/dim]\n")
+
+    # Dataset selection
+    datasets = list_datasets()
+    if not datasets:
+        console.print("[red]✗ No datasets available. Create one first.[/red]")
+        Prompt.ask("Press Enter to continue")
+        return
+
+    console.print("[bold]Available Datasets:[/bold]")
+    show_datasets_table(console)
+    dataset_version = int(Prompt.ask("\nEnter dataset version to train on (e.g., 1)"))
+
+    if not any(v == dataset_version for v, _ in datasets):
+        console.print("[red]✗ Dataset not found[/red]")
+        return
+
+    labeled_file = get_dataset_labeled_path(dataset_version)
+    if not labeled_file:
+        console.print("[red]✗ Dataset labeled.jsonl not found[/red]")
+        return
+
+    duration_minutes = float(Prompt.ask("Training budget (minutes)", default="60"))
+    epochs_per_season = int(Prompt.ask("Max epochs per season", default="50"))
+    early_stopping_patience = int(Prompt.ask("Early stopping patience (epochs)", default="10"))
+
+    # Optional initial checkpoint
+    available = list_checkpoints()
+    checkpoint = None
+    if available:
+        console.print(f"\n[dim]Available checkpoints: {', '.join([f'v{v}' for v in sorted(available)])}[/dim]")
+        if Confirm.ask("Start from an existing checkpoint?", default=False):
+            version = Prompt.ask("Enter checkpoint version", default=str(max(available)))
+            checkpoint = str(get_weights_dir() / f"nnue_weights_v{version}.pt")
+
+    # Training hyperparameters
+    batch_size = int(Prompt.ask("Batch size", default="16384"))
+    subsample_ratio = float(Prompt.ask("Stochastic subsample ratio per epoch (1.0 = all data)", default="1.0"))
+    default_layers = ",".join(str(s) for s in DEFAULT_HIDDEN_SIZES)
+    hidden_layers_str = Prompt.ask(
+        "Hidden layer sizes (comma-separated, e.g. 1536,1024,512,256)",
+        default=default_layers
+    )
+    hidden_sizes = [int(x.strip()) for x in hidden_layers_str.split(",") if x.strip()]
+    arch_str = " → ".join(str(s) for s in [768] + hidden_sizes + [1])
+
+    # Summary
+    console.print("\n[bold]Configuration Summary:[/bold]")
+    console.print(f"  Dataset:             ds_v{dataset_version}")
+    console.print(f"  Architecture:        {arch_str}")
+    console.print(f"  Duration:            {duration_minutes:.0f} minutes")
+    console.print(f"  Epochs per season:   {epochs_per_season}")
+    console.print(f"  Patience:            {early_stopping_patience}")
+    console.print(f"  Batch size:          {batch_size}")
+    console.print(f"  Subsample ratio:     {subsample_ratio:.0%}")
+    console.print(f"  Checkpoint:          {checkpoint or 'None (from scratch)'}")
+
+    if not Confirm.ask("\nStart burst training?", default=True):
+        console.print("[yellow]Burst training cancelled[/yellow]")
+        Prompt.ask("Press Enter to continue")
+        return
+
+    weights_dir = get_weights_dir()
+
+    try:
+        console.print("\n[bold cyan]Burst Training[/bold cyan]")
+        burst_train(
+            data_file=str(labeled_file),
+            output_file=str(weights_dir / "nnue_weights.pt"),
+            duration_minutes=duration_minutes,
+            epochs_per_season=epochs_per_season,
+            early_stopping_patience=early_stopping_patience,
+            batch_size=batch_size,
+            initial_checkpoint=checkpoint,
+            use_versioning=True,
+            subsample_ratio=subsample_ratio,
+            hidden_sizes=hidden_sizes,
+        )
+        console.print("[green]✓ Burst training complete[/green]")
+
+        available = list_checkpoints()
+        if available:
+            console.print(f"[bold]Latest checkpoint: v{max(available)}[/bold]")
+        Prompt.ask("Press Enter to continue")
+
+    except Exception as e:
+        console.print(f"[red]✗ Error: {e}[/red]")
+        import traceback
+        traceback.print_exc()
+        Prompt.ask("Press Enter to continue")
+
+
+def export_interactive():
+    """Interactive export menu."""
+    console = Console()
+    show_header()
+
+    console.print("\n[bold cyan]📦 Export Configuration[/bold cyan]")
+
+    # Select weights version
+    available = list_checkpoints()
+    if not available:
+        console.print("[red]✗ No checkpoints available to export[/red]")
+        Prompt.ask("Press Enter to continue")
+        return
+
+    console.print(f"[dim]Available versions: {', '.join([f'v{v}' for v in sorted(available)])}[/dim]")
+    version = Prompt.ask("Enter version to export (e.g., 2)")
+
+    weights_file = f"nnue_weights_v{version}.pt"
+    output_file = str(Path(__file__).parent.parent / "src" / "main" / "resources" / "nnue_weights.nbai")
+
+    console.print(f"\n[bold]Export Configuration:[/bold]")
+    console.print(f"  Source: {weights_file}")
+    console.print(f"  Destination: {output_file}")
+
+    if not Confirm.ask("\nExport weights?", default=True):
+        console.print("[yellow]Export cancelled[/yellow]")
+        return
+
+    try:
+        weights_dir = get_weights_dir()
+        weights_path = weights_dir / weights_file
+
+        if not weights_path.exists():
+            console.print(f"[red]✗ {weights_file} not found[/red]")
+            return
+
+        console.print("\n[bold cyan]Exporting Weights[/bold cyan]")
+        export_to_nbai(str(weights_path), output_file)
+        console.print(f"\n[green]✓ Export complete![/green]")
+        console.print(f"[bold]Weights saved to:[/bold] {output_file}")
+        Prompt.ask("Press Enter to continue")
+
+    except Exception as e:
+        console.print(f"[red]✗ Error: {e}[/red]")
+        import traceback
+        traceback.print_exc()
+        Prompt.ask("Press Enter to continue")
+
+
+def main():
+    try:
+        show_main_menu()
+        return 0
+    except KeyboardInterrupt:
+        console = Console()
+        console.print("\n[yellow]Interrupted by user[/yellow]")
+        return 1
+    except Exception as e:
+        console = Console()
+        console.print(f"[red]Error:[/red] {e}")
+        import traceback
+        traceback.print_exc()
+        return 1
+
+
+if __name__ == "__main__":
+    sys.exit(main())
@@ -0,0 +1,6 @@
+chess==1.11.2
+torch==2.11.0
+tqdm==4.67.3
+numpy==2.4.4
+rich==13.7.0
+zstandard==0.23.0
@@ -0,0 +1,66 @@
+@echo off
+REM NNUE Training Pipeline for Windows
+
+setlocal enabledelayedexpansion
+
+echo.
+echo === NNUE Training Pipeline ===
+echo.
+
+REM Get the directory where this script is located
+set SCRIPT_DIR=%~dp0
+
+cd /d "%SCRIPT_DIR%"
+
+REM Step 1: Generate positions
+echo Step 1: Generating 500,000 random positions...
+python generate_positions.py positions.txt
+if not exist positions.txt (
+    echo ERROR: positions.txt not created
+    exit /b 1
+)
+echo [OK] Positions generated
+echo.
+
+REM Step 2: Label positions with Stockfish
+echo Step 2: Labeling positions with Stockfish (depth 12^)...
+if "%STOCKFISH_PATH%"=="" (
+    set STOCKFISH_PATH=stockfish
+)
+python label_positions.py positions.txt training_data.jsonl "%STOCKFISH_PATH%"
+if not exist training_data.jsonl (
+    echo ERROR: training_data.jsonl not created
+    exit /b 1
+)
+echo [OK] Positions labeled
+echo.
+
+REM Step 3: Train NNUE model
+echo Step 3: Training NNUE model (20 epochs^)...
+python train_nnue.py training_data.jsonl nnue_weights.pt
+if not exist nnue_weights.pt (
+    echo ERROR: nnue_weights.pt not created
+    exit /b 1
+)
+echo [OK] Model trained
+echo.
+
+REM Step 4: Export weights to Scala
+echo Step 4: Exporting weights to Scala...
+python export_weights.py nnue_weights.pt ..\src\main\scala\de\nowchess\bot\bots\nnue\NNUEWeights.scala
+if not exist ..\src\main\scala\de\nowchess\bot\bots\nnue\NNUEWeights.scala (
+    echo ERROR: NNUEWeights.scala not created
+    exit /b 1
+)
+echo [OK] Weights exported
+echo.
+
+echo === Pipeline Complete ===
+echo.
+echo Next steps:
+echo 1. Navigate to project root: cd ..\..
+echo 2. Compile: .\compile.bat
+echo 3. Test: .\test.bat
+echo.
+
+endlocal
@@ -0,0 +1,39 @@
+#!/bin/bash
+
+# NNUE Training Pipeline (bash version)
+# Uses the central CLI (nnue.py) for all operations
+# Works on Linux, macOS, and Windows (with Git Bash or WSL)
+
+set -e  # Exit on error
+
+SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
+cd "$SCRIPT_DIR"
+
+# Use python or python3 (check which is available)
+PYTHON_CMD="python3"
+if ! command -v python3 &> /dev/null; then
+    PYTHON_CMD="python"
+fi
+
+echo "=== NNUE Training Pipeline ==="
+echo ""
+echo "Python command: $PYTHON_CMD"
+echo "Working directory: $SCRIPT_DIR"
+echo ""
+
+# Run the unified training pipeline
+$PYTHON_CMD nnue.py train
+
+if [ $? -ne 0 ]; then
+    echo ""
+    echo "ERROR: Training pipeline failed"
+    exit 1
+fi
+
+echo ""
+echo "=== Pipeline Complete ==="
+echo ""
+echo "Next steps:"
+echo "1. Navigate to project root: cd ../.."
+echo "2. Compile: ./compile"
+echo "3. Test: ./test"
@@ -0,0 +1,287 @@
+#!/usr/bin/env python3
+"""Dataset versioning and management for NNUE training data."""
+
+import json
+from pathlib import Path
+from datetime import datetime
+from typing import Optional, Dict, List, Tuple
+from rich.console import Console
+from rich.table import Table
+
+
+def get_datasets_dir() -> Path:
+    """Get/create datasets directory."""
+    datasets_dir = Path(__file__).parent.parent / "datasets"
+    datasets_dir.mkdir(exist_ok=True)
+    return datasets_dir
+
+
+def next_dataset_version() -> int:
+    """Find the next available dataset version number."""
+    datasets_dir = get_datasets_dir()
+    versions = []
+
+    for d in datasets_dir.iterdir():
+        if d.is_dir() and d.name.startswith("ds_v"):
+            try:
+                v = int(d.name.split("_v")[1])
+                versions.append(v)
+            except (ValueError, IndexError):
+                pass
+
+    return max(versions) + 1 if versions else 1
+
+
+def list_datasets() -> List[Tuple[int, Dict]]:
+    """List all datasets with their metadata.
+
+    Returns:
+        List of (version, metadata_dict) tuples, sorted by version.
+    """
+    datasets_dir = get_datasets_dir()
+    datasets = []
+
+    for d in datasets_dir.iterdir():
+        if d.is_dir() and d.name.startswith("ds_v"):
+            try:
+                v = int(d.name.split("_v")[1])
+                metadata_file = d / "metadata.json"
+                if metadata_file.exists():
+                    with open(metadata_file, 'r') as f:
+                        metadata = json.load(f)
+                    datasets.append((v, metadata))
+            except (ValueError, IndexError, json.JSONDecodeError):
+                pass
+
+    return sorted(datasets, key=lambda x: x[0])
+
+
+def load_dataset_metadata(version: int) -> Optional[Dict]:
+    """Load metadata for a specific dataset version.
+
+    Returns:
+        Metadata dict or None if not found.
+    """
+    datasets_dir = get_datasets_dir()
+    metadata_file = datasets_dir / f"ds_v{version}" / "metadata.json"
+
+    if not metadata_file.exists():
+        return None
+
+    with open(metadata_file, 'r') as f:
+        return json.load(f)
+
+
+def save_dataset_metadata(version: int, metadata: Dict) -> None:
+    """Save metadata for a dataset version."""
+    datasets_dir = get_datasets_dir()
+    dataset_dir = datasets_dir / f"ds_v{version}"
+    dataset_dir.mkdir(exist_ok=True)
+
+    metadata_file = dataset_dir / "metadata.json"
+    with open(metadata_file, 'w') as f:
+        json.dump(metadata, f, indent=2, default=str)
+
+
+def create_dataset(
+    version: int,
+    labeled_jsonl_path: str,
+    sources: List[Dict],
+    stockfish_depth: int = 12
+) -> Path:
+    """Create a new versioned dataset.
+
+    Args:
+        version: Dataset version number
+        labeled_jsonl_path: Path to labeled.jsonl to copy
+        sources: List of source dicts (see plan for schema)
+        stockfish_depth: Depth used for labeling
+
+    Returns:
+        Path to the created dataset directory.
+    """
+    datasets_dir = get_datasets_dir()
+    dataset_dir = datasets_dir / f"ds_v{version}"
+    dataset_dir.mkdir(exist_ok=True)
+
+    # Copy labeled data with deduplication (in case source has duplicates)
+    source_path = Path(labeled_jsonl_path)
+    if source_path.exists():
+        dest_path = dataset_dir / "labeled.jsonl"
+        seen_fens = set()
+        unique_count = 0
+
+        with open(source_path, 'r') as src, open(dest_path, 'w') as dst:
+            for line in src:
+                try:
+                    data = json.loads(line)
+                    fen = data.get('fen')
+                    if fen and fen not in seen_fens:
+                        dst.write(line)
+                        seen_fens.add(fen)
+                        unique_count += 1
+                except json.JSONDecodeError:
+                    # Skip malformed lines
+                    pass
+
+    # Count positions
+    total_positions = 0
+    if (dataset_dir / "labeled.jsonl").exists():
+        with open(dataset_dir / "labeled.jsonl", 'r') as f:
+            total_positions = sum(1 for _ in f)
+
+    # Create metadata
+    metadata = {
+        "version": version,
+        "created": datetime.now().isoformat(),
+        "total_positions": total_positions,
+        "stockfish_depth": stockfish_depth,
+        "sources": sources
+    }
+
+    save_dataset_metadata(version, metadata)
+    return dataset_dir
+
+
+def extend_dataset(
+    version: int,
+    new_labeled_path: str,
+    new_source_entry: Dict
+) -> bool:
+    """Extend an existing dataset with new labeled positions (with deduplication).
+
+    Args:
+        version: Dataset version to extend
+        new_labeled_path: Path to new labeled.jsonl to merge
+        new_source_entry: Source entry to add to metadata
+
+    Returns:
+        True if successful, False otherwise.
+    """
+    datasets_dir = get_datasets_dir()
+    dataset_dir = datasets_dir / f"ds_v{version}"
+
+    if not dataset_dir.exists():
+        return False
+
+    labeled_file = dataset_dir / "labeled.jsonl"
+    new_labeled_file = Path(new_labeled_path)
+
+    if not new_labeled_file.exists():
+        return False
+
+    # Load existing FENs (dedup set) — must load entire file to avoid duplicates
+    existing_fens = set()
+    if labeled_file.exists():
+        with open(labeled_file, 'r') as f:
+            for line in f:
+                try:
+                    data = json.loads(line)
+                    fen = data.get('fen')
+                    if fen:
+                        existing_fens.add(fen)
+                except json.JSONDecodeError:
+                    pass
+
+    # Merge new positions, skipping duplicates
+    new_count = 0
+    new_lines = []
+    with open(new_labeled_file, 'r') as f_new:
+        for line in f_new:
+            try:
+                data = json.loads(line)
+                fen = data.get('fen')
+                if fen and fen not in existing_fens:
+                    new_lines.append(line)
+                    existing_fens.add(fen)
+                    new_count += 1
+            except json.JSONDecodeError:
+                pass
+
+    # Append only the new, unique positions
+    if new_lines:
+        with open(labeled_file, 'a') as f_append:
+            for line in new_lines:
+                f_append.write(line)
+
+    # Update metadata
+    metadata = load_dataset_metadata(version)
+    if metadata:
+        # Count total positions
+        total_positions = 0
+        with open(labeled_file, 'r') as f:
+            total_positions = sum(1 for _ in f)
+
+        metadata['total_positions'] = total_positions
+        # Update the source entry with actual count of new positions added
+        new_source_entry['actual_count'] = new_count
+        metadata['sources'].append(new_source_entry)
+        save_dataset_metadata(version, metadata)
+
+    return True
+
+
+def get_dataset_labeled_path(version: int) -> Optional[Path]:
+    """Get the path to a dataset's labeled.jsonl file.
+
+    Returns:
+        Path to labeled.jsonl or None if dataset doesn't exist.
+    """
+    datasets_dir = get_datasets_dir()
+    labeled_file = datasets_dir / f"ds_v{version}" / "labeled.jsonl"
+
+    if labeled_file.exists():
+        return labeled_file
+    return None
+
+
+def delete_dataset(version: int) -> bool:
+    """Delete a dataset (recursively removes directory).
+
+    Args:
+        version: Dataset version to delete
+
+    Returns:
+        True if successful.
+    """
+    datasets_dir = get_datasets_dir()
+    dataset_dir = datasets_dir / f"ds_v{version}"
+
+    if not dataset_dir.exists():
+        return False
+
+    import shutil
+    shutil.rmtree(dataset_dir)
+    return True
+
+
+def show_datasets_table(console: Console = None) -> None:
+    """Display all datasets in a Rich table."""
+    if console is None:
+        console = Console()
+
+    datasets = list_datasets()
+
+    if not datasets:
+        console.print("[yellow]ℹ No datasets found yet[/yellow]")
+        return
+
+    table = Table(title="Available Datasets", show_header=True, header_style="bold cyan")
+    table.add_column("Version", style="dim")
+    table.add_column("Positions", justify="right")
+    table.add_column("Sources", justify="left")
+    table.add_column("Depth", justify="center")
+    table.add_column("Created", justify="left")
+
+    for v, metadata in datasets:
+        positions = metadata.get('total_positions', 0)
+        sources = metadata.get('sources', [])
+        source_str = ", ".join([s.get('type', '?') for s in sources])
+        depth = metadata.get('stockfish_depth', '?')
+        created = metadata.get('created', '?')
+        if created != '?':
+            created = created.split('T')[0]  # Just the date
+
+        table.add_row(f"v{v}", f"{positions:,}", source_str, str(depth), created)
+
+    console.print(table)
@@ -0,0 +1,137 @@
+#!/usr/bin/env python3
+"""Export NNUE weights to .nbai format for runtime loading."""
+
+import json
+import struct
+import sys
+from datetime import datetime
+from pathlib import Path
+
+import torch
+
+MAGIC = 0x4942_414E   # bytes 'N','B','A','I' as little-endian int32
+VERSION = 1
+
+
+def _read_sidecar(weights_file: str) -> dict:
+    sidecar = weights_file.replace(".pt", "_metadata.json")
+    if Path(sidecar).exists():
+        with open(sidecar) as f:
+            return json.load(f)
+    return {}
+
+
+def _infer_layers(state_dict: dict) -> list[dict]:
+    """Derive layer descriptors from state_dict weight shapes.
+
+    Assumes layers named l1, l2, ..., lN.
+    All hidden layers get activation 'relu'; the last gets 'linear'.
+    """
+    names = sorted(
+        {k.split(".")[0] for k in state_dict if k.endswith(".weight")},
+        key=lambda n: int(n[1:]),
+    )
+    layers = []
+    for i, name in enumerate(names):
+        out_size, in_size = state_dict[f"{name}.weight"].shape
+        activation = "linear" if i == len(names) - 1 else "relu"
+        layers.append({"activation": activation, "inputSize": int(in_size), "outputSize": int(out_size)})
+    return layers
+
+
+def _write_floats(f, tensor):
+    data = tensor.float().flatten().cpu().numpy()
+    f.write(struct.pack("<I", len(data)))
+    f.write(struct.pack(f"<{len(data)}f", *data))
+
+
+def export_to_nbai(
+    weights_file: str,
+    output_file: str,
+    trained_by: str = "unknown",
+    train_loss: float = 0.0,
+):
+    if not Path(weights_file).exists():
+        print(f"Error: weights file not found at {weights_file}")
+        sys.exit(1)
+
+    loaded = torch.load(weights_file, map_location="cpu")
+    state_dict = (
+        loaded["model_state_dict"]
+        if isinstance(loaded, dict) and "model_state_dict" in loaded
+        else loaded
+    )
+
+    sidecar = _read_sidecar(weights_file)
+    val_loss = float(loaded.get("best_val_loss", sidecar.get("final_val_loss", 0.0))) if isinstance(loaded, dict) else 0.0
+    trained_at = sidecar.get("date", datetime.now().isoformat())
+    training_data_count = int(sidecar.get("num_positions", 0))
+
+    metadata = {
+        "trainedBy": trained_by,
+        "trainedAt": trained_at,
+        "trainingDataCount": training_data_count,
+        "valLoss": val_loss,
+        "trainLoss": train_loss,
+    }
+
+    layers = _infer_layers(state_dict)
+    layer_names = sorted(
+        {k.split(".")[0] for k in state_dict if k.endswith(".weight")},
+        key=lambda n: int(n[1:]),
+    )
+
+    print(f"Architecture ({len(layers)} layers):")
+    for i, l in enumerate(layers):
+        print(f"  l{i + 1}: {l['inputSize']} -> {l['outputSize']}  [{l['activation']}]")
+
+    Path(output_file).parent.mkdir(parents=True, exist_ok=True)
+
+    with open(output_file, "wb") as f:
+        # Header
+        f.write(struct.pack("<I", MAGIC))
+        f.write(struct.pack("<H", VERSION))
+
+        # Metadata (length-prefixed UTF-8 JSON)
+        meta_bytes = json.dumps(metadata, indent=2).encode("utf-8")
+        f.write(struct.pack("<I", len(meta_bytes)))
+        f.write(meta_bytes)
+
+        # Layer descriptors
+        f.write(struct.pack("<H", len(layers)))
+        for layer in layers:
+            name_bytes = layer["activation"].encode("ascii")
+            f.write(struct.pack("<B", len(name_bytes)))
+            f.write(name_bytes)
+            f.write(struct.pack("<I", layer["inputSize"]))
+            f.write(struct.pack("<I", layer["outputSize"]))
+
+        # Weights: weight tensor then bias tensor per layer
+        for name in layer_names:
+            w = state_dict[f"{name}.weight"]
+            b = state_dict[f"{name}.bias"]
+            _write_floats(f, w)
+            _write_floats(f, b)
+            print(f"  Wrote {name}: weight {tuple(w.shape)}, bias {tuple(b.shape)}")
+
+    size_mb = Path(output_file).stat().st_size / (1024 ** 2)
+    print(f"\nExported to {output_file} ({size_mb:.2f} MB)")
+    print(f"Metadata: {json.dumps(metadata, indent=2)}")
+
+
+if __name__ == "__main__":
+    weights_file = "nnue_weights.pt"
+    output_file = "../src/main/resources/nnue_weights.nbai"
+    trained_by = "unknown"
+    train_loss = 0.0
+
+    if len(sys.argv) > 1:
+        weights_file = sys.argv[1]
+    if len(sys.argv) > 2:
+        output_file = sys.argv[2]
+    if len(sys.argv) > 3:
+        trained_by = sys.argv[3]
+    if len(sys.argv) > 4:
+        train_loss = float(sys.argv[4])
+
+    export_to_nbai(weights_file, output_file, trained_by, train_loss)
@@ -0,0 +1,171 @@
+#!/usr/bin/env python3
+"""Generate random chess positions for NNUE training with multiprocessing."""
+
+import chess
+import random
+import sys
+from pathlib import Path
+from tqdm import tqdm
+from multiprocessing import Pool, Queue
+from datetime import datetime
+import time
+
+def _worker_generate_games(worker_id, games_per_worker, samples_per_game, min_move, max_move):
+    """Generate games for one worker.
+
+    Returns:
+        list of FENs generated by this worker
+    """
+    positions = []
+
+    for game_num in range(games_per_worker):
+        board = chess.Board()
+        move_history = []
+
+        # Play a complete random game
+        while not board.is_game_over() and len(move_history) < 200:
+            legal_moves = list(board.legal_moves)
+            if not legal_moves:
+                break
+            move = random.choice(legal_moves)
+            board.push(move)
+            move_history.append(board.copy())
+
+        # Determine the range of moves to sample from
+        game_length = len(move_history)
+        valid_start = max(min_move, 0)
+        valid_end = min(max_move, game_length)
+
+        if valid_start >= valid_end:
+            continue
+
+        # Randomly sample positions from this game
+        sample_count = min(samples_per_game, valid_end - valid_start)
+        if sample_count > 0:
+            sample_indices = random.sample(
+                range(valid_start, valid_end),
+                k=sample_count
+            )
+
+            for idx in sample_indices:
+                sampled_board = move_history[idx]
+
+                # Only filter truly invalid or terminal positions
+                if not sampled_board.is_valid() or sampled_board.is_game_over():
+                    continue
+
+                # Save position (include check, captures, all positions)
+                fen = sampled_board.fen()
+                positions.append(fen)
+
+    return positions
+
+
+def play_random_game_and_collect_positions(
+    output_file,
+    total_positions=3000000,
+    samples_per_game=1,
+    min_move=1,
+    max_move=50,
+    num_workers=8
+):
+    """Generate positions using multiprocessing with multiple workers.
+
+    Args:
+        output_file: Output file for positions
+        total_positions: Target number of positions to generate
+        samples_per_game: Number of positions to sample per game (1-N)
+        min_move: Minimum move number to start sampling from
+        max_move: Maximum move number for sampling
+        num_workers: Number of parallel worker processes
+
+    Returns:
+        Number of valid positions saved
+    """
+    # Estimate games needed (roughly 1 position per game on average)
+    total_games = max(total_positions // samples_per_game, num_workers)
+    games_per_worker = total_games // num_workers
+
+    print(f"Generating {total_positions:,} positions using {num_workers} workers")
+    print(f"Total games: ~{total_games:,} ({games_per_worker:,} per worker)")
+    print()
+
+    start_time = datetime.now()
+
+    # Generate positions in parallel
+    worker_tasks = [
+        (i, games_per_worker, samples_per_game, min_move, max_move)
+        for i in range(num_workers)
+    ]
+
+    positions_count = 0
+    all_positions = []
+
+    with Pool(num_workers) as pool:
+        with tqdm(total=num_workers, desc="Workers generating games") as pbar:
+            for positions in pool.starmap(_worker_generate_games, worker_tasks):
+                all_positions.extend(positions)
+                positions_count += len(positions)
+                pbar.update(1)
+
+    # Write all positions to file
+    print(f"Writing {positions_count:,} positions to {output_file}...")
+    with open(output_file, 'w') as f:
+        for fen in all_positions:
+            f.write(fen + '\n')
+
+    elapsed_time = datetime.now() - start_time
+    elapsed_seconds = elapsed_time.total_seconds()
+    positions_per_second = positions_count / elapsed_seconds if elapsed_seconds > 0 else 0
+
+    # Print summary
+    print()
+    print("=" * 60)
+    print("POSITION GENERATION SUMMARY")
+    print("=" * 60)
+    print(f"Target positions:          {total_positions:,}")
+    print(f"Actual positions saved:    {positions_count:,}")
+    print(f"Workers:                   {num_workers}")
+    print(f"Games per worker:          {games_per_worker:,}")
+    print(f"Samples per game:          {samples_per_game}")
+    print(f"Move range:                {min_move}-{max_move}")
+    print(f"Elapsed time:              {elapsed_time}")
+    print(f"Throughput:                {positions_per_second:.0f} positions/second")
+    print("=" * 60)
+    print()
+
+    if positions_count == 0:
+        print("WARNING: No valid positions were generated!")
+        return 0
+
+    return positions_count
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser(description="Generate random chess positions for NNUE training")
+    parser.add_argument("output_file", nargs="?", default="positions.txt",
+                        help="Output file for positions (default: positions.txt)")
+    parser.add_argument("--positions", type=int, default=3000000,
+                        help="Target number of positions to generate (default: 3000000)")
+    parser.add_argument("--samples-per-game", type=int, default=1,
+                        help="Number of positions to sample per game (default: 1)")
+    parser.add_argument("--min-move", type=int, default=1,
+                        help="Minimum move number to sample from (default: 1)")
+    parser.add_argument("--max-move", type=int, default=50,
+                        help="Maximum move number to sample from (default: 50)")
+    parser.add_argument("--workers", type=int, default=8,
+                        help="Number of parallel worker processes (default: 8)")
+
+    args = parser.parse_args()
+
+    count = play_random_game_and_collect_positions(
+        output_file=args.output_file,
+        total_positions=args.positions,
+        samples_per_game=args.samples_per_game,
+        min_move=args.min_move,
+        max_move=args.max_move,
+        num_workers=args.workers
+    )
+
+    sys.exit(0 if count > 0 else 1)
@@ -0,0 +1,326 @@
+#!/usr/bin/env python3
+"""Label positions with Stockfish evaluations and analyze distribution."""
+
+import json
+import chess.engine
+import sys
+import os
+import numpy as np
+from pathlib import Path
+from tqdm import tqdm
+from multiprocessing import Pool
+from functools import partial
+
+def normalize_evaluation(cp_value, method='tanh', scale=300.0):
+    """Normalize centipawn evaluation to a bounded range.
+
+    Args:
+        cp_value: Centipawn evaluation from Stockfish
+        method: 'tanh' (default) or 'sigmoid'
+        scale: Scale factor (tanh: 300 is typical)
+
+    Returns:
+        Normalized value in approximately [-1, 1] (tanh) or [0, 1] (sigmoid)
+    """
+    if method == 'tanh':
+        return np.tanh(cp_value / scale)
+    elif method == 'sigmoid':
+        return 1.0 / (1.0 + np.exp(-cp_value / scale))
+    else:
+        return cp_value / 100.0
+
+def _evaluate_fen_batch(args):
+    """Worker function to evaluate a batch of FENs with Stockfish threading.
+
+    Args:
+        args: tuple of (fens, stockfish_path, depth, normalize)
+
+    Returns:
+        list of (fen, eval_normalized, eval_raw) tuples
+    """
+    fens, stockfish_path, depth, normalize = args
+
+    results = []
+
+    try:
+        engine = chess.engine.SimpleEngine.popen_uci(stockfish_path)
+    except Exception:
+        return []
+
+    try:
+        for fen in fens:
+            try:
+                board = chess.Board(fen)
+                if not board.is_valid():
+                    continue
+
+                info = engine.analyse(board, chess.engine.Limit(depth=depth))
+
+                if info.get('score') is None:
+                    continue
+
+                score = info['score'].white()
+
+                if score.is_mate():
+                    eval_cp = 2000 if score.mate() > 0 else -2000
+                else:
+                    eval_cp = score.cp
+
+                eval_cp = max(-2000, min(2000, eval_cp))
+                eval_normalized = normalize_evaluation(eval_cp) if normalize else eval_cp
+
+                results.append((fen, eval_normalized, eval_cp))
+
+            except Exception:
+                continue
+    finally:
+        engine.quit()
+
+    return results
+
+
+def label_positions_with_stockfish(positions_file, output_file, stockfish_path, batch_size=1000, depth=12, verbose=False, normalize=True, num_workers=1):
+    """Read positions and label them with Stockfish evaluations.
+
+    Args:
+        positions_file: Path to positions.txt
+        output_file: Path to training_data.jsonl
+        stockfish_path: Path to stockfish binary
+        batch_size: Batch size for processing (positions per worker task, default: 1000)
+        depth: Stockfish depth
+        verbose: Print detailed error messages
+        normalize: If True, normalize evals using tanh
+        num_workers: Number of parallel Stockfish processes
+    """
+
+    # Check if stockfish exists
+    if not Path(stockfish_path).exists():
+        print(f"Error: Stockfish not found at {stockfish_path}")
+        print(f"Tried: {stockfish_path}")
+        print(f"Set STOCKFISH_PATH environment variable or pass as argument")
+        sys.exit(1)
+
+    print(f"Using Stockfish: {stockfish_path}")
+    print(f"Number of workers: {num_workers}")
+
+    # Check if positions file exists
+    if not Path(positions_file).exists():
+        print(f"Error: Positions file not found at {positions_file}")
+        sys.exit(1)
+
+    # Load existing evaluations if resuming
+    evaluated_fens = set()
+    position_count = 0
+
+    if Path(output_file).exists():
+        with open(output_file, 'r') as f:
+            for line in f:
+                try:
+                    data = json.loads(line)
+                    evaluated_fens.add(data['fen'])
+                    position_count += 1
+                except json.JSONDecodeError:
+                    pass
+        print(f"Resuming from {position_count} already evaluated positions")
+
+    # Load all FENs that need evaluation
+    fens_to_evaluate = []
+    fens_seen_in_batch = set()  # Track duplicates within current batch
+    skipped_invalid = 0
+    skipped_duplicate = 0
+
+    with open(positions_file, 'r') as f:
+        for fen in f:
+            fen = fen.strip()
+
+            if not fen:
+                skipped_invalid += 1
+                continue
+
+            if fen in evaluated_fens:
+                skipped_duplicate += 1
+                continue
+
+            if fen in fens_seen_in_batch:
+                skipped_duplicate += 1
+                continue
+
+            fens_to_evaluate.append(fen)
+            fens_seen_in_batch.add(fen)
+
+    total_to_evaluate = len(fens_to_evaluate)
+    total_lines = position_count + skipped_duplicate + skipped_invalid + total_to_evaluate
+
+    if total_to_evaluate == 0:
+        if position_count == 0:
+            print(f"Error: No valid positions to evaluate in {positions_file}")
+            sys.exit(1)
+        else:
+            print(f"All positions already evaluated. No new positions to process.")
+            return True
+
+    print(f"Total positions to process: {total_lines}")
+    print(f"New positions to evaluate: {total_to_evaluate}")
+    print(f"Using depth: {depth}")
+    print()
+
+    # Split FENs into batches for workers
+    batches = []
+    for i in range(0, total_to_evaluate, batch_size):
+        batch = fens_to_evaluate[i:i+batch_size]
+        batches.append((batch, stockfish_path, depth, normalize))
+
+    # Process batches in parallel
+    evaluated = 0
+    errors = 0
+    raw_evals = []
+    normalized_evals = []
+
+    import time
+    start_time = time.time()
+
+    with Pool(num_workers) as pool:
+        with tqdm(total=total_lines, initial=position_count, desc="Labeling positions") as pbar:
+            with open(output_file, 'a') as out:
+                for batch_idx, batch_results in enumerate(pool.imap_unordered(_evaluate_fen_batch, batches)):
+                    for fen, eval_normalized, eval_cp in batch_results:
+                        # Skip if already evaluated in output file during this run
+                        if fen in evaluated_fens:
+                            continue
+
+                        data = {"fen": fen, "eval": eval_normalized, "eval_raw": eval_cp}
+                        out.write(json.dumps(data) + '\n')
+                        evaluated_fens.add(fen)  # Track as evaluated
+                        evaluated += 1
+                        raw_evals.append(eval_cp)
+                        normalized_evals.append(eval_normalized)
+                        pbar.update(1)
+
+                    # Update progress for any failed evaluations in the batch
+                    batch_size_actual = len(batches[0][0]) if batches else batch_size
+                    failed = batch_size_actual - len(batch_results)
+                    if failed > 0:
+                        errors += failed
+                        pbar.update(failed)
+
+                    # Calculate and show throughput and ETA
+                    elapsed = time.time() - start_time
+                    throughput = evaluated / elapsed if elapsed > 0 else 0
+                    remaining_positions = total_to_evaluate - evaluated
+                    eta_seconds = remaining_positions / throughput if throughput > 0 else 0
+                    eta_str = f"{int(eta_seconds // 60)}:{int(eta_seconds % 60):02d}"
+
+                    if (batch_idx + 1) % max(1, len(batches) // 10) == 0:
+                        pbar.set_postfix({
+                            'rate': f'{throughput:.0f} pos/s',
+                            'eta': eta_str
+                        })
+
+    # Print summary and analysis
+    print()
+    print("=" * 60)
+    print("LABELING SUMMARY")
+    print("=" * 60)
+    print(f"Successfully evaluated: {evaluated}")
+    print(f"Skipped (duplicates):   {skipped_duplicate}")
+    print(f"Skipped (invalid):      {skipped_invalid}")
+    print(f"Errors:                 {errors}")
+    print(f"Total processed:        {evaluated + skipped_duplicate + skipped_invalid + errors}")
+    print("=" * 60)
+    print()
+
+    if evaluated == 0:
+        print("WARNING: No positions were successfully evaluated!")
+        print("Check that:")
+        print("  1. positions.txt is not empty")
+        print("  2. positions.txt contains valid FENs")
+        print("  3. Stockfish is installed and working")
+        print("  4. Stockfish path is correct")
+        return False
+
+    # Print distribution analysis
+    if raw_evals:
+        raw_evals_arr = np.array(raw_evals)
+        norm_evals_arr = np.array(normalized_evals)
+
+        print("=" * 60)
+        print("EVALUATION DISTRIBUTION ANALYSIS")
+        print("=" * 60)
+        print()
+        print("Raw Evaluations (centipawns):")
+        print(f"  Min:    {raw_evals_arr.min():.1f}")
+        print(f"  Max:    {raw_evals_arr.max():.1f}")
+        print(f"  Mean:   {raw_evals_arr.mean():.1f}")
+        print(f"  Median: {np.median(raw_evals_arr):.1f}")
+        print(f"  Std:    {raw_evals_arr.std():.1f}")
+        print()
+
+        print("Normalized Evaluations (tanh):")
+        print(f"  Min:    {norm_evals_arr.min():.4f}")
+        print(f"  Max:    {norm_evals_arr.max():.4f}")
+        print(f"  Mean:   {norm_evals_arr.mean():.4f}")
+        print(f"  Median: {np.median(norm_evals_arr):.4f}")
+        print(f"  Std:    {norm_evals_arr.std():.4f}")
+        print()
+
+        # Distribution buckets
+        print("Raw Evaluation Buckets (counts):")
+        buckets = [
+            (-float('inf'), -500, "< -5.00"),
+            (-500, -300, "[-5.00, -3.00)"),
+            (-300, -100, "[-3.00, -1.00)"),
+            (-100, 0, "[-1.00, 0.00)"),
+            (0, 100, "[0.00, 1.00)"),
+            (100, 300, "[1.00, 3.00)"),
+            (300, 500, "[3.00, 5.00)"),
+            (500, float('inf'), "> 5.00"),
+        ]
+        for low, high, label in buckets:
+            count = np.sum((raw_evals_arr > low) & (raw_evals_arr <= high))
+            pct = 100.0 * count / len(raw_evals_arr)
+            print(f"  {label}: {count:6d} ({pct:5.1f}%)")
+
+        print("=" * 60)
+        print()
+
+    print(f"✓ Labeling complete. Output saved to {output_file}")
+    return True
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser(description="Label chess positions with Stockfish evaluations")
+    parser.add_argument("positions_file", nargs="?", default="positions.txt",
+                        help="Input positions file (default: positions.txt)")
+    parser.add_argument("output_file", nargs="?", default="training_data.jsonl",
+                        help="Output file (default: training_data.jsonl)")
+    parser.add_argument("stockfish_path", nargs="?", default=None,
+                        help="Path to Stockfish binary (default: $STOCKFISH_PATH or 'stockfish')")
+    parser.add_argument("--depth", type=int, default=12,
+                        help="Stockfish depth (default: 12)")
+    parser.add_argument("--batch-size", type=int, default=1000,
+                        help="Batch size for processing (default: 1000)")
+    parser.add_argument("--no-normalize", action="store_true",
+                        help="Disable evaluation normalization (keep raw centipawns)")
+    parser.add_argument("--verbose", action="store_true",
+                        help="Print detailed error messages")
+    parser.add_argument("--workers", type=int, default=1,
+                        help="Number of parallel Stockfish processes (default: 1)")
+
+    args = parser.parse_args()
+
+    # Determine Stockfish path
+    stockfish_path = args.stockfish_path or os.environ.get("STOCKFISH_PATH", "stockfish")
+
+    success = label_positions_with_stockfish(
+        positions_file=args.positions_file,
+        output_file=args.output_file,
+        stockfish_path=stockfish_path,
+        batch_size=args.batch_size,
+        depth=args.depth,
+        normalize=not args.no_normalize,
+        verbose=args.verbose,
+        num_workers=args.workers
+    )
+
+    sys.exit(0 if success else 1)
@@ -0,0 +1,208 @@
+#!/usr/bin/env python3
+"""Import pre-labeled positions from the Lichess evaluation database.
+
+Source: https://database.lichess.org/#evals
+Format: lichess_db_eval.jsonl.zst — compressed JSONL, one position per line.
+
+Each line:
+  {
+    "fen": "<pieces> <turn> <castling> <ep>",
+    "evals": [
+      {
+        "knodes": <int>,
+        "depth": <int>,
+        "pvs": [{"cp": <int>, "line": "..."} | {"mate": <int>, "line": "..."}]
+      }
+    ]
+  }
+
+cp and mate are from White's perspective (positive = White winning), matching
+the sign convention used by label.py (score.white()) and expected by train.py.
+"""
+
+import json
+import sys
+import numpy as np
+from pathlib import Path
+from tqdm import tqdm
+
+MATE_CP = 20000
+SCALE   = 300.0
+
+
+def _best_eval(evals: list) -> dict | None:
+    """Return the highest-depth evaluation entry, using knodes as tiebreaker."""
+    if not evals:
+        return None
+    return max(evals, key=lambda e: (e.get("depth", 0), e.get("knodes", 0)))
+
+
+def _cp_from_pv(pv: dict) -> int | None:
+    """Extract centipawn value from a principal variation entry."""
+    if "cp" in pv:
+        return max(-MATE_CP, min(MATE_CP, pv["cp"]))
+    if "mate" in pv:
+        return MATE_CP if pv["mate"] > 0 else -MATE_CP
+    return None
+
+
+def _normalize(cp: int) -> float:
+    return float(np.tanh(cp / SCALE))
+
+
+def import_lichess_evals(
+    input_path: str,
+    output_file: str,
+    max_positions: int | None = None,
+    min_depth: int = 0,
+    verbose: bool = False,
+) -> int:
+    """Stream the Lichess eval database and write a labeled.jsonl file.
+
+    Args:
+        input_path:    Path to lichess_db_eval.jsonl.zst (or uncompressed .jsonl).
+        output_file:   Destination labeled.jsonl (appended — supports resuming).
+        max_positions: Stop after this many new positions (None = no limit).
+        min_depth:     Skip positions whose best eval has depth < min_depth.
+        verbose:       Print warnings for skipped lines.
+
+    Returns:
+        Number of new positions written.
+    """
+    import zstandard as zstd
+
+    input_path = Path(input_path)
+    if not input_path.exists():
+        print(f"Error: {input_path} not found")
+        sys.exit(1)
+
+    # Resume: collect already-written FENs so we skip duplicates.
+    seen_fens: set[str] = set()
+    if Path(output_file).exists():
+        with open(output_file, "r") as f:
+            for line in f:
+                try:
+                    seen_fens.add(json.loads(line)["fen"])
+                except (json.JSONDecodeError, KeyError):
+                    pass
+        if seen_fens:
+            print(f"Resuming — skipping {len(seen_fens):,} already-imported positions")
+
+    written = 0
+    skipped_depth = 0
+    skipped_no_eval = 0
+    skipped_dup = 0
+
+    def iter_lines():
+        """Yield decoded text lines from either a .zst or plain .jsonl file."""
+        import io
+        if input_path.suffix == ".zst":
+            dctx = zstd.ZstdDecompressor()
+            with open(input_path, "rb") as fh:
+                with dctx.stream_reader(fh) as reader:
+                    text_stream = io.TextIOWrapper(reader, encoding="utf-8")
+                    yield from text_stream
+        else:
+            with open(input_path, "r", encoding="utf-8") as fh:
+                yield from fh
+
+    try:
+        with open(output_file, "a") as out:
+            with tqdm(desc="Importing Lichess evals", unit=" pos") as pbar:
+                for raw_line in iter_lines():
+                    line = raw_line.strip()
+                    if not line:
+                        continue
+
+                    try:
+                        data = json.loads(line)
+                    except json.JSONDecodeError:
+                        if verbose:
+                            print("Warning: malformed JSON line skipped")
+                        continue
+
+                    fen = data.get("fen", "")
+                    if not fen:
+                        skipped_no_eval += 1
+                        continue
+
+                    if fen in seen_fens:
+                        skipped_dup += 1
+                        continue
+
+                    best = _best_eval(data.get("evals", []))
+                    if best is None:
+                        skipped_no_eval += 1
+                        continue
+
+                    if best.get("depth", 0) < min_depth:
+                        skipped_depth += 1
+                        continue
+
+                    pvs = best.get("pvs", [])
+                    if not pvs:
+                        skipped_no_eval += 1
+                        continue
+
+                    cp = _cp_from_pv(pvs[0])
+                    if cp is None:
+                        skipped_no_eval += 1
+                        continue
+
+                    record = {
+                        "fen":      fen,
+                        "eval":     _normalize(cp),
+                        "eval_raw": cp,
+                    }
+                    out.write(json.dumps(record) + "\n")
+                    seen_fens.add(fen)
+                    written += 1
+                    pbar.update(1)
+
+                    if max_positions and written >= max_positions:
+                        print(f"\nReached max_positions limit ({max_positions:,})")
+                        break
+
+    except Exception:
+        raise
+
+    print()
+    print("=" * 60)
+    print("LICHESS IMPORT SUMMARY")
+    print("=" * 60)
+    print(f"Positions written:  {written:,}")
+    print(f"Skipped (dup):      {skipped_dup:,}")
+    print(f"Skipped (no eval):  {skipped_no_eval:,}")
+    print(f"Skipped (depth<{min_depth}): {skipped_depth:,}")
+    print("=" * 60)
+    print(f"\n✓ Output: {output_file}")
+
+    return written
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser(
+        description="Import Lichess pre-labeled positions into labeled.jsonl"
+    )
+    parser.add_argument("input_path",
+                        help="Path to lichess_db_eval.jsonl.zst")
+    parser.add_argument("output_file", nargs="?", default="training_data.jsonl",
+                        help="Output labeled.jsonl (default: training_data.jsonl)")
+    parser.add_argument("--max-positions", type=int, default=None,
+                        help="Stop after N positions (default: no limit)")
+    parser.add_argument("--min-depth", type=int, default=0,
+                        help="Minimum eval depth to accept (default: 0)")
+    parser.add_argument("--verbose", action="store_true",
+                        help="Print warnings for skipped lines")
+
+    args = parser.parse_args()
+    count = import_lichess_evals(
+        input_path=args.input_path,
+        output_file=args.output_file,
+        max_positions=args.max_positions,
+        min_depth=args.min_depth,
+        verbose=args.verbose,
+    )
+    sys.exit(0 if count > 0 else 1)
@@ -0,0 +1,249 @@
+import chess
+import csv
+import json
+import sys
+import urllib.request
+from pathlib import Path
+from typing import Set, Tuple
+
+try:
+    import zstandard as zstd
+except ImportError:
+    print("zstandard library not found. Install with: pip install zstandard")
+    sys.exit(1)
+
+from generate import play_random_game_and_collect_positions
+
+
+def download_and_extract_puzzle_db(
+        url: str = 'https://database.lichess.org/lichess_db_puzzle.csv.zst',
+        output_dir: str = 'tactical_data'
+):
+    """Download and extract the Lichess puzzle database."""
+    output_path = Path(output_dir)
+    output_path.mkdir(parents=True, exist_ok=True)
+
+    csv_file = output_path / 'lichess_db_puzzle.csv'
+    zst_file = output_path / 'lichess_db_puzzle.csv.zst'
+
+    # Download if not already present
+    if not zst_file.exists():
+        print(f"Downloading puzzle database from {url}...")
+        try:
+            urllib.request.urlretrieve(url, zst_file)
+            print(f"Downloaded to {zst_file}")
+        except Exception as e:
+            print(f"Failed to download: {e}")
+            return None
+
+    # Extract if CSV doesn't exist
+    if not csv_file.exists():
+        print(f"Extracting {zst_file}...")
+        try:
+            with open(zst_file, 'rb') as f:
+                dctx = zstd.ZstdDecompressor()
+                with dctx.stream_reader(f) as reader:
+                    with open(csv_file, 'wb') as out:
+                        out.write(reader.read())
+            print(f"Extracted to {csv_file}")
+        except Exception as e:
+            print(f"Failed to extract: {e}")
+            return None
+
+    return str(csv_file)
+
+
+def extract_puzzle_positions(
+        puzzle_csv: str,
+        max_puzzles: int = 300_000
+) -> Set[str]:
+    """
+    Extract the position BEFORE the blunder from each puzzle.
+    This is exactly the type of position where tactical
+    recognition matters most.
+
+    Returns a set of unique FENs.
+    """
+    positions = set()
+
+    with open(puzzle_csv) as f:
+        reader = csv.DictReader(f)
+        for row in reader:
+            if len(positions) >= max_puzzles:
+                break
+
+            try:
+                board = chess.Board(row['FEN'])
+
+                # The puzzle FEN is AFTER the blunder move
+                # We want the position BEFORE — so it learns
+                # to find the tactic, not just play it
+                moves = row['Moves'].split()
+
+                # Undo one move to get pre-tactic position
+                board.push_uci(moves[0])  # opponent blunder
+                fen = board.fen()
+
+                # Filter for useful tactical themes
+                themes = row.get('Themes', '')
+                useful = any(t in themes for t in [
+                    'fork', 'pin', 'skewer', 'discoveredAttack',
+                    'mate', 'mateIn2', 'mateIn3', 'hangingPiece',
+                    'trappedPiece', 'sacrifice'
+                ])
+
+                if useful:
+                    positions.add(fen)
+
+            except Exception:
+                continue
+
+    return positions
+
+
+def load_positions_from_file(file_path: str) -> Set[str]:
+    """Load positions from a text file (one FEN per line)."""
+    positions = set()
+    try:
+        with open(file_path) as f:
+            for line in f:
+                line = line.strip()
+                if line:
+                    positions.add(line)
+        print(f"Loaded {len(positions)} positions from {file_path}")
+        return positions
+    except Exception as e:
+        print(f"Failed to load from {file_path}: {e}")
+        return set()
+
+
+def merge_positions(
+        tactical: Set[str],
+        other: Set[str],
+        output_file: str = 'position.txt'
+):
+    """Merge two position sets and write to file."""
+    merged = tactical | other
+
+    with open(output_file, 'w') as f:
+        for fen in merged:
+            f.write(fen + '\n')
+
+    overlap = len(tactical & other)
+    print(f"\n{'='*60}")
+    print(f"MERGE SUMMARY")
+    print(f"{'='*60}")
+    print(f"Tactical positions:        {len(tactical):,}")
+    print(f"Other positions:           {len(other):,}")
+    print(f"Overlap (deduplicated):    {overlap:,}")
+    print(f"Total merged positions:    {len(merged):,}")
+    print(f"Written to:                {output_file}")
+    print(f"{'='*60}\n")
+
+
+def extract_tactical_only(
+        puzzle_csv: str,
+        output_file: str,
+        max_puzzles: int = 300_000
+) -> int:
+    """Extract tactical positions and save to file (no merge prompts).
+
+    Args:
+        puzzle_csv: Path to Lichess puzzle CSV
+        output_file: Where to save the FEN positions
+        max_puzzles: Maximum puzzles to extract
+
+    Returns:
+        Number of positions extracted
+    """
+    print("Extracting tactical positions from puzzle database...")
+    tactical_positions = extract_puzzle_positions(puzzle_csv, max_puzzles)
+
+    with open(output_file, 'w') as f:
+        for fen in tactical_positions:
+            f.write(fen + '\n')
+
+    return len(tactical_positions)
+
+
+def interactive_merge_positions(
+        puzzle_csv: str,
+        output_file: str = 'position.txt',
+        max_puzzles: int = 300_000
+):
+    """Interactive workflow: extract tactical positions and merge with user selection."""
+    print("\n" + "="*60)
+    print("TACTICAL POSITION EXTRACTOR & MERGER")
+    print("="*60 + "\n")
+
+    # Extract tactical positions
+    print("Extracting tactical positions from puzzle database...")
+    tactical_positions = extract_puzzle_positions(puzzle_csv, max_puzzles)
+    print(f"Extracted {len(tactical_positions):,} unique tactical positions\n")
+
+    # Ask what to merge with
+    print("What would you like to merge with these tactical positions?")
+    print("1. Load from a position file")
+    print("2. Generate random positions")
+    print("3. Skip merging (save tactical only)")
+
+    choice = input("\nEnter choice (1-3): ").strip()
+
+    other_positions = set()
+
+    if choice == '1':
+        file_path = input("Enter path to position file: ").strip()
+        other_positions = load_positions_from_file(file_path)
+
+    elif choice == '2':
+        positions_to_gen = input("How many positions to generate? (default 1000000): ").strip()
+        try:
+            positions_to_gen = int(positions_to_gen) if positions_to_gen else 1000000
+        except ValueError:
+            positions_to_gen = 1000000
+
+        temp_file = 'temp_generated_positions.txt'
+        print(f"\nGenerating {positions_to_gen:,} random positions...")
+        play_random_game_and_collect_positions(
+            output_file=temp_file,
+            total_positions=positions_to_gen,
+            samples_per_game=1,
+            min_move=1,
+            max_move=50,
+            num_workers=8
+        )
+        other_positions = load_positions_from_file(temp_file)
+
+    elif choice == '3':
+        print("Skipping merge, saving tactical positions only...")
+
+    else:
+        print("Invalid choice, saving tactical positions only...")
+
+    merge_positions(tactical_positions, other_positions, output_file)
+
+
+if __name__ == '__main__':
+    import argparse
+
+    parser = argparse.ArgumentParser(description="Extract and merge tactical positions")
+    parser.add_argument("--url", default='https://database.lichess.org/lichess_db_puzzle.csv.zst',
+                        help="URL to download puzzle database from")
+    parser.add_argument("--output-dir", default='trainingdata',
+                        help="Directory to extract puzzle database to")
+    parser.add_argument("--max-puzzles", type=int, default=300_000,
+                        help="Maximum puzzles to extract (default: 300000)")
+    parser.add_argument("--output-file", default='position.txt',
+                        help="Output file for merged positions (default: position.txt)")
+
+    args = parser.parse_args()
+
+    # Download and extract
+    csv_path = download_and_extract_puzzle_db(args.url, args.output_dir)
+
+    if csv_path:
+        # Interactive merge
+        interactive_merge_positions(csv_path, args.output_file, args.max_puzzles)
+    else:
+        print("Failed to download/extract puzzle database")
+        sys.exit(1)
@@ -0,0 +1,676 @@
+#!/usr/bin/env python3
+"""Train NNUE neural network for chess evaluation."""
+
+import json
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import DataLoader, Dataset
+import sys
+from pathlib import Path
+from tqdm import tqdm
+import chess
+from datetime import datetime, timedelta
+import re
+import numpy as np
+
+class NNUEDataset(Dataset):
+    """Dataset of chess positions with evaluations."""
+
+    def __init__(self, data_file):
+        self.positions = []
+        self.evals = []
+        self.evals_raw = []
+        self.is_normalized = None
+
+        with open(data_file, 'r') as f:
+            for line in f:
+                try:
+                    data = json.loads(line)
+                    fen = data['fen']
+                    eval_val = data['eval']
+                    self.positions.append(fen)
+                    self.evals.append(eval_val)
+
+                    # Check if normalized or raw
+                    if self.is_normalized is None:
+                        # If eval is in range [-1, 1], assume normalized
+                        self.is_normalized = abs(eval_val) <= 1.0
+
+                    # Store raw if available
+                    if 'eval_raw' in data:
+                        self.evals_raw.append(data['eval_raw'])
+                    else:
+                        self.evals_raw.append(eval_val)
+                except (json.JSONDecodeError, KeyError):
+                    pass
+
+    def __len__(self):
+        return len(self.positions)
+
+    def __getitem__(self, idx):
+        fen = self.positions[idx]
+        eval_val = self.evals[idx]
+        features = fen_to_features(fen)
+
+        # Use evaluation as-is if normalized, otherwise apply sigmoid scaling
+        if self.is_normalized:
+            target = torch.tensor(eval_val, dtype=torch.float32)
+        else:
+            target = torch.sigmoid(torch.tensor(eval_val / 400.0, dtype=torch.float32))
+
+        return features, target
+
+def fen_to_features(fen):
+    """Convert FEN to 768-dimensional binary feature vector."""
+    # Piece type to index: pawn=0, knight=1, bishop=2, rook=3, queen=4, king=5
+    piece_to_idx = {'p': 0, 'n': 1, 'b': 2, 'r': 3, 'q': 4, 'k': 5,
+                    'P': 6, 'N': 7, 'B': 8, 'R': 9, 'Q': 10, 'K': 11}
+
+    features = torch.zeros(768, dtype=torch.float32)
+
+    try:
+        board = chess.Board(fen)
+
+        # 12 piece types × 64 squares = 768
+        for square in chess.SQUARES:
+            piece = board.piece_at(square)
+            if piece is not None:
+                piece_char = piece.symbol()
+                if piece_char in piece_to_idx:
+                    piece_idx = piece_to_idx[piece_char]
+                    feature_idx = piece_idx * 64 + square
+                    features[feature_idx] = 1.0
+    except:
+        pass
+
+    return features
+
+DEFAULT_HIDDEN_SIZES = [1536, 1024, 512, 256]
+
+
+class NNUE(nn.Module):
+    """NNUE neural network with configurable hidden layers.
+
+    Architecture: 768 → hidden_sizes[0] → ... → hidden_sizes[-1] → 1
+    Layer attributes follow the naming l1, l2, ..., lN so export.py can
+    infer the architecture directly from the state_dict.
+    """
+
+    def __init__(self, hidden_sizes=None, dropout_rate=0.2):
+        super().__init__()
+        if hidden_sizes is None:
+            hidden_sizes = DEFAULT_HIDDEN_SIZES
+        self.hidden_sizes = list(hidden_sizes)
+        sizes = [768] + self.hidden_sizes + [1]
+        num_hidden = len(self.hidden_sizes)
+
+        for i in range(num_hidden):
+            setattr(self, f"l{i + 1}", nn.Linear(sizes[i], sizes[i + 1]))
+            setattr(self, f"relu{i + 1}", nn.ReLU())
+            setattr(self, f"drop{i + 1}", nn.Dropout(dropout_rate))
+        setattr(self, f"l{num_hidden + 1}", nn.Linear(sizes[-2], sizes[-1]))
+        self._num_hidden = num_hidden
+
+    def forward(self, x):
+        for i in range(1, self._num_hidden + 1):
+            layer = getattr(self, f"l{i}")
+            relu = getattr(self, f"relu{i}")
+            drop = getattr(self, f"drop{i}")
+            x = drop(relu(layer(x)))
+        return getattr(self, f"l{self._num_hidden + 1}")(x)
+
+def find_next_version(base_name="nnue_weights"):
+    """Find the next version number for model versioning.
+
+    Looks for nnue_weights_v*.pt files and returns the next version number.
+    If no versioned files exist, returns 1.
+    """
+    base_path = Path(base_name)
+    directory = base_path.parent
+    filename = base_path.name
+
+    pattern = re.compile(rf"{re.escape(filename)}_v(\d+)\.pt")
+    versions = []
+
+    for file in directory.glob(f"{filename}_v*.pt"):
+        match = pattern.match(file.name)
+        if match:
+            versions.append(int(match.group(1)))
+
+    if versions:
+        return max(versions) + 1
+    return 1
+
+def save_metadata(weights_file, metadata):
+    """Save training metadata alongside the weights file.
+
+    Args:
+        weights_file: Path to the .pt file (e.g., nnue_weights_v1.pt)
+        metadata: Dictionary with training info
+    """
+    metadata_file = weights_file.replace(".pt", "_metadata.json")
+
+    with open(metadata_file, "w") as f:
+        json.dump(metadata, f, indent=2, default=str)
+
+    return metadata_file
+
+def _setup_training(data_file, batch_size, subsample_ratio):
+    """Set up device, dataset, and data loaders.
+
+    Returns:
+        (device, dataset, train_dataset, val_dataset, train_loader, val_loader, num_positions)
+    """
+    print("Checking GPU availability...")
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    if torch.cuda.is_available():
+        print(f"✓ GPU available: {torch.cuda.get_device_name(0)}")
+        print(f"  GPU memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.2f} GB")
+    else:
+        print("⚠ GPU not available, using CPU")
+    print(f"Using device: {device}")
+    print()
+
+    print("Loading dataset...")
+    dataset = NNUEDataset(data_file)
+    num_positions = len(dataset)
+    print(f"Dataset size: {num_positions}")
+    print(f"Data normalization: {'Yes (tanh)' if dataset.is_normalized else 'No (raw centipawns)'})")
+
+    evals_array = np.array(dataset.evals)
+    print()
+    print("=" * 60)
+    print("TRAINING DATASET DIAGNOSTICS")
+    print("=" * 60)
+    print(f"Min evaluation:     {evals_array.min():.4f}")
+    print(f"Max evaluation:     {evals_array.max():.4f}")
+    print(f"Mean evaluation:    {evals_array.mean():.4f}")
+    print(f"Median evaluation:  {np.median(evals_array):.4f}")
+    print(f"Std deviation:      {evals_array.std():.4f}")
+    print("=" * 60)
+    print()
+
+    train_size = int(0.9 * len(dataset))
+    val_size = len(dataset) - train_size
+
+    from torch.utils.data import random_split, RandomSampler
+    generator = torch.Generator().manual_seed(42)
+    train_dataset, val_dataset = random_split(dataset, [train_size, val_size], generator=generator)
+
+    subsample_size = max(1, int(subsample_ratio * len(train_dataset)))
+    train_sampler = RandomSampler(train_dataset, replacement=False, num_samples=subsample_size)
+    train_loader = DataLoader(
+        train_dataset,
+        batch_size=batch_size,
+        sampler=train_sampler,
+        num_workers=8,
+        pin_memory=True,
+        persistent_workers=True
+    )
+    val_loader = DataLoader(
+        val_dataset,
+        batch_size=batch_size,
+        shuffle=False,
+        num_workers=8,
+        pin_memory=True,
+        persistent_workers=True
+    )
+
+    return device, dataset, train_dataset, val_dataset, train_loader, val_loader, num_positions
+
+def _run_training_season(
+    model, optimizer, scheduler, scaler,
+    train_loader, val_loader, train_dataset, val_dataset,
+    device, criterion, output_file,
+    start_epoch, epochs, early_stopping_patience,
+    season_start_time, deadline=None, initial_best_val_loss=float('inf')
+):
+    """Run one training season until epoch limit, early stopping, or deadline.
+
+    Args:
+        initial_best_val_loss: Baseline to beat — epochs that don't improve on this count
+                               toward early stopping and do not save snapshots.
+    Returns:
+        (best_val_loss, best_model_state, last_epoch)
+        best_model_state is None if no epoch beat initial_best_val_loss.
+    """
+    best_val_loss = initial_best_val_loss
+    best_model_state = None
+    epochs_without_improvement = 0
+    total_epochs = start_epoch + epochs
+    last_epoch = start_epoch - 1
+
+    for epoch in range(start_epoch, start_epoch + epochs):
+        if deadline and datetime.now() >= deadline:
+            print("Time limit reached, stopping season.")
+            break
+
+        epoch_display = epoch + 1
+
+        # Train
+        model.train()
+        train_loss = 0.0
+        with tqdm(total=len(train_loader), desc=f"Epoch {epoch_display}/{total_epochs} - Train") as pbar:
+            for batch_features, batch_targets in train_loader:
+                batch_features = batch_features.to(device)
+                batch_targets = batch_targets.to(device).unsqueeze(1)
+
+                optimizer.zero_grad()
+
+                with torch.amp.autocast('cuda' if torch.cuda.is_available() else 'cpu'):
+                    outputs = model(batch_features)
+                    loss = criterion(outputs, batch_targets)
+
+                scaler.scale(loss).backward()
+                scaler.step(optimizer)
+                scaler.update()
+
+                train_loss += loss.item() * batch_features.size(0)
+                pbar.update(1)
+
+        train_loss /= len(train_dataset)
+
+        # Validation
+        model.eval()
+        val_loss = 0.0
+        with torch.no_grad():
+            with tqdm(total=len(val_loader), desc=f"Epoch {epoch_display}/{total_epochs} - Val") as pbar:
+                for batch_features, batch_targets in val_loader:
+                    batch_features = batch_features.to(device)
+                    batch_targets = batch_targets.to(device).unsqueeze(1)
+
+                    with torch.amp.autocast('cuda' if torch.cuda.is_available() else 'cpu'):
+                        outputs = model(batch_features)
+                        loss = criterion(outputs, batch_targets)
+                    val_loss += loss.item() * batch_features.size(0)
+                    pbar.update(1)
+
+        val_loss /= len(val_dataset)
+
+        scheduler.step()
+
+        if torch.cuda.is_available():
+            gpu_mem_used = torch.cuda.memory_allocated(device) / 1e9
+            gpu_mem_reserved = torch.cuda.memory_reserved(device) / 1e9
+            print(f"GPU Memory: {gpu_mem_used:.2f}GB used, {gpu_mem_reserved:.2f}GB reserved")
+
+        elapsed_time = datetime.now() - season_start_time
+        time_per_epoch = elapsed_time.total_seconds() / (epoch + 1)
+        remaining_epochs = total_epochs - epoch_display
+        eta_seconds = time_per_epoch * remaining_epochs
+        eta_str = str(datetime.fromtimestamp(eta_seconds) - datetime.fromtimestamp(0)).split('.')[0]
+        print(f"Epoch {epoch_display}: Train Loss = {train_loss:.6f}, Val Loss = {val_loss:.6f} | ETA: {eta_str}")
+
+        checkpoint_file = output_file.replace(".pt", "_checkpoint.pt")
+        torch.save({
+            "epoch": epoch,
+            "model_state_dict": model.state_dict(),
+            "optimizer_state_dict": optimizer.state_dict(),
+            "scheduler_state_dict": scheduler.state_dict(),
+            "scaler_state_dict": scaler.state_dict(),
+            "best_val_loss": best_val_loss,
+            "hidden_sizes": model.hidden_sizes,
+        }, checkpoint_file)
+
+        if val_loss < best_val_loss:
+            best_val_loss = val_loss
+            best_model_state = model.state_dict().copy()
+            epochs_without_improvement = 0
+            snapshot_file = output_file.replace(".pt", "_best_snapshot.pt")
+            torch.save(best_model_state, snapshot_file)
+            print(f"  Best model snapshot saved: {snapshot_file} (val_loss: {val_loss:.6f})")
+        else:
+            epochs_without_improvement += 1
+
+        last_epoch = epoch
+
+        if early_stopping_patience and epochs_without_improvement >= early_stopping_patience:
+            print(f"Early stopping: no improvement for {early_stopping_patience} epochs")
+            break
+
+    return best_val_loss, best_model_state, last_epoch
+
+def _save_versioned_model(best_model_state, optimizer, scheduler, scaler, last_epoch,
+                           best_val_loss, output_file, use_versioning, num_positions,
+                           stockfish_depth, training_start_time, hidden_sizes=None,
+                           extra_metadata=None):
+    """Save the best model with optional versioning and metadata."""
+    final_output_file = output_file
+    metadata = {}
+    architecture = [768] + list(hidden_sizes or DEFAULT_HIDDEN_SIZES) + [1]
+
+    if use_versioning:
+        base_name = output_file.replace(".pt", "")
+        version = find_next_version(base_name)
+        final_output_file = f"{base_name}_v{version}.pt"
+
+        metadata = {
+            "version": version,
+            "date": training_start_time.isoformat(),
+            "num_positions": num_positions,
+            "stockfish_depth": stockfish_depth,
+            "final_val_loss": float(best_val_loss),
+            "architecture": architecture,
+            "device": str(torch.device("cuda" if torch.cuda.is_available() else "cpu")),
+            "notes": "Win rate vs classical eval: TBD (requires benchmark games)"
+        }
+        if extra_metadata:
+            metadata.update(extra_metadata)
+
+    torch.save({
+        "model_state_dict": best_model_state,
+        "optimizer_state_dict": optimizer.state_dict(),
+        "scheduler_state_dict": scheduler.state_dict(),
+        "scaler_state_dict": scaler.state_dict(),
+        "epoch": last_epoch,
+        "best_val_loss": best_val_loss,
+        "hidden_sizes": list(hidden_sizes or DEFAULT_HIDDEN_SIZES),
+    }, final_output_file)
+    print(f"Best model saved to {final_output_file}")
+
+    if use_versioning and metadata:
+        metadata_file = save_metadata(final_output_file, metadata)
+        print(f"Metadata saved to {metadata_file}")
+        print(f"\nTraining Summary:")
+        for key, val in metadata.items():
+            print(f"  {key}: {val}")
+
+def train_nnue(data_file, output_file="nnue_weights.pt", epochs=100, batch_size=16384, lr=0.001, checkpoint=None, stockfish_depth=12, use_versioning=True, early_stopping_patience=None, weight_decay=1e-4, subsample_ratio=1.0, hidden_sizes=None):
+    """Train the NNUE model with GPU optimizations and automatic mixed precision.
+
+    Args:
+        data_file: Path to training_data.jsonl
+        output_file: Where to save best weights (or base name if use_versioning=True)
+        epochs: Number of training epochs (default: 100)
+        batch_size: Training batch size (default: 16384)
+        lr: Learning rate (default: 0.001)
+        checkpoint: Optional path to checkpoint file to resume from
+        stockfish_depth: Depth used in Stockfish evaluation (for metadata)
+        use_versioning: If True, save as nnue_weights_v{N}.pt with metadata
+        early_stopping_patience: Stop if val loss doesn't improve for N epochs (None to disable)
+        weight_decay: L2 regularization strength (default: 1e-4, helps prevent overfitting)
+        subsample_ratio: Fraction of training data to sample per epoch (default: 1.0 = all data)
+        hidden_sizes: Hidden layer sizes (default: [1536, 1024, 512, 256])
+    """
+    device, dataset, train_dataset, val_dataset, train_loader, val_loader, num_positions = \
+        _setup_training(data_file, batch_size, subsample_ratio)
+
+    start_epoch = 0
+    best_val_loss = float('inf')
+    resolved_hidden_sizes = list(hidden_sizes or DEFAULT_HIDDEN_SIZES)
+
+    if checkpoint:
+        print(f"Loading checkpoint: {checkpoint}")
+        ckpt = torch.load(checkpoint, map_location=device)
+        if isinstance(ckpt, dict) and "model_state_dict" in ckpt:
+            ckpt_hidden = ckpt.get("hidden_sizes")
+            if ckpt_hidden and ckpt_hidden != resolved_hidden_sizes:
+                print(f"  Using architecture from checkpoint: {ckpt_hidden}")
+                resolved_hidden_sizes = ckpt_hidden
+
+    model = NNUE(hidden_sizes=resolved_hidden_sizes).to(device)
+    criterion = nn.MSELoss()
+    optimizer = optim.Adam(model.parameters(), lr=lr, weight_decay=weight_decay)
+    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs)
+    scaler = torch.amp.GradScaler('cuda') if torch.cuda.is_available() else torch.amp.GradScaler('cpu')
+
+    if checkpoint:
+        ckpt = torch.load(checkpoint, map_location=device)
+        if isinstance(ckpt, dict) and "model_state_dict" in ckpt:
+            model.load_state_dict(ckpt["model_state_dict"])
+            optimizer.load_state_dict(ckpt["optimizer_state_dict"])
+            scheduler.load_state_dict(ckpt["scheduler_state_dict"])
+            scaler.load_state_dict(ckpt["scaler_state_dict"])
+            start_epoch = ckpt["epoch"] + 1
+            best_val_loss = ckpt.get("best_val_loss", float('inf'))
+            print(f"Resumed from epoch {start_epoch} (best val loss so far: {best_val_loss:.6f})")
+        else:
+            model.load_state_dict(ckpt)
+            print("Loaded weights-only checkpoint (no optimizer state)")
+
+    checkpoint_val_loss = best_val_loss if checkpoint else float('inf')
+
+    subsample_size = max(1, int(subsample_ratio * len(train_dataset)))
+    arch_str = " → ".join(str(s) for s in [768] + resolved_hidden_sizes + [1])
+    print(f"Architecture: {arch_str}")
+    print(f"Training for {epochs} epochs with batch_size={batch_size}, lr={lr}...")
+    print(f"Learning rate scheduler: Cosine annealing (T_max={epochs})")
+    print(f"Mixed precision training: enabled")
+    print(f"Regularization: Dropout (20%) + L2 weight decay ({weight_decay})")
+    if subsample_ratio < 1.0:
+        print(f"Stochastic sampling: {subsample_ratio:.0%} of train set per epoch ({subsample_size:,} positions)")
+    if early_stopping_patience:
+        print(f"Early stopping enabled (patience: {early_stopping_patience} epochs)")
+    print()
+
+    training_start_time = datetime.now()
+
+    best_val_loss, best_model_state, last_epoch = _run_training_season(
+        model, optimizer, scheduler, scaler,
+        train_loader, val_loader, train_dataset, val_dataset,
+        device, criterion, output_file,
+        start_epoch, epochs, early_stopping_patience,
+        training_start_time
+    )
+
+    if best_model_state is None or best_val_loss >= checkpoint_val_loss:
+        print(f"\nNo improvement over checkpoint (best: {best_val_loss:.6f} vs checkpoint: {checkpoint_val_loss:.6f})")
+        print("No new model created.")
+        return
+
+    _save_versioned_model(
+        best_model_state, optimizer, scheduler, scaler, last_epoch,
+        best_val_loss, output_file, use_versioning, num_positions,
+        stockfish_depth, training_start_time,
+        hidden_sizes=resolved_hidden_sizes,
+        extra_metadata={"epochs": epochs, "batch_size": batch_size, "learning_rate": lr,
+                        "checkpoint": str(checkpoint) if checkpoint else None}
+    )
+
+def burst_train(data_file, output_file="nnue_weights.pt", duration_minutes=60,
+                epochs_per_season=50, early_stopping_patience=10,
+                batch_size=16384, lr=0.001, initial_checkpoint=None,
+                stockfish_depth=12, use_versioning=True,
+                weight_decay=1e-4, subsample_ratio=1.0, hidden_sizes=None):
+    """Train in burst mode: repeatedly restart from the best checkpoint until the time budget expires.
+
+    Each season trains with early stopping. When early stopping fires, the model reloads the
+    global best weights and begins a fresh season with a reset optimizer and scheduler.
+    This prevents the model from drifting away from its best known state.
+
+    Args:
+        data_file: Path to training_data.jsonl
+        output_file: Output file base name
+        duration_minutes: Total training budget in minutes
+        epochs_per_season: Max epochs per restart season (default: 50)
+        early_stopping_patience: Patience for early stopping within each season (default: 10)
+        batch_size: Training batch size (default: 16384)
+        lr: Learning rate reset to this value at the start of each season (default: 0.001)
+        initial_checkpoint: Optional weights-only .pt file to start from
+        stockfish_depth: Depth used in Stockfish evaluation (for metadata)
+        use_versioning: If True, save as nnue_weights_v{N}.pt with metadata
+        weight_decay: L2 regularization strength (default: 1e-4)
+        subsample_ratio: Fraction of training data to sample per epoch (default: 1.0)
+        hidden_sizes: Hidden layer sizes (default: [1536, 1024, 512, 256])
+    """
+    deadline = datetime.now() + timedelta(minutes=duration_minutes)
+
+    device, dataset, train_dataset, val_dataset, train_loader, val_loader, num_positions = \
+        _setup_training(data_file, batch_size, subsample_ratio)
+
+    resolved_hidden_sizes = list(hidden_sizes or DEFAULT_HIDDEN_SIZES)
+
+    if initial_checkpoint:
+        print(f"Loading initial weights: {initial_checkpoint}")
+        ckpt = torch.load(initial_checkpoint, map_location=device)
+        if isinstance(ckpt, dict) and "model_state_dict" in ckpt:
+            ckpt_hidden = ckpt.get("hidden_sizes")
+            if ckpt_hidden and ckpt_hidden != resolved_hidden_sizes:
+                print(f"  Using architecture from checkpoint: {ckpt_hidden}")
+                resolved_hidden_sizes = ckpt_hidden
+
+    model = NNUE(hidden_sizes=resolved_hidden_sizes).to(device)
+    criterion = nn.MSELoss()
+    best_global_val_loss = float('inf')
+
+    if initial_checkpoint:
+        ckpt = torch.load(initial_checkpoint, map_location=device)
+        if isinstance(ckpt, dict) and "model_state_dict" in ckpt:
+            model.load_state_dict(ckpt["model_state_dict"])
+            best_global_val_loss = ckpt.get("best_val_loss", float('inf'))
+            if best_global_val_loss < float('inf'):
+                print(f"Resumed from checkpoint (best val loss: {best_global_val_loss:.6f})")
+            else:
+                print("Initial weights loaded (no val loss in checkpoint).")
+        else:
+            model.load_state_dict(ckpt)
+            print("Loaded weights-only checkpoint (no val loss info).")
+
+    arch_str = " → ".join(str(s) for s in [768] + resolved_hidden_sizes + [1])
+    print(f"Architecture: {arch_str}")
+    print(f"Burst training: {duration_minutes}m budget, {epochs_per_season} epochs/season, patience={early_stopping_patience}")
+    print(f"Deadline: {deadline.strftime('%H:%M:%S')}")
+    print()
+
+    burst_start_time = datetime.now()
+    season = 0
+    best_global_state = None
+    last_optimizer = None
+    last_scheduler = None
+    last_scaler = None
+    last_epoch = 0
+
+    while datetime.now() < deadline:
+        season += 1
+        remaining_minutes = (deadline - datetime.now()).total_seconds() / 60
+        print(f"\n{'=' * 60}")
+        print(f"BURST SEASON {season} | {remaining_minutes:.1f} minutes remaining")
+        if best_global_val_loss < float('inf'):
+            print(f"Global best val loss so far: {best_global_val_loss:.6f}")
+        print(f"{'=' * 60}\n")
+
+        optimizer = optim.Adam(model.parameters(), lr=lr, weight_decay=weight_decay)
+        scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs_per_season)
+        scaler = torch.amp.GradScaler('cuda') if torch.cuda.is_available() else torch.amp.GradScaler('cpu')
+
+        season_start_time = datetime.now()
+        val_loss, model_state, last_epoch = _run_training_season(
+            model, optimizer, scheduler, scaler,
+            train_loader, val_loader, train_dataset, val_dataset,
+            device, criterion, output_file,
+            0, epochs_per_season, early_stopping_patience,
+            season_start_time, deadline=deadline,
+            initial_best_val_loss=best_global_val_loss
+        )
+
+        last_optimizer = optimizer
+        last_scheduler = scheduler
+        last_scaler = scaler
+
+        if model_state is not None and val_loss < best_global_val_loss:
+            best_global_val_loss = val_loss
+            best_global_state = model_state
+            print(f"  New global best: {best_global_val_loss:.6f} (season {season})")
+
+        # Reload global best for the next season so we never drift backwards
+        if best_global_state is not None:
+            model.load_state_dict(best_global_state)
+
+    total_minutes = (datetime.now() - burst_start_time).total_seconds() / 60
+    print(f"\n{'=' * 60}")
+    print(f"Burst training complete: {season} season(s) in {total_minutes:.1f}m")
+    print(f"Best val loss: {best_global_val_loss:.6f}")
+    print(f"{'=' * 60}\n")
+
+    if best_global_state is None:
+        print("No model improvement found. No file saved.")
+        return
+
+    _save_versioned_model(
+        best_global_state, last_optimizer, last_scheduler, last_scaler, last_epoch,
+        best_global_val_loss, output_file, use_versioning, num_positions,
+        stockfish_depth, burst_start_time,
+        hidden_sizes=resolved_hidden_sizes,
+        extra_metadata={
+            "mode": "burst",
+            "duration_minutes": duration_minutes,
+            "epochs_per_season": epochs_per_season,
+            "early_stopping_patience": early_stopping_patience,
+            "seasons_completed": season,
+            "batch_size": batch_size,
+            "learning_rate": lr,
+            "initial_checkpoint": str(initial_checkpoint) if initial_checkpoint else None,
+        }
+    )
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser(description="Train NNUE neural network for chess evaluation")
+    parser.add_argument("data_file", nargs="?", default="training_data.jsonl",
+                        help="Path to training_data.jsonl (default: training_data.jsonl)")
+    parser.add_argument("output_file", nargs="?", default="nnue_weights.pt",
+                        help="Output file base name (default: nnue_weights.pt)")
+    parser.add_argument("--checkpoint", type=str, default=None,
+                        help="Path to checkpoint file to resume training from (optional)")
+    parser.add_argument("--epochs", type=int, default=100,
+                        help="Number of epochs to train (default: 100)")
+    parser.add_argument("--batch-size", type=int, default=16384,
+                        help="Batch size (default: 16384)")
+    parser.add_argument("--lr", type=float, default=0.001,
+                        help="Learning rate (default: 0.001)")
+    parser.add_argument("--early-stopping", type=int, default=None,
+                        help="Stop if val loss doesn't improve for N epochs (optional)")
+    parser.add_argument("--stockfish-depth", type=int, default=12,
+                        help="Stockfish depth used for evaluations (for metadata, default: 12)")
+    parser.add_argument("--no-versioning", action="store_true",
+                        help="Disable automatic versioning (save directly to output file)")
+    parser.add_argument("--weight-decay", type=float, default=5e-5,
+                        help="L2 regularization strength (default: 1e-4, helps prevent overfitting)")
+    parser.add_argument("--subsample-ratio", type=float, default=1.0,
+                        help="Fraction of training data to sample per epoch (default: 1.0 = all data)")
+    parser.add_argument("--hidden-layers", type=str, default=None,
+                        help="Comma-separated hidden layer sizes (default: 1536,1024,512,256)")
+
+    # Burst mode
+    parser.add_argument("--burst-duration", type=float, default=None,
+                        help="Enable burst mode: total training budget in minutes")
+    parser.add_argument("--epochs-per-season", type=int, default=50,
+                        help="Max epochs per burst season before restarting (default: 50, burst mode only)")
+
+    args = parser.parse_args()
+
+    hidden_sizes = [int(x) for x in args.hidden_layers.split(",")] if args.hidden_layers else None
+
+    if args.burst_duration is not None:
+        burst_train(
+            data_file=args.data_file,
+            output_file=args.output_file,
+            duration_minutes=args.burst_duration,
+            epochs_per_season=args.epochs_per_season,
+            early_stopping_patience=args.early_stopping or 10,
+            batch_size=args.batch_size,
+            lr=args.lr,
+            initial_checkpoint=args.checkpoint,
+            stockfish_depth=args.stockfish_depth,
+            use_versioning=not args.no_versioning,
+            weight_decay=args.weight_decay,
+            subsample_ratio=args.subsample_ratio,
+            hidden_sizes=hidden_sizes,
+        )
+    else:
+        train_nnue(
+            data_file=args.data_file,
+            output_file=args.output_file,
+            epochs=args.epochs,
+            batch_size=args.batch_size,
+            lr=args.lr,
+            checkpoint=args.checkpoint,
+            stockfish_depth=args.stockfish_depth,
+            use_versioning=not args.no_versioning,
+            early_stopping_patience=args.early_stopping,
+            weight_decay=args.weight_decay,
+            subsample_ratio=args.subsample_ratio,
+            hidden_sizes=hidden_sizes,
+        )
@@ -0,0 +1,30 @@
+# Setup and run NNUE training pipeline
+
+$ScriptDir = Split-Path -Parent $MyInvocation.MyCommand.Path
+$VenvDir = Join-Path $ScriptDir ".venv"
+
+# Check if virtual environment exists
+if (-not (Test-Path $VenvDir)) {
+    Write-Host "Creating virtual environment..."
+    python -m venv $VenvDir
+    if ($LASTEXITCODE -ne 0) {
+        Write-Host "Error: Failed to create virtual environment. Make sure python is installed."
+        exit 1
+    }
+}
+
+# Activate virtual environment
+Write-Host "Activating virtual environment..."
+$ActivateScript = Join-Path $VenvDir "Scripts\Activate.ps1"
+& $ActivateScript
+
+# Install/update dependencies if requirements.txt exists
+$RequirementsFile = Join-Path $ScriptDir "requirements.txt"
+if (Test-Path $RequirementsFile) {
+    Write-Host "Installing dependencies..."
+    pip install -q -r $RequirementsFile
+}
+
+# Run nnue.py
+Write-Host "Starting NNUE Training Pipeline..."
+python (Join-Path $ScriptDir "nnue.py")
@@ -0,0 +1,29 @@
+#!/bin/bash
+# Setup and run NNUE training pipeline
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+VENV_DIR="$SCRIPT_DIR/.venv"
+
+# Check if virtual environment exists
+if [ ! -d "$VENV_DIR" ]; then
+    echo "Creating virtual environment..."
+    python3 -m venv "$VENV_DIR"
+    if [ $? -ne 0 ]; then
+        echo "Error: Failed to create virtual environment. Make sure python3 is installed."
+        exit 1
+    fi
+fi
+
+# Activate virtual environment
+echo "Activating virtual environment..."
+source "$VENV_DIR/bin/activate"
+
+# Install/update dependencies if requirements.txt exists
+if [ -f "$SCRIPT_DIR/requirements.txt" ]; then
+    echo "Installing dependencies..."
+    pip install -q -r "$SCRIPT_DIR/requirements.txt"
+fi
+
+# Run nnue.py
+echo "Starting NNUE Training Pipeline..."
+python "$SCRIPT_DIR/nnue.py"
@@ -0,0 +1,21 @@
+{
+  "version": 10,
+  "date": "2026-04-14T22:18:38.824577",
+  "num_positions": 3022562,
+  "stockfish_depth": 12,
+  "final_val_loss": 6.248612448225196e-05,
+  "architecture": [
+    768,
+    1536,
+    1024,
+    512,
+    256,
+    1
+  ],
+  "device": "cuda",
+  "notes": "Win rate vs classical eval: TBD (requires benchmark games)",
+  "epochs": 100,
+  "batch_size": 16384,
+  "learning_rate": 0.001,
+  "checkpoint": null
+}
@@ -0,0 +1,13 @@
+{
+  "version": 1,
+  "date": "2026-04-07T22:56:23.259658",
+  "num_positions": 2086,
+  "stockfish_depth": 12,
+  "epochs": 20,
+  "batch_size": 4096,
+  "learning_rate": 0.001,
+  "final_val_loss": 0.016311248764395714,
+  "device": "cuda",
+  "checkpoint": null,
+  "notes": "Win rate vs classical eval: TBD (requires benchmark games)"
+}
@@ -0,0 +1,13 @@
+{
+  "version": 2,
+  "date": "2026-04-07T23:50:05.390402",
+  "num_positions": 6886,
+  "stockfish_depth": 12,
+  "epochs": 100,
+  "batch_size": 4096,
+  "learning_rate": 0.001,
+  "final_val_loss": 0.007848377339541912,
+  "device": "cuda",
+  "checkpoint": "/mnt/d/Workspaces/NowChessSystems/modules/bot/python/weights/nnue_weights_v1.pt",
+  "notes": "Win rate vs classical eval: TBD (requires benchmark games)"
+}
@@ -0,0 +1,13 @@
+{
+  "version": 3,
+  "date": "2026-04-08T09:43:28.000579",
+  "num_positions": 71610,
+  "stockfish_depth": 12,
+  "epochs": 20,
+  "batch_size": 4096,
+  "learning_rate": 0.001,
+  "final_val_loss": 0.006398905136849695,
+  "device": "cpu",
+  "checkpoint": "/home/janis/Workspaces/IntelliJ/NowChess/NowChessSystems/modules/bot/python/weights/nnue_weights_v2.pt",
+  "notes": "Win rate vs classical eval: TBD (requires benchmark games)"
+}
@@ -0,0 +1,13 @@
+{
+  "version": 4,
+  "date": "2026-04-09T00:28:07.572209",
+  "num_positions": 2009355,
+  "stockfish_depth": 12,
+  "epochs": 40,
+  "batch_size": 4096,
+  "learning_rate": 0.001,
+  "final_val_loss": 9.106677896235248e-05,
+  "device": "cuda",
+  "checkpoint": "/mnt/d/Workspaces/NowChessSystems/modules/bot/python/weights/nnue_weights_v3.pt",
+  "notes": "Win rate vs classical eval: TBD (requires benchmark games)"
+}
@@ -0,0 +1,13 @@
+{
+  "version": 5,
+  "date": "2026-04-09T18:50:27.845632",
+  "num_positions": 2009355,
+  "stockfish_depth": 12,
+  "epochs": 100,
+  "batch_size": 16384,
+  "learning_rate": 0.001,
+  "final_val_loss": 9.180421525105905e-05,
+  "device": "cuda",
+  "checkpoint": null,
+  "notes": "Win rate vs classical eval: TBD (requires benchmark games)"
+}
@@ -0,0 +1,13 @@
+{
+  "version": 6,
+  "date": "2026-04-09T21:28:21.000832",
+  "num_positions": 1958728,
+  "stockfish_depth": 12,
+  "epochs": 100,
+  "batch_size": 16384,
+  "learning_rate": 0.001,
+  "final_val_loss": 0.2984530149085532,
+  "device": "cuda",
+  "checkpoint": "/home/janis/Workspaces/NowChessSystems/modules/bot/python/weights/nnue_weights_v5.pt",
+  "notes": "Win rate vs classical eval: TBD (requires benchmark games)"
+}
@@ -0,0 +1,13 @@
+{
+  "version": 7,
+  "date": "2026-04-09T22:06:50.439858",
+  "num_positions": 1958728,
+  "stockfish_depth": 12,
+  "epochs": 100,
+  "batch_size": 16384,
+  "learning_rate": 0.001,
+  "final_val_loss": 0.2997283308762831,
+  "device": "cuda",
+  "checkpoint": null,
+  "notes": "Win rate vs classical eval: TBD (requires benchmark games)"
+}
@@ -0,0 +1,13 @@
+{
+  "version": 8,
+  "date": "2026-04-09T22:22:47.859730",
+  "num_positions": 1958728,
+  "stockfish_depth": 12,
+  "epochs": 100,
+  "batch_size": 16384,
+  "learning_rate": 0.001,
+  "final_val_loss": 0.24803777390839968,
+  "device": "cuda",
+  "checkpoint": "/home/janis/Workspaces/NowChessSystems/modules/bot/python/weights/nnue_weights_v7.pt",
+  "notes": "Win rate vs classical eval: TBD (requires benchmark games)"
+}
@@ -0,0 +1,17 @@
+{
+  "version": 9,
+  "date": "2026-04-13T20:19:08.123315",
+  "num_positions": 2522562,
+  "stockfish_depth": 12,
+  "final_val_loss": 6.994176222619626e-05,
+  "device": "cuda",
+  "notes": "Win rate vs classical eval: TBD (requires benchmark games)",
+  "mode": "burst",
+  "duration_minutes": 30.0,
+  "epochs_per_season": 50,
+  "early_stopping_patience": 10,
+  "seasons_completed": 3,
+  "batch_size": 16384,
+  "learning_rate": 0.001,
+  "initial_checkpoint": "/home/janis/Workspaces/NowChess/NowChessSystems/modules/bot/python/weights/nnue_weights_v8.pt"
+}
@@ -0,0 +1,21 @@
+package de.nowchess.bot
+
+import de.nowchess.api.bot.Bot
+import de.nowchess.bot.bots.ClassicalBot
+
+object BotController {
+
+  private val bots: Map[String, Bot] = Map(
+    "easy"   -> ClassicalBot(BotDifficulty.Easy),
+    "medium" -> ClassicalBot(BotDifficulty.Medium),
+    "hard"   -> ClassicalBot(BotDifficulty.Hard),
+    "expert" -> ClassicalBot(BotDifficulty.Expert),
+  )
+
+  /** Get a bot by name. */
+  def getBot(name: String): Option[Bot] = bots.get(name.toLowerCase)
+
+  /** List all available bot names. */
+  def listBots: List[String] = bots.keys.toList.sorted
+
+}
@@ -0,0 +1,7 @@
+package de.nowchess.bot
+
+enum BotDifficulty:
+  case Easy
+  case Medium
+  case Hard
+  case Expert
@@ -0,0 +1,19 @@
+package de.nowchess.bot
+
+import de.nowchess.api.game.GameContext
+import de.nowchess.api.move.Move
+
+object BotMoveRepetition:
+
+  private val maxConsecutiveMoves = 3
+
+  def blockedMoves(context: GameContext): Set[Move] = repeatedMove(context).toSet
+
+  def repeatedMove(context: GameContext): Option[Move] =
+    context.moves.takeRight(maxConsecutiveMoves) match
+      case first :: second :: third :: Nil if first == second && second == third => Some(first)
+      case _                                                                     => None
+
+  def filterAllowed(context: GameContext, moves: List[Move]): List[Move] =
+    val blocked = blockedMoves(context)
+    moves.filterNot(blocked.contains)
@@ -0,0 +1,11 @@
+package de.nowchess.bot
+
+object Config:
+
+  /** Threshold in centipawns: if classical evaluation differs from NNUE by more than this, the move is vetoed (not
+    * accepted as a suggestion).
+    */
+  val VETO_THRESHOLD: Int = 150
+
+  /** Time budget per move for iterative deepening (milliseconds). */
+  val TIME_LIMIT_MS: Long = 2000L
@@ -0,0 +1,29 @@
+package de.nowchess.bot.ai
+
+import de.nowchess.api.game.GameContext
+import de.nowchess.api.move.Move
+
+trait Evaluation:
+
+  def CHECKMATE_SCORE: Int
+  def DRAW_SCORE: Int
+
+  def evaluate(context: GameContext): Int
+
+  // ── Accumulator hooks ─────────────────────────────────────────────────────
+  // Default implementations fall back to full re-evaluation each call.
+  // Override in NNUE-capable evaluators for incremental L1 speedup.
+
+  /** Initialise the accumulator for the root position at ply 0. */
+  def initAccumulator(context: GameContext): Unit = ()
+
+  /** Copy parent ply's accumulator to childPly without move deltas (null-move). */
+  def copyAccumulator(parentPly: Int, childPly: Int): Unit = ()
+
+  /** Derive childPly's accumulator from parentPly by applying move deltas. */
+  def pushAccumulator(childPly: Int, move: Move, parent: GameContext, child: GameContext): Unit = ()
+
+  /** Evaluate from the pre-computed accumulator at ply, using hash for the eval cache. Falls back to full evaluate when
+    * not overridden.
+    */
+  def evaluateAccumulator(ply: Int, context: GameContext, hash: Long): Int = evaluate(context)
@@ -0,0 +1,29 @@
+package de.nowchess.bot.bots
+
+import de.nowchess.api.bot.Bot
+import de.nowchess.api.game.GameContext
+import de.nowchess.api.move.Move
+import de.nowchess.bot.bots.classic.EvaluationClassic
+import de.nowchess.bot.logic.AlphaBetaSearch
+import de.nowchess.bot.util.PolyglotBook
+import de.nowchess.bot.{BotDifficulty, BotMoveRepetition}
+import de.nowchess.rules.RuleSet
+import de.nowchess.rules.sets.DefaultRules
+
+final class ClassicalBot(
+    difficulty: BotDifficulty,
+    rules: RuleSet = DefaultRules,
+    book: Option[PolyglotBook] = None,
+) extends Bot:
+
+  private val search: AlphaBetaSearch = AlphaBetaSearch(rules, weights = EvaluationClassic)
+  private val TIME_BUDGET_MS          = 1000L
+
+  override val name: String = s"ClassicalBot(${difficulty.toString})"
+
+  override def nextMove(context: GameContext): Option[Move] =
+    val blockedMoves = BotMoveRepetition.blockedMoves(context)
+    book
+      .flatMap(_.probe(context))
+      .filterNot(blockedMoves.contains)
+      .orElse(search.bestMoveWithTime(context, TIME_BUDGET_MS, blockedMoves))
@@ -0,0 +1,43 @@
+package de.nowchess.bot.bots
+
+import de.nowchess.api.bot.Bot
+import de.nowchess.api.game.GameContext
+import de.nowchess.api.move.Move
+import de.nowchess.bot.ai.Evaluation
+import de.nowchess.bot.bots.classic.EvaluationClassic
+import de.nowchess.bot.bots.nnue.EvaluationNNUE
+import de.nowchess.bot.logic.{AlphaBetaSearch, TranspositionTable}
+import de.nowchess.bot.util.PolyglotBook
+import de.nowchess.bot.{BotDifficulty, BotMoveRepetition, Config}
+import de.nowchess.rules.RuleSet
+import de.nowchess.rules.sets.DefaultRules
+
+final class HybridBot(
+    difficulty: BotDifficulty,
+    rules: RuleSet = DefaultRules,
+    book: Option[PolyglotBook] = None,
+    nnueEvaluation: Evaluation = EvaluationNNUE,
+    classicalEvaluation: Evaluation = EvaluationClassic,
+    vetoReporter: String => Unit = println(_),
+) extends Bot:
+
+  private val search = AlphaBetaSearch(rules, TranspositionTable(), classicalEvaluation)
+
+  override val name: String = s"HybridBot(${difficulty.toString})"
+
+  override def nextMove(context: GameContext): Option[Move] =
+    val blockedMoves = BotMoveRepetition.blockedMoves(context)
+    book.flatMap(_.probe(context)).filterNot(blockedMoves.contains).orElse(searchWithVeto(context, blockedMoves))
+
+  private def searchWithVeto(context: GameContext, blockedMoves: Set[Move]): Option[Move] =
+    search.bestMoveWithTime(context, Config.TIME_LIMIT_MS, blockedMoves).map { move =>
+      val next       = rules.applyMove(context)(move)
+      val staticNnue = nnueEvaluation.evaluate(next)
+      val classical  = classicalEvaluation.evaluate(next)
+      val diff       = (classical - staticNnue).abs
+      if diff > Config.VETO_THRESHOLD then
+        vetoReporter(
+          f"[Veto] ${move.from}->${move.to}: nnue=$staticNnue  classical=$classical  diff=$diff — flagged but trusted (deep search)",
+        )
+      move
+    }
@@ -0,0 +1,60 @@
+package de.nowchess.bot.bots
+
+import de.nowchess.api.bot.Bot
+import de.nowchess.api.game.GameContext
+import de.nowchess.api.move.Move
+import de.nowchess.bot.bots.nnue.EvaluationNNUE
+import de.nowchess.bot.logic.AlphaBetaSearch
+import de.nowchess.bot.util.{PolyglotBook, ZobristHash}
+import de.nowchess.bot.{BotDifficulty, BotMoveRepetition}
+import de.nowchess.rules.RuleSet
+import de.nowchess.rules.sets.DefaultRules
+
+final class NNUEBot(
+    difficulty: BotDifficulty,
+    rules: RuleSet = DefaultRules,
+    book: Option[PolyglotBook] = None,
+) extends Bot:
+
+  private val search: AlphaBetaSearch = AlphaBetaSearch(rules, weights = EvaluationNNUE)
+
+  override val name: String = s"NNUEBot(${difficulty.toString})"
+
+  override def nextMove(context: GameContext): Option[Move] =
+    val blockedMoves = BotMoveRepetition.blockedMoves(context)
+    book
+      .flatMap(_.probe(context))
+      .filterNot(blockedMoves.contains)
+      .orElse {
+        val moves = BotMoveRepetition.filterAllowed(context, rules.allLegalMoves(context))
+        if moves.isEmpty then None
+        else
+          val scored   = batchEvaluateRoot(context, moves)
+          val bestMove = scored.maxBy(_._2)._1
+          search.bestMoveWithTime(context, allocateTime(scored), blockedMoves).orElse(Some(bestMove))
+      }
+
+  /** Evaluate all root moves shallowly via incremental NNUE accumulator updates. Returns (move, score) pairs with score
+    * from the root player's perspective.
+    */
+  private def batchEvaluateRoot(context: GameContext, moves: List[Move]): List[(Move, Int)] =
+    EvaluationNNUE.initAccumulator(context)
+    val rootHash = ZobristHash.hash(context)
+    moves.map { move =>
+      val child     = rules.applyMove(context)(move)
+      val childHash = ZobristHash.nextHash(context, rootHash, move, child)
+      EvaluationNNUE.pushAccumulator(1, move, context, child)
+      val score = -EvaluationNNUE.evaluateAccumulator(1, child, childHash)
+      (move, score)
+    }
+
+  /** Allocate more time for complex positions; less when one move clearly dominates. */
+  private def allocateTime(scored: List[(Move, Int)]): Long =
+    val moveCount = scored.length
+    if moveCount > 30 then 1500L
+    else if moveCount < 5 then 500L
+    else
+      val scores = scored.map(_._2)
+      val best   = scores.max
+      val second = scores.filter(_ < best).maxOption.getOrElse(best)
+      if best - second > 200 then 600L else 1000L
@@ -0,0 +1,361 @@
+package de.nowchess.bot.bots.classic
+
+import de.nowchess.api.board.{Color, PieceType, Square}
+import de.nowchess.api.game.GameContext
+import de.nowchess.bot.ai.Evaluation
+
+object EvaluationClassic extends Evaluation:
+
+  val CHECKMATE_SCORE: Int = 10_000_000
+  val DRAW_SCORE: Int      = 0
+
+  // Material values in centipawns (indexed by PieceType.ordinal: Pawn=0, Knight=1, Bishop=2, Rook=3, Queen=4, King=5)
+  private val mgMaterial = Array(100, 325, 335, 500, 900, 20_000)
+  private val egMaterial = Array(110, 310, 310, 530, 1_000, 20_000)
+
+  private val TEMPO_BONUS: Int = 10
+
+  // Piece-square tables (Simplified Evaluation Function, Michniewski)
+  // Indexed by squareIndex = rank.ordinal * 8 + file.ordinal
+  // White's perspective: rank 0 = home (r1), rank 7 = back rank (r8)
+  // Black is vertically mirrored
+
+  private val mgPawnTable: Array[Int] = Array(
+    0, 0, 0, 0, 0, 0, 0, 0, 50, 50, 50, 50, 50, 50, 50, 50, 10, 10, 20, 30, 30, 20, 10, 10, 5, 5, 10, 25, 25, 10, 5, 5,
+    0, 0, 0, 20, 20, 0, 0, 0, 5, -5, -10, 0, 0, -10, -5, 5, 5, 10, 10, -20, -20, 10, 10, 5, 0, 0, 0, 0, 0, 0, 0, 0,
+  )
+
+  private val egPawnTable: Array[Int] = Array(
+    0, 0, 0, 0, 0, 0, 0, 0, 70, 70, 70, 70, 70, 70, 70, 70, 40, 40, 40, 40, 40, 40, 40, 40, 30, 30, 30, 30, 30, 30, 30,
+    30, 20, 20, 20, 20, 20, 20, 20, 20, 10, 10, 10, 10, 10, 10, 10, 10, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0,
+  )
+
+  private val mgKnightTable: Array[Int] = Array(
+    -50, -40, -30, -30, -30, -30, -40, -50, -40, -20, 0, 0, 0, 0, -20, -40, -30, 0, 10, 15, 15, 10, 0, -30, -30, 5, 15,
+    20, 20, 15, 5, -30, -30, 0, 15, 20, 20, 15, 0, -30, -30, 5, 10, 15, 15, 10, 5, -30, -40, -20, 0, 5, 5, 0, -20, -40,
+    -50, -40, -30, -30, -30, -30, -40, -50,
+  )
+
+  private val egKnightTable: Array[Int] = Array(
+    -30, -20, -10, -10, -10, -10, -20, -30, -20, 0, 5, 5, 5, 5, 0, -20, -10, 5, 15, 20, 20, 15, 5, -10, -10, 5, 20, 25,
+    25, 20, 5, -10, -10, 5, 20, 25, 25, 20, 5, -10, -10, 5, 15, 20, 20, 15, 5, -10, -20, 0, 5, 5, 5, 5, 0, -20, -30,
+    -20, -10, -10, -10, -10, -20, -30,
+  )
+
+  private val mgBishopTable: Array[Int] = Array(
+    -20, -10, -10, -10, -10, -10, -10, -20, -10, 0, 0, 0, 0, 0, 0, -10, -10, 0, 5, 10, 10, 5, 0, -10, -10, 5, 5, 10, 10,
+    5, 5, -10, -10, 0, 10, 10, 10, 10, 0, -10, -10, 10, 10, 10, 10, 10, 10, -10, -10, 5, 0, 0, 0, 0, 5, -10, -20, -10,
+    -10, -10, -10, -10, -10, -20,
+  )
+
+  private val egBishopTable: Array[Int] = Array(
+    -20, -10, -5, -5, -5, -5, -10, -20, -10, 0, 5, 5, 5, 5, 0, -10, -5, 5, 10, 10, 10, 10, 5, -5, -5, 5, 10, 15, 15, 10,
+    5, -5, -5, 5, 10, 15, 15, 10, 5, -5, -5, 5, 10, 10, 10, 10, 5, -5, -10, 0, 5, 5, 5, 5, 0, -10, -20, -10, -5, -5, -5,
+    -5, -10, -20,
+  )
+
+  private val mgRookTable: Array[Int] = Array(
+    0, 0, 0, 0, 0, 0, 0, 0, 5, 10, 10, 10, 10, 10, 10, 5, -5, 0, 0, 0, 0, 0, 0, -5, -5, 0, 0, 0, 0, 0, 0, -5, -5, 0, 0,
+    0, 0, 0, 0, -5, -5, 0, 0, 0, 0, 0, 0, -5, -5, 0, 0, 0, 0, 0, 0, -5, 0, 0, 0, 5, 5, 0, 0, 0,
+  )
+
+  private val egRookTable: Array[Int] = Array(
+    0, 0, 0, 0, 0, 0, 0, 0, 5, 10, 10, 10, 10, 10, 10, 5, -5, 0, 0, 0, 0, 0, 0, -5, -5, 0, 0, 0, 0, 0, 0, -5, -5, 0, 0,
+    0, 0, 0, 0, -5, -5, 0, 0, 0, 0, 0, 0, -5, -5, 0, 0, 0, 0, 0, 0, -5, 0, 0, 0, 5, 5, 0, 0, 0,
+  )
+
+  private val mgQueenTable: Array[Int] = Array(
+    -20, -10, -10, -5, -5, -10, -10, -20, -10, 0, 0, 0, 0, 0, 0, -10, -10, 0, 5, 5, 5, 5, 0, -10, -5, 0, 5, 5, 5, 5, 0,
+    -5, 0, 0, 5, 5, 5, 5, 0, -5, -10, 5, 5, 5, 5, 5, 0, -10, -10, 0, 5, 0, 0, 0, 0, -10, -20, -10, -10, -5, -5, -10,
+    -10, -20,
+  )
+
+  private val egQueenTable: Array[Int] = Array(
+    -15, -10, -8, -5, -5, -8, -10, -15, -10, 0, 3, 5, 5, 3, 0, -10, -8, 3, 10, 10, 10, 10, 3, -8, -5, 5, 10, 15, 15, 10,
+    5, -5, -5, 5, 10, 15, 15, 10, 5, -5, -8, 3, 10, 10, 10, 10, 3, -8, -10, 0, 3, 5, 5, 3, 0, -10, -15, -10, -8, -5, -5,
+    -8, -10, -15,
+  )
+
+  private val mgKingTable: Array[Int] = Array(
+    -30, -40, -40, -50, -50, -40, -40, -30, -30, -40, -40, -50, -50, -40, -40, -30, -30, -40, -40, -50, -50, -40, -40,
+    -30, -30, -40, -40, -50, -50, -40, -40, -30, -20, -30, -30, -40, -40, -30, -30, -20, -10, -20, -20, -20, -20, -20,
+    -20, -10, 20, 20, 0, 0, 0, 0, 20, 20, 20, 30, 10, 0, 0, 10, 30, 20,
+  )
+
+  private val egKingTable: Array[Int] = Array(
+    -50, -40, -30, -20, -20, -30, -40, -50, -30, -20, -10, 0, 0, -10, -20, -30, -30, -10, 20, 30, 30, 20, -10, -30, -30,
+    -10, 30, 40, 40, 30, -10, -30, -30, -10, 30, 40, 40, 30, -10, -30, -30, -10, 20, 30, 30, 20, -10, -30, -30, -30, 0,
+    0, 0, 0, -30, -30, -50, -30, -30, -30, -30, -30, -30, -50,
+  )
+
+  private val phaseWeight: Map[PieceType, Int] = Map(
+    PieceType.Knight -> 1,
+    PieceType.Bishop -> 1,
+    PieceType.Rook   -> 2,
+    PieceType.Queen  -> 4,
+  )
+  private val maxPhase = 24 // 4*4 + 4*2 + 4*1 + 4*1
+
+  private val passedPawnBonus: Array[Int]   = Array(0, 5, 10, 20, 35, 60, 100, 0)
+  private val egPassedPawnBonus: Array[Int] = Array(0, 20, 40, 80, 150, 250, 400, 0)
+
+  // Pawn structure penalties
+  private val doubledMg  = -10
+  private val doubledEg  = -25
+  private val isolatedMg = -15
+  private val isolatedEg = -20
+
+  // Mobility weights: centipawns per reachable square (indexed by PieceType.ordinal)
+  private val mobilityMg = Array(0, 4, 3, 2, 1, 0, 0)
+  private val mobilityEg = Array(0, 4, 3, 4, 2, 0, 0)
+
+  // Direction offsets for sliding pieces
+  private val diagonals     = List((-1, -1), (-1, 1), (1, -1), (1, 1))
+  private val orthogonals   = List((-1, 0), (1, 0), (0, -1), (0, 1))
+  private val knightOffsets = List((-2, -1), (-2, 1), (-1, -2), (-1, 2), (1, -2), (1, 2), (2, -1), (2, 1))
+
+  // Rook and bishop bonuses
+  private val bishopPairMg = 50
+  private val bishopPairEg = 70
+  private val rookOn7thMg  = 20
+  private val rookOn7thEg  = 10
+
+  /** Evaluate the position from the perspective of context.turn. Positive = good for context.turn.
+    */
+  def evaluate(context: GameContext): Int =
+    val phase      = gamePhase(context.board)
+    val isEg       = isEndgame(phase)
+    val material   = materialAndPositional(context, phase)
+    val structure  = pawnStructure(context, phase)
+    val mobility   = mobilityScore(context, phase)
+    val rookBishop = rookAndBishopBonuses(context, phase)
+    val bonuses    = positionalBonuses(context, phase, isEg)
+    val egBonuses  = if isEg then endgameBonus(context) else 0
+    material + structure + mobility + rookBishop + bonuses + egBonuses + TEMPO_BONUS
+
+  private def gamePhase(board: de.nowchess.api.board.Board): Int =
+    val phase = board.pieces.values.foldLeft(0) { (acc, piece) =>
+      acc + phaseWeight.getOrElse(piece.pieceType, 0)
+    }
+    math.min(phase, maxPhase)
+
+  private def isEndgame(phase: Int): Boolean =
+    phase < 8 // Significantly reduced material indicates endgame
+
+  private def taper(mg: Int, eg: Int, phase: Int): Int =
+    (mg * phase + eg * (maxPhase - phase)) / maxPhase
+
+  private def materialAndPositional(context: GameContext, phase: Int): Int =
+    val (mg, eg) = context.board.pieces.foldLeft((0, 0)) { case ((mg, eg), (square, piece)) =>
+      val (psqMg, psqEg) = squareBonus(piece.pieceType, piece.color, square)
+      val pieceMg        = mgMaterial(piece.pieceType.ordinal) + psqMg
+      val pieceEg        = egMaterial(piece.pieceType.ordinal) + psqEg
+      val sign           = if piece.color == context.turn then 1 else -1
+      (mg + sign * pieceMg, eg + sign * pieceEg)
+    }
+    taper(mg, eg, phase)
+
+  private def squareBonus(pieceType: PieceType, color: Color, sq: Square): (Int, Int) =
+    val rankIdx   = if color == Color.White then sq.rank.ordinal else 7 - sq.rank.ordinal
+    val fileIdx   = sq.file.ordinal
+    val squareIdx = rankIdx * 8 + fileIdx
+
+    pieceType match
+      case PieceType.Pawn   => (mgPawnTable(squareIdx), egPawnTable(squareIdx))
+      case PieceType.Knight => (mgKnightTable(squareIdx), egKnightTable(squareIdx))
+      case PieceType.Bishop => (mgBishopTable(squareIdx), egBishopTable(squareIdx))
+      case PieceType.Rook   => (mgRookTable(squareIdx), egRookTable(squareIdx))
+      case PieceType.Queen  => (mgQueenTable(squareIdx), egQueenTable(squareIdx))
+      case PieceType.King   => (mgKingTable(squareIdx), egKingTable(squareIdx))
+
+  private def pawnStructure(context: GameContext, phase: Int): Int =
+    val friendlyPawns = context.board.pieces.filter((_, p) => p.color == context.turn && p.pieceType == PieceType.Pawn)
+    val enemyPawns    = context.board.pieces.filter((_, p) => p.color != context.turn && p.pieceType == PieceType.Pawn)
+
+    val friendlyByFile = friendlyPawns.groupMap(s => s._1.file.ordinal)(s => s._1.rank.ordinal)
+    val enemyByFile    = enemyPawns.groupMap(s => s._1.file.ordinal)(s => s._1.rank.ordinal)
+
+    val (fMg, fEg) = structureScore(friendlyByFile)
+    val (eMg, eEg) = structureScore(enemyByFile)
+    taper(fMg - eMg, fEg - eEg, phase)
+
+  private def structureScore(byFile: Map[Int, Iterable[Int]]): (Int, Int) =
+    byFile.foldLeft((0, 0)) { case ((mg, eg), (file, ranks)) =>
+      val doubled     = (ranks.size - 1).max(0)
+      val hasAdjacent = (file - 1 to file + 1).filter(f => f >= 0 && f < 8 && f != file).exists(byFile.contains)
+      val isolated    = if !hasAdjacent then ranks.size else 0
+      (mg + doubled * doubledMg + isolated * isolatedMg, eg + doubled * doubledEg + isolated * isolatedEg)
+    }
+
+  private def positionalBonuses(context: GameContext, phase: Int, isEg: Boolean): Int =
+    context.board.pieces.foldLeft(0) { case (score, (sq, piece)) =>
+      val bonus = piece.pieceType match
+        case PieceType.Pawn =>
+          if isPassedPawn(context.board, sq, piece.color) then
+            if isEg then egPassedPawnBonus(sq.rank.ordinal) else passedPawnBonus(sq.rank.ordinal)
+          else 0
+        case PieceType.Rook => rookOpenFileBonus(context.board, sq, piece.color)
+        case PieceType.King => kingShieldBonus(context.board, sq, piece.color, phase)
+        case _              => 0
+      if piece.color == context.turn then score + bonus else score - bonus
+    }
+
+  private def isPassedPawn(board: de.nowchess.api.board.Board, sq: Square, color: Color): Boolean =
+    val enemyColor = color.opposite
+    val pawnRank   = sq.rank.ordinal
+    val fileRange  = (sq.file.ordinal - 1 to sq.file.ordinal + 1).filter(f => f >= 0 && f < 8)
+    val rankCheck  = if color == Color.White then (r: Int) => r > pawnRank else (r: Int) => r < pawnRank
+
+    board.pieces.forall { (enemySq, enemyPiece) =>
+      !(enemyPiece.color == enemyColor &&
+        enemyPiece.pieceType == PieceType.Pawn &&
+        fileRange.contains(enemySq.file.ordinal) &&
+        rankCheck(enemySq.rank.ordinal))
+    }
+
+  private def rookOpenFileBonus(board: de.nowchess.api.board.Board, rookSq: Square, color: Color): Int =
+    val hasFriendlyPawn = board.pieces.exists { (sq, piece) =>
+      piece.color == color && piece.pieceType == PieceType.Pawn && sq.file == rookSq.file
+    }
+    val hasEnemyPawn = board.pieces.exists { (sq, piece) =>
+      piece.color != color && piece.pieceType == PieceType.Pawn && sq.file == rookSq.file
+    }
+    if !hasFriendlyPawn && !hasEnemyPawn then 20 // open file
+    else if !hasFriendlyPawn then 10             // semi-open file
+    else 0
+
+  private def kingShieldBonus(board: de.nowchess.api.board.Board, kingSq: Square, color: Color, phase: Int): Int =
+    val shieldRankDelta = if color == Color.White then 1 else -1
+    val shieldFiles     = (kingSq.file.ordinal - 1 to kingSq.file.ordinal + 1).filter(f => f >= 0 && f < 8)
+    val shieldRank      = kingSq.rank.ordinal + shieldRankDelta
+
+    if shieldRank < 0 || shieldRank > 7 then 0
+    else
+      val rawBonus = board.pieces.count { (sq, piece) =>
+        piece.color == color &&
+        piece.pieceType == PieceType.Pawn &&
+        shieldFiles.contains(sq.file.ordinal) &&
+        sq.rank.ordinal == shieldRank
+      } * 10
+      (rawBonus * phase) / maxPhase
+
+  private def slidingCount(
+      sq: Square,
+      board: de.nowchess.api.board.Board,
+      color: Color,
+      directions: List[(Int, Int)],
+  ): Int =
+    directions.foldLeft(0) { case (total, (fileDelta, rankDelta)) =>
+      @scala.annotation.tailrec
+      def countRay(current: Option[Square], acc: Int): Int =
+        current match
+          case None => acc
+          case Some(target) =>
+            board.pieceAt(target) match
+              case Some(piece) if piece.color == color => acc
+              case Some(_)                             => acc + 1
+              case None                                => countRay(target.offset(fileDelta, rankDelta), acc + 1)
+      total + countRay(sq.offset(fileDelta, rankDelta), 0)
+    }
+
+  private def knightCount(sq: Square, board: de.nowchess.api.board.Board, color: Color): Int =
+    knightOffsets.count { case (fileDelta, rankDelta) =>
+      sq.offset(fileDelta, rankDelta).forall { target =>
+        board.pieceAt(target).forall(_.color != color)
+      }
+    }
+
+  private def mobilityScore(context: GameContext, phase: Int): Int =
+    val (mg, eg) = context.board.pieces.foldLeft((0, 0)) { case ((mg, eg), (sq, piece)) =>
+      val count = piece.pieceType match
+        case PieceType.Knight => knightCount(sq, context.board, piece.color)
+        case PieceType.Bishop => slidingCount(sq, context.board, piece.color, diagonals)
+        case PieceType.Rook   => slidingCount(sq, context.board, piece.color, orthogonals)
+        case PieceType.Queen  => slidingCount(sq, context.board, piece.color, diagonals ++ orthogonals)
+        case _                => 0
+      val pieceMg = count * mobilityMg(piece.pieceType.ordinal)
+      val pieceEg = count * mobilityEg(piece.pieceType.ordinal)
+      val sign    = if piece.color == context.turn then 1 else -1
+      (mg + sign * pieceMg, eg + sign * pieceEg)
+    }
+    taper(mg, eg, phase)
+
+  private def rookAndBishopBonuses(context: GameContext, phase: Int): Int =
+    val (baseMg, baseEg) = bishopPairBase(context)
+    val (rookMg, rookEg) = rookOn7thDelta(context)
+    taper(baseMg + rookMg, baseEg + rookEg, phase)
+
+  private def bishopPairBase(context: GameContext): (Int, Int) =
+    val friendlyHasPair = hasBishopPair(context, context.turn)
+    val enemyHasPair    = hasBishopPair(context, context.turn.opposite)
+    val mg              = pairDelta(friendlyHasPair, enemyHasPair, bishopPairMg)
+    val eg              = pairDelta(friendlyHasPair, enemyHasPair, bishopPairEg)
+    (mg, eg)
+
+  private def hasBishopPair(context: GameContext, color: Color): Boolean =
+    val bishopSquares = context.board.pieces.collect {
+      case (sq, piece) if piece.color == color && piece.pieceType == PieceType.Bishop => sq
+    }
+    bishopSquares.exists(isEvenSquare) && bishopSquares.exists(sq => !isEvenSquare(sq))
+
+  private def isEvenSquare(square: Square): Boolean =
+    (square.file.ordinal + square.rank.ordinal) % 2 == 0
+
+  private def pairDelta(friendlyHasPair: Boolean, enemyHasPair: Boolean, bonus: Int): Int =
+    (if friendlyHasPair then bonus else 0) - (if enemyHasPair then bonus else 0)
+
+  private def rookOn7thDelta(context: GameContext): (Int, Int) =
+    context.board.pieces.foldLeft((0, 0)) { case ((mg, eg), (sq, piece)) =>
+      rookOn7thContribution(piece, sq, context.turn).fold((mg, eg)) { case (dMg, dEg) =>
+        (mg + dMg, eg + dEg)
+      }
+    }
+
+  private def rookOn7thContribution(piece: de.nowchess.api.board.Piece, sq: Square, turn: Color): Option[(Int, Int)] =
+    Option.when(piece.pieceType == PieceType.Rook && isRookOn7th(piece.color, sq)) {
+      val sign = if piece.color == turn then 1 else -1
+      (sign * rookOn7thMg, sign * rookOn7thEg)
+    }
+
+  private def isRookOn7th(color: Color, sq: Square): Boolean =
+    if color == Color.White then sq.rank.ordinal == 6 else sq.rank.ordinal == 1
+
+  private def endgameBonus(context: GameContext): Int =
+    val friendlyKing = context.board.pieces.find((_, p) => p.color == context.turn && p.pieceType == PieceType.King)
+    val enemyKing    = context.board.pieces.find((_, p) => p.color != context.turn && p.pieceType == PieceType.King)
+
+    val kingCentralBonus =
+      friendlyKing.fold(0)((kSq, _) => (8 - kingCentralizationDistance(kSq)) * 15) -
+        enemyKing.fold(0)((kSq, _) => (8 - kingCentralizationDistance(kSq)) * 15)
+
+    val friendlyMaterial = materialCount(context, context.turn)
+    val enemyMaterial    = materialCount(context, context.turn.opposite)
+    val edgeBonus =
+      if friendlyMaterial > enemyMaterial then enemyKing.fold(0)((kSq, _) => (7 - kingEdgeDistance(kSq)) * 10)
+      else 0
+
+    kingCentralBonus + edgeBonus
+
+  private def kingCentralizationDistance(sq: Square): Int =
+    val fileFromCenter = (sq.file.ordinal - 3.5).abs.toInt
+    val rankFromCenter = (sq.rank.ordinal - 3.5).abs.toInt
+    math.max(fileFromCenter, rankFromCenter)
+
+  private def kingEdgeDistance(sq: Square): Int =
+    val fileFromEdge = math.min(sq.file.ordinal, 7 - sq.file.ordinal)
+    val rankFromEdge = math.min(sq.rank.ordinal, 7 - sq.rank.ordinal)
+    math.min(fileFromEdge, rankFromEdge)
+
+  private def materialCount(context: GameContext, color: Color): Int =
+    context.board.pieces.foldLeft(0) { case (sum, (_, piece)) =>
+      if piece.color == color then
+        sum + (piece.pieceType match
+          case PieceType.Knight => 300
+          case PieceType.Bishop => 300
+          case PieceType.Rook   => 500
+          case PieceType.Queen  => 900
+          case PieceType.Pawn   => 0
+          case PieceType.King   => 0
+        )
+      else sum
+    }
@@ -0,0 +1,31 @@
+package de.nowchess.bot.bots.nnue
+
+import de.nowchess.api.game.GameContext
+import de.nowchess.api.move.Move
+import de.nowchess.bot.ai.Evaluation
+
+object EvaluationNNUE extends Evaluation:
+
+  private val nnue = NNUE(NbaiLoader.loadDefault())
+
+  val CHECKMATE_SCORE: Int = 10_000_000
+  val DRAW_SCORE: Int      = 0
+
+  /** Full-board evaluate — used as fallback and by non-search callers. */
+  def evaluate(context: GameContext): Int = nnue.evaluate(context)
+
+  // ── Accumulator hooks (incremental L1) ───────────────────────────────────
+
+  override def initAccumulator(context: GameContext): Unit =
+    nnue.initAccumulator(context.board)
+
+  override def copyAccumulator(parentPly: Int, childPly: Int): Unit =
+    nnue.copyAccumulator(parentPly, childPly)
+
+  override def pushAccumulator(childPly: Int, move: Move, parent: GameContext, child: GameContext): Unit =
+    // Use incremental updates, but recompute from scratch every 10 plies to prevent accumulation errors
+    if childPly % 10 == 0 then nnue.recomputeAccumulator(childPly, child.board)
+    else nnue.pushAccumulator(childPly, move, parent.board)
+
+  override def evaluateAccumulator(ply: Int, context: GameContext, hash: Long): Int =
+    nnue.evaluateAtPlyWithValidation(ply, context.turn, hash, context.board)
@@ -0,0 +1,231 @@
+package de.nowchess.bot.bots.nnue
+
+import de.nowchess.api.board.{Board, Color, File, Piece, PieceType, Square}
+import de.nowchess.api.game.GameContext
+import de.nowchess.api.move.{Move, MoveType, PromotionPiece}
+
+class NNUE(model: NbaiModel):
+
+  private val featureSize   = model.layers(0).inputSize
+  private val accSize       = model.layers(0).outputSize
+  private val validateAccum = sys.env.contains("NNUE_VALIDATE") // Enable with NNUE_VALIDATE=1
+
+  // Column-major L1 weights for cache-friendly sparse & incremental updates.
+  // l1WeightsT(featureIdx * accSize + outputIdx) = l1Weights(outputIdx * featureSize + featureIdx)
+  private val l1WeightsT: Array[Float] =
+    val w = model.weights(0).weights
+    val t = new Array[Float](featureSize * accSize)
+    for j <- 0 until featureSize; i <- 0 until accSize do t(j * accSize + i) = w(i * featureSize + j)
+    t
+
+  // ── Accumulator stack ────────────────────────────────────────────────────
+
+  private val MAX_PLY                      = 128
+  private val l1Stack: Array[Array[Float]] = Array.fill(MAX_PLY + 1)(new Array[Float](accSize))
+
+  // Shared evaluation buffers: index i holds the output of layers(i) (all except the scalar output layer).
+  private val evalBuffers: Array[Array[Float]] = model.layers.init.map(l => new Array[Float](l.outputSize))
+
+  // ── Eval cache ───────────────────────────────────────────────────────────
+
+  private val EVAL_CACHE_MASK = (1 << 18) - 1L
+  private val evalCacheHashes = new Array[Long](1 << 18)
+  private val evalCacheScores = new Array[Int](1 << 18)
+
+  // ── Feature helpers ──────────────────────────────────────────────────────
+
+  private def squareNum(sq: Square): Int = sq.rank.ordinal * 8 + sq.file.ordinal
+
+  private def featureIndex(piece: Piece, sqNum: Int): Int =
+    val colorOffset = if piece.color == Color.White then 6 else 0
+    (colorOffset + piece.pieceType.ordinal) * 64 + sqNum
+
+  private def addColumn(l1Pre: Array[Float], featureIdx: Int): Unit =
+    val offset = featureIdx * accSize
+    for i <- 0 until accSize do l1Pre(i) += l1WeightsT(offset + i)
+
+  private def subtractColumn(l1Pre: Array[Float], featureIdx: Int): Unit =
+    val offset = featureIdx * accSize
+    for i <- 0 until accSize do l1Pre(i) -= l1WeightsT(offset + i)
+
+  // ── Accumulator init ─────────────────────────────────────────────────────
+
+  def initAccumulator(board: Board): Unit =
+    System.arraycopy(model.weights(0).bias, 0, l1Stack(0), 0, accSize)
+    for (sq, piece) <- board.pieces do addColumn(l1Stack(0), featureIndex(piece, squareNum(sq)))
+
+  // ── Accumulator push (incremental updates) ───────────────────────────────
+
+  def pushAccumulator(childPly: Int, move: Move, board: Board): Unit =
+    System.arraycopy(l1Stack(childPly - 1), 0, l1Stack(childPly), 0, accSize)
+    val l1 = l1Stack(childPly)
+    move.moveType match
+      case MoveType.Normal(_)                                 => applyNormalDelta(l1, move, board)
+      case MoveType.EnPassant                                 => applyEnPassantDelta(l1, move, board)
+      case MoveType.CastleKingside | MoveType.CastleQueenside => applyCastleDelta(l1, move, board)
+      case MoveType.Promotion(p)                              => applyPromotionDelta(l1, move, p, board)
+
+  def copyAccumulator(parentPly: Int, childPly: Int): Unit =
+    System.arraycopy(l1Stack(parentPly), 0, l1Stack(childPly), 0, accSize)
+
+  def recomputeAccumulator(ply: Int, board: Board): Unit =
+    System.arraycopy(model.weights(0).bias, 0, l1Stack(ply), 0, accSize)
+    for (sq, piece) <- board.pieces do addColumn(l1Stack(ply), featureIndex(piece, squareNum(sq)))
+
+  def validateAccumulator(ply: Int, board: Board): Boolean =
+    // Compute what L1 should be from scratch
+    val expectedL1 = new Array[Float](accSize)
+    System.arraycopy(model.weights(0).bias, 0, expectedL1, 0, accSize)
+    for (sq, piece) <- board.pieces do addColumn(expectedL1, featureIndex(piece, squareNum(sq)))
+
+    // Compare with actual L1
+    val actual = l1Stack(ply)
+    val maxError =
+      (0 until accSize).foldLeft(0f) { (currentMax, i) =>
+        val error = math.abs(actual(i) - expectedL1(i))
+        math.max(currentMax, error)
+      }
+
+    maxError < 0.001f // Allow small floating-point errors
+
+  private def applyNormalDelta(l1: Array[Float], move: Move, board: Board): Unit =
+    // Extract source and destination square indices early
+    val fromNum = squareNum(move.from)
+    val toNum   = squareNum(move.to)
+
+    // Get the moving piece
+    board.pieceAt(move.from).foreach { mover =>
+      subtractColumn(l1, featureIndex(mover, fromNum))
+
+      // If there's a capture, subtract the captured piece
+      board.pieceAt(move.to).foreach { cap =>
+        subtractColumn(l1, featureIndex(cap, toNum))
+      }
+
+      // Add the piece to its new location
+      addColumn(l1, featureIndex(mover, toNum))
+    }
+
+  private def applyEnPassantDelta(l1: Array[Float], move: Move, board: Board): Unit =
+    board.pieceAt(move.from).foreach { pawn =>
+      val capturedSq = Square(move.to.file, move.from.rank)
+      subtractColumn(l1, featureIndex(pawn, squareNum(move.from)))
+      board.pieceAt(capturedSq).foreach(cap => subtractColumn(l1, featureIndex(cap, squareNum(capturedSq))))
+      addColumn(l1, featureIndex(pawn, squareNum(move.to)))
+    }
+
+  private def applyCastleDelta(l1: Array[Float], move: Move, board: Board): Unit =
+    board.pieceAt(move.from).foreach { king =>
+      val rank     = move.from.rank
+      val kingside = move.moveType == MoveType.CastleKingside
+      val (rookFrom, rookTo) =
+        if kingside then (Square(File.H, rank), Square(File.F, rank))
+        else (Square(File.A, rank), Square(File.D, rank))
+      val rook = Piece(king.color, PieceType.Rook)
+      subtractColumn(l1, featureIndex(king, squareNum(move.from)))
+      addColumn(l1, featureIndex(king, squareNum(move.to)))
+      subtractColumn(l1, featureIndex(rook, squareNum(rookFrom)))
+      addColumn(l1, featureIndex(rook, squareNum(rookTo)))
+    }
+
+  private def applyPromotionDelta(l1: Array[Float], move: Move, promo: PromotionPiece, board: Board): Unit =
+    board.pieceAt(move.from).foreach { pawn =>
+      val toNum = squareNum(move.to)
+      subtractColumn(l1, featureIndex(pawn, squareNum(move.from)))
+      board.pieceAt(move.to).foreach(cap => subtractColumn(l1, featureIndex(cap, toNum)))
+      addColumn(l1, featureIndex(Piece(pawn.color, promotedType(promo)), toNum))
+    }
+
+  private def promotedType(promo: PromotionPiece): PieceType = promo match
+    case PromotionPiece.Knight => PieceType.Knight
+    case PromotionPiece.Bishop => PieceType.Bishop
+    case PromotionPiece.Rook   => PieceType.Rook
+    case PromotionPiece.Queen  => PieceType.Queen
+
+  // ── Evaluation from accumulator ──────────────────────────────────────────
+
+  def evaluateAtPly(ply: Int, turn: Color, hash: Long): Int =
+    val idx = (hash & EVAL_CACHE_MASK).toInt
+    if evalCacheHashes(idx) == hash then evalCacheScores(idx)
+    else
+      val score = runL2toOutput(l1Stack(ply), turn)
+      evalCacheHashes(idx) = hash
+      evalCacheScores(idx) = score
+      score
+
+  def evaluateAtPlyWithValidation(ply: Int, turn: Color, hash: Long, board: Board): Int =
+    // For debugging: validate that incremental accumulator matches recomputation
+    if validateAccum && ply > 0 && ply % 10 != 0 then
+      val isValid = validateAccumulator(ply, board)
+      if !isValid then System.err.println(s"WARNING: NNUE accumulator diverged at ply $ply")
+    evaluateAtPly(ply, turn, hash)
+
+  private def runL2toOutput(l1Pre: Array[Float], turn: Color): Int =
+    val l1ReLU = evalBuffers(0)
+    for i <- 0 until accSize do l1ReLU(i) = if l1Pre(i) > 0f then l1Pre(i) else 0f
+
+    val finalInput =
+      (1 until model.layers.length - 1).foldLeft(l1ReLU) { (input, i) =>
+        val lw  = model.weights(i)
+        val out = evalBuffers(i)
+        val ld  = model.layers(i)
+        runDenseReLU(input, ld.inputSize, lw.weights, lw.bias, out, ld.outputSize)
+        out
+      }
+
+    val lastIdx = model.layers.length - 1
+    val output  = runOutputLayer(finalInput, model.layers(lastIdx).inputSize, model.weights(lastIdx))
+    scoreFromOutput(output, turn)
+
+  private def runDenseReLU(
+      input: Array[Float],
+      inSize: Int,
+      weights: Array[Float],
+      bias: Array[Float],
+      output: Array[Float],
+      outSize: Int,
+  ): Unit =
+    for i <- 0 until outSize do
+      val sum = (0 until inSize).foldLeft(bias(i))((s, j) => s + input(j) * weights(i * inSize + j))
+      output(i) = if sum > 0f then sum else 0f
+
+  private def runOutputLayer(input: Array[Float], inSize: Int, lw: LayerWeights): Float =
+    (0 until inSize).foldLeft(lw.bias(0))((sum, j) => sum + input(j) * lw.weights(j))
+
+  private def scoreFromOutput(output: Float, turn: Color): Int =
+    val cp =
+      if math.abs(output) >= 0.9999f then if output > 0f then 20000 else -20000
+      else
+        val atanh = 0.5f * math.log((1f + output) / (1f - output)).toFloat
+        (300f * atanh).toInt
+    val cpFromTurn = if turn == Color.Black then -cp else cp
+    math.max(-20000, math.min(20000, cpFromTurn))
+
+  // ── Legacy full-board evaluate ────────────────────────────────────────────
+
+  private val legacyL1 = new Array[Float](accSize)
+
+  def evaluate(context: GameContext): Int =
+    System.arraycopy(model.weights(0).bias, 0, legacyL1, 0, accSize)
+    for (sq, piece) <- context.board.pieces do addColumn(legacyL1, featureIndex(piece, squareNum(sq)))
+    runL2toOutput(legacyL1, context.turn)
+
+  def benchmark(): Unit =
+    val context    = GameContext.initial
+    val iterations = 1_000_000
+    for _ <- 0 until 10000 do evaluate(context)
+    val startNanos = System.nanoTime()
+    for _ <- 0 until iterations do evaluate(context)
+    val endNanos     = System.nanoTime()
+    val totalNanos   = endNanos - startNanos
+    val nanosPerEval = totalNanos.toDouble / iterations
+    println()
+    println("=" * 60)
+    println("NNUE BENCHMARK RESULTS")
+    println("=" * 60)
+    println(f"Iterations:        $iterations%,d")
+    println(f"Total time:        ${totalNanos / 1e9}%.2f seconds")
+    println(f"ns/eval:           $nanosPerEval%.2f ns")
+    println(f"evals/second:      ${1e9 / nanosPerEval}%.0f evals/s")
+    println("=" * 60)
+    println()
@@ -0,0 +1,52 @@
+package de.nowchess.bot.bots.nnue
+
+import java.io.InputStream
+import java.nio.{ByteBuffer, ByteOrder}
+import java.nio.charset.StandardCharsets
+
+object NbaiLoader:
+
+  /** Little-endian encoding of ASCII bytes 'N','B','A','I'. */
+  val MAGIC: Int = 0x4942_414e
+
+  def load(stream: InputStream): NbaiModel =
+    val buf = ByteBuffer.wrap(stream.readAllBytes()).order(ByteOrder.LITTLE_ENDIAN)
+    checkHeader(buf)
+    val metadata = readMetadata(buf)
+    val descs    = readLayerDescriptors(buf)
+    val weights  = descs.map(_ => readLayerWeights(buf))
+    NbaiModel(metadata, descs, weights)
+
+  /** Tries /nnue_weights.nbai on the classpath; falls back to migrating /nnue_weights.bin. */
+  def loadDefault(): NbaiModel =
+    Option(getClass.getResourceAsStream("/nnue_weights.nbai")) match
+      case Some(s) =>
+        try load(s)
+        finally s.close()
+      case None => NbaiMigrator.migrateFromBin()
+
+  private def checkHeader(buf: ByteBuffer): Unit =
+    val magic = buf.getInt()
+    if magic != MAGIC then sys.error(s"Invalid NBAI magic: 0x${magic.toHexString}")
+    val version = buf.getShort() & 0xffff
+    if version != 1 then sys.error(s"Unsupported NBAI version: $version")
+
+  private def readMetadata(buf: ByteBuffer): NbaiMetadata =
+    val bytes = new Array[Byte](buf.getInt())
+    buf.get(bytes)
+    NbaiMetadata.fromJson(new String(bytes, StandardCharsets.UTF_8))
+
+  private def readLayerDescriptors(buf: ByteBuffer): Array[LayerDescriptor] =
+    Array.tabulate(buf.getShort() & 0xffff) { _ =>
+      val nameBytes = new Array[Byte](buf.get() & 0xff)
+      buf.get(nameBytes)
+      LayerDescriptor(new String(nameBytes, StandardCharsets.US_ASCII), buf.getInt(), buf.getInt())
+    }
+
+  private def readLayerWeights(buf: ByteBuffer): LayerWeights =
+    LayerWeights(readFloats(buf), readFloats(buf))
+
+  private def readFloats(buf: ByteBuffer): Array[Float] =
+    val arr = new Array[Float](buf.getInt())
+    for i <- arr.indices do arr(i) = buf.getFloat()
+    arr
@@ -0,0 +1,43 @@
+package de.nowchess.bot.bots.nnue
+
+import java.nio.{ByteBuffer, ByteOrder}
+
+/** Converts the legacy nnue_weights.bin resource into an NbaiModel. Used as fallback when no .nbai file exists. */
+object NbaiMigrator:
+
+  private val BinMagic   = 0x4555_4e4e
+  private val BinVersion = 1
+
+  private val DefaultLayers: Array[LayerDescriptor] = Array(
+    LayerDescriptor("relu", 768, 1536),
+    LayerDescriptor("relu", 1536, 1024),
+    LayerDescriptor("relu", 1024, 512),
+    LayerDescriptor("relu", 512, 256),
+    LayerDescriptor("linear", 256, 1),
+  )
+
+  private val UnknownMetadata: NbaiMetadata =
+    NbaiMetadata(trainedBy = "unknown", trainedAt = "unknown", trainingDataCount = 0L, valLoss = 0.0, trainLoss = 0.0)
+
+  def migrateFromBin(): NbaiModel =
+    val stream = Option(getClass.getResourceAsStream("/nnue_weights.bin"))
+      .getOrElse(sys.error("Neither nnue_weights.nbai nor nnue_weights.bin found in resources"))
+    try
+      val buf = ByteBuffer.wrap(stream.readAllBytes()).order(ByteOrder.LITTLE_ENDIAN)
+      checkBinHeader(buf)
+      val weights = DefaultLayers.map(_ => readBinLayerWeights(buf))
+      NbaiModel(UnknownMetadata, DefaultLayers, weights)
+    finally stream.close()
+
+  private def checkBinHeader(buf: ByteBuffer): Unit =
+    val magic = buf.getInt()
+    if magic != BinMagic then sys.error(s"Invalid bin magic: 0x${magic.toHexString}")
+    val version = buf.getInt()
+    if version != BinVersion then sys.error(s"Unsupported bin version: $version")
+
+  private def readBinLayerWeights(buf: ByteBuffer): LayerWeights =
+    LayerWeights(readBinTensor(buf), readBinTensor(buf))
+
+  private def readBinTensor(buf: ByteBuffer): Array[Float] =
+    val shape = Array.tabulate(buf.getInt())(_ => buf.getInt())
+    Array.tabulate(shape.product)(_ => buf.getFloat())
@@ -0,0 +1,45 @@
+package de.nowchess.bot.bots.nnue
+
+/** Descriptor for a single dense layer stored in a .nbai file. */
+case class LayerDescriptor(activation: String, inputSize: Int, outputSize: Int)
+
+/** Training metadata embedded in every .nbai file. */
+case class NbaiMetadata(
+    trainedBy: String,
+    trainedAt: String,
+    trainingDataCount: Long,
+    valLoss: Double,
+    trainLoss: Double,
+):
+  def toJson: String =
+    s"""{
+       |  "trainedBy": "$trainedBy",
+       |  "trainedAt": "$trainedAt",
+       |  "trainingDataCount": $trainingDataCount,
+       |  "valLoss": $valLoss,
+       |  "trainLoss": $trainLoss
+       |}""".stripMargin
+
+object NbaiMetadata:
+  def fromJson(json: String): NbaiMetadata =
+    def str(key: String) = raw""""$key"\s*:\s*"([^"]*)"""".r.findFirstMatchIn(json).map(_.group(1)).getOrElse("")
+    def num(key: String) = raw""""$key"\s*:\s*([0-9.eE+\-]+)""".r.findFirstMatchIn(json).map(_.group(1)).getOrElse("0")
+    NbaiMetadata(
+      str("trainedBy"),
+      str("trainedAt"),
+      num("trainingDataCount").toLong,
+      num("valLoss").toDouble,
+      num("trainLoss").toDouble,
+    )
+
+/** Weights and biases for a single layer. Weights are row-major: (outputSize × inputSize). */
+case class LayerWeights(weights: Array[Float], bias: Array[Float])
+
+/** A fully deserialized .nbai model ready to initialize NNUE. */
+case class NbaiModel(
+    metadata: NbaiMetadata,
+    layers: Array[LayerDescriptor],
+    weights: Array[LayerWeights],
+):
+  require(layers.length == weights.length, "Layer count must match weight count")
+  require(layers.length >= 2, "Model must have at least 2 layers")
@@ -0,0 +1,51 @@
+package de.nowchess.bot.bots.nnue
+
+import java.io.{ByteArrayOutputStream, OutputStream}
+import java.nio.{ByteBuffer, ByteOrder}
+import java.nio.charset.StandardCharsets
+
+object NbaiWriter:
+
+  def write(model: NbaiModel, out: OutputStream): Unit =
+    val acc = new ByteArrayOutputStream()
+    writeHeader(acc)
+    writeMetadata(acc, model.metadata)
+    writeLayerDescriptors(acc, model.layers)
+    model.weights.foreach(lw => writeLayerWeights(acc, lw))
+    out.write(acc.toByteArray)
+
+  private def writeHeader(out: ByteArrayOutputStream): Unit =
+    val buf = ByteBuffer.allocate(6).order(ByteOrder.LITTLE_ENDIAN)
+    buf.putInt(NbaiLoader.MAGIC)
+    buf.putShort(1.toShort)
+    out.write(buf.array())
+
+  private def writeMetadata(out: ByteArrayOutputStream, meta: NbaiMetadata): Unit =
+    val json = meta.toJson.getBytes(StandardCharsets.UTF_8)
+    val buf  = ByteBuffer.allocate(4 + json.length).order(ByteOrder.LITTLE_ENDIAN)
+    buf.putInt(json.length)
+    buf.put(json)
+    out.write(buf.array())
+
+  private def writeLayerDescriptors(out: ByteArrayOutputStream, layers: Array[LayerDescriptor]): Unit =
+    val nameBytes = layers.map(_.activation.getBytes(StandardCharsets.US_ASCII))
+    val capacity  = 2 + layers.indices.map(i => 1 + nameBytes(i).length + 8).sum
+    val buf       = ByteBuffer.allocate(capacity).order(ByteOrder.LITTLE_ENDIAN)
+    buf.putShort(layers.length.toShort)
+    layers.zip(nameBytes).foreach { (l, nb) =>
+      buf.put(nb.length.toByte)
+      buf.put(nb)
+      buf.putInt(l.inputSize)
+      buf.putInt(l.outputSize)
+    }
+    out.write(buf.array())
+
+  private def writeLayerWeights(out: ByteArrayOutputStream, lw: LayerWeights): Unit =
+    writeFloats(out, lw.weights)
+    writeFloats(out, lw.bias)
+
+  private def writeFloats(out: ByteArrayOutputStream, floats: Array[Float]): Unit =
+    val buf = ByteBuffer.allocate(4 + floats.length * 4).order(ByteOrder.LITTLE_ENDIAN)
+    buf.putInt(floats.length)
+    floats.foreach(buf.putFloat)
+    out.write(buf.array())
@@ -0,0 +1,419 @@
+package de.nowchess.bot.logic
+
+import de.nowchess.api.board.PieceType
+import de.nowchess.api.game.GameContext
+import de.nowchess.api.move.{Move, MoveType}
+import de.nowchess.bot.ai.Evaluation
+import de.nowchess.bot.util.ZobristHash
+import de.nowchess.rules.RuleSet
+import de.nowchess.rules.sets.DefaultRules
+import java.util.concurrent.atomic.{AtomicInteger, AtomicLong}
+
+final class AlphaBetaSearch(
+    rules: RuleSet = DefaultRules,
+    tt: TranspositionTable = TranspositionTable(),
+    weights: Evaluation,
+    numThreads: Int = Runtime.getRuntime.availableProcessors,
+):
+
+  private val INF                  = Int.MaxValue / 2
+  private val MAX_QUIESCENCE_PLY   = 64
+  private val NULL_MOVE_R          = 2
+  private val ASPIRATION_DELTA     = 50
+  private val ASPIRATION_DELTA_MAX = 150
+  private val TIME_CHECK_FREQUENCY = 1000
+  private val FUTILITY_MARGIN      = 100
+  private val CHECK_EXTENSION      = 1
+
+  private val timeStartMs = AtomicLong(0L)
+  private val timeLimitMs = AtomicLong(0L)
+  private val nodeCount   = AtomicInteger(0)
+  private val ordering    = MoveOrdering.OrderingContext()
+
+  private final case class QuiescenceNode(
+      context: GameContext,
+      ply: Int,
+      alpha: Int,
+      beta: Int,
+      hash: Long,
+  )
+
+  /** Return the best move for the side to move, searching to maxDepth plies. Uses iterative deepening with aspiration
+    * windows.
+    */
+  def bestMove(context: GameContext, maxDepth: Int): Option[Move] =
+    bestMove(context, maxDepth, Set.empty)
+
+  def bestMove(context: GameContext, maxDepth: Int, excludedRootMoves: Set[Move]): Option[Move] =
+    tt.clear()
+    ordering.clear()
+    weights.initAccumulator(context)
+    timeStartMs.set(System.currentTimeMillis)
+    timeLimitMs.set(Long.MaxValue / 4)
+    nodeCount.set(0)
+    val rootHash = ZobristHash.hash(context)
+    (1 to maxDepth)
+      .foldLeft((None: Option[Move], 0)) { case ((bestSoFar, prevScore), depth) =>
+        val (alpha, beta) =
+          if depth == 1 then (-INF, INF) else (prevScore - ASPIRATION_DELTA, prevScore + ASPIRATION_DELTA)
+        val (score, move) = searchWithAspiration(
+          context,
+          depth,
+          alpha,
+          beta,
+          ASPIRATION_DELTA,
+          rootHash,
+          excludedRootMoves,
+        )
+        (move.orElse(bestSoFar), score)
+      }
+      ._1
+
+  /** Return the best move for the side to move within a time budget (ms). Uses iterative deepening, stopping when time
+    * runs out.
+    */
+  def bestMoveWithTime(context: GameContext, timeBudgetMs: Long): Option[Move] =
+    bestMoveWithTime(context, timeBudgetMs, Set.empty)
+
+  def bestMoveWithTime(context: GameContext, timeBudgetMs: Long, excludedRootMoves: Set[Move]): Option[Move] =
+    tt.clear()
+    ordering.clear()
+    weights.initAccumulator(context)
+    timeStartMs.set(System.currentTimeMillis)
+    timeLimitMs.set(timeBudgetMs)
+    nodeCount.set(0)
+    val rootHash = ZobristHash.hash(context)
+
+    @scala.annotation.tailrec
+    def loop(bestSoFar: Option[Move], prevScore: Int, depth: Int): Option[Move] =
+      if isOutOfTime then bestSoFar
+      else
+        val (alpha, beta) =
+          if depth == 1 then (-INF, INF) else (prevScore - ASPIRATION_DELTA, prevScore + ASPIRATION_DELTA)
+        val (score, move) = searchWithAspiration(
+          context,
+          depth,
+          alpha,
+          beta,
+          ASPIRATION_DELTA,
+          rootHash,
+          excludedRootMoves,
+        )
+        loop(move.orElse(bestSoFar), score, depth + 1)
+
+    loop(None, 0, 1)
+
+  private def isOutOfTime: Boolean =
+    System.currentTimeMillis - timeStartMs.get >= timeLimitMs.get
+
+  private def searchWithAspiration(
+      context: GameContext,
+      depth: Int,
+      alpha: Int,
+      beta: Int,
+      initialWindow: Int,
+      rootHash: Long,
+      excludedRootMoves: Set[Move],
+  ): (Int, Option[Move]) =
+    val state = SearchState(rootHash, Map(rootHash -> 1))
+
+    @scala.annotation.tailrec
+    def loop(currentAlpha: Int, currentBeta: Int, delta: Int, attempt: Int): (Int, Option[Move]) =
+      if attempt >= 3 || attempt >= depth then search(context, depth, 0, Window(-INF, INF), state, excludedRootMoves)
+      else
+        val (score, move) = search(context, depth, 0, Window(currentAlpha, currentBeta), state, excludedRootMoves)
+        if score > currentAlpha && score < currentBeta then (score, move)
+        else if score <= currentAlpha then
+          loop(score - delta, currentBeta, math.min(delta * 2, ASPIRATION_DELTA_MAX), attempt + 1)
+        else loop(currentAlpha, score + delta, math.min(delta * 2, ASPIRATION_DELTA_MAX), attempt + 1)
+
+    loop(alpha, beta, initialWindow, 0)
+
+  private def hasNonPawnMaterial(context: GameContext): Boolean =
+    context.board.pieces.values.exists { piece =>
+      piece.color == context.turn &&
+      piece.pieceType != PieceType.Pawn &&
+      piece.pieceType != PieceType.King
+    }
+
+  private def nullMoveContext(context: GameContext): GameContext =
+    context.withTurn(context.turn.opposite).withEnPassantSquare(None)
+
+  private def tryNullMove(
+      context: GameContext,
+      depth: Int,
+      ply: Int,
+      beta: Int,
+      state: SearchState,
+      excludedRootMoves: Set[Move],
+  ): Option[Int] =
+    val nullCtx        = nullMoveContext(context)
+    val nullState      = state.advance(ZobristHash.hash(nullCtx))
+    val reductionDepth = math.max(0, depth - 1 - NULL_MOVE_R)
+    weights.copyAccumulator(ply, ply + 1)
+    val (score, _) = search(nullCtx, reductionDepth, ply + 1, Window(-beta, -beta + 1), nullState, excludedRootMoves)
+    if -score >= beta then Some(beta) else None
+
+  /** Negamax alpha-beta search returning (score, best move). */
+  private def search(
+      context: GameContext,
+      depth: Int,
+      ply: Int,
+      window: Window,
+      state: SearchState,
+      excludedRootMoves: Set[Move],
+  ): (Int, Option[Move]) =
+    val params = SearchParams(context, depth, ply, window, state, excludedRootMoves)
+    searchNode(params)
+
+  private def searchNode(params: SearchParams): (Int, Option[Move]) =
+    val count = nodeCount.incrementAndGet()
+    immediateSearchResult(params, count).getOrElse {
+      val legalMoves = rules.allLegalMoves(params.context)
+      terminalSearchResult(params, legalMoves).getOrElse(searchDeeper(params, legalMoves))
+    }
+
+  private def immediateSearchResult(
+      params: SearchParams,
+      count: Int,
+  ): Option[(Int, Option[Move])] =
+    if count % TIME_CHECK_FREQUENCY == 0 && isOutOfTime then
+      Some((weights.evaluateAccumulator(params.ply, params.context, params.state.hash), None))
+    else if params.state.repetitions.getOrElse(params.state.hash, 0) >= 3 then Some((weights.DRAW_SCORE, None))
+    else ttCutoff(params)
+
+  private def ttCutoff(params: SearchParams): Option[(Int, Option[Move])] =
+    tt.probe(params.state.hash).filter(_.depth >= params.depth).flatMap { entry =>
+      entry.flag match
+        case TTFlag.Exact => Some((entry.score, entry.bestMove))
+        case TTFlag.Lower =>
+          val newAlpha = math.max(params.window.alpha, entry.score)
+          Option.when(newAlpha >= params.window.beta)((entry.score, entry.bestMove))
+        case TTFlag.Upper =>
+          val newBeta = math.min(params.window.beta, entry.score)
+          Option.when(params.window.alpha >= newBeta)((entry.score, entry.bestMove))
+    }
+
+  private def terminalSearchResult(
+      params: SearchParams,
+      legalMoves: List[Move],
+  ): Option[(Int, Option[Move])] =
+    if legalMoves.isEmpty then
+      Some(
+        (
+          if rules.isCheckmate(params.context) then -(weights.CHECKMATE_SCORE - params.ply) else weights.DRAW_SCORE,
+          None,
+        ),
+      )
+    else if rules.isInsufficientMaterial(params.context) || rules.isFiftyMoveRule(params.context) then
+      Some((weights.DRAW_SCORE, None))
+    else if params.depth == 0 then
+      Some((quiescence(params.context, params.ply, params.window.alpha, params.window.beta, params.state.hash), None))
+    else None
+
+  private def searchDeeper(
+      params: SearchParams,
+      legalMoves: List[Move],
+  ): (Int, Option[Move]) =
+    val nullResult =
+      Option
+        .when(canTryNullMove(params))(
+          tryNullMove(
+            params.context,
+            params.depth,
+            params.ply,
+            params.window.beta,
+            params.state,
+            params.excludedRootMoves,
+          ),
+        )
+        .flatten
+
+    nullResult.map((_, None)).getOrElse {
+      val ttBest  = tt.probe(params.state.hash).flatMap(_.bestMove)
+      val ordered = MoveOrdering.sort(params.context, legalMoves, ttBest, params.ply, ordering)
+      searchSequential(
+        params.context,
+        params.depth,
+        params.ply,
+        params.window,
+        ordered,
+        params.state,
+        params.excludedRootMoves,
+      )
+    }
+
+  private def canTryNullMove(params: SearchParams): Boolean =
+    params.depth >= 3 &&
+      !rules.isCheck(params.context) &&
+      hasNonPawnMaterial(params.context)
+
+  private def isQuietMove(context: GameContext, move: Move): Boolean =
+    !isCapture(context, move) &&
+      move.moveType != MoveType.CastleKingside &&
+      move.moveType != MoveType.CastleQueenside
+
+  private def scoreMove(
+      child: GameContext,
+      childState: SearchState,
+      params: SearchParams,
+      extension: Int,
+      reduction: Int,
+      a: Int,
+  ): Int =
+    val betaNeg = -params.window.beta
+    if reduction > 0 then
+      val (rs, _) = search(
+        child,
+        math.max(0, params.depth - 1 - reduction + extension),
+        params.ply + 1,
+        Window(-a - 1, -a),
+        childState,
+        params.excludedRootMoves,
+      )
+      val s = -rs
+      if s > a then
+        val (fs, _) = search(
+          child,
+          math.max(0, params.depth - 1 + extension),
+          params.ply + 1,
+          Window(betaNeg, -a),
+          childState,
+          params.excludedRootMoves,
+        )
+        -fs
+      else s
+    else
+      val (rs, _) = search(
+        child,
+        math.max(0, params.depth - 1 + extension),
+        params.ply + 1,
+        Window(betaNeg, -a),
+        childState,
+        params.excludedRootMoves,
+      )
+      -rs
+
+  private def evalSingleMove(
+      move: Move,
+      moveNumber: Int,
+      a: Int,
+      params: SearchParams,
+  ): Option[(Int, Boolean)] =
+    val skipRoot = params.ply == 0 && params.excludedRootMoves.contains(move)
+    val isQuiet  = isQuietMove(params.context, move)
+    val futility = params.depth == 1 && isQuiet && moveNumber > 2 &&
+      weights.evaluateAccumulator(params.ply, params.context, params.state.hash) + FUTILITY_MARGIN < params.window.alpha
+    if skipRoot || futility then None
+    else
+      val child     = rules.applyMove(params.context)(move)
+      val childHash = ZobristHash.nextHash(params.context, params.state.hash, move, child)
+      weights.pushAccumulator(params.ply + 1, move, params.context, child)
+      val childState = params.state.advance(childHash)
+      val extension  = if rules.isCheck(child) then CHECK_EXTENSION else 0
+      val reduction  = if moveNumber > 4 && params.depth >= 3 && isQuiet then 1 else 0
+      Some((scoreMove(child, childState, params, extension, reduction, a), isQuiet))
+
+  private def recordCutoff(move: Move, depth: Int, ply: Int): Unit =
+    ordering.addHistory(
+      move.from.rank.ordinal * 8 + move.from.file.ordinal,
+      move.to.rank.ordinal * 8 + move.to.file.ordinal,
+      depth * depth,
+    )
+    ordering.addKillerMove(ply, move)
+
+  @scala.annotation.tailrec
+  private def searchLoop(
+      idx: Int,
+      moveNumber: Int,
+      acc: LoopAcc,
+      params: SearchParams,
+      ordered: List[Move],
+  ): (Option[Move], Int, Boolean) =
+    if idx >= ordered.length then (acc.bestMove, acc.bestScore, false)
+    else
+      val move = ordered(idx)
+      evalSingleMove(move, moveNumber, acc.a, params) match
+        case None => searchLoop(idx + 1, moveNumber + 1, acc, params, ordered)
+        case Some((score, isQuiet)) =>
+          val newAcc = LoopAcc(
+            if score > acc.bestScore then Some(move) else acc.bestMove,
+            math.max(acc.bestScore, score),
+            math.max(acc.a, score),
+          )
+          if newAcc.a >= params.window.beta then
+            if isQuiet then recordCutoff(move, params.depth, params.ply)
+            (newAcc.bestMove, newAcc.bestScore, true)
+          else searchLoop(idx + 1, moveNumber + 1, newAcc, params, ordered)
+
+  private def searchSequential(
+      context: GameContext,
+      depth: Int,
+      ply: Int,
+      window: Window,
+      ordered: List[Move],
+      state: SearchState,
+      excludedRootMoves: Set[Move],
+  ): (Int, Option[Move]) =
+    val params                        = SearchParams(context, depth, ply, window, state, excludedRootMoves)
+    val (bestMove, bestScore, cutoff) = searchLoop(0, 0, LoopAcc(None, -INF, window.alpha), params, ordered)
+    val flag =
+      if cutoff then TTFlag.Lower
+      else if bestScore <= window.alpha then TTFlag.Upper
+      else TTFlag.Exact
+    tt.store(TTEntry(state.hash, depth, bestScore, flag, bestMove))
+    (bestScore, bestMove)
+
+  /** Quiescence search: only captures until position is quiet. */
+  private def quiescence(
+      context: GameContext,
+      ply: Int,
+      alpha: Int,
+      beta: Int,
+      hash: Long,
+  ): Int =
+    quiescenceNode(QuiescenceNode(context, ply, alpha, beta, hash))
+
+  private def quiescenceNode(node: QuiescenceNode): Int =
+    val inCheck  = rules.isCheck(node.context)
+    val standPat = if inCheck then -INF else weights.evaluateAccumulator(node.ply, node.context, node.hash)
+
+    if !inCheck && standPat >= node.beta then node.beta
+    else if node.ply >= MAX_QUIESCENCE_PLY then quiescenceAtDepthLimit(node, inCheck, standPat)
+    else
+      val moves = tacticalMoves(node.context, inCheck)
+      if inCheck && moves.isEmpty then -(weights.CHECKMATE_SCORE - node.ply)
+      else
+        val ordered = MoveOrdering.sort(node.context, moves, None)
+        val a0      = if inCheck then node.alpha else math.max(node.alpha, standPat)
+        quiescenceLoop(node, ordered, 0, a0)
+
+  private def quiescenceAtDepthLimit(node: QuiescenceNode, inCheck: Boolean, standPat: Int): Int =
+    if inCheck then weights.evaluateAccumulator(node.ply, node.context, node.hash) else standPat
+
+  private def tacticalMoves(context: GameContext, inCheck: Boolean): List[Move] =
+    val allMoves = rules.allLegalMoves(context)
+    if inCheck then allMoves else allMoves.filter(m => isCapture(context, m))
+
+  @scala.annotation.tailrec
+  private def quiescenceLoop(
+      node: QuiescenceNode,
+      ordered: List[Move],
+      idx: Int,
+      a: Int,
+  ): Int =
+    if idx >= ordered.length then a
+    else
+      val move      = ordered(idx)
+      val child     = rules.applyMove(node.context)(move)
+      val childHash = ZobristHash.nextHash(node.context, node.hash, move, child)
+      weights.pushAccumulator(node.ply + 1, move, node.context, child)
+      val score = -quiescence(child, node.ply + 1, -node.beta, -a, childHash)
+      if score >= node.beta then node.beta
+      else quiescenceLoop(node, ordered, idx + 1, math.max(a, score))
+
+  private def isCapture(context: GameContext, move: Move): Boolean = move.moveType match
+    case MoveType.Normal(true) => true
+    case MoveType.EnPassant    => true
+    case MoveType.Promotion(_) => context.board.pieceAt(move.to).exists(_.color != context.turn)
+    case _                     => false
@@ -0,0 +1,177 @@
+package de.nowchess.bot.logic
+
+import de.nowchess.api.board.{Board, Color, Piece, PieceType, Square}
+import de.nowchess.api.game.GameContext
+import de.nowchess.api.move.{Move, MoveType, PromotionPiece}
+
+import scala.annotation.tailrec
+import scala.collection.mutable
+
+object MoveOrdering:
+
+  class OrderingContext:
+    private val killerMoves  = mutable.Map[Int, List[Move]]()
+    private val historyTable = mutable.Map[(Int, Int), Int]()
+
+    def addKillerMove(ply: Int, move: Move): Unit =
+      val current = killerMoves.getOrElse(ply, List())
+      if current.isEmpty || (current.head.from != move.from || current.head.to != move.to) then
+        killerMoves(ply) = (move :: current).take(2)
+
+    def getKillerMoves(ply: Int): List[Move] =
+      killerMoves.getOrElse(ply, List())
+
+    def addHistory(from: Int, to: Int, bonus: Int): Unit =
+      val key = (from, to)
+      historyTable(key) = historyTable.getOrElse(key, 0) + bonus
+
+    def getHistory(from: Int, to: Int): Int =
+      historyTable.getOrElse((from, to), 0)
+
+    def clear(): Unit =
+      killerMoves.clear()
+      historyTable.clear()
+
+  def score(
+      context: GameContext,
+      move: Move,
+      ttBestMove: Option[Move],
+      ply: Int = 0,
+      ordering: OrderingContext = new OrderingContext(),
+  ): Int =
+    if ttBestMove.exists(m => m.from == move.from && m.to == move.to) then Int.MaxValue
+    else
+      move.moveType match
+        case MoveType.Promotion(PromotionPiece.Queen) =>
+          1_000_000 + promotionCaptureBonus(context, move)
+        case MoveType.Normal(true) | MoveType.EnPassant =>
+          captureScore(context, move)
+        case MoveType.Promotion(_) =>
+          50_000 + promotionCaptureBonus(context, move)
+        case _ => scoreQuietMove(move, ply, ordering)
+
+  def sort(
+      context: GameContext,
+      moves: List[Move],
+      ttBestMove: Option[Move],
+      ply: Int = 0,
+      ordering: OrderingContext = new OrderingContext(),
+  ): List[Move] =
+    moves.sortBy(m => -score(context, m, ttBestMove, ply, ordering))
+
+  private def scoreQuietMove(move: Move, ply: Int, ordering: OrderingContext): Int =
+    val isKiller = ordering.getKillerMoves(ply).exists(k => k.from == move.from && k.to == move.to)
+    val fromIdx  = move.from.rank.ordinal * 8 + move.from.file.ordinal
+    val toIdx    = move.to.rank.ordinal * 8 + move.to.file.ordinal
+    val history  = ordering.getHistory(fromIdx, toIdx)
+    if isKiller then 10_000 + (history / 10) else history / 10
+
+  private def promotionCaptureBonus(context: GameContext, move: Move): Int =
+    if isCapture(context, move) then captureScore(context, move) else 0
+
+  private def captureScore(context: GameContext, move: Move): Int =
+    val see     = staticExchange(context, move)
+    val seeBias = if see >= 0 then 20_000 else -20_000
+    100_000 + mvvLva(context, move) + seeBias + see
+
+  private def mvvLva(context: GameContext, move: Move): Int =
+    (victimValue(context, move) * 10) - attackerValue(context, move)
+
+  private def attackerValue(context: GameContext, move: Move): Int =
+    context.board.pieceAt(move.from).map(pieceValue).getOrElse(0)
+
+  private def victimValue(context: GameContext, move: Move): Int =
+    move.moveType match
+      case MoveType.Normal(true) => context.board.pieceAt(move.to).map(pieceValue).getOrElse(0)
+      case MoveType.EnPassant    => 1
+      case MoveType.Promotion(_) => context.board.pieceAt(move.to).map(pieceValue).getOrElse(0)
+      case _                     => 0
+
+  private def pieceValue(piece: Piece): Int = piece.pieceType match
+    case PieceType.Pawn   => 1
+    case PieceType.Knight => 3
+    case PieceType.Bishop => 3
+    case PieceType.Rook   => 5
+    case PieceType.Queen  => 9
+    case PieceType.King   => 200
+
+  private def isCapture(context: GameContext, move: Move): Boolean = move.moveType match
+    case MoveType.Normal(true) => true
+    case MoveType.EnPassant    => true
+    case MoveType.Promotion(_) => context.board.pieceAt(move.to).exists(_.color != context.turn)
+    case _                     => false
+
+  private def staticExchange(context: GameContext, move: Move): Int =
+    if !isCapture(context, move) then 0
+    else
+      val target      = move.to
+      val initialGain = victimValue(context, move)
+      movedPieceAfterMove(context, move).fold(initialGain) { moved =>
+        val boardAfterMove = applySeeMove(context.board, move, moved)
+        initialGain - seeGain(boardAfterMove, target, context.turn.opposite, pieceValue(moved))
+      }
+
+  private def movedPieceAfterMove(context: GameContext, move: Move): Option[Piece] =
+    move.moveType match
+      case MoveType.Promotion(pp) => Some(Piece(context.turn, promotionPieceType(pp)))
+      case _                      => context.board.pieceAt(move.from)
+
+  private def seeGain(board: Board, target: Square, side: Color, currentValue: Int): Int =
+    leastValuableAttacker(board, target, side) match
+      case None => 0
+      case Some((from, attacker)) =>
+        val nextBoard = board.removed(from).updated(target, attacker)
+        val replyGain = seeGain(nextBoard, target, side.opposite, pieceValue(attacker))
+        math.max(0, currentValue - replyGain)
+
+  private def applySeeMove(board: Board, move: Move, moved: Piece): Board =
+    move.moveType match
+      case MoveType.EnPassant =>
+        val capturedSquare = Square(move.to.file, move.from.rank)
+        board.removed(move.from).removed(capturedSquare).updated(move.to, moved)
+      case _ => board.removed(move.from).updated(move.to, moved)
+
+  private def leastValuableAttacker(board: Board, target: Square, color: Color): Option[(Square, Piece)] =
+    board.pieces
+      .collect {
+        case (sq, piece) if piece.color == color && attacksSquare(board, sq, target, piece) => (sq, piece)
+      }
+      .toList
+      .sortBy { case (_, piece) => pieceValue(piece) }
+      .headOption
+
+  private def attacksSquare(board: Board, from: Square, target: Square, piece: Piece): Boolean =
+    val df = target.file.ordinal - from.file.ordinal
+    val dr = target.rank.ordinal - from.rank.ordinal
+    piece.pieceType match
+      case PieceType.Pawn =>
+        val dir = if piece.color == Color.White then 1 else -1
+        dr == dir && math.abs(df) == 1
+      case PieceType.Knight =>
+        val adf = math.abs(df)
+        val adr = math.abs(dr)
+        (adf == 1 && adr == 2) || (adf == 2 && adr == 1)
+      case PieceType.Bishop => clearLine(board, from, target, df, dr, diagonal = true)
+      case PieceType.Rook   => clearLine(board, from, target, df, dr, diagonal = false)
+      case PieceType.Queen =>
+        clearLine(board, from, target, df, dr, diagonal = true) ||
+        clearLine(board, from, target, df, dr, diagonal = false)
+      case PieceType.King => math.abs(df) <= 1 && math.abs(dr) <= 1
+
+  private def clearLine(board: Board, from: Square, target: Square, df: Int, dr: Int, diagonal: Boolean): Boolean =
+    val valid =
+      if diagonal then math.abs(df) == math.abs(dr) && df != 0 else (df == 0 && dr != 0) || (dr == 0 && df != 0)
+    valid && pathClear(board, from, target, Integer.compare(df, 0), Integer.compare(dr, 0))
+
+  @tailrec
+  private def pathClear(board: Board, from: Square, target: Square, stepF: Int, stepR: Int): Boolean =
+    from.offset(stepF, stepR) match
+      case None                         => false
+      case Some(next) if next == target => true
+      case Some(next)                   => board.pieceAt(next).isEmpty && pathClear(board, next, target, stepF, stepR)
+
+  private def promotionPieceType(piece: PromotionPiece): PieceType = piece match
+    case PromotionPiece.Knight => PieceType.Knight
+    case PromotionPiece.Bishop => PieceType.Bishop
+    case PromotionPiece.Rook   => PieceType.Rook
+    case PromotionPiece.Queen  => PieceType.Queen
@@ -0,0 +1,61 @@
+package de.nowchess.bot.logic
+
+import de.nowchess.api.game.GameContext
+import de.nowchess.api.move.Move
+
+final case class Window(alpha: Int, beta: Int)
+
+final case class LoopAcc(bestMove: Option[Move], bestScore: Int, a: Int)
+
+final case class SearchParams(
+    context: GameContext,
+    depth: Int,
+    ply: Int,
+    window: Window,
+    state: SearchState,
+    excludedRootMoves: Set[Move],
+)
+
+final case class SearchState(hash: Long, repetitions: Map[Long, Int]):
+  def advance(nextHash: Long): SearchState =
+    SearchState(
+      nextHash,
+      repetitions.updatedWith(nextHash) {
+        case Some(v) => Some(v + 1)
+        case None    => Some(1)
+      },
+    )
+
+enum TTFlag:
+  case Exact // Score is exact
+  case Lower // Score is a lower bound
+  case Upper // Score is an upper bound
+
+final case class TTEntry(
+    hash: Long,
+    depth: Int,
+    score: Int,
+    flag: TTFlag,
+    bestMove: Option[Move],
+)
+
+final class TranspositionTable(val sizePow2: Int = 20):
+  private val size                          = 1 << sizePow2
+  private val mask                          = size - 1L
+  private val locks                         = Array.fill(size)(new Object())
+  private val table: Array[Option[TTEntry]] = Array.fill(size)(None)
+
+  def probe(hash: Long): Option[TTEntry] =
+    val index = (hash & mask).toInt
+    locks(index).synchronized {
+      table(index).filter(_.hash == hash)
+    }
+
+  def store(entry: TTEntry): Unit =
+    val index = (entry.hash & mask).toInt
+    locks(index).synchronized {
+      table(index) = Some(entry)
+    }
+
+  def clear(): Unit =
+    for i <- 0 until size do locks(i).synchronized { table(i) = None }
@@ -0,0 +1,137 @@
+package de.nowchess.bot.util
+
+import de.nowchess.api.board.*
+import de.nowchess.api.game.GameContext
+import de.nowchess.api.move.{Move, MoveType, PromotionPiece}
+
+import java.io.{DataInputStream, FileInputStream}
+import scala.collection.mutable
+import scala.util.Random
+
+/** Reads a Polyglot opening book (.bin file) and probes it for moves.
+  *
+  * Polyglot books are binary files with 16-byte big-endian records:
+  *   - key: 8 bytes (Long) — Zobrist hash of the position
+  *   - move: 2 bytes (Short) — packed as (to_file | to_rank | from_file | from_rank | promotion)
+  *   - weight: 2 bytes (Short) — move weight (higher = preferred)
+  *   - learn: 4 bytes (Int) — learning data (unused)
+  */
+final class PolyglotBook(path: String):
+
+  private val entries: Map[Long, Vector[BookEntry]] =
+    try {
+      val r = loadBookFile(path)
+      println(s"Book loaded successfully. ${r.size} entries found.")
+      r
+    } catch
+      case e: Exception =>
+        println(s"Error loading book: $e")
+        // Gracefully fail: return empty map if book cannot be loaded
+        // This allows the bot to work even if the book file is missing
+        scala.collection.immutable.Map.empty
+
+  /** Probe the book for a move in the given position. Returns a weighted random move, or None if not in book. */
+  def probe(context: GameContext): Option[Move] =
+    val hash = PolyglotHash.hash(context)
+    println(f"0x$hash%016X")
+    entries.get(hash).flatMap { bookEntries =>
+      if bookEntries.isEmpty then None
+      else
+        val entry = weightedRandom(bookEntries)
+        decodeMove(entry.move, context)
+    }
+
+  private def loadBookFile(path: String): Map[Long, Vector[BookEntry]] =
+    val input = DataInputStream(FileInputStream(path))
+    try
+      val result = mutable.Map[Long, Vector[BookEntry]]()
+      while input.available() > 0 do
+        val key    = input.readLong()
+        val move   = input.readShort()
+        val weight = input.readShort()
+        input.readInt() // learning data (unused)
+
+        val entry = BookEntry(key, move, weight)
+        result.updateWith(key) {
+          case Some(entries) => Some(entries :+ entry)
+          case None          => Some(Vector(entry))
+        }
+      result.toMap
+    finally input.close()
+
+  /** Decode a packed Polyglot move short into an Option[Move].
+    *
+    * Bit layout of the move Short:
+    *   - bits 0-2: to_file (0-7)
+    *   - bits 3-5: to_rank (0-7)
+    *   - bits 6-8: from_file (0-7)
+    *   - bits 9-11: from_rank (0-7)
+    *   - bits 12-14: promotion piece (0=none, 1=knight, 2=bishop, 3=rook, 4=queen)
+    */
+  private def decodeMove(raw: Short, context: GameContext): Option[Move] =
+    val toFile        = raw & 0x07
+    val toRank        = (raw >> 3) & 0x07
+    val fromFile      = (raw >> 6) & 0x07
+    val fromRank      = (raw >> 9) & 0x07
+    val promotionBits = (raw >> 12) & 0x07
+
+    if toFile > 7 || toRank > 7 || fromFile > 7 || fromRank > 7 then None
+    else
+      val from = Square(File.values(fromFile), Rank.values(fromRank))
+      val to   = Square(File.values(toFile), Rank.values(toRank))
+
+      if isKingMove(context, from) && isRookSquare(to, context) then Some(decodeCastling(from, to))
+      else
+        val moveTypeOpt: Option[MoveType] =
+          if promotionBits > 0 then
+            promotionBits match
+              case 1 => Some(MoveType.Promotion(PromotionPiece.Knight))
+              case 2 => Some(MoveType.Promotion(PromotionPiece.Bishop))
+              case 3 => Some(MoveType.Promotion(PromotionPiece.Rook))
+              case 4 => Some(MoveType.Promotion(PromotionPiece.Queen))
+              case _ => None
+          else Some(MoveType.Normal(context.board.pieces.contains(to)))
+
+        moveTypeOpt.map(moveType => Move(from, to, moveType))
+
+  private def isKingMove(context: GameContext, square: Square): Boolean =
+    context.board.pieces.get(square).exists { piece =>
+      piece.pieceType == PieceType.King
+    }
+
+  private def isRookSquare(square: Square, context: GameContext): Boolean =
+    context.board.pieces.get(square).exists { piece =>
+      piece.pieceType == PieceType.Rook
+    }
+
+  /** Decode castling from king-to-rook square to the standard move.
+    *
+    * Polyglot encodes castling as:
+    *   - e1→h1 = White kingside (move to g1)
+    *   - e1→a1 = White queenside (move to c1)
+    *   - e8→h8 = Black kingside (move to g8)
+    *   - e8→a8 = Black queenside (move to c8)
+    */
+  private def decodeCastling(from: Square, to: Square): Move =
+    if to.file == File.H then Move(from, Square(File.G, to.rank), MoveType.CastleKingside)
+    else if to.file == File.A then Move(from, Square(File.C, to.rank), MoveType.CastleQueenside)
+    else
+      // Fallback (should not happen in a valid book)
+      Move(from, to, MoveType.Normal())
+
+  /** Select a weighted random move from the list of book entries. */
+  private def weightedRandom(entries: Vector[BookEntry]): BookEntry =
+    if entries.length == 1 then entries.head
+    else
+      val totalWeight = entries.map(_.weight).sum
+      val pick        = Random.nextInt(totalWeight.max(1)) // NOSONAR
+
+      @scala.annotation.tailrec
+      def select(remaining: Int, idx: Int): BookEntry =
+        if idx >= entries.length then entries.last
+        else if remaining < entries(idx).weight then entries(idx)
+        else select(remaining - entries(idx).weight, idx + 1)
+
+      select(pick, 0)
+
+private case class BookEntry(key: Long, move: Short, weight: Int)
@@ -0,0 +1,204 @@
+package de.nowchess.bot.util
+
+import de.nowchess.api.board.{Color, Piece, PieceType, Square}
+import de.nowchess.api.game.GameContext
+
+object PolyglotHash:
+
+  /** 781-entry Zobrist random table from the Polyglot spec. */
+  private val Random: Array[Long] = Array(
+    0x9d39247e33776d41L, 0x2af7398005aaa5c7L, 0x44db015024623547L, 0x9c15f73e62a76ae2L, 0x75834465489c0c89L,
+    0x3290ac3a203001bfL, 0x0fbbad1f61042279L, 0xe83a908ff2fb60caL, 0x0d7e765d58755c10L, 0x1a083822ceafe02dL,
+    0x9605d5f0e25ec3b0L, 0xd021ff5cd13a2ed5L, 0x40bdf15d4a672e32L, 0x011355146fd56395L, 0x5db4832046f3d9e5L,
+    0x239f8b2d7ff719ccL, 0x05d1a1ae85b49aa1L, 0x679f848f6e8fc971L, 0x7449bbff801fed0bL, 0x7d11cdb1c3b7adf0L,
+    0x82c7709e781eb7ccL, 0xf3218f1c9510786cL, 0x331478f3af51bbe6L, 0x4bb38de5e7219443L, 0xaa649c6ebcfd50fcL,
+    0x8dbd98a352afd40bL, 0x87d2074b81d79217L, 0x19f3c751d3e92ae1L, 0xb4ab30f062b19abfL, 0x7b0500ac42047ac4L,
+    0xc9452ca81a09d85dL, 0x24aa6c514da27500L, 0x4c9f34427501b447L, 0x14a68fd73c910841L, 0xa71b9b83461cbd93L,
+    0x03488b95b0f1850fL, 0x637b2b34ff93c040L, 0x09d1bc9a3dd90a94L, 0x3575668334a1dd3bL, 0x735e2b97a4c45a23L,
+    0x18727070f1bd400bL, 0x1fcbacd259bf02e7L, 0xd310a7c2ce9b6555L, 0xbf983fe0fe5d8244L, 0x9f74d14f7454a824L,
+    0x51ebdc4ab9ba3035L, 0x5c82c505db9ab0faL, 0xfcf7fe8a3430b241L, 0x3253a729b9ba3ddeL, 0x8c74c368081b3075L,
+    0xb9bc6c87167c33e7L, 0x7ef48f2b83024e20L, 0x11d505d4c351bd7fL, 0x6568fca92c76a243L, 0x4de0b0f40f32a7b8L,
+    0x96d693460cc37e5dL, 0x42e240cb63689f2fL, 0x6d2bdcdae2919661L, 0x42880b0236e4d951L, 0x5f0f4a5898171bb6L,
+    0x39f890f579f92f88L, 0x93c5b5f47356388bL, 0x63dc359d8d231b78L, 0xec16ca8aea98ad76L, 0x5355f900c2a82dc7L,
+    0x07fb9f855a997142L, 0x5093417aa8a7ed5eL, 0x7bcbc38da25a7f3cL, 0x19fc8a768cf4b6d4L, 0x637a7780decfc0d9L,
+    0x8249a47aee0e41f7L, 0x79ad695501e7d1e8L, 0x14acbaf4777d5776L, 0xf145b6beccdea195L, 0xdabf2ac8201752fcL,
+    0x24c3c94df9c8d3f6L, 0xbb6e2924f03912eaL, 0x0ce26c0b95c980d9L, 0xa49cd132bfbf7cc4L, 0xe99d662af4243939L,
+    0x27e6ad7891165c3fL, 0x8535f040b9744ff1L, 0x54b3f4fa5f40d873L, 0x72b12c32127fed2bL, 0xee954d3c7b411f47L,
+    0x9a85ac909a24eaa1L, 0x70ac4cd9f04f21f5L, 0xf9b89d3e99a075c2L, 0x87b3e2b2b5c907b1L, 0xa366e5b8c54f48b8L,
+    0xae4a9346cc3f7cf2L, 0x1920c04d47267bbdL, 0x87bf02c6b49e2ae9L, 0x092237ac237f3859L, 0xff07f64ef8ed14d0L,
+    0x8de8dca9f03cc54eL, 0x9c1633264db49c89L, 0xb3f22c3d0b0b38edL, 0x390e5fb44d01144bL, 0x5bfea5b4712768e9L,
+    0x1e1032911fa78984L, 0x9a74acb964e78cb3L, 0x4f80f7a035dafb04L, 0x6304d09a0b3738c4L, 0x2171e64683023a08L,
+    0x5b9b63eb9ceff80cL, 0x506aacf489889342L, 0x1881afc9a3a701d6L, 0x6503080440750644L, 0xdfd395339cdbf4a7L,
+    0xef927dbcf00c20f2L, 0x7b32f7d1e03680ecL, 0xb9fd7620e7316243L, 0x05a7e8a57db91b77L, 0xb5889c6e15630a75L,
+    0x4a750a09ce9573f7L, 0xcf464cec899a2f8aL, 0xf538639ce705b824L, 0x3c79a0ff5580ef7fL, 0xede6c87f8477609dL,
+    0x799e81f05bc93f31L, 0x86536b8cf3428a8cL, 0x97d7374c60087b73L, 0xa246637cff328532L, 0x043fcae60cc0eba0L,
+    0x920e449535dd359eL, 0x70eb093b15b290ccL, 0x73a1921916591cbdL, 0x56436c9fe1a1aa8dL, 0xefac4b70633b8f81L,
+    0xbb215798d45df7afL, 0x45f20042f24f1768L, 0x930f80f4e8eb7462L, 0xff6712ffcfd75ea1L, 0xae623fd67468aa70L,
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+    0x4e9d2827355fc492L, 0x19ebb029435dcb0fL, 0x4659d2b743848a2cL, 0x963ef2c96b33be31L, 0x74f85198b05a2e7dL,
+    0x5a0f544dd2b1fb18L, 0x03727073c2e134b1L, 0xc7f6aa2de59aea61L, 0x352787baa0d7c22fL, 0x9853eab63b5e0b35L,
+    0xabbdcdd7ed5c0860L, 0xcf05daf5ac8d77b0L, 0x49cad48cebf4a71eL, 0x7a4c10ec2158c4a6L, 0xd9e92aa246bf719eL,
+    0x13ae978d09fe5557L, 0x730499af921549ffL, 0x4e4b705b92903ba4L, 0xff577222c14f0a3aL, 0x55b6344cf97aafaeL,
+    0xb862225b055b6960L, 0xcac09afbddd2cdb4L, 0xdaf8e9829fe96b5fL, 0xb5fdfc5d3132c498L, 0x310cb380db6f7503L,
+    0xe87fbb46217a360eL, 0x2102ae466ebb1148L, 0xf8549e1a3aa5e00dL, 0x07a69afdcc42261aL, 0xc4c118bfe78feaaeL,
+    0xf9f4892ed96bd438L, 0x1af3dbe25d8f45daL, 0xf5b4b0b0d2deeeb4L, 0x962aceefa82e1c84L, 0x046e3ecaaf453ce9L,
+    0xf05d129681949a4cL, 0x964781ce734b3c84L, 0x9c2ed44081ce5fbdL, 0x522e23f3925e319eL, 0x177e00f9fc32f791L,
+    0x2bc60a63a6f3b3f2L, 0x222bbfae61725606L, 0x486289ddcc3d6780L, 0x7dc7785b8efdfc80L, 0x8af38731c02ba980L,
+    0x1fab64ea29a2ddf7L, 0xe4d9429322cd065aL, 0x9da058c67844f20cL, 0x24c0e332b70019b0L, 0x233003b5a6cfe6adL,
+    0xd586bd01c5c217f6L, 0x5e5637885f29bc2bL, 0x7eba726d8c94094bL, 0x0a56a5f0bfe39272L, 0xd79476a84ee20d06L,
+    0x9e4c1269baa4bf37L, 0x17efee45b0dee640L, 0x1d95b0a5fcf90bc6L, 0x93cbe0b699c2585dL, 0x65fa4f227a2b6d79L,
+    0xd5f9e858292504d5L, 0xc2b5a03f71471a6fL, 0x59300222b4561e00L, 0xce2f8642ca0712dcL, 0x7ca9723fbb2e8988L,
+    0x2785338347f2ba08L, 0xc61bb3a141e50e8cL, 0x150f361dab9dec26L, 0x9f6a419d382595f4L, 0x64a53dc924fe7ac9L,
+    0x142de49fff7a7c3dL, 0x0c335248857fa9e7L, 0x0a9c32d5eae45305L, 0xe6c42178c4bbb92eL, 0x71f1ce2490d20b07L,
+    0xf1bcc3d275afe51aL, 0xe728e8c83c334074L, 0x96fbf83a12884624L, 0x81a1549fd6573da5L, 0x5fa7867caf35e149L,
+    0x56986e2ef3ed091bL, 0x917f1dd5f8886c61L, 0xd20d8c88c8ffe65fL, 0x31d71dce64b2c310L, 0xf165b587df898190L,
+    0xa57e6339dd2cf3a0L, 0x1ef6e6dbb1961ec9L, 0x70cc73d90bc26e24L, 0xe21a6b35df0c3ad7L, 0x003a93d8b2806962L,
+    0x1c99ded33cb890a1L, 0xcf3145de0add4289L, 0xd0e4427a5514fb72L, 0x77c621cc9fb3a483L, 0x67a34dac4356550bL,
+    0xf8d626aaaf278509L,
+  )
+
+  def hash(context: GameContext): Long =
+    val piecesHash = context.board.pieces.foldLeft(0L) { case (h, (sq, piece)) =>
+      h ^ Random(pieceIndex(piece) * 64 + squareIndex(sq))
+    }
+    val h1 = if context.castlingRights.whiteKingSide then piecesHash ^ Random(768) else piecesHash
+    val h2 = if context.castlingRights.whiteQueenSide then h1 ^ Random(769) else h1
+    val h3 = if context.castlingRights.blackKingSide then h2 ^ Random(770) else h2
+    val h4 = if context.castlingRights.blackQueenSide then h3 ^ Random(771) else h3
+    val h5 = context.enPassantSquare.fold(h4) { sq =>
+      if canCaptureEnPassant(context, sq) then h4 ^ Random(772 + sq.file.ordinal) else h4
+    }
+    if context.turn == Color.White then h5 ^ Random(780) else h5
+
+  private def pieceIndex(piece: Piece): Int =
+    val typeIdx = piece.pieceType match
+      case PieceType.Pawn   => 0
+      case PieceType.Knight => 1
+      case PieceType.Bishop => 2
+      case PieceType.Rook   => 3
+      case PieceType.Queen  => 4
+      case PieceType.King   => 5
+    val colorOffset = if piece.color == Color.White then 1 else 0
+    typeIdx * 2 + colorOffset
+
+  private def squareIndex(sq: Square): Int =
+    sq.file.ordinal + 8 * sq.rank.ordinal
+
+  private def canCaptureEnPassant(context: GameContext, epSquare: Square): Boolean =
+    val pawn      = Piece(context.turn, PieceType.Pawn)
+    val rankDelta = if context.turn == Color.White then -1 else 1
+    List(-1, 1).exists { fileDelta =>
+      epSquare
+        .offset(fileDelta, rankDelta)
+        .flatMap(context.board.pieces.get)
+        .contains(pawn)
+    }
@@ -0,0 +1,125 @@
+package de.nowchess.bot.util
+
+import de.nowchess.api.board.{Color, Piece, PieceType, Rank, Square}
+import de.nowchess.api.game.GameContext
+import de.nowchess.api.move.{Move, MoveType, PromotionPiece}
+
+import scala.util.Random
+
+object ZobristHash:
+
+  // 768 entries: 64 squares * 12 piece variants (2 colors * 6 piece types)
+  private val pieceRands: Array[Long] = Array.ofDim(768)
+
+  // Side-to-move: XOR when Black to move
+  private val sideToMoveRand: Long = Random(0x1badb002L).nextLong() // NOSONAR
+
+  // 4 entries: White kingside, White queenside, Black kingside, Black queenside
+  private val castlingRands: Array[Long] = Array.ofDim(4)
+
+  // 8 entries: one per file (a-h)
+  private val enPassantRands: Array[Long] = Array.ofDim(8)
+
+  // Initialize all random values using a seeded RNG for reproducibility
+  locally:
+    val rng = Random(0x1badb002L) // NOSONAR
+    for i <- 0 until 768 do pieceRands(i) = rng.nextLong()
+    for i <- 0 until 4 do castlingRands(i) = rng.nextLong()
+    for i <- 0 until 8 do enPassantRands(i) = rng.nextLong()
+
+  /** Compute a 64-bit Zobrist hash for a GameContext. */
+  def hash(context: GameContext): Long =
+    val piecesHash = context.board.pieces.foldLeft(0L) { case (h, (square, piece)) =>
+      val squareIndex = square.rank.ordinal * 8 + square.file.ordinal
+      val colorIndex  = if piece.color == Color.White then 0 else 1
+      val pieceIndex  = colorIndex * 6 + piece.pieceType.ordinal
+      h ^ pieceRands(squareIndex * 12 + pieceIndex)
+    }
+    val h1 = if context.turn == Color.Black then piecesHash ^ sideToMoveRand else piecesHash
+    val h2 = if context.castlingRights.whiteKingSide then h1 ^ castlingRands(0) else h1
+    val h3 = if context.castlingRights.whiteQueenSide then h2 ^ castlingRands(1) else h2
+    val h4 = if context.castlingRights.blackKingSide then h3 ^ castlingRands(2) else h3
+    val h5 = if context.castlingRights.blackQueenSide then h4 ^ castlingRands(3) else h4
+    context.enPassantSquare.fold(h5)(sq => h5 ^ enPassantRands(sq.file.ordinal))
+
+  def nextHash(context: GameContext, currentHash: Long, move: Move, nextContext: GameContext): Long =
+    val h0 = currentHash ^ sideToMoveRand
+    val h1 = toggleCastling(h0, context, nextContext)
+    val h2 = toggleEnPassant(h1, context, nextContext)
+    move.moveType match
+      case MoveType.CastleKingside | MoveType.CastleQueenside =>
+        applyCastleDelta(h2, context.turn, move.moveType == MoveType.CastleKingside)
+      case MoveType.EnPassant =>
+        applyEnPassantDelta(h2, context, move)
+      case MoveType.Promotion(piece) =>
+        applyPromotionDelta(h2, context, move, piece)
+      case MoveType.Normal(_) =>
+        applyNormalDelta(h2, context, move)
+
+  private def applyNormalDelta(h0: Long, context: GameContext, move: Move): Long =
+    context.board.pieceAt(move.from).fold(h0) { mover =>
+      val h1 = h0 ^ pieceKey(move.from, mover)
+      val h2 = context.board.pieceAt(move.to).fold(h1)(captured => h1 ^ pieceKey(move.to, captured))
+      h2 ^ pieceKey(move.to, mover)
+    }
+
+  private def applyPromotionDelta(h0: Long, context: GameContext, move: Move, promoted: PromotionPiece): Long =
+    context.board.pieceAt(move.from).fold(h0) { pawn =>
+      val h1 = h0 ^ pieceKey(move.from, pawn)
+      val h2 = context.board.pieceAt(move.to).fold(h1)(captured => h1 ^ pieceKey(move.to, captured))
+      h2 ^ pieceKey(move.to, Piece(context.turn, promotedPieceType(promoted)))
+    }
+
+  private def applyEnPassantDelta(h0: Long, context: GameContext, move: Move): Long =
+    context.board.pieceAt(move.from).fold(h0) { pawn =>
+      val capturedSquare = Square(move.to.file, move.from.rank)
+      val h1             = h0 ^ pieceKey(move.from, pawn)
+      val h2 = context.board.pieceAt(capturedSquare).fold(h1)(captured => h1 ^ pieceKey(capturedSquare, captured))
+      h2 ^ pieceKey(move.to, pawn)
+    }
+
+  private def applyCastleDelta(h0: Long, color: Color, kingside: Boolean): Long =
+    val rank = if color == Color.White then Rank.R1 else Rank.R8
+    val (kingFrom, kingTo, rookFrom, rookTo) =
+      if kingside then
+        (
+          Square(de.nowchess.api.board.File.E, rank),
+          Square(de.nowchess.api.board.File.G, rank),
+          Square(de.nowchess.api.board.File.H, rank),
+          Square(de.nowchess.api.board.File.F, rank),
+        )
+      else
+        (
+          Square(de.nowchess.api.board.File.E, rank),
+          Square(de.nowchess.api.board.File.C, rank),
+          Square(de.nowchess.api.board.File.A, rank),
+          Square(de.nowchess.api.board.File.D, rank),
+        )
+    val king = Piece(color, PieceType.King)
+    val rook = Piece(color, PieceType.Rook)
+    h0 ^ pieceKey(kingFrom, king) ^ pieceKey(kingTo, king) ^ pieceKey(rookFrom, rook) ^ pieceKey(rookTo, rook)
+
+  private def promotedPieceType(promotion: PromotionPiece): PieceType = promotion match
+    case PromotionPiece.Knight => PieceType.Knight
+    case PromotionPiece.Bishop => PieceType.Bishop
+    case PromotionPiece.Rook   => PieceType.Rook
+    case PromotionPiece.Queen  => PieceType.Queen
+
+  private def toggleCastling(h0: Long, before: GameContext, after: GameContext): Long =
+    val h1 =
+      if before.castlingRights.whiteKingSide != after.castlingRights.whiteKingSide then h0 ^ castlingRands(0) else h0
+    val h2 =
+      if before.castlingRights.whiteQueenSide != after.castlingRights.whiteQueenSide then h1 ^ castlingRands(1) else h1
+    val h3 =
+      if before.castlingRights.blackKingSide != after.castlingRights.blackKingSide then h2 ^ castlingRands(2) else h2
+    if before.castlingRights.blackQueenSide != after.castlingRights.blackQueenSide then h3 ^ castlingRands(3) else h3
+
+  private def toggleEnPassant(h0: Long, before: GameContext, after: GameContext): Long =
+    val h1 = before.enPassantSquare.fold(h0)(sq => h0 ^ enPassantRands(sq.file.ordinal))
+    after.enPassantSquare.fold(h1)(sq => h1 ^ enPassantRands(sq.file.ordinal))
+
+  private def pieceKey(square: Square, piece: Piece): Long =
+    val squareIndex = square.rank.ordinal * 8 + square.file.ordinal
+    val colorIndex  = if piece.color == Color.White then 0 else 1
+    val pieceIndex  = colorIndex * 6 + piece.pieceType.ordinal
+    pieceRands(squareIndex * 12 + pieceIndex)
@@ -0,0 +1,336 @@
+package de.nowchess.bot
+
+import de.nowchess.api.board.{Board, Color, File, Piece, Rank, Square}
+import de.nowchess.api.game.GameContext
+import de.nowchess.api.move.{Move, MoveType, PromotionPiece}
+import de.nowchess.bot.ai.Evaluation
+import de.nowchess.bot.bots.classic.EvaluationClassic
+import de.nowchess.bot.logic.AlphaBetaSearch
+import de.nowchess.rules.RuleSet
+import org.scalatest.funsuite.AnyFunSuite
+import org.scalatest.matchers.should.Matchers
+import de.nowchess.rules.sets.DefaultRules
+
+import java.util.concurrent.atomic.AtomicBoolean
+
+class AlphaBetaSearchTest extends AnyFunSuite with Matchers:
+
+  private object ZeroEval extends Evaluation:
+    val CHECKMATE_SCORE: Int                = 1_000_000
+    val DRAW_SCORE: Int                     = 0
+    def evaluate(context: GameContext): Int = 0
+
+  test("bestMove on initial position returns a move"):
+    val search = AlphaBetaSearch(DefaultRules, weights = EvaluationClassic)
+    val move   = search.bestMove(GameContext.initial, maxDepth = 2)
+    move should not be None
+
+  test("bestMove on a position with one legal move returns that move"):
+    // Create a simple position: White king on h1, Black rook on a2
+    // (set up so there's only one legal move available)
+    // For simplicity, just test that a position with forced mate returns a move
+    val search  = AlphaBetaSearch(DefaultRules, weights = EvaluationClassic)
+    val context = GameContext.initial
+    val move    = search.bestMove(context, maxDepth = 1)
+    move should not be None
+
+  test("bestMoveWithTime skips excluded root moves"):
+    val blockedMove = Move(Square(File.E, Rank.R2), Square(File.E, Rank.R4), MoveType.Normal())
+    val stubRules = new RuleSet:
+      def candidateMoves(context: GameContext)(square: Square): List[Move] = Nil
+      def legalMoves(context: GameContext)(square: Square): List[Move]     = Nil
+      def allLegalMoves(context: GameContext): List[Move]                  = List(blockedMove)
+      def isCheck(context: GameContext): Boolean                           = false
+      def isCheckmate(context: GameContext): Boolean                       = false
+      def isStalemate(context: GameContext): Boolean                       = false
+      def isInsufficientMaterial(context: GameContext): Boolean            = false
+      def isFiftyMoveRule(context: GameContext): Boolean                   = false
+      def isThreefoldRepetition(context: GameContext): Boolean             = false
+      def applyMove(context: GameContext)(move: Move): GameContext         = context
+
+    val search = AlphaBetaSearch(stubRules, weights = EvaluationClassic)
+    val move   = search.bestMoveWithTime(GameContext.initial, 1000L, Set(blockedMove))
+    move should be(None)
+
+  test("bestMove returns None for initial position has no legal moves"):
+    // Use a stub RuleSet that returns empty legal moves
+    val stubRules = new RuleSet:
+      def candidateMoves(context: GameContext)(square: Square): List[Move] = Nil
+      def legalMoves(context: GameContext)(square: Square): List[Move]     = Nil
+      def allLegalMoves(context: GameContext): List[Move]                  = Nil
+      def isCheck(context: GameContext): Boolean                           = false
+      def isCheckmate(context: GameContext): Boolean                       = true
+      def isStalemate(context: GameContext): Boolean                       = false
+      def isInsufficientMaterial(context: GameContext): Boolean            = false
+      def isFiftyMoveRule(context: GameContext): Boolean                   = false
+      def isThreefoldRepetition(context: GameContext): Boolean             = false
+      def applyMove(context: GameContext)(move: Move): GameContext         = context
+
+    val search = AlphaBetaSearch(stubRules, weights = EvaluationClassic)
+    val move   = search.bestMove(GameContext.initial, maxDepth = 2)
+    move should be(None)
+
+  test("transposition table is cleared at start of bestMove"):
+    val search  = AlphaBetaSearch(DefaultRules, weights = EvaluationClassic)
+    val context = GameContext.initial
+    // Call bestMove twice and verify both work independently
+    val move1 = search.bestMove(context, maxDepth = 1)
+    val move2 = search.bestMove(context, maxDepth = 1)
+    move1 should be(move2)
+
+  test("quiescence captures are ordered"):
+    val search = AlphaBetaSearch(DefaultRules, weights = EvaluationClassic)
+    // A position with multiple captures to verify quiescence orders them
+    val context = GameContext.initial
+    val move    = search.bestMove(context, maxDepth = 2)
+    // Just verify it completes without error
+    move.isDefined should be(true)
+
+  test("search respects alpha-beta bounds"):
+    // This is implicit in the structure, but we test via behavior
+    val search  = AlphaBetaSearch(DefaultRules, weights = EvaluationClassic)
+    val context = GameContext.initial
+    val move    = search.bestMove(context, maxDepth = 3)
+    move should not be None
+
+  test("iterative deepening finds a move at each depth"):
+    val search  = AlphaBetaSearch(DefaultRules, weights = EvaluationClassic)
+    val context = GameContext.initial
+    // Searching to depth 3 should use iterative deepening (depths 1, 2, 3)
+    val move = search.bestMove(context, maxDepth = 3)
+    move should not be None
+
+  test("stalemate position returns score 0"):
+    // Create a stalemate stub: white to move, no legal moves, not checkmate
+    val stalematRules = new RuleSet:
+      def candidateMoves(context: GameContext)(square: Square): List[Move] = Nil
+      def legalMoves(context: GameContext)(square: Square): List[Move]     = Nil
+      def allLegalMoves(context: GameContext): List[Move]                  = Nil
+      def isCheck(context: GameContext): Boolean                           = false
+      def isCheckmate(context: GameContext): Boolean                       = false
+      def isStalemate(context: GameContext): Boolean                       = true
+      def isInsufficientMaterial(context: GameContext): Boolean            = false
+      def isFiftyMoveRule(context: GameContext): Boolean                   = false
+      def isThreefoldRepetition(context: GameContext): Boolean             = false
+      def applyMove(context: GameContext)(move: Move): GameContext         = context
+
+    val search = AlphaBetaSearch(stalematRules, weights = EvaluationClassic)
+    val move   = search.bestMove(GameContext.initial, maxDepth = 1)
+    move should be(None)
+
+  test("insufficient material returns score 0"):
+    val insufficientRules = new RuleSet:
+      def candidateMoves(context: GameContext)(square: Square): List[Move] = Nil
+      def legalMoves(context: GameContext)(square: Square): List[Move]     = Nil
+      def allLegalMoves(context: GameContext): List[Move]                  = Nil
+      def isCheck(context: GameContext): Boolean                           = false
+      def isCheckmate(context: GameContext): Boolean                       = false
+      def isStalemate(context: GameContext): Boolean                       = false
+      def isInsufficientMaterial(context: GameContext): Boolean            = true
+      def isFiftyMoveRule(context: GameContext): Boolean                   = false
+      def isThreefoldRepetition(context: GameContext): Boolean             = false
+      def applyMove(context: GameContext)(move: Move): GameContext         = context
+
+    val search = AlphaBetaSearch(insufficientRules, weights = EvaluationClassic)
+    val move   = search.bestMove(GameContext.initial, maxDepth = 1)
+    move should be(None)
+
+  test("fifty move rule returns score 0"):
+    val fiftyMoveRules = new RuleSet:
+      def candidateMoves(context: GameContext)(square: Square): List[Move] = Nil
+      def legalMoves(context: GameContext)(square: Square): List[Move]     = Nil
+      def allLegalMoves(context: GameContext): List[Move]                  = Nil
+      def isCheck(context: GameContext): Boolean                           = false
+      def isCheckmate(context: GameContext): Boolean                       = false
+      def isStalemate(context: GameContext): Boolean                       = false
+      def isInsufficientMaterial(context: GameContext): Boolean            = false
+      def isFiftyMoveRule(context: GameContext): Boolean                   = true
+      def isThreefoldRepetition(context: GameContext): Boolean             = false
+      def applyMove(context: GameContext)(move: Move): GameContext         = context
+
+    val search = AlphaBetaSearch(fiftyMoveRules, weights = EvaluationClassic)
+    val move   = search.bestMove(GameContext.initial, maxDepth = 1)
+    move should be(None)
+
+  test("capture moves are recognized in quiescence search"):
+    // Create a position with a capture available
+    val board = Board(
+      Map(
+        Square(File.E, Rank.R4) -> Piece.WhiteQueen,
+        Square(File.E, Rank.R5) -> Piece.BlackPawn,
+      ),
+    )
+    val context = GameContext.initial.withBoard(board)
+
+    val captureMove = Move(Square(File.E, Rank.R4), Square(File.E, Rank.R5), MoveType.Normal(true))
+    val rulesWithCapture = new RuleSet:
+      def candidateMoves(context: GameContext)(square: Square): List[Move] = Nil
+      def legalMoves(context: GameContext)(square: Square): List[Move]     = Nil
+      def allLegalMoves(context: GameContext): List[Move]                  = List(captureMove)
+      def isCheck(context: GameContext): Boolean                           = false
+      def isCheckmate(context: GameContext): Boolean                       = false
+      def isStalemate(context: GameContext): Boolean                       = false
+      def isInsufficientMaterial(context: GameContext): Boolean            = false
+      def isFiftyMoveRule(context: GameContext): Boolean                   = false
+      def isThreefoldRepetition(context: GameContext): Boolean             = false
+      def applyMove(context: GameContext)(move: Move): GameContext         = context
+
+    val search = AlphaBetaSearch(rulesWithCapture, weights = EvaluationClassic)
+    val move   = search.bestMove(context, maxDepth = 1)
+    move should be(Some(captureMove))
+
+  test("non-capture moves are not included in quiescence"):
+    val quietMove = Move(Square(File.E, Rank.R4), Square(File.E, Rank.R5), MoveType.Normal())
+    val rulesQuiet = new RuleSet:
+      def candidateMoves(context: GameContext)(square: Square): List[Move] = Nil
+      def legalMoves(context: GameContext)(square: Square): List[Move]     = Nil
+      def allLegalMoves(context: GameContext): List[Move]                  = List(quietMove)
+      def isCheck(context: GameContext): Boolean                           = false
+      def isCheckmate(context: GameContext): Boolean                       = false
+      def isStalemate(context: GameContext): Boolean                       = false
+      def isInsufficientMaterial(context: GameContext): Boolean            = false
+      def isFiftyMoveRule(context: GameContext): Boolean                   = false
+      def isThreefoldRepetition(context: GameContext): Boolean             = false
+      def applyMove(context: GameContext)(move: Move): GameContext         = context
+
+    val search = AlphaBetaSearch(rulesQuiet, weights = EvaluationClassic)
+    val move   = search.bestMove(GameContext.initial, maxDepth = 1)
+    move should be(Some(quietMove)) // bestMove returns the quiet move since it's the only legal move
+
+  test("default constructor uses DefaultRules"):
+    val search = AlphaBetaSearch(weights = EvaluationClassic)
+    val move   = search.bestMove(GameContext.initial, maxDepth = 1)
+    move should not be None
+
+  test("bestMoveWithTime without excluded moves overload"):
+    val search = AlphaBetaSearch(DefaultRules, weights = EvaluationClassic)
+    val move   = search.bestMoveWithTime(GameContext.initial, 500L)
+    move should not be None
+
+  test("en passant move is treated as capture in quiescence"):
+    val epMove = Move(Square(File.E, Rank.R5), Square(File.D, Rank.R6), MoveType.EnPassant)
+    val board = Board(
+      Map(
+        Square(File.E, Rank.R5) -> Piece.WhitePawn,
+        Square(File.D, Rank.R5) -> Piece.BlackPawn,
+      ),
+    )
+    val ctx = GameContext.initial.withBoard(board).withTurn(Color.White)
+    val epRules = new RuleSet:
+      def candidateMoves(context: GameContext)(square: Square): List[Move] = Nil
+      def legalMoves(context: GameContext)(square: Square): List[Move]     = Nil
+      def allLegalMoves(context: GameContext): List[Move]                  = List(epMove)
+      def isCheck(context: GameContext): Boolean                           = false
+      def isCheckmate(context: GameContext): Boolean                       = false
+      def isStalemate(context: GameContext): Boolean                       = false
+      def isInsufficientMaterial(context: GameContext): Boolean            = false
+      def isFiftyMoveRule(context: GameContext): Boolean                   = false
+      def isThreefoldRepetition(context: GameContext): Boolean             = false
+      def applyMove(context: GameContext)(move: Move): GameContext         = context
+    val search = AlphaBetaSearch(epRules, weights = EvaluationClassic)
+    search.bestMove(ctx, maxDepth = 1) should be(Some(epMove))
+
+  test("promotion capture move is treated as capture in quiescence"):
+    val promoCapture = Move(Square(File.E, Rank.R7), Square(File.D, Rank.R8), MoveType.Promotion(PromotionPiece.Queen))
+    val board = Board(
+      Map(
+        Square(File.E, Rank.R7) -> Piece.WhitePawn,
+        Square(File.D, Rank.R8) -> Piece.BlackRook,
+      ),
+    )
+    val ctx = GameContext.initial.withBoard(board).withTurn(Color.White)
+    val promoCaptureRules = new RuleSet:
+      def candidateMoves(context: GameContext)(square: Square): List[Move] = Nil
+      def legalMoves(context: GameContext)(square: Square): List[Move]     = Nil
+      def allLegalMoves(context: GameContext): List[Move]                  = List(promoCapture)
+      def isCheck(context: GameContext): Boolean                           = false
+      def isCheckmate(context: GameContext): Boolean                       = false
+      def isStalemate(context: GameContext): Boolean                       = false
+      def isInsufficientMaterial(context: GameContext): Boolean            = false
+      def isFiftyMoveRule(context: GameContext): Boolean                   = false
+      def isThreefoldRepetition(context: GameContext): Boolean             = false
+      def applyMove(context: GameContext)(move: Move): GameContext         = context
+    val search = AlphaBetaSearch(promoCaptureRules, weights = EvaluationClassic)
+    search.bestMove(ctx, maxDepth = 1) should be(Some(promoCapture))
+
+  test("draw when isInsufficientMaterial with legal moves present"):
+    val legalMove = Move(Square(File.E, Rank.R2), Square(File.E, Rank.R4), MoveType.Normal())
+    val drawRules = new RuleSet:
+      def candidateMoves(context: GameContext)(square: Square): List[Move] = List(legalMove)
+      def legalMoves(context: GameContext)(square: Square): List[Move]     = List(legalMove)
+      def allLegalMoves(context: GameContext): List[Move]                  = List(legalMove)
+      def isCheck(context: GameContext): Boolean                           = false
+      def isCheckmate(context: GameContext): Boolean                       = false
+      def isStalemate(context: GameContext): Boolean                       = false
+      def isInsufficientMaterial(context: GameContext): Boolean            = true
+      def isFiftyMoveRule(context: GameContext): Boolean                   = false
+      def isThreefoldRepetition(context: GameContext): Boolean             = false
+      def applyMove(context: GameContext)(move: Move): GameContext         = context
+    val search = AlphaBetaSearch(drawRules, weights = EvaluationClassic)
+    search.bestMove(GameContext.initial, maxDepth = 2) should be(None)
+
+  test("repetition cutoff is reached on forced self-loop positions"):
+    // Use a no-op move from an empty square so nextHash alternates between a tiny set of hashes.
+    // This forces repetition counts >= 3 and exercises immediateSearchResult's repetition cutoff.
+    val loopMove = Move(Square(File.A, Rank.R3), Square(File.A, Rank.R4), MoveType.Normal())
+    val loopRules = new RuleSet:
+      def candidateMoves(context: GameContext)(square: Square): List[Move] = List(loopMove)
+      def legalMoves(context: GameContext)(square: Square): List[Move]     = List(loopMove)
+      def allLegalMoves(context: GameContext): List[Move]                  = List(loopMove)
+      def isCheck(context: GameContext): Boolean                           = false
+      def isCheckmate(context: GameContext): Boolean                       = false
+      def isStalemate(context: GameContext): Boolean                       = false
+      def isInsufficientMaterial(context: GameContext): Boolean            = false
+      def isFiftyMoveRule(context: GameContext): Boolean                   = false
+      def isThreefoldRepetition(context: GameContext): Boolean             = false
+      def applyMove(context: GameContext)(move: Move): GameContext         = context
+
+    val search = AlphaBetaSearch(loopRules, weights = ZeroEval)
+    search.bestMove(GameContext.initial, maxDepth = 8) should be(Some(loopMove))
+
+  test("quiescence returns checkmate score when side is in check and has no tactical moves"):
+    val rootMove = Move(Square(File.E, Rank.R2), Square(File.E, Rank.R3), MoveType.Normal())
+    val capMove  = Move(Square(File.D, Rank.R2), Square(File.D, Rank.R3), MoveType.Normal(true))
+    val qRules = new RuleSet:
+      def candidateMoves(context: GameContext)(square: Square): List[Move] = allLegalMoves(context)
+      def legalMoves(context: GameContext)(square: Square): List[Move]     = allLegalMoves(context)
+      def allLegalMoves(context: GameContext): List[Move] =
+        context.moves.length match
+          case 0 => List(rootMove)
+          case 1 => List(capMove)
+          case _ => Nil
+      def isCheck(context: GameContext): Boolean =
+        context.moves.length >= 2
+      def isCheckmate(context: GameContext): Boolean            = false
+      def isStalemate(context: GameContext): Boolean            = false
+      def isInsufficientMaterial(context: GameContext): Boolean = false
+      def isFiftyMoveRule(context: GameContext): Boolean        = false
+      def isThreefoldRepetition(context: GameContext): Boolean  = false
+      def applyMove(context: GameContext)(move: Move): GameContext =
+        context.copy(turn = context.turn.opposite, moves = context.moves :+ move)
+
+    val search = AlphaBetaSearch(qRules, weights = ZeroEval)
+    search.bestMove(GameContext.initial, maxDepth = 1) should be(Some(rootMove))
+
+  test("quiescence depth-limit in-check branch is exercised"):
+    val rootMove            = Move(Square(File.E, Rank.R2), Square(File.E, Rank.R3), MoveType.Normal())
+    val capMove             = Move(Square(File.D, Rank.R2), Square(File.D, Rank.R3), MoveType.Normal(true))
+    val firstChildCheckCall = AtomicBoolean(true)
+    val deepQRules = new RuleSet:
+      def candidateMoves(context: GameContext)(square: Square): List[Move] = allLegalMoves(context)
+      def legalMoves(context: GameContext)(square: Square): List[Move]     = allLegalMoves(context)
+      def allLegalMoves(context: GameContext): List[Move] =
+        if context.moves.isEmpty then List(rootMove) else List(capMove)
+      def isCheck(context: GameContext): Boolean =
+        if context.moves.length == 1 && firstChildCheckCall.compareAndSet(true, false) then false
+        else context.moves.nonEmpty
+      def isCheckmate(context: GameContext): Boolean            = false
+      def isStalemate(context: GameContext): Boolean            = false
+      def isInsufficientMaterial(context: GameContext): Boolean = false
+      def isFiftyMoveRule(context: GameContext): Boolean        = false
+      def isThreefoldRepetition(context: GameContext): Boolean  = false
+      def applyMove(context: GameContext)(move: Move): GameContext =
+        context.copy(turn = context.turn.opposite, moves = context.moves :+ move)
+
+    val search = AlphaBetaSearch(deepQRules, weights = ZeroEval)
+    search.bestMove(GameContext.initial, maxDepth = 1) should be(Some(rootMove))
@@ -0,0 +1,18 @@
+package de.nowchess.bot
+
+import org.scalatest.funsuite.AnyFunSuite
+import org.scalatest.matchers.should.Matchers
+
+class BotControllerTest extends AnyFunSuite with Matchers:
+
+  test("BotController can be instantiated"):
+    BotController.listBots should not be empty
+
+  test("getBot returns known bots by name"):
+    BotController.getBot("easy") should not be None
+    BotController.getBot("medium") should not be None
+    BotController.getBot("hard") should not be None
+    BotController.getBot("expert") should not be None
+
+  test("getBot returns None for unknown bot"):
+    BotController.getBot("unknown") should be(None)
@@ -0,0 +1,14 @@
+package de.nowchess.bot
+
+import org.scalatest.funsuite.AnyFunSuite
+import org.scalatest.matchers.should.Matchers
+
+class BotDifficultyTest extends AnyFunSuite with Matchers:
+
+  test("all difficulty values are defined"):
+    val difficulties = BotDifficulty.values
+    difficulties should contain(BotDifficulty.Easy)
+    difficulties should contain(BotDifficulty.Medium)
+    difficulties should contain(BotDifficulty.Hard)
+    difficulties should contain(BotDifficulty.Expert)
+    difficulties should have length 4
@@ -0,0 +1,30 @@
+package de.nowchess.bot
+
+import de.nowchess.api.board.{File, Rank, Square}
+import de.nowchess.api.game.GameContext
+import de.nowchess.api.move.{Move, MoveType}
+import org.scalatest.funsuite.AnyFunSuite
+import org.scalatest.matchers.should.Matchers
+
+class BotMoveRepetitionTest extends AnyFunSuite with Matchers:
+
+  private val move1 = Move(Square(File.E, Rank.R2), Square(File.E, Rank.R4), MoveType.Normal())
+  private val move2 = Move(Square(File.D, Rank.R2), Square(File.D, Rank.R4), MoveType.Normal())
+
+  test("filterAllowed passes through moves when none are blocked"):
+    val ctx     = GameContext.initial
+    val allowed = BotMoveRepetition.filterAllowed(ctx, List(move1, move2))
+    allowed should contain(move1)
+    allowed should contain(move2)
+
+  test("filterAllowed removes the move repeated three times"):
+    val ctx     = GameContext.initial.copy(moves = List(move1, move1, move1))
+    val allowed = BotMoveRepetition.filterAllowed(ctx, List(move1, move2))
+    allowed should not contain move1
+    allowed should contain(move2)
+
+  test("filterAllowed keeps all moves when repetition is below threshold"):
+    val ctx     = GameContext.initial.copy(moves = List(move1, move1))
+    val allowed = BotMoveRepetition.filterAllowed(ctx, List(move1, move2))
+    allowed should contain(move1)
+    allowed should contain(move2)
@@ -0,0 +1,98 @@
+package de.nowchess.bot
+
+import de.nowchess.api.board.Square
+import de.nowchess.api.game.GameContext
+import de.nowchess.api.move.Move
+import de.nowchess.api.move.MoveType
+import de.nowchess.bot.bots.ClassicalBot
+import de.nowchess.rules.RuleSet
+import org.scalatest.funsuite.AnyFunSuite
+import org.scalatest.matchers.should.Matchers
+import de.nowchess.rules.sets.DefaultRules
+
+class ClassicalBotTest extends AnyFunSuite with Matchers:
+
+  test("name returns expected format"):
+    val botEasy = ClassicalBot(BotDifficulty.Easy)
+    botEasy.name should include("ClassicalBot")
+    botEasy.name should include("Easy")
+
+    val botMedium = ClassicalBot(BotDifficulty.Medium)
+    botMedium.name should include("Medium")
+
+  test("nextMove on initial position returns a move"):
+    val bot  = ClassicalBot(BotDifficulty.Easy)
+    val move = bot.nextMove(GameContext.initial)
+    move should not be None
+
+  test("nextMove returns None for position with no legal moves"):
+    val stubRules = new RuleSet:
+      def candidateMoves(context: GameContext)(square: Square): List[Move] = Nil
+      def legalMoves(context: GameContext)(square: Square): List[Move]     = Nil
+      def allLegalMoves(context: GameContext): List[Move]                  = Nil
+      def isCheck(context: GameContext): Boolean                           = false
+      def isCheckmate(context: GameContext): Boolean                       = true
+      def isStalemate(context: GameContext): Boolean                       = false
+      def isInsufficientMaterial(context: GameContext): Boolean            = false
+      def isFiftyMoveRule(context: GameContext): Boolean                   = false
+      def isThreefoldRepetition(context: GameContext): Boolean             = false
+      def applyMove(context: GameContext)(move: Move): GameContext         = context
+
+    val bot  = ClassicalBot(BotDifficulty.Easy, stubRules)
+    val move = bot.nextMove(GameContext.initial)
+    move should be(None)
+
+  test("all BotDifficulty values work"):
+    BotDifficulty.values.foreach { difficulty =>
+      val bot  = ClassicalBot(difficulty)
+      val move = bot.nextMove(GameContext.initial)
+      // All difficulties should return a move on the initial position
+      move should not be None
+    }
+
+  test("custom RuleSet injection works"):
+    val moveToReturn = Move(
+      de.nowchess.api.board.Square(de.nowchess.api.board.File.E, de.nowchess.api.board.Rank.R2),
+      de.nowchess.api.board.Square(de.nowchess.api.board.File.E, de.nowchess.api.board.Rank.R4),
+      de.nowchess.api.move.MoveType.Normal(),
+    )
+
+    val stubRules = new RuleSet:
+      def candidateMoves(context: GameContext)(square: Square): List[Move] = Nil
+      def legalMoves(context: GameContext)(square: Square): List[Move]     = Nil
+      def allLegalMoves(context: GameContext): List[Move]                  = List(moveToReturn)
+      def isCheck(context: GameContext): Boolean                           = false
+      def isCheckmate(context: GameContext): Boolean                       = false
+      def isStalemate(context: GameContext): Boolean                       = false
+      def isInsufficientMaterial(context: GameContext): Boolean            = false
+      def isFiftyMoveRule(context: GameContext): Boolean                   = false
+      def isThreefoldRepetition(context: GameContext): Boolean             = false
+      def applyMove(context: GameContext)(move: Move): GameContext         = context
+
+    val bot  = ClassicalBot(BotDifficulty.Easy, stubRules)
+    val move = bot.nextMove(GameContext.initial)
+    move should be(Some(moveToReturn))
+
+  test("nextMove skips a move repeated three times in a row"):
+    val repeatedMove = Move(
+      de.nowchess.api.board.Square(de.nowchess.api.board.File.E, de.nowchess.api.board.Rank.R2),
+      de.nowchess.api.board.Square(de.nowchess.api.board.File.E, de.nowchess.api.board.Rank.R4),
+      MoveType.Normal(),
+    )
+
+    val stubRules = new RuleSet:
+      def candidateMoves(context: GameContext)(square: Square): List[Move] = Nil
+      def legalMoves(context: GameContext)(square: Square): List[Move]     = Nil
+      def allLegalMoves(context: GameContext): List[Move]                  = List(repeatedMove)
+      def isCheck(context: GameContext): Boolean                           = false
+      def isCheckmate(context: GameContext): Boolean                       = false
+      def isStalemate(context: GameContext): Boolean                       = false
+      def isInsufficientMaterial(context: GameContext): Boolean            = false
+      def isFiftyMoveRule(context: GameContext): Boolean                   = false
+      def isThreefoldRepetition(context: GameContext): Boolean             = false
+      def applyMove(context: GameContext)(move: Move): GameContext         = context
+
+    val context = GameContext.initial.copy(moves = List(repeatedMove, repeatedMove, repeatedMove))
+    val bot     = ClassicalBot(BotDifficulty.Easy, stubRules)
+
+    bot.nextMove(context) should be(None)
@@ -0,0 +1,142 @@
+package de.nowchess.bot
+
+import de.nowchess.api.board.{Board, Color, File, Piece, PieceType, Rank, Square}
+import de.nowchess.api.game.GameContext
+import de.nowchess.api.board.Board
+import de.nowchess.bot.bots.classic.EvaluationClassic
+import org.scalatest.funsuite.AnyFunSuite
+import org.scalatest.matchers.should.Matchers
+
+class EvaluationTest extends AnyFunSuite with Matchers:
+
+  test("initial position evaluates to tempo bonus"):
+    val eval = EvaluationClassic.evaluate(GameContext.initial)
+    eval should equal(10) // TEMPO_BONUS only
+
+  test("remove white queen gives negative evaluation"):
+    val initial           = GameContext.initial
+    val board             = initial.board
+    val emptySquare       = Square(File.D, Rank.R1)
+    val boardWithoutQueen = board.pieces.filter((sq, _) => sq != emptySquare)
+    val newContext        = initial.withBoard(Board(boardWithoutQueen))
+    val eval              = EvaluationClassic.evaluate(newContext)
+    eval should be < 0
+
+  test("remove black queen gives positive evaluation"):
+    val initial           = GameContext.initial
+    val board             = initial.board
+    val emptySquare       = Square(File.D, Rank.R8)
+    val boardWithoutQueen = board.pieces.filter((sq, _) => sq != emptySquare)
+    val newContext        = initial.withBoard(Board(boardWithoutQueen))
+    val eval              = EvaluationClassic.evaluate(newContext)
+    eval should be > 0
+
+  test("different piece-square bonuses are applied"):
+    // Knight on d4 (center) vs knight on a1 (corner) - center should be better
+    val knightD4Board = Board(Map(Square(File.D, Rank.R4) -> Piece.WhiteKnight))
+    val knightA1Board = Board(Map(Square(File.A, Rank.R1) -> Piece.WhiteKnight))
+    val knightD4      = GameContext.initial.withBoard(knightD4Board)
+    val knightA1      = GameContext.initial.withBoard(knightA1Board)
+
+    val eval1 = EvaluationClassic.evaluate(knightD4)
+    val eval2 = EvaluationClassic.evaluate(knightA1)
+    eval1 should be > eval2 // d4 (center) is better than a1 (corner) for knight
+
+  test("all piece types are in material map"):
+    PieceType.values.length should be > 0
+    // Just verify evaluate works with all piece types
+    val eval = EvaluationClassic.evaluate(GameContext.initial)
+    eval should not be (EvaluationClassic.CHECKMATE_SCORE)
+
+  test("CHECKMATE_SCORE and DRAW_SCORE are accessible"):
+    EvaluationClassic.CHECKMATE_SCORE should equal(10_000_000)
+    EvaluationClassic.DRAW_SCORE should equal(0)
+
+  test("active knight (center) scores higher than passive knight (corner)"):
+    val knightD4Board   = Board(Map(Square(File.D, Rank.R4) -> Piece.WhiteKnight))
+    val knightA1Board   = Board(Map(Square(File.A, Rank.R1) -> Piece.WhiteKnight))
+    val knightD4Context = GameContext.initial.withBoard(knightD4Board)
+    val knightA1Context = GameContext.initial.withBoard(knightA1Board)
+    val evalD4          = EvaluationClassic.evaluate(knightD4Context)
+    val evalA1          = EvaluationClassic.evaluate(knightA1Context)
+    evalD4 should be > evalA1 // Knight on d4 (center, more mobility) should score higher
+
+  test("bishop pair scores higher than bishop + knight"):
+    val bishopPairBoard = Board(
+      Map(
+        Square(File.C, Rank.R1) -> Piece.WhiteBishop,
+        Square(File.F, Rank.R1) -> Piece.WhiteBishop,
+      ),
+    )
+    val bishopKnightBoard = Board(
+      Map(
+        Square(File.C, Rank.R1) -> Piece.WhiteBishop,
+        Square(File.B, Rank.R1) -> Piece.WhiteKnight,
+      ),
+    )
+    val pairContext   = GameContext.initial.withBoard(bishopPairBoard)
+    val knightContext = GameContext.initial.withBoard(bishopKnightBoard)
+    val evalPair      = EvaluationClassic.evaluate(pairContext)
+    val evalKnight    = EvaluationClassic.evaluate(knightContext)
+    evalPair should be > evalKnight // Bishop pair should score higher
+
+  test("rook on 7th rank scores higher than rook on 4th rank"):
+    val rook7thBoard   = Board(Map(Square(File.A, Rank.R7) -> Piece.WhiteRook))
+    val rook4thBoard   = Board(Map(Square(File.A, Rank.R4) -> Piece.WhiteRook))
+    val rook7thContext = GameContext.initial.withBoard(rook7thBoard)
+    val rook4thContext = GameContext.initial.withBoard(rook4thBoard)
+    val eval7th        = EvaluationClassic.evaluate(rook7thContext)
+    val eval4th        = EvaluationClassic.evaluate(rook4thContext)
+    eval7th should be > eval4th // Rook on 7th rank should score higher
+
+  test("enemy rook on 7th rank is penalised"):
+    // Black rook on rank 2 (7th for black) with white to move — hits the enemy branch
+    val board   = Board(Map(Square(File.A, Rank.R2) -> Piece.BlackRook))
+    val context = GameContext.initial.withBoard(board).withTurn(Color.White)
+    val eval    = EvaluationClassic.evaluate(context)
+    eval should be < 0 // disadvantageous for white
+
+  test("king at edge rank yields zero king-shield bonus"):
+    // White king on rank 8 — shieldRank would be 9, out of bounds → guard fires
+    val board   = Board(Map(Square(File.H, Rank.R8) -> Piece.WhiteKing, Square(File.H, Rank.R1) -> Piece.BlackKing))
+    val context = GameContext.initial.withBoard(board).withTurn(Color.White)
+    // Evaluating does not throw and uses the guard path
+    noException should be thrownBy EvaluationClassic.evaluate(context)
+
+  test("endgame bonus is applied when material is low"):
+    // Kings + one rook: phase = 2 < 8, triggers endgameBonus with friendly material advantage
+    val board = Board(
+      Map(
+        Square(File.D, Rank.R4) -> Piece.WhiteKing,
+        Square(File.D, Rank.R6) -> Piece.BlackKing,
+        Square(File.A, Rank.R1) -> Piece.WhiteRook,
+      ),
+    )
+    val context = GameContext.initial.withBoard(board).withTurn(Color.White)
+    noException should be thrownBy EvaluationClassic.evaluate(context)
+
+  test("endgame bonus else branch when material is equal"):
+    // Both sides have a rook: friendlyMaterial == enemyMaterial → edgeBonus = 0
+    val board = Board(
+      Map(
+        Square(File.D, Rank.R4) -> Piece.WhiteKing,
+        Square(File.D, Rank.R6) -> Piece.BlackKing,
+        Square(File.A, Rank.R1) -> Piece.WhiteRook,
+        Square(File.H, Rank.R8) -> Piece.BlackRook,
+      ),
+    )
+    val context = GameContext.initial.withBoard(board).withTurn(Color.White)
+    noException should be thrownBy EvaluationClassic.evaluate(context)
+
+  test("passed pawn bonus is applied in endgame"):
+    // No enemy pawns anywhere → white pawn on e5 is passed; phase = 0 → endgame → egPassedPawnBonus
+    val board = Board(
+      Map(
+        Square(File.E, Rank.R5) -> Piece.WhitePawn,
+        Square(File.E, Rank.R1) -> Piece.WhiteKing,
+        Square(File.E, Rank.R8) -> Piece.BlackKing,
+      ),
+    )
+    val context = GameContext.initial.withBoard(board).withTurn(Color.White)
+    val eval    = EvaluationClassic.evaluate(context)
+    eval should be > 0
@@ -0,0 +1,160 @@
+package de.nowchess.bot
+
+import de.nowchess.api.board.{File, Rank, Square}
+import de.nowchess.api.game.GameContext
+import de.nowchess.api.move.{Move, MoveType}
+import de.nowchess.bot.ai.Evaluation
+import de.nowchess.bot.bots.HybridBot
+import de.nowchess.bot.util.{PolyglotBook, PolyglotHash}
+import de.nowchess.rules.RuleSet
+import org.scalatest.funsuite.AnyFunSuite
+import org.scalatest.matchers.should.Matchers
+
+import java.io.{DataOutputStream, FileOutputStream}
+import java.nio.file.Files
+import java.util.concurrent.atomic.AtomicBoolean
+import scala.util.Using
+
+class HybridBotTest extends AnyFunSuite with Matchers:
+
+  test("HybridBot name includes difficulty"):
+    val bot = HybridBot(BotDifficulty.Easy)
+    bot.name should include("HybridBot")
+    bot.name should include("Easy")
+
+  test("HybridBot nextMove returns a move on the initial position"):
+    val bot  = HybridBot(BotDifficulty.Easy)
+    val move = bot.nextMove(GameContext.initial)
+    move should not be None
+
+  test("HybridBot nextMove returns None when no legal moves"):
+    val noMovesRules = new RuleSet:
+      def candidateMoves(context: GameContext)(square: Square): List[Move] = Nil
+      def legalMoves(context: GameContext)(square: Square): List[Move]     = Nil
+      def allLegalMoves(context: GameContext): List[Move]                  = Nil
+      def isCheck(context: GameContext): Boolean                           = false
+      def isCheckmate(context: GameContext): Boolean                       = true
+      def isStalemate(context: GameContext): Boolean                       = false
+      def isInsufficientMaterial(context: GameContext): Boolean            = false
+      def isFiftyMoveRule(context: GameContext): Boolean                   = false
+      def isThreefoldRepetition(context: GameContext): Boolean             = false
+      def applyMove(context: GameContext)(move: Move): GameContext         = context
+    val bot  = HybridBot(BotDifficulty.Easy, noMovesRules)
+    val move = bot.nextMove(GameContext.initial)
+    move should be(None)
+
+  test("HybridBot with empty book falls through to search"):
+    val emptyBook = PolyglotBook("/nonexistent/book.bin")
+    val bot       = HybridBot(BotDifficulty.Easy, book = Some(emptyBook))
+    val move      = bot.nextMove(GameContext.initial)
+    move should not be None
+
+  test("HybridBot skips move repeated three times"):
+    val repeatedMove = Move(Square(File.E, Rank.R2), Square(File.E, Rank.R4), MoveType.Normal())
+    val onlyMoveRules = new RuleSet:
+      def candidateMoves(context: GameContext)(square: Square): List[Move] = Nil
+      def legalMoves(context: GameContext)(square: Square): List[Move]     = Nil
+      def allLegalMoves(context: GameContext): List[Move]                  = List(repeatedMove)
+      def isCheck(context: GameContext): Boolean                           = false
+      def isCheckmate(context: GameContext): Boolean                       = false
+      def isStalemate(context: GameContext): Boolean                       = false
+      def isInsufficientMaterial(context: GameContext): Boolean            = false
+      def isFiftyMoveRule(context: GameContext): Boolean                   = false
+      def isThreefoldRepetition(context: GameContext): Boolean             = false
+      def applyMove(context: GameContext)(move: Move): GameContext         = context
+    val ctx = GameContext.initial.copy(moves = List(repeatedMove, repeatedMove, repeatedMove))
+    val bot = HybridBot(BotDifficulty.Easy, onlyMoveRules)
+    bot.nextMove(ctx) should be(None)
+
+  test("HybridBot uses book move when available"):
+    val tempFile = Files.createTempFile("hybrid_book", ".bin")
+    try
+      val ctx         = GameContext.initial
+      val hash        = PolyglotHash.hash(ctx)
+      val e2e4: Short = (4 | (3 << 3) | (4 << 6) | (1 << 9)).toShort
+
+      Using(DataOutputStream(FileOutputStream(tempFile.toFile))) { dos =>
+        dos.writeLong(hash)
+        dos.writeShort(e2e4)
+        dos.writeShort(100)
+        dos.writeInt(0)
+      }.get
+
+      val book = PolyglotBook(tempFile.toString)
+      val bot  = HybridBot(BotDifficulty.Easy, book = Some(book))
+      bot.nextMove(ctx) should be(Some(Move(Square(File.E, Rank.R2), Square(File.E, Rank.R4), MoveType.Normal())))
+    finally Files.deleteIfExists(tempFile)
+
+  test("HybridBot reports veto when classical and NNUE differ above threshold"):
+    val forcedMove = Move(Square(File.E, Rank.R2), Square(File.E, Rank.R3), MoveType.Normal())
+    val oneMoveRules = new RuleSet:
+      def candidateMoves(context: GameContext)(square: Square): List[Move] = List(forcedMove)
+      def legalMoves(context: GameContext)(square: Square): List[Move]     = List(forcedMove)
+      def allLegalMoves(context: GameContext): List[Move]                  = List(forcedMove)
+      def isCheck(context: GameContext): Boolean                           = false
+      def isCheckmate(context: GameContext): Boolean                       = false
+      def isStalemate(context: GameContext): Boolean                       = false
+      def isInsufficientMaterial(context: GameContext): Boolean            = false
+      def isFiftyMoveRule(context: GameContext): Boolean                   = false
+      def isThreefoldRepetition(context: GameContext): Boolean             = false
+      def applyMove(context: GameContext)(move: Move): GameContext =
+        context.copy(turn = context.turn.opposite, moves = context.moves :+ move)
+
+    object LowNnue extends Evaluation:
+      val CHECKMATE_SCORE: Int                = 10_000_000
+      val DRAW_SCORE: Int                     = 0
+      def evaluate(context: GameContext): Int = 0
+
+    object HighClassic extends Evaluation:
+      val CHECKMATE_SCORE: Int                = 10_000_000
+      val DRAW_SCORE: Int                     = 0
+      def evaluate(context: GameContext): Int = 10_000
+
+    val reported = AtomicBoolean(false)
+    val bot = HybridBot(
+      BotDifficulty.Easy,
+      rules = oneMoveRules,
+      nnueEvaluation = LowNnue,
+      classicalEvaluation = HighClassic,
+      vetoReporter = _ => reported.set(true),
+    )
+
+    bot.nextMove(GameContext.initial) should be(Some(forcedMove))
+    reported.get should be(true)
+
+  test("HybridBot default veto reporter prints when threshold is exceeded"):
+    val forcedMove = Move(Square(File.E, Rank.R2), Square(File.E, Rank.R3), MoveType.Normal())
+    val oneMoveRules = new RuleSet:
+      def candidateMoves(context: GameContext)(square: Square): List[Move] = List(forcedMove)
+      def legalMoves(context: GameContext)(square: Square): List[Move]     = List(forcedMove)
+      def allLegalMoves(context: GameContext): List[Move]                  = List(forcedMove)
+      def isCheck(context: GameContext): Boolean                           = false
+      def isCheckmate(context: GameContext): Boolean                       = false
+      def isStalemate(context: GameContext): Boolean                       = false
+      def isInsufficientMaterial(context: GameContext): Boolean            = false
+      def isFiftyMoveRule(context: GameContext): Boolean                   = false
+      def isThreefoldRepetition(context: GameContext): Boolean             = false
+      def applyMove(context: GameContext)(move: Move): GameContext =
+        context.copy(turn = context.turn.opposite, moves = context.moves :+ move)
+
+    object LowNnue extends Evaluation:
+      val CHECKMATE_SCORE: Int                = 10_000_000
+      val DRAW_SCORE: Int                     = 0
+      def evaluate(context: GameContext): Int = 0
+
+    object HighClassic extends Evaluation:
+      val CHECKMATE_SCORE: Int                = 10_000_000
+      val DRAW_SCORE: Int                     = 0
+      def evaluate(context: GameContext): Int = 10_000
+
+    val bot = HybridBot(
+      BotDifficulty.Easy,
+      rules = oneMoveRules,
+      nnueEvaluation = LowNnue,
+      classicalEvaluation = HighClassic,
+    )
+
+    val printed = Console.withOut(new java.io.ByteArrayOutputStream()) {
+      bot.nextMove(GameContext.initial)
+    }
+    printed should be(Some(forcedMove))
@@ -0,0 +1,219 @@
+package de.nowchess.bot
+
+import de.nowchess.api.board.{Board, Color, File, Piece, Rank, Square}
+import de.nowchess.api.game.GameContext
+import de.nowchess.api.move.{Move, MoveType, PromotionPiece}
+import de.nowchess.bot.logic.MoveOrdering
+import org.scalatest.funsuite.AnyFunSuite
+import org.scalatest.matchers.should.Matchers
+
+class MoveOrderingTest extends AnyFunSuite with Matchers:
+
+  test("queen capture ranks higher than rook capture"):
+    val board = Board(
+      Map(
+        Square(File.E, Rank.R4) -> Piece.WhiteQueen,
+        Square(File.E, Rank.R5) -> Piece.BlackQueen,
+        Square(File.E, Rank.R6) -> Piece.BlackRook,
+      ),
+    )
+    val context      = GameContext.initial.withBoard(board).withTurn(Color.White)
+    val queenCapture = Move(Square(File.E, Rank.R4), Square(File.E, Rank.R5), MoveType.Normal(true))
+    val rookCapture  = Move(Square(File.E, Rank.R4), Square(File.E, Rank.R6), MoveType.Normal(true))
+
+    val queenScore = MoveOrdering.score(context, queenCapture, None)
+    val rookScore  = MoveOrdering.score(context, rookCapture, None)
+    queenScore should be > rookScore
+
+  test("quiet move ranks lower than capture"):
+    val board = Board(
+      Map(
+        Square(File.E, Rank.R4) -> Piece.WhiteQueen,
+        Square(File.E, Rank.R5) -> Piece.BlackPawn,
+      ),
+    )
+    val context = GameContext.initial.withBoard(board).withTurn(Color.White)
+    val capture = Move(Square(File.E, Rank.R4), Square(File.E, Rank.R5), MoveType.Normal(true))
+    val quiet   = Move(Square(File.E, Rank.R4), Square(File.D, Rank.R5))
+
+    val captureScore = MoveOrdering.score(context, capture, None)
+    val quietScore   = MoveOrdering.score(context, quiet, None)
+    captureScore should be > quietScore
+
+  test("TT best move ranks first"):
+    val board = Board(
+      Map(
+        Square(File.E, Rank.R4) -> Piece.WhiteQueen,
+        Square(File.E, Rank.R5) -> Piece.BlackPawn,
+        Square(File.D, Rank.R5) -> Piece.BlackPawn,
+      ),
+    )
+    val context      = GameContext.initial.withBoard(board).withTurn(Color.White)
+    val bestMove     = Move(Square(File.E, Rank.R4), Square(File.D, Rank.R5), MoveType.Normal(true))
+    val otherCapture = Move(Square(File.E, Rank.R4), Square(File.E, Rank.R5), MoveType.Normal(true))
+
+    val bestScore  = MoveOrdering.score(context, bestMove, Some(bestMove))
+    val otherScore = MoveOrdering.score(context, otherCapture, Some(bestMove))
+    bestScore should equal(Int.MaxValue)
+    otherScore should be < bestScore
+
+  test("promotion to queen ranks high"):
+    val board = Board(
+      Map(
+        Square(File.E, Rank.R7) -> Piece.WhitePawn,
+      ),
+    )
+    val context = GameContext.initial.withBoard(board).withTurn(Color.White)
+    val promotionQueen =
+      Move(Square(File.E, Rank.R7), Square(File.E, Rank.R8), MoveType.Promotion(PromotionPiece.Queen))
+    val promotionKnight =
+      Move(Square(File.E, Rank.R7), Square(File.E, Rank.R8), MoveType.Promotion(PromotionPiece.Knight))
+
+    val queenScore  = MoveOrdering.score(context, promotionQueen, None)
+    val knightScore = MoveOrdering.score(context, promotionKnight, None)
+    queenScore should be > knightScore
+    queenScore should be > 100_000 // Queen promotion score is > 100_000
+
+  test("en passant is treated as capture"):
+    val board = Board(
+      Map(
+        Square(File.E, Rank.R5) -> Piece.WhitePawn,
+        Square(File.D, Rank.R5) -> Piece.BlackPawn,
+      ),
+    )
+    val context   = GameContext.initial.withBoard(board).withTurn(Color.White)
+    val epCapture = Move(Square(File.E, Rank.R5), Square(File.D, Rank.R6), MoveType.EnPassant)
+    val quiet     = Move(Square(File.E, Rank.R5), Square(File.E, Rank.R6))
+
+    val epScore    = MoveOrdering.score(context, epCapture, None)
+    val quietScore = MoveOrdering.score(context, quiet, None)
+    epScore should be > quietScore
+
+  test("sort returns moves ordered by score"):
+    val board = Board(
+      Map(
+        Square(File.E, Rank.R4) -> Piece.WhiteQueen,
+        Square(File.E, Rank.R5) -> Piece.BlackPawn,
+        Square(File.D, Rank.R5) -> Piece.BlackRook,
+      ),
+    )
+    val context = GameContext.initial.withBoard(board).withTurn(Color.White)
+    val moves = List(
+      Move(Square(File.E, Rank.R4), Square(File.D, Rank.R5), MoveType.Normal(true)), // Rook capture
+      Move(Square(File.E, Rank.R4), Square(File.E, Rank.R5), MoveType.Normal(true)), // Pawn capture
+      Move(Square(File.E, Rank.R4), Square(File.E, Rank.R6)),                        // Quiet
+    )
+    val sorted = MoveOrdering.sort(context, moves, None)
+    // Rook capture should be first (higher victim value)
+    sorted.head.to should equal(Square(File.D, Rank.R5))
+    // Pawn capture should be second
+    sorted(1).to should equal(Square(File.E, Rank.R5))
+    // Quiet should be last
+    sorted.last.to should equal(Square(File.E, Rank.R6))
+
+  test("castling move is quiet (not capture)"):
+    val board = Board(
+      Map(
+        Square(File.E, Rank.R1) -> Piece.WhiteKing,
+        Square(File.H, Rank.R1) -> Piece.WhiteRook,
+      ),
+    )
+    val context    = GameContext.initial.withBoard(board)
+    val castleMove = Move(Square(File.E, Rank.R1), Square(File.G, Rank.R1), MoveType.CastleKingside)
+    val score      = MoveOrdering.score(context, castleMove, None)
+    score should equal(0) // Quiet move
+
+  test("all MoveType variants are handled in victimValue"):
+    val board = Board(
+      Map(
+        Square(File.E, Rank.R1) -> Piece.WhiteKing,
+        Square(File.H, Rank.R1) -> Piece.WhiteRook,
+        Square(File.E, Rank.R2) -> Piece.WhitePawn,
+      ),
+    )
+    val context = GameContext.initial.withBoard(board)
+    // Test castling queenside - should have victim value 0
+    val castleQs = Move(Square(File.E, Rank.R1), Square(File.C, Rank.R1), MoveType.CastleQueenside)
+    val scoreQs  = MoveOrdering.score(context, castleQs, None)
+    scoreQs should equal(0)
+
+  test("attackerValue covers all piece types"):
+    val board = Board(
+      Map(
+        Square(File.A, Rank.R1) -> Piece.WhiteRook,
+        Square(File.B, Rank.R1) -> Piece.WhiteKnight,
+        Square(File.C, Rank.R1) -> Piece.WhiteBishop,
+        Square(File.D, Rank.R1) -> Piece.WhiteQueen,
+        Square(File.E, Rank.R1) -> Piece.WhiteKing,
+        Square(File.F, Rank.R2) -> Piece.WhitePawn,
+      ),
+    )
+    val context = GameContext.initial.withBoard(board)
+    // Create captures with each piece type
+    val rookCapture   = Move(Square(File.A, Rank.R1), Square(File.A, Rank.R8), MoveType.Normal(true))
+    val knightCapture = Move(Square(File.B, Rank.R1), Square(File.A, Rank.R8), MoveType.Normal(true))
+    val bishopCapture = Move(Square(File.C, Rank.R1), Square(File.A, Rank.R8), MoveType.Normal(true))
+    val queenCapture  = Move(Square(File.D, Rank.R1), Square(File.A, Rank.R8), MoveType.Normal(true))
+    val kingCapture   = Move(Square(File.E, Rank.R1), Square(File.A, Rank.R8), MoveType.Normal(true))
+    val pawnCapture   = Move(Square(File.F, Rank.R2), Square(File.A, Rank.R8), MoveType.Normal(true))
+
+    // Just verify all are scored without error
+    MoveOrdering.score(context, rookCapture, None) should be >= 0
+    MoveOrdering.score(context, knightCapture, None) should be >= 0
+    MoveOrdering.score(context, bishopCapture, None) should be >= 0
+    MoveOrdering.score(context, queenCapture, None) should be >= 0
+    MoveOrdering.score(context, kingCapture, None) should be >= 0
+    MoveOrdering.score(context, pawnCapture, None) should be >= 0
+
+  test("promotion capture is distinct from quiet promotion"):
+    val board = Board(
+      Map(
+        Square(File.E, Rank.R7) -> Piece.WhitePawn,
+        Square(File.D, Rank.R8) -> Piece.BlackPawn,
+      ),
+    )
+    val context = GameContext.initial.withBoard(board)
+    // Promotion with capture
+    val promotionWithCapture =
+      Move(Square(File.E, Rank.R7), Square(File.D, Rank.R8), MoveType.Promotion(PromotionPiece.Queen))
+    // Regular queen promotion (no capture)
+    val quietPromotion =
+      Move(Square(File.E, Rank.R7), Square(File.E, Rank.R8), MoveType.Promotion(PromotionPiece.Queen))
+    val score1 = MoveOrdering.score(context, promotionWithCapture, None)
+    val score2 = MoveOrdering.score(context, quietPromotion, None)
+    score1 should be > score2
+
+  test("non-Queen promotion captures trigger promotionPieceType for Knight, Bishop, Rook"):
+    val board = Board(
+      Map(
+        Square(File.E, Rank.R7) -> Piece.WhitePawn,
+        Square(File.D, Rank.R8) -> Piece.BlackRook,
+      ),
+    )
+    val context     = GameContext.initial.withBoard(board).withTurn(Color.White)
+    val knightPromo = Move(Square(File.E, Rank.R7), Square(File.D, Rank.R8), MoveType.Promotion(PromotionPiece.Knight))
+    val bishopPromo = Move(Square(File.E, Rank.R7), Square(File.D, Rank.R8), MoveType.Promotion(PromotionPiece.Bishop))
+    val rookPromo   = Move(Square(File.E, Rank.R7), Square(File.D, Rank.R8), MoveType.Promotion(PromotionPiece.Rook))
+    MoveOrdering.score(context, knightPromo, None) should be > 0
+    MoveOrdering.score(context, bishopPromo, None) should be > 0
+    MoveOrdering.score(context, rookPromo, None) should be > 0
+
+  test("negative SEE capture path is scored below neutral capture baseline"):
+    val board = Board(
+      Map(
+        Square(File.D, Rank.R4) -> Piece.WhiteQueen,
+        Square(File.D, Rank.R5) -> Piece.BlackPawn,
+        Square(File.D, Rank.R8) -> Piece.BlackRook,
+      ),
+    )
+    val context = GameContext.initial.withBoard(board).withTurn(Color.White)
+    val move    = Move(Square(File.D, Rank.R4), Square(File.D, Rank.R5), MoveType.Normal(true))
+
+    MoveOrdering.score(context, move, None) should be < 100_000
+
+  test("non-capture move keeps fallback scoring at zero"):
+    val board   = Board(Map(Square(File.E, Rank.R1) -> Piece.WhiteKing, Square(File.A, Rank.R8) -> Piece.BlackKing))
+    val context = GameContext.initial.withBoard(board).withTurn(Color.White)
+    val castle  = Move(Square(File.E, Rank.R1), Square(File.G, Rank.R1), MoveType.CastleKingside)
+
+    MoveOrdering.score(context, castle, None) should be(0)
@@ -0,0 +1,153 @@
+package de.nowchess.bot
+
+import de.nowchess.api.board.{Color, File, Rank, Square}
+import de.nowchess.api.game.GameContext
+import de.nowchess.api.move.{Move, MoveType, PromotionPiece}
+import de.nowchess.bot.bots.ClassicalBot
+import de.nowchess.bot.util.{PolyglotBook, PolyglotHash}
+import de.nowchess.rules.sets.DefaultRules
+import org.scalatest.funsuite.AnyFunSuite
+import org.scalatest.matchers.should.Matchers
+
+import java.io.{DataOutputStream, FileOutputStream}
+import java.nio.file.Files
+import scala.util.Using
+
+class PolyglotBookTest extends AnyFunSuite with Matchers:
+
+  test("Book probe returns None for non-existent file"):
+    val book = PolyglotBook("/nonexistent/path/book.bin")
+    book.probe(GameContext.initial) shouldEqual None
+
+  test("Book probe returns None when position not in book"):
+    val tempFile = Files.createTempFile("test_book", ".bin")
+    try
+      // Write a single entry with a different key
+      Using(DataOutputStream(FileOutputStream(tempFile.toFile))) { dos =>
+        dos.writeLong(12345L) // some random key
+        dos.writeShort(0)     // move
+        dos.writeShort(100)   // weight
+        dos.writeInt(0)       // learn
+      }.get
+
+      val book = PolyglotBook(tempFile.toString)
+      book.probe(GameContext.initial) shouldEqual None
+    finally Files.delete(tempFile)
+
+  test("Book returns a move when position is in book"):
+    val tempFile = Files.createTempFile("test_book", ".bin")
+    try
+      val ctx  = GameContext.initial
+      val hash = PolyglotHash.hash(ctx)
+
+      // Write an entry: e2-e4 (normal move, non-capture)
+      // from_file=4, from_rank=1, to_file=4, to_rank=3, promotion=0
+      val move: Short = (4 | (3 << 3) | (4 << 6) | (1 << 9)).toShort
+
+      Using(DataOutputStream(FileOutputStream(tempFile.toFile))) { dos =>
+        dos.writeLong(hash)
+        dos.writeShort(move)
+        dos.writeShort(100) // weight
+        dos.writeInt(0)
+      }.get
+
+      val book   = PolyglotBook(tempFile.toString)
+      val result = book.probe(ctx)
+      result shouldNot be(None)
+      result.get.from shouldEqual Square(File.E, Rank.R2)
+      result.get.to shouldEqual Square(File.E, Rank.R4)
+    finally Files.delete(tempFile)
+
+  test("Weighted random sampling works"):
+    val tempFile = Files.createTempFile("test_book", ".bin")
+    try
+      val ctx  = GameContext.initial
+      val hash = PolyglotHash.hash(ctx)
+
+      // Two moves: e2-e4 with high weight, d2-d4 with low weight
+      val moveE4: Short = (4 | (3 << 3) | (4 << 6) | (1 << 9)).toShort
+      val moveD4: Short = (3 | (3 << 3) | (3 << 6) | (1 << 9)).toShort
+
+      Using(DataOutputStream(FileOutputStream(tempFile.toFile))) { dos =>
+        dos.writeLong(hash)
+        dos.writeShort(moveE4)
+        dos.writeShort(900) // high weight
+        dos.writeInt(0)
+
+        dos.writeLong(hash)
+        dos.writeShort(moveD4)
+        dos.writeShort(100) // low weight
+        dos.writeInt(0)
+      }.get
+
+      val book = PolyglotBook(tempFile.toString)
+
+      // Sample multiple times; high-weight move should be picked more often
+      val samples = (0 until 100).map(_ => book.probe(ctx)).flatten
+      samples.length should be > 0
+
+      val e4Count = samples.count(m => m.from == Square(File.E, Rank.R2) && m.to == Square(File.E, Rank.R4))
+      val d4Count = samples.count(m => m.from == Square(File.D, Rank.R2) && m.to == Square(File.D, Rank.R4))
+
+      // With 900:100 weight ratio, e4 should appear more frequently
+      e4Count should be > d4Count
+    finally Files.delete(tempFile)
+
+  test("ClassicalBot without book falls back to search"):
+    val ctx  = GameContext.initial
+    val bot  = ClassicalBot(BotDifficulty.Easy) // no book
+    val move = bot.nextMove(ctx)
+    move shouldNot be(None)
+    // The move should be legal
+    val allLegalMoves = DefaultRules.allLegalMoves(ctx)
+    allLegalMoves should contain(move.get)
+
+  test("ClassicalBot with book uses book move"):
+    val tempFile = Files.createTempFile("test_book", ".bin")
+    try
+      val ctx  = GameContext.initial
+      val hash = PolyglotHash.hash(ctx)
+
+      // e2-e4
+      val moveE4: Short = (4 | (3 << 3) | (4 << 6) | (1 << 9)).toShort
+
+      Using(DataOutputStream(FileOutputStream(tempFile.toFile))) { dos =>
+        dos.writeLong(hash)
+        dos.writeShort(moveE4)
+        dos.writeShort(100)
+        dos.writeInt(0)
+      }.get
+
+      val book        = PolyglotBook(tempFile.toString)
+      val botWithBook = ClassicalBot(BotDifficulty.Easy, book = Some(book))
+      val move        = botWithBook.nextMove(ctx)
+
+      // Book should return e2-e4
+      move shouldEqual Some(Move(Square(File.E, Rank.R2), Square(File.E, Rank.R4), MoveType.Normal()))
+    finally Files.delete(tempFile)
+
+  test("Promotion moves are decoded correctly"):
+    val tempFile = Files.createTempFile("test_book", ".bin")
+    try
+      val ctx  = GameContext.initial
+      val hash = PolyglotHash.hash(ctx)
+
+      // Pawn promotion: a7-a8=Q
+      // from_file=0, from_rank=6, to_file=0, to_rank=7, promotion=4 (queen)
+      val promoteMove: Short = (0 | (7 << 3) | (0 << 6) | (6 << 9) | (4 << 12)).toShort
+
+      Using(DataOutputStream(FileOutputStream(tempFile.toFile))) { dos =>
+        dos.writeLong(hash)
+        dos.writeShort(promoteMove)
+        dos.writeShort(100)
+        dos.writeInt(0)
+      }.get
+
+      val book = PolyglotBook(tempFile.toString)
+      val move = book.probe(ctx)
+
+      move shouldNot be(None)
+      move.get.moveType match
+        case MoveType.Promotion(piece) => piece shouldEqual PromotionPiece.Queen
+        case _                         => fail("Expected promotion move")
+    finally Files.delete(tempFile)
@@ -0,0 +1,58 @@
+package de.nowchess.bot
+
+import de.nowchess.api.board.{Color, File, Rank, Square}
+import de.nowchess.api.game.GameContext
+import de.nowchess.bot.util.PolyglotHash
+import de.nowchess.io.fen.FenParser
+import org.scalatest.funsuite.AnyFunSuite
+import org.scalatest.matchers.should.Matchers
+
+class PolyglotHashTest extends AnyFunSuite with Matchers:
+
+  test("Initial position matches Polyglot reference key"):
+    val ctx = GameContext.initial
+    PolyglotHash.hash(ctx) shouldEqual java.lang.Long.parseUnsignedLong("463b96181691fc9c", 16)
+
+  test("Known Polyglot FEN vector matches reference key"):
+    val fen = "rnbqkbnr/pppppppp/8/8/4P3/8/PPPP1PPP/RNBQKBNR b KQkq e3 0 1"
+    val ctx = FenParser.parseFen(fen).toOption.getOrElse(fail("FEN parse failed"))
+    PolyglotHash.hash(ctx) shouldEqual java.lang.Long.parseUnsignedLong("823c9b50fd114196", 16)
+
+  test("Hash changes when turn changes"):
+    val ctx          = GameContext.initial
+    val hash1        = PolyglotHash.hash(ctx)
+    val ctxBlackTurn = ctx.withTurn(Color.Black)
+    val hash2        = PolyglotHash.hash(ctxBlackTurn)
+    hash1 should not equal hash2
+
+  test("Hash changes when castling rights change"):
+    val ctx   = GameContext.initial
+    val hash1 = PolyglotHash.hash(ctx)
+    val noCastling = ctx.withCastlingRights(
+      de.nowchess.api.board.CastlingRights(false, false, false, false),
+    )
+    val hash2 = PolyglotHash.hash(noCastling)
+    hash1 should not equal hash2
+
+  test("En passant file is ignored when no side-to-move pawn can capture"):
+    val fenWithEp    = "rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR b KQkq e3 0 1"
+    val fenWithoutEp = "rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR b KQkq - 0 1"
+    val withEp       = FenParser.parseFen(fenWithEp).toOption.getOrElse(fail("FEN parse failed"))
+    val withoutEp    = FenParser.parseFen(fenWithoutEp).toOption.getOrElse(fail("FEN parse failed"))
+    PolyglotHash.hash(withEp) shouldEqual PolyglotHash.hash(withoutEp)
+
+  test("Different en passant files produce different hashes when capture is possible"):
+    val ctx     = GameContext.initial
+    val epFileE = ctx.withEnPassantSquare(Some(Square(File.E, Rank.R3)))
+    val epFileD = ctx.withEnPassantSquare(Some(Square(File.D, Rank.R3)))
+    val hash1   = PolyglotHash.hash(epFileE)
+    val hash2   = PolyglotHash.hash(epFileD)
+    hash1 should not equal hash2
+
+  test("Removing en passant changes hash"):
+    val ctx    = GameContext.initial
+    val withEP = ctx.withEnPassantSquare(Some(Square(File.E, Rank.R3)))
+    val hash1  = PolyglotHash.hash(withEP)
+    val noEP   = withEP.withEnPassantSquare(None)
+    val hash2  = PolyglotHash.hash(noEP)
+    hash1 should not equal hash2
@@ -0,0 +1,73 @@
+package de.nowchess.bot
+
+import de.nowchess.api.board.{File, Rank, Square}
+import de.nowchess.api.move.{Move, MoveType}
+import de.nowchess.bot.logic.{TTEntry, TTFlag, TranspositionTable}
+import org.scalatest.funsuite.AnyFunSuite
+import org.scalatest.matchers.should.Matchers
+
+class TranspositionTableTest extends AnyFunSuite with Matchers:
+
+  test("probe on empty table returns None"):
+    val tt = TranspositionTable(sizePow2 = 4)
+    tt.probe(12345L) should be(None)
+
+  test("store then probe returns the stored entry"):
+    val tt    = TranspositionTable(sizePow2 = 4)
+    val move  = Move(Square(File.E, Rank.R2), Square(File.E, Rank.R4), MoveType.Normal())
+    val entry = TTEntry(hash = 12345L, depth = 3, score = 50, flag = TTFlag.Exact, bestMove = Some(move))
+    tt.store(entry)
+    val retrieved = tt.probe(12345L)
+    retrieved should be(Some(entry))
+
+  test("probe returns None when hash differs (collision guard)"):
+    val tt    = TranspositionTable(sizePow2 = 4)
+    val move  = Move(Square(File.E, Rank.R2), Square(File.E, Rank.R4), MoveType.Normal())
+    val entry = TTEntry(hash = 12345L, depth = 3, score = 50, flag = TTFlag.Exact, bestMove = Some(move))
+    tt.store(entry)
+    tt.probe(54321L) should be(None)
+
+  test("clear removes all entries"):
+    val tt    = TranspositionTable(sizePow2 = 4)
+    val move  = Move(Square(File.E, Rank.R2), Square(File.E, Rank.R4), MoveType.Normal())
+    val entry = TTEntry(hash = 12345L, depth = 3, score = 50, flag = TTFlag.Exact, bestMove = Some(move))
+    tt.store(entry)
+    tt.probe(12345L) should be(Some(entry))
+    tt.clear()
+    tt.probe(12345L) should be(None)
+
+  test("all TTFlag values store and retrieve correctly"):
+    val tt   = TranspositionTable(sizePow2 = 4)
+    val move = Move(Square(File.E, Rank.R2), Square(File.E, Rank.R4), MoveType.Normal())
+
+    TTFlag.values.foreach { flag =>
+      val entry = TTEntry(hash = 12345L + flag.ordinal, depth = 2, score = 100, flag = flag, bestMove = Some(move))
+      tt.store(entry)
+      val retrieved = tt.probe(12345L + flag.ordinal)
+      retrieved.map(_.flag) should be(Some(flag))
+    }
+
+  test("bestMove = None roundtrips"):
+    val tt    = TranspositionTable(sizePow2 = 4)
+    val entry = TTEntry(hash = 99999L, depth = 1, score = 0, flag = TTFlag.Upper, bestMove = None)
+    tt.store(entry)
+    val retrieved = tt.probe(99999L)
+    retrieved.map(_.bestMove) should be(Some(None))
+
+  test("always-replace overwrites at same slot"):
+    val tt     = TranspositionTable(sizePow2 = 4)
+    val move1  = Move(Square(File.E, Rank.R2), Square(File.E, Rank.R4), MoveType.Normal())
+    val move2  = Move(Square(File.E, Rank.R2), Square(File.E, Rank.R3), MoveType.Normal())
+    val entry1 = TTEntry(hash = 12345L, depth = 2, score = 50, flag = TTFlag.Exact, bestMove = Some(move1))
+    val entry2 = TTEntry(hash = 12345L, depth = 3, score = 100, flag = TTFlag.Lower, bestMove = Some(move2))
+
+    tt.store(entry1)
+    tt.probe(12345L).map(_.score) should be(Some(50))
+    tt.store(entry2)
+    tt.probe(12345L).map(_.score) should be(Some(100))
+
+  test("size is 1 << sizePow2"):
+    val tt = TranspositionTable(sizePow2 = 4)
+    (1 << 4) should equal(16)
+    val tt2 = TranspositionTable(sizePow2 = 10)
+    (1 << 10) should equal(1024)
@@ -0,0 +1,162 @@
+package de.nowchess.bot
+
+import de.nowchess.api.board.{Board, CastlingRights, Color, File, Piece, Rank, Square}
+import de.nowchess.api.game.GameContext
+import de.nowchess.api.move.{Move, MoveType, PromotionPiece}
+import de.nowchess.bot.util.ZobristHash
+import de.nowchess.rules.sets.DefaultRules
+import org.scalatest.funsuite.AnyFunSuite
+import org.scalatest.matchers.should.Matchers
+
+class ZobristHashTest extends AnyFunSuite with Matchers:
+
+  test("hash is deterministic"):
+    val hash1 = ZobristHash.hash(GameContext.initial)
+    val hash2 = ZobristHash.hash(GameContext.initial)
+    hash1 should equal(hash2)
+
+  test("hash differs after a pawn move"):
+    val initial = GameContext.initial
+    // Move pawn from e2 to e4
+    val board     = initial.board.pieces
+    val newBoard  = board.removed(Square(File.E, Rank.R2)).updated(Square(File.E, Rank.R4), Piece.WhitePawn)
+    val afterMove = initial.withBoard(Board(newBoard)).withTurn(Color.Black)
+    val hash1     = ZobristHash.hash(initial)
+    val hash2     = ZobristHash.hash(afterMove)
+    hash1 should not equal hash2
+
+  test("hash includes castling rights"):
+    val ctx1  = GameContext.initial
+    val ctx2  = ctx1.withCastlingRights(CastlingRights.None)
+    val hash1 = ZobristHash.hash(ctx1)
+    val hash2 = ZobristHash.hash(ctx2)
+    hash1 should not equal hash2
+
+  test("hash includes en-passant square"):
+    val ctx1  = GameContext.initial
+    val ctx2  = ctx1.withEnPassantSquare(Some(Square(File.E, Rank.R3)))
+    val hash1 = ZobristHash.hash(ctx1)
+    val hash2 = ZobristHash.hash(ctx2)
+    hash1 should not equal hash2
+
+  test("hash includes side to move"):
+    val ctx1  = GameContext.initial
+    val ctx2  = ctx1.withTurn(Color.Black)
+    val hash1 = ZobristHash.hash(ctx1)
+    val hash2 = ZobristHash.hash(ctx2)
+    hash1 should not equal hash2
+
+  test("nextHash matches recomputed hash for a normal move"):
+    val context     = GameContext.initial
+    val move        = Move(Square(File.E, Rank.R2), Square(File.E, Rank.R4))
+    val next        = DefaultRules.applyMove(context)(move)
+    val incremental = ZobristHash.nextHash(context, ZobristHash.hash(context), move, next)
+    incremental should equal(ZobristHash.hash(next))
+
+  test("nextHash matches recomputed hash for promotion and castling"):
+    val promotionBoard = Board(
+      Map(
+        Square(File.E, Rank.R7) -> Piece.WhitePawn,
+        Square(File.E, Rank.R1) -> Piece.WhiteKing,
+        Square(File.H, Rank.R1) -> Piece.WhiteRook,
+        Square(File.E, Rank.R8) -> Piece.BlackKing,
+      ),
+    )
+    val promotionContext = GameContext.initial
+      .withBoard(promotionBoard)
+      .withTurn(Color.White)
+      .withCastlingRights(CastlingRights.All)
+    val promotionMove = Move(Square(File.E, Rank.R7), Square(File.E, Rank.R8), MoveType.Promotion(PromotionPiece.Queen))
+    val promotionNext = DefaultRules.applyMove(promotionContext)(promotionMove)
+    val promotionHash =
+      ZobristHash.nextHash(promotionContext, ZobristHash.hash(promotionContext), promotionMove, promotionNext)
+    promotionHash should equal(ZobristHash.hash(promotionNext))
+
+    val castleBoard = Board(
+      Map(
+        Square(File.E, Rank.R1) -> Piece.WhiteKing,
+        Square(File.H, Rank.R1) -> Piece.WhiteRook,
+        Square(File.E, Rank.R8) -> Piece.BlackKing,
+      ),
+    )
+    val castleContext = GameContext.initial
+      .withBoard(castleBoard)
+      .withTurn(Color.White)
+      .withCastlingRights(
+        CastlingRights(whiteKingSide = true, whiteQueenSide = false, blackKingSide = false, blackQueenSide = false),
+      )
+    val castleMove = Move(Square(File.E, Rank.R1), Square(File.G, Rank.R1), MoveType.CastleKingside)
+    val castleNext = DefaultRules.applyMove(castleContext)(castleMove)
+    val castleHash = ZobristHash.nextHash(castleContext, ZobristHash.hash(castleContext), castleMove, castleNext)
+    castleHash should equal(ZobristHash.hash(castleNext))
+
+  test("nextHash matches recomputed hash for queenside castling"):
+    val board = Board(
+      Map(
+        Square(File.E, Rank.R1) -> Piece.WhiteKing,
+        Square(File.A, Rank.R1) -> Piece.WhiteRook,
+        Square(File.E, Rank.R8) -> Piece.BlackKing,
+      ),
+    )
+    val ctx = GameContext.initial
+      .withBoard(board)
+      .withTurn(Color.White)
+      .withCastlingRights(
+        CastlingRights(whiteKingSide = false, whiteQueenSide = true, blackKingSide = false, blackQueenSide = false),
+      )
+    val move = Move(Square(File.E, Rank.R1), Square(File.C, Rank.R1), MoveType.CastleQueenside)
+    val next = DefaultRules.applyMove(ctx)(move)
+    ZobristHash.nextHash(ctx, ZobristHash.hash(ctx), move, next) should equal(ZobristHash.hash(next))
+
+  test("nextHash matches recomputed hash for en passant"):
+    val board = Board(
+      Map(
+        Square(File.E, Rank.R5) -> Piece.WhitePawn,
+        Square(File.D, Rank.R5) -> Piece.BlackPawn,
+        Square(File.E, Rank.R1) -> Piece.WhiteKing,
+        Square(File.E, Rank.R8) -> Piece.BlackKing,
+      ),
+    )
+    val ctx = GameContext.initial
+      .withBoard(board)
+      .withTurn(Color.White)
+      .withEnPassantSquare(Some(Square(File.D, Rank.R6)))
+    val move = Move(Square(File.E, Rank.R5), Square(File.D, Rank.R6), MoveType.EnPassant)
+    val next = DefaultRules.applyMove(ctx)(move)
+    ZobristHash.nextHash(ctx, ZobristHash.hash(ctx), move, next) should equal(ZobristHash.hash(next))
+
+  test("nextHash matches recomputed hash for black kingside castling"):
+    val board = Board(
+      Map(
+        Square(File.E, Rank.R8) -> Piece.BlackKing,
+        Square(File.H, Rank.R8) -> Piece.BlackRook,
+        Square(File.E, Rank.R1) -> Piece.WhiteKing,
+      ),
+    )
+    val ctx = GameContext.initial
+      .withBoard(board)
+      .withTurn(Color.Black)
+      .withCastlingRights(
+        CastlingRights(whiteKingSide = false, whiteQueenSide = false, blackKingSide = true, blackQueenSide = false),
+      )
+    val move = Move(Square(File.E, Rank.R8), Square(File.G, Rank.R8), MoveType.CastleKingside)
+    val next = DefaultRules.applyMove(ctx)(move)
+    ZobristHash.nextHash(ctx, ZobristHash.hash(ctx), move, next) should equal(ZobristHash.hash(next))
+
+  test("nextHash matches recomputed hash for knight and rook promotions"):
+    val board = Board(
+      Map(
+        Square(File.E, Rank.R7) -> Piece.WhitePawn,
+        Square(File.E, Rank.R1) -> Piece.WhiteKing,
+        Square(File.E, Rank.R8) -> Piece.BlackKing,
+      ),
+    )
+    val ctx = GameContext.initial
+      .withBoard(board)
+      .withTurn(Color.White)
+      .withCastlingRights(CastlingRights(false, false, false, false))
+
+    for pp <- List(PromotionPiece.Knight, PromotionPiece.Bishop, PromotionPiece.Rook) do
+      val move = Move(Square(File.E, Rank.R7), Square(File.E, Rank.R8), MoveType.Promotion(pp))
+      val next = DefaultRules.applyMove(ctx)(move)
+      ZobristHash.nextHash(ctx, ZobristHash.hash(ctx), move, next) should equal(ZobristHash.hash(next))
@@ -260,3 +260,29 @@
 * correct test board positions and captureOutput/withInput interaction ([f0481e2](https://git.janis-eccarius.de/NowChess/NowChessSystems/commit/f0481e2561b779df00925b46ee281dc36a795150))
 * update main class path in build configuration and adjust VCS directory mapping ([7b1f8b1](https://git.janis-eccarius.de/NowChess/NowChessSystems/commit/7b1f8b117623d327232a1a92a8a44d18582e0189))
 * update move validation to check for king safety ([#13](https://git.janis-eccarius.de/NowChess/NowChessSystems/issues/13)) ([e5e20c5](https://git.janis-eccarius.de/NowChess/NowChessSystems/commit/e5e20c566e368b12ca1dc59680c34e9112bf6762))
+##  (2026-04-16)
+
+### Features
+
+* add GameRules stub with PositionStatus enum ([76d4168](https://git.janis-eccarius.de/NowChess/NowChessSystems/commit/76d4168038de23e5d6083d4e8f0504fbf31d15a3))
+* add MovedInCheck/Checkmate/Stalemate MoveResult variants (stub dispatch) ([8b7ec57](https://git.janis-eccarius.de/NowChess/NowChessSystems/commit/8b7ec57e5ea6ee1615a1883848a426dc07d26364))
+* implement GameRules with isInCheck, legalMoves, gameStatus ([94a02ff](https://git.janis-eccarius.de/NowChess/NowChessSystems/commit/94a02ff6849436d9496c70a0f16c21666dae8e4e))
+* implement legal castling ([#1](https://git.janis-eccarius.de/NowChess/NowChessSystems/issues/1)) ([00d326c](https://git.janis-eccarius.de/NowChess/NowChessSystems/commit/00d326c1ba67711fbe180f04e1100c3f01dd0254))
+* NCS-10 Implement Pawn Promotion ([#12](https://git.janis-eccarius.de/NowChess/NowChessSystems/issues/12)) ([13bfc16](https://git.janis-eccarius.de/NowChess/NowChessSystems/commit/13bfc16cfe25db78ec607db523ca6d993c13430c))
+* NCS-11 50-move rule ([#9](https://git.janis-eccarius.de/NowChess/NowChessSystems/issues/9)) ([412ed98](https://git.janis-eccarius.de/NowChess/NowChessSystems/commit/412ed986a95703a3b282276540153480ceed229d))
+* NCS-13 Implement Threefold Repetition ([#31](https://git.janis-eccarius.de/NowChess/NowChessSystems/issues/31)) ([767d305](https://git.janis-eccarius.de/NowChess/NowChessSystems/commit/767d3051a76c266050b6335774d66e2db2273c16))
+* NCS-14 implemented insufficient moves rule ([#30](https://git.janis-eccarius.de/NowChess/NowChessSystems/issues/30)) ([b0399a4](https://git.janis-eccarius.de/NowChess/NowChessSystems/commit/b0399a4e489950083066c9538df9a84dcc7a4613))
+* NCS-16 Core Separation via Patterns ([#10](https://git.janis-eccarius.de/NowChess/NowChessSystems/issues/10)) ([1361dfc](https://git.janis-eccarius.de/NowChess/NowChessSystems/commit/1361dfc89553b146864fb8ff3526cf12cf3f293a))
+* NCS-17 Implement basic ScalaFX UI ([#14](https://git.janis-eccarius.de/NowChess/NowChessSystems/issues/14)) ([3ff8031](https://git.janis-eccarius.de/NowChess/NowChessSystems/commit/3ff80318b4f16c59733a46498581a5c27f048287))
+* NCS-21 Write Scripts to automate certain tasks ([#15](https://git.janis-eccarius.de/NowChess/NowChessSystems/issues/15)) ([8051871](https://git.janis-eccarius.de/NowChess/NowChessSystems/commit/80518719d536a087d339fe02530825dc07f8b388))
+* NCS-25 Add linters to keep quality up ([#27](https://git.janis-eccarius.de/NowChess/NowChessSystems/issues/27)) ([fd4e67d](https://git.janis-eccarius.de/NowChess/NowChessSystems/commit/fd4e67d4f782a7e955822d90cb909d0a81676fb2))
+* NCS-6 Implementing FEN & PGN ([#7](https://git.janis-eccarius.de/NowChess/NowChessSystems/issues/7)) ([f28e69d](https://git.janis-eccarius.de/NowChess/NowChessSystems/commit/f28e69dc181416aa2f221fdc4b45c2cda5efbf07))
+* NCS-9 En passant implementation ([#8](https://git.janis-eccarius.de/NowChess/NowChessSystems/issues/8)) ([919beb3](https://git.janis-eccarius.de/NowChess/NowChessSystems/commit/919beb3b4bfa8caf2f90976a415fe9b19b7e9747))
+* wire check/checkmate/stalemate into processMove and gameLoop ([5264a22](https://git.janis-eccarius.de/NowChess/NowChessSystems/commit/5264a225418b885c5e6ea6411b96f85e38837f6c))
+
+### Bug Fixes
+
+* add missing kings to gameLoop capture test board ([aedd787](https://git.janis-eccarius.de/NowChess/NowChessSystems/commit/aedd787b77203c2af934751dba7b784eaf165032))
+* correct test board positions and captureOutput/withInput interaction ([f0481e2](https://git.janis-eccarius.de/NowChess/NowChessSystems/commit/f0481e2561b779df00925b46ee281dc36a795150))
+* update main class path in build configuration and adjust VCS directory mapping ([7b1f8b1](https://git.janis-eccarius.de/NowChess/NowChessSystems/commit/7b1f8b117623d327232a1a92a8a44d18582e0189))
+* update move validation to check for king safety ([#13](https://git.janis-eccarius.de/NowChess/NowChessSystems/issues/13)) ([e5e20c5](https://git.janis-eccarius.de/NowChess/NowChessSystems/commit/e5e20c566e368b12ca1dc59680c34e9112bf6762))
@@ -41,6 +41,7 @@ dependencies {
    implementation(project(":modules:api"))
    implementation(project(":modules:io"))
    implementation(project(":modules:rule"))
+    implementation(project(":modules:bot"))

    testImplementation(platform("org.junit:junit-bom:5.13.4"))
    testImplementation("org.junit.jupiter:junit-jupiter")
@@ -1,21 +1,28 @@
 package de.nowchess.chess.controller

 import de.nowchess.api.board.{File, Rank, Square}
+import de.nowchess.api.move.PromotionPiece

 object Parser:

-  /** Parses coordinate notation such as "e2e4" or "g1f3". Returns None for any input that does not match the expected
-    * format.
+  /** Parses UCI move notation: "e2e4" (4 chars) or "e7e8q" (5 chars with promotion piece suffix). The promotion suffix
+    * is q=Queen, r=Rook, b=Bishop, n=Knight. Returns None for invalid input.
    */
-  def parseMove(input: String): Option[(Square, Square)] =
+  def parseMove(input: String): Option[(Square, Square, Option[PromotionPiece])] =
    val trimmed = input.trim.toLowerCase
-    Option
-      .when(trimmed.length == 4)(trimmed)
-      .flatMap: s =>
+    trimmed.length match
+      case 4 =>
        for
-          from <- parseSquare(s.substring(0, 2))
-          to   <- parseSquare(s.substring(2, 4))
-        yield (from, to)
+          from <- parseSquare(trimmed.substring(0, 2))
+          to   <- parseSquare(trimmed.substring(2, 4))
+        yield (from, to, None)
+      case 5 =>
+        for
+          from  <- parseSquare(trimmed.substring(0, 2))
+          to    <- parseSquare(trimmed.substring(2, 4))
+          promo <- parsePromotion(trimmed(4))
+        yield (from, to, Some(promo))
+      case _ => None

  private def parseSquare(s: String): Option[Square] =
    Option
@@ -26,3 +33,10 @@ object Parser:
        Option.when(fileIdx >= 0 && fileIdx <= 7 && rankIdx >= 0 && rankIdx <= 7)(
          Square(File.values(fileIdx), Rank.values(rankIdx)),
        )
+
+  private def parsePromotion(c: Char): Option[PromotionPiece] = c match
+    case 'q' => Some(PromotionPiece.Queen)
+    case 'r' => Some(PromotionPiece.Rook)
+    case 'b' => Some(PromotionPiece.Bishop)
+    case 'n' => Some(PromotionPiece.Knight)
+    case _   => None
@@ -2,7 +2,8 @@ package de.nowchess.chess.engine

 import de.nowchess.api.board.{Board, Color, Piece, PieceType, Square}
 import de.nowchess.api.move.{Move, MoveType, PromotionPiece}
-import de.nowchess.api.game.{DrawReason, GameContext, GameResult}
+import de.nowchess.api.game.{BotParticipant, DrawReason, GameContext, GameResult, Human, Participant}
+import de.nowchess.api.player.{PlayerId, PlayerInfo}
 import de.nowchess.chess.controller.Parser
 import de.nowchess.chess.observer.*
 import de.nowchess.chess.command.{CommandInvoker, MoveCommand, MoveResult}
@@ -10,29 +11,29 @@ import de.nowchess.io.{GameContextExport, GameContextImport}
 import de.nowchess.rules.RuleSet
 import de.nowchess.rules.sets.DefaultRules

+import scala.concurrent.{ExecutionContext, Future}
+
 /** Pure game engine that manages game state and notifies observers of state changes. All rule queries delegate to the
  * injected RuleSet. All user interactions go through Commands; state changes are broadcast via GameEvents.
  */
 class GameEngine(
    val initialContext: GameContext = GameContext.initial,
    val ruleSet: RuleSet = DefaultRules,
+    val participants: Map[Color, Participant] = Map(
+      Color.White -> Human(PlayerInfo(PlayerId("p1"), "Player 1")),
+      Color.Black -> Human(PlayerInfo(PlayerId("p2"), "Player 2")),
+    ),
 ) extends Observable:
  // Ensure that initialBoard is set correctly for threefold repetition detection
-  private val contextWithInitialBoard = if initialContext.moves.isEmpty && initialContext.board != initialContext.initialBoard then
-    initialContext.copy(initialBoard = initialContext.board)
-  else
-    initialContext
+  private val contextWithInitialBoard =
+    if initialContext.moves.isEmpty && initialContext.board != initialContext.initialBoard then
+      initialContext.copy(initialBoard = initialContext.board)
+    else initialContext
  @SuppressWarnings(Array("DisableSyntax.var"))
  private var currentContext: GameContext = contextWithInitialBoard
  private val invoker                     = new CommandInvoker()

-  /** Pending promotion: the Move that triggered it (from/to only, moveType filled in later). */
-  private case class PendingPromotion(from: Square, to: Square, contextBefore: GameContext)
-  @SuppressWarnings(Array("DisableSyntax.var"))
-  private var pendingPromotion: Option[PendingPromotion] = None
-
-  /** True if a pawn promotion move is pending and needs a piece choice. */
-  def isPendingPromotion: Boolean = synchronized(pendingPromotion.isDefined)
+  private implicit val ec: ExecutionContext = ExecutionContext.global

  // Synchronized accessors for current state
  def board: Board         = synchronized(currentContext.board)
@@ -92,11 +93,11 @@ class GameEngine(
                s"Invalid move format '$moveInput'. Use coordinate notation, e.g. e2e4.",
              ),
            )
-          case Some((from, to)) =>
-            handleParsedMove(from, to)
+          case Some((from, to, promotionPiece: Option[PromotionPiece])) =>
+            handleParsedMove(from, to, promotionPiece)
  }

-  private def handleParsedMove(from: Square, to: Square): Unit =
+  private def handleParsedMove(from: Square, to: Square, promotionPiece: Option[PromotionPiece]): Unit =
    currentContext.board.pieceAt(from) match
      case None =>
        notifyObservers(InvalidMoveEvent(currentContext, "No piece on that square."))
@@ -109,11 +110,18 @@ class GameEngine(
        candidates match
          case Nil =>
            notifyObservers(InvalidMoveEvent(currentContext, "Illegal move."))
-          case moves if isPromotionMove(piece, to) =>
-            // Multiple moves (one per promotion piece) — ask user to choose
-            val contextBefore = currentContext
-            pendingPromotion = Some(PendingPromotion(from, to, contextBefore))
-            notifyObservers(PromotionRequiredEvent(currentContext, from, to))
+          case _ if isPromotionMove(piece, to) =>
+            if promotionPiece.isEmpty then
+              notifyObservers(
+                InvalidMoveEvent(currentContext, "Promotion piece required: append q, r, b, or n to the move."),
+              )
+            else
+              candidates.find(_.moveType == MoveType.Promotion(promotionPiece.get)) match
+                case None =>
+                  notifyObservers(
+                    InvalidMoveEvent(currentContext, "Error completing promotion: no matching legal move."),
+                  )
+                case Some(move) => executeMove(move)
          case move :: _ =>
            executeMove(move)

@@ -123,21 +131,6 @@ class GameEngine(
      to.rank.ordinal == promoRank
    }

-  /** Apply a player's promotion piece choice. Must only be called when isPendingPromotion is true.
-    */
-  def completePromotion(piece: PromotionPiece): Unit = synchronized {
-    pendingPromotion match
-      case None =>
-        notifyObservers(InvalidMoveEvent(currentContext, "No promotion pending."))
-      case Some(pending) =>
-        pendingPromotion = None
-        val move = Move(pending.from, pending.to, MoveType.Promotion(piece))
-        // Verify it's actually legal
-        val legal = ruleSet.legalMoves(currentContext)(pending.from)
-        if legal.contains(move) then executeMove(move)
-        else notifyObservers(InvalidMoveEvent(currentContext, "Error completing promotion."))
-  }
-
  /** Undo the last move. */
  def undo(): Unit = synchronized(performUndo())

@@ -159,7 +152,6 @@ class GameEngine(
  private def replayGame(ctx: GameContext): Either[String, Unit] =
    val savedContext = currentContext
    currentContext = GameContext.initial
-    pendingPromotion = None
    invoker.clear()

    if ctx.moves.isEmpty then
@@ -175,14 +167,13 @@ class GameEngine(
    result

  private def applyReplayMove(move: Move): Either[String, Unit] =
-    handleParsedMove(move.from, move.to)
-    move.moveType match
-      case MoveType.Promotion(pp) if pendingPromotion.isDefined =>
-        completePromotion(pp)
-        Right(())
-      case MoveType.Promotion(_) =>
-        Left(s"Promotion required for move ${move.from}${move.to}")
-      case _ => Right(())
+    val legal = ruleSet.legalMoves(currentContext)(move.from)
+    val candidate = move.moveType match
+      case MoveType.Promotion(pp) => legal.find(m => m.to == move.to && m.moveType == MoveType.Promotion(pp))
+      case _                      => legal.find(_.to == move.to)
+    candidate match
+      case None     => Left("Illegal move.")
+      case Some(lm) => executeMove(lm); Right(())

  /** Export the current game context using the provided exporter. */
  def exportGame(exporter: GameContextExport): String = synchronized {
@@ -191,12 +182,10 @@ class GameEngine(

  /** Load an arbitrary board position, clearing all history and undo/redo state. */
  def loadPosition(newContext: GameContext): Unit = synchronized {
-    val contextWithInitialBoard = if newContext.moves.isEmpty then
-      newContext.copy(initialBoard = newContext.board)
-    else
-      newContext
+    val contextWithInitialBoard =
+      if newContext.moves.isEmpty then newContext.copy(initialBoard = newContext.board)
+      else newContext
    currentContext = contextWithInitialBoard
-    pendingPromotion = None
    invoker.clear()
    notifyObservers(BoardResetEvent(currentContext))
  }
@@ -208,6 +197,9 @@ class GameEngine(
    notifyObservers(BoardResetEvent(currentContext))
  }

+  /** Kick off play when the side to move is a bot (e.g. bot-vs-bot from initial position). */
+  def startGame(): Unit = synchronized(requestBotMoveIfNeeded())
+
  // ──── Private helpers ────

  private def executeMove(move: Move): Unit =
@@ -250,7 +242,9 @@ class GameEngine(
    else if ruleSet.isCheck(currentContext) then notifyObservers(CheckDetectedEvent(currentContext))

    if currentContext.halfMoveClock >= 100 then notifyObservers(FiftyMoveRuleAvailableEvent(currentContext))
-    if ruleSet.isThreefoldRepetition(currentContext) then notifyObservers(ThreefoldRepetitionAvailableEvent(currentContext))
+    if ruleSet.isThreefoldRepetition(currentContext) then
+      notifyObservers(ThreefoldRepetitionAvailableEvent(currentContext))
+    else requestBotMoveIfNeeded()

  private def translateMoveToNotation(move: Move, boardBefore: Board): String =
    move.moveType match
@@ -301,6 +295,47 @@ class GameEngine(
      case _ =>
        context.board.pieceAt(move.to)

+  /** Request a move from the opponent bot if it's their turn. Spawns an async task to avoid blocking the engine.
+    */
+  private def requestBotMoveIfNeeded(): Unit =
+    val pendingBotMove = synchronized {
+      participants.get(currentContext.turn) match
+        case Some(BotParticipant(bot)) => Some((bot, currentContext))
+        case _                         => None
+    }
+
+    pendingBotMove.foreach { case (bot, contextAtRequest) =>
+      Future {
+        bot.nextMove(contextAtRequest) match
+          case Some(move) => applyBotMove(move)
+          case None       => handleBotNoMove()
+      }
+    }
+
+  private def applyBotMove(move: Move): Unit =
+    synchronized {
+      val color = currentContext.turn
+      val from  = move.from
+      val to    = move.to
+      currentContext.board.pieceAt(from) match
+        case Some(piece) if piece.color == color =>
+          val legal = ruleSet.legalMoves(currentContext)(from)
+          legal.find(m => m.to == to && m.moveType == move.moveType) match
+            case Some(legalMove) => executeMove(legalMove)
+            case None =>
+              notifyObservers(InvalidMoveEvent(currentContext, s"Bot move ${from}${to} is illegal"))
+        case _ =>
+          notifyObservers(InvalidMoveEvent(currentContext, "Bot move has invalid source square"))
+    }
+
+  private def handleBotNoMove(): Unit =
+    synchronized {
+      if ruleSet.isCheckmate(currentContext) then
+        val winner = currentContext.turn.opposite
+        notifyObservers(CheckmateEvent(currentContext, winner))
+      else if ruleSet.isStalemate(currentContext) then notifyObservers(DrawEvent(currentContext, DrawReason.Stalemate))
+    }
+
  private def performUndo(): Unit =
    if invoker.canUndo then
      val cmd = invoker.history(invoker.getCurrentIndex)
@@ -1,6 +1,6 @@
 package de.nowchess.chess.observer

-import de.nowchess.api.board.{Color, Square}
+import de.nowchess.api.board.Color
 import de.nowchess.api.game.{DrawReason, GameContext}

 /** Base trait for all game state events. Events are immutable snapshots of game state changes.
@@ -39,13 +39,6 @@ case class InvalidMoveEvent(
    reason: String,
 ) extends GameEvent

-/** Fired when a pawn reaches the back rank and the player must choose a promotion piece. */
-case class PromotionRequiredEvent(
-    context: GameContext,
-    from: Square,
-    to: Square,
-) extends GameEvent
-
 /** Fired when the board is reset. */
 case class BoardResetEvent(
    context: GameContext,
@@ -18,8 +18,8 @@ class CommandInvokerBranchTest extends AnyFunSuite with Matchers:
      initialShouldFailOnUndo: Boolean = false,
      initialShouldFailOnExecute: Boolean = false,
  ) extends Command:
-    val shouldFailOnUndo    = new java.util.concurrent.atomic.AtomicBoolean(initialShouldFailOnUndo)
-    val shouldFailOnExecute = new java.util.concurrent.atomic.AtomicBoolean(initialShouldFailOnExecute)
+    val shouldFailOnUndo             = new java.util.concurrent.atomic.AtomicBoolean(initialShouldFailOnUndo)
+    val shouldFailOnExecute          = new java.util.concurrent.atomic.AtomicBoolean(initialShouldFailOnExecute)
    override def execute(): Boolean  = !shouldFailOnExecute.get()
    override def undo(): Boolean     = !shouldFailOnUndo.get()
    override def description: String = "Conditional fail"
@@ -1,25 +1,26 @@
 package de.nowchess.chess.controller

 import de.nowchess.api.board.{File, Rank, Square}
+import de.nowchess.api.move.PromotionPiece
 import org.scalatest.funsuite.AnyFunSuite
 import org.scalatest.matchers.should.Matchers

 class ParserTest extends AnyFunSuite with Matchers:

  test("parseMove parses valid 'e2e4'"):
-    Parser.parseMove("e2e4") shouldBe Some((Square(File.E, Rank.R2), Square(File.E, Rank.R4)))
+    Parser.parseMove("e2e4") shouldBe Some((Square(File.E, Rank.R2), Square(File.E, Rank.R4), None))

  test("parseMove is case-insensitive"):
-    Parser.parseMove("E2E4") shouldBe Some((Square(File.E, Rank.R2), Square(File.E, Rank.R4)))
+    Parser.parseMove("E2E4") shouldBe Some((Square(File.E, Rank.R2), Square(File.E, Rank.R4), None))

  test("parseMove trims leading and trailing whitespace"):
-    Parser.parseMove("  e2e4  ") shouldBe Some((Square(File.E, Rank.R2), Square(File.E, Rank.R4)))
+    Parser.parseMove("  e2e4  ") shouldBe Some((Square(File.E, Rank.R2), Square(File.E, Rank.R4), None))

  test("parseMove handles corner squares a1h8"):
-    Parser.parseMove("a1h8") shouldBe Some((Square(File.A, Rank.R1), Square(File.H, Rank.R8)))
+    Parser.parseMove("a1h8") shouldBe Some((Square(File.A, Rank.R1), Square(File.H, Rank.R8), None))

  test("parseMove handles corner squares h8a1"):
-    Parser.parseMove("h8a1") shouldBe Some((Square(File.H, Rank.R8), Square(File.A, Rank.R1)))
+    Parser.parseMove("h8a1") shouldBe Some((Square(File.H, Rank.R8), Square(File.A, Rank.R1), None))

  test("parseMove returns None for empty string"):
    Parser.parseMove("") shouldBe None
@@ -27,8 +28,8 @@ class ParserTest extends AnyFunSuite with Matchers:
  test("parseMove returns None for input shorter than 4 chars"):
    Parser.parseMove("e2e") shouldBe None

-  test("parseMove returns None for input longer than 4 chars"):
-    Parser.parseMove("e2e44") shouldBe None
+  test("parseMove returns None for input longer than 5 chars"):
+    Parser.parseMove("e2e4qq") shouldBe None

  test("parseMove returns None when from-file is out of range"):
    Parser.parseMove("z2e4") shouldBe None
@@ -41,3 +42,31 @@ class ParserTest extends AnyFunSuite with Matchers:

  test("parseMove returns None when to-rank is out of range"):
    Parser.parseMove("e2e9") shouldBe None
+
+  test("parseMove parses queen promotion 'e7e8q'"):
+    Parser.parseMove("e7e8q") shouldBe Some(
+      (Square(File.E, Rank.R7), Square(File.E, Rank.R8), Some(PromotionPiece.Queen)),
+    )
+
+  test("parseMove parses rook promotion 'a7a8r'"):
+    Parser.parseMove("a7a8r") shouldBe Some(
+      (Square(File.A, Rank.R7), Square(File.A, Rank.R8), Some(PromotionPiece.Rook)),
+    )
+
+  test("parseMove parses bishop promotion 'e7e8b'"):
+    Parser.parseMove("e7e8b") shouldBe Some(
+      (Square(File.E, Rank.R7), Square(File.E, Rank.R8), Some(PromotionPiece.Bishop)),
+    )
+
+  test("parseMove parses knight promotion 'e7e8n'"):
+    Parser.parseMove("e7e8n") shouldBe Some(
+      (Square(File.E, Rank.R7), Square(File.E, Rank.R8), Some(PromotionPiece.Knight)),
+    )
+
+  test("parseMove returns None for 5-char input with invalid promotion char"):
+    Parser.parseMove("e7e8x") shouldBe None
+
+  test("parseMove parses black promotion 'e2e1q'"):
+    Parser.parseMove("e2e1q") shouldBe Some(
+      (Square(File.E, Rank.R2), Square(File.E, Rank.R1), Some(PromotionPiece.Queen)),
+    )
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Janis	8744bee2dd	feat: NCS-41 Bot Platform (#33 ) Co-authored-by: Janis <janis@nowchess.de> Reviewed-on: #33 Co-authored-by: Janis <janis.e.20@gmx.de> Co-committed-by: Janis <janis.e.20@gmx.de>	2026-04-19 15:52:08 +02:00
TeamCity	5f4d33f3ca	ci: bump version with Build-41	2026-04-16 16:55:00 +00:00